# Astria — full public content # Astria Fine-tuning API - Overview Source: https://docs.astria.ai/docs/api/overview/ ## Billing The API uses account balance just like the web interface. See the [pricing](https://www.astria.ai/pricing) page for more details. ## Mock testing The API allows creating a mock `Tune` object with attribute `branch=fast`. See [Create a tune documentation](https://docs.astria.ai/docs/api/tune/create#branch-optional). API call for creating `fast` tune is free, and subsequent prompt calls will return mock images without incurring any charges. This is useful for testing your integration with the API. Once you're ready for production, you can [purchase credits](https://www.astria.ai/users/edit#billing) and adjust the quantity on the checkout page to how much you need. ## Beta features Astria maintains backward compatability and avoid making changes that could alter the behavior of the API. However, features marked as BETA are subject to change as we try to improve the product. ## Auto top off To allow your account to work without disruption, top-off feature can be enabled to refill account each time account balance reaches zero. Auto top-off can be enabled in the [billing page](https://www.astria.ai/users/edit#billing). It is recommended to set the top-off amount to at least a week worth of API calls to avoid hitting the rate limit on top-off - Frequent top-offs can cause credit card charges to fail or bank declines. ## Storage Generated images, training pictures and models, will be automatically deleted 30 days after the training has ended. You may delete the fine-tune object including the trained model, training images, and generated images at any time before using the delete API calls. You may opt-in to automatically [extend model storage](https://www.astria.ai/users/edit#billing) or set the `auto_extend` for [tune](https://docs.astria.ai/docs/api/tune/create/). ## Authorization The API uses bearer `Authorization` headers scheme. API calls should include the header: ```text Authorization: Bearer sd_XXXXXX ``` Find your API key on the [API settings](https://www.astria.ai/users/edit#api) under your account settings. ## REST API Astria API is built as a REST API. Main resources are `tune` and `prompt` and each one has all the CRUD operations. Prompts are usually used as a nested resource on top of tunes (i.e: `/tunes/:id/prompts`). ## Error Codes tip Pay attention to 504 error below and turn on idempotency for your account to avoid duplicate objects. `422` - Validation error - Log these errors, specifically the response body, and use an exception tracking system such as Rollbar or Sentry to get notified. Do not retry these requests. `429` - Rate limiting - This error might show when using polling. Use callbacks to avoid this. `500` - Internal server error - Such requests should be retried with exponential backoff and wait start time of 30 seconds. `504` - Gateway timeout - This error indicates that the request processing took more than the 30 seconds which is set as the max request timeout. In most cases the request would probably have been processed and you should avoid retrying it. In order to get the response see Idempotency section ## Idempotency Idempotency is a principle in programming which means that the same operation (a request in our case) can be made multiple times without changing the result. This can be useful when a request is interrupted and you want to make sure it is processed only once, and avoid duplicate objects. Idempotency can be enabled for the account in the [API settings](https://www.astria.ai/users/edit#api). For tunes request with the same `title`, idempotency will return the existing tune object and will not create a new one. This is useful for cases where you want to make sure you don't create duplicate tunes. In this case set the tune title to a unique value such as a UUID which identifies the transaction. For prompts request with the same attributes, idempotency will return the existing prompt object and will not create a new one. Prompt attributes considered for the idempotency are `text, tune_id, cfg_scale, steps, seed, callback, negative_prompt, super_resolution, face_correct, ar, num_images, controlnet, denoising_strength, use_lpw, controlnet_conditioning_scale, w, h, hires_fix, scheduler, controlnet_txt2img, inpaint_faces` When retrying a 504 error, and idempotency is enabled, make sure to wait 60 seconds before retrying the request to allow the previous request to finish processing. ## Callbacks Callbacks for prompts and tunes are `POST` requests containing the entity object in the request body. Callbacks work in test mode with `branch=fast`. Callbacks are currently not retried if they fail. To test your callbacks, we recommend a local tunnel tool like [ngrok](https://ngrok.com/). If you need your backend server to receive some context arguments/metadata for the callback, like internal user-id or transaction id, we recommend adding those to the callback query string. --- # Create a like Source: https://docs.astria.ai/docs/api/like/create/ Adds a like to a prompt. If the prompt was created using a pack (i.e: `orig_prompt_id` is set) - a like will be added to the original prompt as well. ### Parameters ### Returns Returns status code 201 if successful and no content. #### POST /prompts/:prompt\_id/likes ```bash curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/prompts/:prompt_id/like ``` ```javascript const fetch = require(`node-fetch'); const FormData = require('form-data'); const API_URL = 'https://api.astria.ai/prompts/:prompt_id/like'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } fetch(API_URL, { method: 'POST', headers: headers, }).then(response => response.json()) ``` ```python import requests API_URL = 'https://api.astria.ai/prompts/:prompt_id/like' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } response = requests.post(API_URL, headers=headers) ``` #### Response `Status code: 201 Created` --- # Create a prompt Source: https://docs.astria.ai/docs/api/prompt/create/ Creates a new fine-tune model from training images which in turn will be used to create prompts and generate images. ### Parameters #### `text` (required) Description of the image. #### `negative_prompt` (optional) A comma separated list of words that should not appear in the image. #### `callback` (optional) a URL that will be called when the prompt is done processing. The callback is a POST request where the body contains the prompt object. See [more](https://docs.astria.ai/docs/api/overview/#callbacks). #### `workspace_id` (optional) Integer. Creates and bills the prompt in a workspace that the authenticated user can access. Omit it to use the user's personal scope. #### `community` (optional) Boolean. Share the finished prompt to the Astria community feed. #### `num_images` (optional) Number of images to generate. Range: 1-8. #### `seed` (optional) Random number to create consistent results. Range: 0 to 2^32. #### `super_resolution` (optional) Boolean. X4 super-resolution. #### `inpaint_faces` (optional) Boolean. Requires super-resolution on. Inpaints faces. #### `hires_fix` (optional) Boolean. Super resolution details. Available only when super\_resolution is true. Adds details. #### `face_correct` (optional) Boolean. Runs another AI model on top to correct the face in the image. #### `face_swap` (optional) Boolean. Uses training images to swap face and enhance resemblance. #### `cfg_scale` (optional) Float. How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt). Range 0-15 #### `steps` (optional) Integer. Number of diffusion steps to run . Range 0-50 #### `use_lpw` (optional) Boolean. Use weighted prompts. #### `aspect_ratio` (optional) enum: `1:1`, `16:9`, `9:16`, `21:9`, `9:21`, `3:2`, `2:3`, `5:4`, `4:5`, `4:3`, `3:4` #### `ar` (optional) Legacy aspect-ratio field. enum: `1:1`, `portrait`, `16:9`, `landscape`. New integrations should use `aspect_ratio`. #### `resolution` (optional) Output resolution tier. enum: `1K`, `2K`, `4K`. Availability depends on the selected foundation model; unsupported combinations return a validation error. #### `w` (optional) width - In multiples of 8. #### `h` (optional) height - In multiples of 8. #### `scheduler` (optional) enum: `euler`, `euler_a`, `dpm++2m_karras`, `dpm++sde_karras`, `dpm++2m`, `dpm++sde`, `lcm`, `tcd`. If not specified the default [account scheduler](https://www.astria.ai/users/edit) will be used. #### `backend_version` (optional) enum: null, `1`, If not specified will default to the [account version](https://www.astria.ai/users/edit) will be used. #### `style` (optional) enum: null, `Cinematic`, `Animated`, `Digital Art`, `Photographic`, `Fantasy art`, `Neonpunk`, `Enhance`, `Comic book`, `Lowpoly`, `Line art`. See [more](https://docs.astria.ai/docs/features/styles/). #### `prompt_expansion` (optional) Boolean. Ask supported models to expand the supplied prompt before generation. Defaults to `false`. #### `theme` (optional) String metadata used to group prompts generated as part of the same theme. #### `color_grading` (optional) enum: `Film Velvia`, `Film Portra`, `Ektar`. #### `film_grain` (optional) boolean - Adds noise to the image to make it look more realistic. ## Img2Img / ControlNet #### `controlnet` (optional) Requires input\_image. Possible values: composition, reference, segroom, ipadapter, lineart, canny, depth, mlsd, hed, pose, tile, qr. #### `denoising_strength` (optional) For img2img. 1.0 - Take prompt. 0.0 - Take image. Range: 0.0-1.0. Default: 0.8 #### `controlnet_conditioning_scale` (optional) Strength of controlnet conditioning. 0.0-1.0 #### `controlnet_txt2img` (optional) Boolean toggle. True for text to image controlnet. False for image to image controlnet. #### `input_image` (optional) Binary multi-part request with the image. Used in conjunction with controlnet parameter. #### `input_image_base64` (optional) Base64 data URL alternative to `input_image`, for example `data:image/png;base64,...`. #### `input_image_url` (optional) URL to an image. Used in conjunction with controlnet parameter. #### `mask_image` (optional) Binary multi-part request with one channel mask image. Used in conjunction with input\_image parameter for inpainting #### `mask_image_url` (optional) URL to a one channel mask image. Used in conjunction with input\_image\_url parameter for inpainting. #### `lora_scale` (optional) Available only when used as `prompts_attributes` in `POST /tunes` request to override the default scale of the LoRA model. #### `pack_id` (optional) Integer. Authors the prompt as a template prompt in an accessible pack. A template prompt must reference a fine-tuned tune. #### `base_pack_id` (optional) Integer. Records an accessible pack as the provenance for a one-off prompt without making the prompt a reusable pack template. ## Video Pass these alongside `text` to turn a prompt into a video. The image stage (driven by the chosen tune) renders the first frame from `text`; the video model then animates it using `video_prompt`. For text-to-video models the image stage can be skipped — see the model list. #### `video_model` (required for video) enum: `seedance_480p`, `seedance_v15_720p`, `seedance_v15_audio_720p`, `seedance2_fast_480p`, `seedance2_fast_720p`, `seedance2_fast_1080p`, `seedance2_fast_4k`, `seedance2_480p`, `seedance2_720p`, `seedance2_1080p`, `seedance2_4k`, `wan22_720p`, `wan22_fast_720p`, `wan22_fast_580p`, `wan22_fast_480p`, `wan25_720p`, `wan26_720p`, `wan26_1080p`, `wan27_720p`, `wan27_1080p`, `wan_animate_720p`, `ltx23_720p`, `ltx23_1080p`, `kling25`, `kling30_standard`, `kling30_standard_audio`, `kling30_pro`, `kling30_pro_audio`, `kling30_4k`, `kling30_motion_control`, `kling30_motion_control_pro`, `cinematic_video`, `dreamactor_m2`, `happyhorse_720p`, `happyhorse_1080p`, `happyhorse_motion_control`, `veo31_fast_720p`, `veo31_fast_audio_720p`, `veo31_fast_1080p`, `veo31_fast_audio_1080p`, `veo31_fast_4k`, `veo31_fast_audio_4k`, `veo31_lite_720p`, `veo31_lite_audio_720p`, `veo31_lite_1080p`, `veo31_lite_audio_1080p`. See [Image2Video](https://docs.astria.ai/docs/features/video/) for capabilities, pricing, and allowed durations per model. #### `video_prompt` (required for video) Natural-language description of the motion / camera / scene action. Surround the value with quotes when posting via `multipart/form-data`. For Seedance 2 reference-to-video, include each existing reference tune as ` TUNE_NAME` (or the corresponding `lora` token). The tune's class name must immediately follow the token. Astria resolves those tunes and supplies their images as video references; Seedance 2 accepts up to nine reference images in total. #### `image_references` (optional) Ordered array of binary multipart image uploads used to condition a compatible video model. Repeat `prompt[image_references][]` for each image. The request must be a video prompt and the selected `video_model` must support multiple image references. #### `image_reference_urls` (optional) Ordered array of publicly accessible image URLs, as an alternative to `image_references`. Repeat `prompt[image_reference_urls][]` for each URL. Reference order is preserved within each array; use one form when the exact order of a mixed file-and-URL sequence matters. #### `video_duration` (optional) Integer seconds. Allowed values depend on `video_model` (see [Image2Video](https://docs.astria.ai/docs/features/video/)). Defaults vary per model (commonly 5; 8 for VEO3; 10 for motion-control / animate models). #### `video_first_frame` (optional) Binary multi-part image upload to use as the first frame, overriding the rendered image. When provided, the image stage is skipped and `text` is not required. #### `video_first_frame_url` (optional) URL alternative to `video_first_frame`. #### `video_last_frame` (optional) Binary multi-part image upload for first+last keyframe models (e.g. `seedance_v15_*`, `seedance2_*`, `wan21_*`, `kling*`, `ltx23_*`, `veo31_*`). #### `video_last_frame_url` (optional) URL alternative to `video_last_frame`. #### `source_image_url` (optional) URL of an existing generated image to animate as the first frame. This skips the image-generation stage. If `video_first_frame` or `video_first_frame_url` is also provided, the explicit first frame takes precedence. #### `input_video` (optional) Binary multi-part video upload. Seedance 2 uses it as a reference video. It is also required for motion-control models: `kling30_motion_control`, `kling30_motion_control_pro`, `wan_animate_720p`, `dreamactor_m2`, `happyhorse_motion_control`. #### `input_video_url` (optional) URL alternative to `input_video`. #### `audio_reference` (optional) Binary multi-part audio upload for models that accept reference audio, including `seedance2_*`. Set `video_audio=true` so the output includes audio. You can refer to the track as `@Audio1` in `video_prompt`; Astria adds that token on provider paths that require it when no `@Audio` token is present. #### `audio_reference_url` (optional) URL alternative to `audio_reference`. #### `video_audio` (optional) Boolean. Generate an audio track when the selected model supports generated audio, including `seedance2_*`. Defaults to `false`. Set it to `true` when using `audio_reference`. #### `video_upscale_model` (optional) Post-process the generated video with an upscaler. enum: `seedvr2`. #### `video_upscale_target_resolution` (optional) Target for `video_upscale_model`. enum: `720p`, `1080p`, `2k`, `4k`. Defaults to `1080p` when an upscaler is selected. #### `video_fps_model` (optional) Post-process the generated video with frame interpolation. enum: `video_fps_increaser`. It can be combined with video upscaling. For Seedance 2 and Seedance 2.5, first/last-frame mode cannot be combined with `image_references`, `image_reference_urls`, `input_video`, or `audio_reference`. Use either first/last frames or reference images/video/audio in one request. ### Video with ordered image references ```bash curl -X POST -H "Authorization: Bearer $API_KEY" \ https://api.astria.ai/tunes/$TUNE_ID/prompts \ -F 'prompt[video_model]=seedance2_fast_720p' \ -F 'prompt[video_prompt]=Move from the product detail into the full look, preserving the garment design' \ -F 'prompt[video_duration]=8' \ -F 'prompt[image_references][]=@/path/to/detail.jpg' \ -F 'prompt[image_references][]=@/path/to/full-look.jpg' ``` For hosted images, replace the last two fields with repeated URL fields: ```bash -F 'prompt[image_reference_urls][]=https://example.com/detail.jpg' \ -F 'prompt[image_reference_urls][]=https://example.com/full-look.jpg' ``` The created prompt JSON returns the attached images as an ordered `image_references` array of URLs. Model-specific reference limits still apply. > Backwards-compatible legacy syntax: the `--video --video_model … --video_prompt "…" --duration N` CLI flags inside `text` are still accepted and promoted into the dedicated columns server-side. New integrations should use the form fields above. ### Returns Returns a prompt object if successful which will start processing if tune is processed. #### POST /tunes/:id/prompts ```bash curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/1/prompts \ -F prompt[text]="a painting of ohwx man in the style of Van Gogh" \ -F prompt[negative_prompt]="old, blemish, wrin" \ -F prompt[super_resolution]=true \ -F prompt[face_correct]=true \ -F prompt[callback]="https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1" ``` ```javascript const fetch = require(`node-fetch'); const FormData = require('form-data'); const API_URL = 'https://api.astria.ai/tunes/1/prompts'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } const form = new FormData(); form.append('prompt[text]', 'a painting of ohwx man in the style of Van Gogh'); form.append('prompt[negative_prompt]', 'old, blemish, wrin'); form.append('prompt[super_resolution]', true); form.append('prompt[face_correct]', true); form.append('prompt[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1'); fetch(API_URL, { method: 'POST', headers: headers, body: form }).then(response => response.json()) ``` ```python import requests API_URL = 'https://api.astria.ai/tunes/1/prompts' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } data = { 'prompt[text]': 'a painting of ohwx man in the style of Van Gogh', 'prompt[negative_prompt]': 'old, blemish, wrin', 'prompt[super_resolution]': True, 'prompt[face_correct]': True, 'prompt[callback]': 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1' } files = [] files.append((f"prompt[input_image]", load_image(prompt['input_image']))) response = requests.post(API_URL, headers=headers, files=files, data=data) ``` #### Response ```json { "id": 1, "callback": "https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1", "text": "a painting of ohwx man in the style of Van Gogh", "negative_prompt": "old, blemish, wrinkles, mole", "cfg_scale": null, "steps": null, "seed": null, "trained_at": null, "started_training_at": null, "created_at": "2022-10-06T16:12:54.505Z", "updated_at": "2022-10-06T16:12:54.505Z", "tune_id": 1, "url": "http://api.astria.ai/tunes/1/prompts/1.json" } ``` --- # Create a tune Source: https://docs.astria.ai/docs/api/tune/create/ Creates a new fine-tune model from training images which in turn will be used to create prompts and generate images. info To avoid cloning inference details of different model types such as Flux LoRA vs SD1.5 checkpoint, please consider using the [Packs API](https://docs.astria.ai/docs/api/pack/pack/). Packs will help you abstract the inference logic so that you do not have to hard-code prompts and parameters such as `w,h, cfg_scale` in your backend. Moreover this will allow the creative department to launch packs, make modifications and even track likes, without needing to touch the backend code. ### Parameters #### `name` (required) A class name the describes the fine-tune. e.g: `man`, `woman`, `cat`, `dog`, `boy`, `girl`, `style` #### `title` (required) Describes the fine-tune. Ideally a UUID related to the transaction. See [idempotency](https://docs.astria.ai/docs/api/overview/) for more information. #### `images` (required) An array of images to train the fine-tune with. The images can be uploaded as multipart/form-data or as image\_urls. #### `image_urls` (required) An array of images to train the fine-tune with. The images can be uploaded as multipart/form-data or as image\_urls. #### `callback` (optional) A webhook URL to be called when the tune is finished training. The webhook will receive a POST request with the tune object. See [more on callbacks](https://docs.astria.ai/docs/api/overview/#callbacks). #### `branch` (optional) Enum: `flux1`,`sd15`, `sdxl1`, `fast`. Will default to the `base_tune` branch if not specified, or to `sd15` if `base_tune` is not specified. info Use `branch=fast` for [mock testing](https://docs.astria.ai/docs/api/overview#mock-testing) #### `steps` (optional) Training steps. Recommended leaving blank in order to allow better defaults set by the system. #### `token` (optional) Unique short text to which the features will be embedded into. Default `ohwx` for SDXL and `sks` for SD15. #### `face_crop` (optional) Detects faces in training images and augments training set with cropped faces. Defaults to [account setting](https://www.astria.ai/users/edit) #### `training_face_correct` (optional) Enhance training images using GFPGAN. Consider enabling if input image are low quality or low resolution. May result in over-smoothing. #### `base_tune_id` (optional) Training on top of former fine-tune or a different baseline model from the [gallery](https://www.astria.ai/gallery/tunes) (id in the URL). e.g: `1504944` - Flux1.dev #### `model_type` (optional) Enum: `lora`, `pti`, `faceid`, `null` for checkpoint. For SDXL1 - API will default to `pti` and will ignore `model_type` parameter. #### `auto_extend` (optional) Boolean: `true`, `false`. Default is `false`. If `true`, the tune will be automatically extended when it expires. This is a paid feature; see [pricing](https://www.astria.ai/users/edit/api) for more details. #### `preset` (optional) Enum: `flux-lora-focus`, `flux-lora-portrait`, `flux-lora-fast` see details in the GUI, `null`. See [Flux lora training](https://docs.astria.ai/docs/use-cases/flux-finetuning/) for more information. #### `characteristics` (optional) A free-form object that can be used to templatize the prompts text. e.g: `{"eye_color": "blue eyes"}` would than be used in the prompt text as `ohwx woman, {{eye_color}}, holding flowers`. #### `prompts_attributes` (optional) Array of prompts entities with all attributes. See [create prompt](https://docs.astria.ai/docs/api/prompt/create/) for more information. ### Returns Returns a tune object if successful which will start training immediately and call callback once training is complete. #### POST /tunes ```bash # With imags as multipart/form-data # Hard coded tune id of Flux1.dev - https://www.astria.ai/gallery/tunes curl -X POST -H "Authorization: Bearer $API_KEY" http://localhost:3000/tunes \ -F "tune[title]=Jane Doe - UUID - 1234-6789-1234-56789" \ -F "tune[name]=woman" \ -F "tune[base_tune_id]=1504944" \ -F "tune[model_type]=lora" \ -F "tune[token]=ohwx" \ -F "tune[images][]=@1.jpg" \ -F "tune[images][]=@2.jpg" \ -F "tune[images][]=@3.jpg" \ -F "tune[images][]=@4.jpg" \ -F "tune[prompts_attributes][][text]=ohwx woman holding flowers" \ -F "tune[prompts_attributes][][inpaint_faces]=true" \ -F "tune[prompts_attributes][][super_resolution]=true" # With image_urls as form-data # Hard coded tune id of Flux1.dev - https://www.astria.ai/gallery/tunes curl -X POST -H "Authorization: Bearer $API_KEY" http://localhost:3000/tunes \ -F "tune[title]=Jane Doe - UUID - 1234-6789-1234-56789" \ -F "tune[name]=woman" \ -F "tune[base_tune_id]=1504944" \ -F "tune[model_type]=lora" \ -F "tune[token]=ohwx" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[prompts_attributes][][text]=ohwx woman holding flowers" \ -F "tune[prompts_attributes][][inpaint_faces]=true" \ -F "tune[prompts_attributes][][super_resolution]=true" # As JSON cat > data.json <<- EOM { "tune": { "title": "Jane Doe - UUID - 1234-6789-1234-56789", "name": "cat", "branch": "fast", "callback": "https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1", "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ], "prompts_attributes": [ { "text": " ohwx woman holding flowers", "callback": "https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1&prompt_id=1" } ] } } EOM curl -X POST -H"Content-Type: application/json" -H "Authorization: Bearer $API_KEY" --data @data.json https://api.astria.ai/tunes ``` ```javascript // NodeJS 16 // With image_urls and fetch() // For NodeJS 18 - do NOT import the below as it is built-in import fetch from "node-fetch"; const API_KEY = 'sd_XXXXXX'; const DOMAIN = 'https://api.astria.ai'; function createTune() { let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY, 'Content-Type': 'application/json' }, body: JSON.stringify({ tune: { "title": 'Jane Doe - UUID - 1234-6789-1234-56789', // Hard coded tune id of Flux1.dev - https://www.astria.ai/gallery/tunes "base_tune_id": 1504944, "name": "woman", "model_type": "lora", "token": "ohwx", "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ], "prompts_attributes": [ { "text": "ohwx woman holding flowers", "inpaint_faces": true, "super_resolution": true } ] } }) }; return fetch(DOMAIN + '/tunes', options) .then(r => r.json()) .then(r => console.log(r)) } createTune() ``` ```python import requests API_KEY = 'sd_XXXXXX' headers = {'Authorization': f'Bearer {API_KEY}'} def load_image(file_path): with open(file_path, "rb") as f: return f.read() # With image_urls as JSON data = { "tune": { "title": "Jane Doe - UUID - 1234-6789-1234-56789", "name": "woman", "base_tune_id": 1504944, # Hard coded tune id of Flux1.dev "model_type": "lora", "token": "ohwx", "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ], "prompts_attributes": [ { "text": "ohwx woman holding flowers", "inpaint_faces": True, "super_resolution": True } ] } } API_URL = 'https://api.astria.ai/tunes' response = requests.post(API_URL, json=data, headers=headers) response.raise_for_status() ``` #### Response ```json { "id": 1, "title": "John Doe - UUID - 1234-6789-1234-56789", "name": "woman", "token": "ohwx", "base_tune_id": null, "args": null, "steps": null, "face_crop": null, "training_face_correct": false, "ckpt_url": "https://sdbooth2-production.s3.amazonaws.com/mock", "ckpt_urls": [ "https://sdbooth2-production.s3.amazonaws.com/mock" ], "eta": "2023-10-02T14:32:40.363Z", "trained_at": "2023-10-02T14:32:40.363Z", "started_training_at": "2023-10-02T14:32:05.229Z", "expires_at": "2023-11-01T14:32:40.363Z", "created_at": "2023-10-02T14:32:05.067Z", "branch": "sdxl1", "model_type": "lora", "updated_at": "2023-10-02T14:32:40.363Z", "url": "https://www.astria.ai/tunes/788416.json", "orig_images": [ "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock" ] } ``` --- # Create a tune from a pack Source: https://docs.astria.ai/docs/api/pack/tunes/create/ Creates a new fine-tune model from training images according to the pack base tune id, model type and adds prompts to generate images. info Please make sure to go over the [overview](https://docs.astria.ai/docs/api/pack/pack/) for why using packs and how to create your first pack. ### Parameters #### `name` (required) A class name the describes the fine-tune. e.g: `man`, `woman`, `cat`, `dog`, `boy`, `girl`, `style`. Class name must be supported by pack. #### `title` (required) Describes the fine-tune. Ideally a UUID related to the transaction. See [idempotency](https://docs.astria.ai/docs/api/overview/) for more information. #### `images` (required) An array of images to train the fine-tune with. The images can be uploaded as multipart/form-data or as image\_urls. #### `image_urls` (required) An array of images to train the fine-tune with. The images can be uploaded as multipart/form-data or as image\_urls. #### `callback` (optional) A webhook URL to be called when the tune is finished training. The webhook will receive a POST request with the tune object. See [more on callbacks](https://docs.astria.ai/docs/api/overview/#callbacks). #### `characteristics` (optional) A free-form object that can be used to templatize the prompts text. e.g: `{"eye_color": "blue eyes"}` would than be used in the prompt text as `ohwx woman, {{eye_color}}, holding flowers`. #### `prompt_attributes.callback` (optional) A webhook URL to be called when each prompt is finished inference. The webhook will receive a POST request with the prompt object. See [more on callbacks](https://docs.astria.ai/docs/api/overview/#callbacks). #### `prompts_callback` (optional) A webhook URL to be called when all the prompts are finished training. The webhook will receive a POST request with an array of prompts objects. #### `tune_ids` (optional) Array of tune ids. If provided, will be used instead of the above attributes to create pack prompts only. #### `prompt_ids` (optional) Array of pack prompt ids. If provided, only the specified prompts from the pack will be created. If omitted, all pack prompts are created. Useful for selecting a subset of prompts from a pack. Can be used together with `tune_ids`. ### Returns Returns a tune object if successful which will start training immediately and call callback once training is complete. #### POST /p/:pack\_id/tunes ```bash # With images as multipart/form-data # Hard coded pack id 260 for corporate headshots from the gallery - https://www.astria.ai/gallery/packs.json # https://www.astria.ai/gallery/tunes/690204/prompts curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/p/260/tunes \ -F tune[title]="John Doe - UUID - 1234-6789-1234-56789" \ -F tune[name]=man \ -f tune[preset]=flux-lora-portrait \ -F tune[callback]="https://optional-callback-url.com/webhooks/astria?user_id=1" \ -F tune[characteristics][eye_color]="blue eyes" \ -F tune[prompt_attributes][callback]="https://optional-callback-url.com/webhooks/astria_prompts?user_id=1" \ -F "tune[images][]=@1.jpg" \ -F "tune[images][]=@2.jpg" \ -F "tune[images][]=@3.jpg" \ -F "tune[images][]=@4.jpg" # With image_urls as form-data curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/p/260/tunes \ -F tune[title]="Grumpy cat - UUID - 1234-6789-1234-56789" \ -F tune[name]=cat \ -F tune[callback]="https://optional-callback-url.com/to-your-service-when-ready?user_id=1" \ -F tune[prompt_attributes][callback]="https://optional-callback-url.com/webhooks/astria_prompts?user_id=1" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" # As JSON cat > data.json <<- EOM { "tune": { "title": "Grumpy Cat - UUID - 1234-6789-1234-56789", "name": "cat", "callback": "https://optional-callback-url.com/to-your-service-when-ready?user_id=1", "characteristics": {"eye_color": "blue eyes"}, "prompt_attributes": { "callback": "https://optional-callback-url.com/webhooks/astria_prompts?user_id=1" }, "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ] } } EOM # Hard coded pack id 260 for corporate headshots from the gallery - https://www.astria.ai/gallery/packs.json curl -X POST -H"Content-Type: application/json" -H "Authorization: Bearer $API_KEY" --data @data.json https://api.astria.ai/p/260/tunes ``` ```javascript // NodeJS 16 // With image_urls and fetch() // For NodeJS 18 - do NOT import the below as it is built-in import fetch from "node-fetch"; const API_KEY = 'sd_XXXXXX'; const DOMAIN = 'https://api.astria.ai'; function createTune() { let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY, 'Content-Type': 'application/json' }, body: JSON.stringify({ tune: { "title": 'John Doe - UUID - 1234-6789-1234-56789', "name": "cat", "callback": "https://optional-callback-url.com/to-your-service-when-ready?user_id=1", "characteristics": {"eye_color": "blue eyes"}, "prompt_attributes": { "callback": "https://optional-callback-url.com/webhooks/astria_prompts?user_id=1" }, "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ] } }) }; // Hard coded pack id 260 for corporate headshots from the gallery - https://www.astria.ai/gallery/packs.json return fetch(DOMAIN + '/p/260/tunes', options) .then(r => r.json()) .then(r => console.log(r)) } createTune() /// With form-data, fetch() and nested prompts // For NodeJS 18 - do NOT import the two below as they are built-in import fetch from "node-fetch"; import FormData from 'form-data'; import fs from 'fs'; const API_KEY = 'sd_XXXX'; const DOMAIN = 'https://api.astria.ai'; function createTune() { let formData = new FormData(); formData.append('tune[title]', 'John Doe - UUID - 1234-6789-1234-56789'); formData.append('tune[name]', 'man'); formData.append('tune[preset]', 'flux-lora-portrait'); formData.append('tune[callback]', 'https://optional-callback-url.com/webhooks/astria?user_id=1'); formData.append('tune[characteristics][eye_color]', 'blue eyes'); formData.append('tune[prompt_attributes][callback]', 'https://optional-callback-url.com/webhooks/astria_prompts?user_id=1'); // Load all JPGs from ./samples directory and append to FormData let files = fs.readdirSync('./samples'); files.forEach(file => { if(file.endsWith('.jpg')) { formData.append('tune[images][]', fs.createReadStream(`./samples/${file}`), file); } }); formData.append('tune[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?user_id=1'); let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY }, body: formData }; // Hard coded pack id 260 for corporate headshots from the gallery - https://www.astria.ai/gallery/packs.json return fetch(DOMAIN + '/p/260/tunes', options) .then(r => r.json()) .then(r => console.log(r)); } createTune(); // Create prompts from pack with tune_ids function createTune(tuneIds) { let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY, 'Content-Type': 'application/json' }, body: JSON.stringify({ tune: { tune_ids: tuneIds, } }) }; // Hard coded pack id 260 for corporate headshots from the gallery - https://www.astria.ai/gallery/packs.json return fetch(DOMAIN + '/p/260/tunes', options) .then(r => r.json()) .then(r => console.log(r)) } createTune([89]) ``` ```python import requests headers = {'Authorization': f'Bearer {API_KEY}'} def load_image(file_path): with open(file_path, "rb") as f: return f.read() # Assuming `prompts` and `tune.images` are already defined in your context data = { "tune[title]": "John Doe - UUID - 1234-6789-1234-56789", "tune[name]": "man", "tune[preset]": "flux-lora-portrait", } files = [] for image in tune.images: image_data = load_image(image) # Assuming image is a file path files.append(("tune[images][]", image_data)) API_URL = 'https://api.astria.ai/p/260/tunes' response = requests.post(API_URL, data=data, files=files, headers=headers) response.raise_for_status() ## Alternatively - create prompts from pack with tune_ids data = { "tune": { "tune_ids": [89] } } response = requests.post(API_URL, json=data, headers=headers) ``` #### Response ```json { "id": 1, "title": "John Doe", "name": "woman", "eta": "2023-10-02T14:32:40.363Z", "trained_at": "2023-10-02T14:32:40.363Z", "started_training_at": "2023-10-02T14:32:05.229Z", "expires_at": "2023-11-01T14:32:40.363Z", "created_at": "2023-10-02T14:32:05.067Z", "updated_at": "2023-10-02T14:32:40.363Z", "url": "https://www.astria.ai/tunes/788416.json", "orig_images": [ "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock" ] } ``` --- # Delete a like Source: https://docs.astria.ai/docs/api/like/delete/ Delete a like from a prompt. ### Parameters ### Returns Returns status code 201 if successful and no content. #### POST /prompts/:prompt\_id/likes ```bash curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/prompts/:prompt_id/like ``` ```javascript const fetch = require(`node-fetch'); const FormData = require('form-data'); const API_URL = 'https://api.astria.ai/prompts/:prompt_id/like'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } fetch(API_URL, { method: 'POST', headers: headers, }).then(response => response.json()) ``` ```python import requests API_URL = 'https://api.astria.ai/prompts/:prompt_id/like' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } response = requests.post(API_URL, headers=headers) ``` #### Response `Status code: 201 Created` --- # Delete a prompt Source: https://docs.astria.ai/docs/api/prompt/delete/ Deletes a specific prompt associated with a tune. ### Parameters #### `id` (required) The ID of the prompt to be deleted. ### Returns Returns 200 OK if the prompt was successfully deleted. #### DELETE /tunes/:tune\_id/prompts/:id ```bash curl -X DELETE -H "Authorization: Bearer $API_KEY" https://api.astria.ai/prompts/1 ``` ```javascript const fetch = require('node-fetch'); const API_URL = 'https://api.astria.ai/prompts/1'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } fetch(API_URL, { method: 'DELETE', headers: headers }).then(response => response.json()) ``` ```python import requests API_URL = 'https://api.astria.ai/prompts/1' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } response = requests.delete(API_URL, headers=headers) ``` #### Response ```json { "message": "Prompt successfully deleted" } ``` --- # Delete a tune Source: https://docs.astria.ai/docs/api/tune/delete/ Deletes a specific tune by its ID, and associated prompts in case of a checkpoint. ### Parameters #### `id` (required) The ID of the tune to be deleted. ### Returns Returns 200 OK if the prompt was successfully deleted. #### DELETE /tunes/:id ```bash curl -X DELETE -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/1 ``` ```javascript const headers = { Authorization: `Bearer ${API_KEY}` } fetch('https://api.astria.ai/tunes/1', { method: 'DELETE', headers: headers }); ``` ```python import requests headers = {'Authorization': f'Bearer {API_KEY}'} requests.delete('https://api.astria.ai/tunes/1', headers=headers) ``` #### Response `200 OK` --- # Duplicate a prompt Source: https://docs.astria.ai/docs/api/prompt/duplicate/ Duplicates a prompt, and allows overriding any of its attributes. Any of the parameters available in the [Create a prompt](https://docs.astria.ai/docs/api/prompt/create/) endpoint can be used here to override the original prompt's attributes. For example, to use a different seed for a new prompt, you can use the `seed` attribute. ### Parameters #### `image_index` (required) Unique additional parameter that can be used to bypass the initial creation of the image and re-use the existing image from the original prompt this saving cost and time of the base image creation. This is mostly useful for video2image generation where the first image is used as a base for the video generation. Note that this parameter is provided at the top level and not inside the prompt object. ### Returns Returns a new prompt object if successful. #### POST /prompts/:id/duplicate ```bash curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/prompts/1/duplicate \ -F prompt[seed]=12345 ``` ```javascript const fetch = require(`node-fetch`); const FormData = require('form-data'); const API_URL = 'https://api.astria.ai/prompts/1/duplicate'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } const form = new FormData(); form.append('prompt[seed]', 12345); fetch(API_URL, { method: 'POST', headers: headers, body: form }).then(response => response.json()) ``` ```python import requests API_URL = 'https://api.astria.ai/prompts/1/duplicate' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } data = { 'prompt[seed]': 12345 } response = requests.post(API_URL, headers=headers, data=data) ``` #### Response ```json { "id": 2, "callback": "https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1", "text": "a painting of ohwx man in the style of Van Gogh", "negative_prompt": "old, blemish, wrinkles, mole", "cfg_scale": null, "steps": null, "seed": 12345, "trained_at": null, "started_training_at": null, "created_at": "2022-10-06T16:15:22.505Z", "updated_at": "2022-10-06T16:15:22.505Z", "tune_id": 1, "url": "http://api.astria.ai/tunes/1/prompts/2.json" } ``` ### Video Generation from an existing prompt image ```bash curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/prompts/1/duplicate \ -F image_index=0 \ -F prompt[text]="a painting of ohwx man in the style of Van Gogh --video --video_prompt \"smiling, put their hands on hips\"" ``` ```javascript const fetch = require(`node-fetch`); const FormData = require('form-data'); const API_URL = 'https://api.astria.ai/prompts/1/duplicate'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } const form = new FormData(); form.append('image_index', 0); form.append('prompt[text]', 'a painting of ohwx man in the style of Van Gogh --video --video_prompt "smiling, put their hands on hips"'); fetch(API_URL, { method: 'POST', headers: headers, body: form }).then(response => response.json()) ``` ```python import requests API_URL = 'https://api.astria.ai/prompts/1/duplicate' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } data = { 'image_index': 0, 'prompt[text]': 'a painting of ohwx man in the style of Van Gogh --video --video_prompt "smiling, put their hands on hips"' } response = requests.post(API_URL, headers=headers, data=data) ``` --- # Flux API usage Source: https://docs.astria.ai/docs/api/flux-api/ For an overview of Flux fine-tuning, see [Flux fine-tuning](https://docs.astria.ai/docs/use-cases/flux-finetuning/) - Unlike SD15 checkpoint training, Flux is trained as a LoRA model type. As such, inference is taking place on a base model such as Flux1.dev and `prompt.text` should specify the loaded lora such as `` - will load lora with id=123456 and strength=1 - Flux1.Dev requires commercial license which Astria provides to its customers, and as such LoRA downloading is not available for API usage. See [LoRA docs](https://docs.astria.ai/docs/features/lora/) on lora syntax info To avoid cloning inference details of different model types such as Flux LoRA vs SD1.5 checkpoint, please consider using the [Packs API](https://docs.astria.ai/docs/api/pack/pack/). Packs will help you abstract the inference logic so that you do not have to hard-code prompts and parameters such as `w,h, cfg_scale` in your backend. Moreover this will allow the creative department to launch packs, make modifications and even track likes, without needing to touch the backend code. ### Step 1: Fine-tune a lora model #### POST /tunes ```bash # With images as multipart/form-data # Hard coded tune id of Flux1.dev from the gallery - https://www.astria.ai/gallery/tunes # https://www.astria.ai/gallery/tunes/1504944/prompts curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes \ -F tune[title]="John Doe - UUID - 1234-6789-1234-56789" \ -F tune[name]=man \ -F tune[preset]=flux-lora-portrait \ -F tune[callback]="https://optional-callback-url.com/webhooks/astria?user_id=1&tune_id=1" \ -F tune[base_tune_id]=1504944 \ -F tune[model_type]="lora" \ -F "tune[images][]=@1.jpg" \ -F "tune[images][]=@2.jpg" \ -F "tune[images][]=@3.jpg" \ -F "tune[images][]=@4.jpg" # With image_urls as form-data curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes \ -F tune[title]="Grumpy cat - UUID - 1234-6789-1234-56789" \ -F tune[name]=cat \ -F tune[preset]=flux-lora-portrait \ -F tune[callback]="https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1" \ -F tune[base_tune_id]=1504944 \ -F tune[model_type]="lora" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" # As JSON cat > data.json <<- EOM { "tune": { "title": "Grumpy Cat - UUID - 1234-6789-1234-56789", "name": "cat", "base_tune_id": 1504944, "model_type": "lora", "callback": "https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1", "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ] } } EOM curl -X POST -H"Content-Type: application/json" -H "Authorization: Bearer $API_KEY" --data @data.json https://api.astria.ai/tunes ``` ```javascript // NodeJS 16 // With image_urls and fetch() // For NodeJS 18 - do NOT import the below as it is built-in import fetch from "node-fetch"; const API_KEY = 'sd_XXXXXX'; const DOMAIN = 'https://api.astria.ai'; function createTune() { let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY, 'Content-Type': 'application/json' }, body: JSON.stringify({ tune: { "title": 'John Doe - UUID - 1234-6789-1234-56789', // Hard coded tune id of Flux1.dev from the gallery - https://www.astria.ai/gallery/tunes // https://www.astria.ai/gallery/tunes/1504944/prompts "base_tune_id": 1504944, "model_type": "lora", "name": "cat", "preset": "flux-lora-portrait", "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ], } }) }; return fetch(DOMAIN + '/tunes', options) .then(r => r.json()) .then(r => console.log(r)) } createTune() /// With form-data, fetch() and nested prompts // For NodeJS 18 - do NOT import the two below as they are built-in import fetch from "node-fetch"; import FormData from 'form-data'; import fs from 'fs'; const API_KEY = 'sd_XXXX'; const DOMAIN = 'https://api.astria.ai'; function createTune() { let formData = new FormData(); formData.append('tune[title]', 'John Doe - UUID - 1234-6789-1234-56789'); // Hard coded tune id of Flux1.dev from the gallery - https://www.astria.ai/gallery/tunes // https://www.astria.ai/gallery/tunes/1504944/prompts formData.append('tune[base_tune_id]', 1504944); formData.append('tune[model_type]', 'lora'); formData.append('tune[name]', 'man'); formData.append('tune[preset]', 'flux-lora-portrait'); // Load all JPGs from ./samples directory and append to FormData let files = fs.readdirSync('./samples'); files.forEach(file => { if(file.endsWith('.jpg')) { formData.append('tune[images][]', fs.createReadStream(`./samples/${file}`), file); } }); formData.append('tune[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1'); let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY }, body: formData }; return fetch(DOMAIN + '/tunes', options) .then(r => r.json()) .then(r => console.log(r)); } createTune(); ``` ```python import requests headers = {'Authorization': f'Bearer {API_KEY}'} def load_image(file_path): with open(file_path, "rb") as f: return f.read() # Assuming `prompts` and `tune.images` are already defined in your context image_data = load_image("assets/image.jpeg") data = { "tune[title]": "John Doe - UUID - 1234-6789-1234-56789", "tune[name]": "man", "tune[preset]": "flux-lora-portrait", "tune[base_tune_id]": 1504944, "tune[model_type]": "lora", } files = [] for image in tune.images: image_data = load_image(image) # Assuming image is a file path files.append(("tune[images][]", image_data)) API_URL = 'https://api.astria.ai/tunes' response = requests.post(API_URL, data=data, files=files, headers=headers) response.raise_for_status() ``` ### Step 2: Generate images using the fine-tuned model #### POST /tunes/:id/prompts ```bash # Note the hard-coded 1504944 which is the tune_id of Flux1.dev from the gallery curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/1504944/prompts \ -F prompt[text]=" a painting of ohwx man in the style of Van Gogh" \ -F prompt[callback]="https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1" ``` ```javascript const fetch = require('node-fetch'); const FormData = require('form-data'); // Note the hard-coded 1504944 which is the tune_id of Flux1.dev from the gallery const API_URL = 'https://api.astria.ai/tunes/1504944/prompts'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } const form = new FormData(); form.append('prompt[text]', ' a painting of ohwx man in the style of Van Gogh'); form.append('prompt[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1'); fetch(API_URL, { method: 'POST', headers: headers, body: form }).then(response => response.json()) ``` ```python import requests # Note the hard-coded 1504944 which is the tune_id of Flux1.dev from the gallery API_URL = 'https://api.astria.ai/tunes/1504944/prompts' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } data = { 'prompt[text]': ' a painting of ohwx man in the style of Van Gogh', 'prompt[callback]': 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1' } files = [] response = requests.post(API_URL, headers=headers, data=data) ``` --- # Inspect image Source: https://docs.astria.ai/docs/api/images/inspect/ Inspects an image to extract attributes both for filtering/warning about bad training set images and as `characteristics` to be added to [the fine tune](https://docs.astria.ai/docs/api/tune/create/#characteristics-optional). ### Parameters #### `name` (required) Class name of the object to be inspected in the image. This should be the same class name that will be used to create the tune object. #### `file` (required) The image to inspect. #### `file_url` (optional) The image url to inspect if `file` is not provided. ### Returns The below JSON schema is returned. It is possible that some attributes will not be returned. ```JSON { "type": "object", "properties": { "name": { "type": "string", "enum": [ "man", "woman", "boy", "girl", "baby", "cat", "dog", "NONE" ] }, "body_type": { "type": "string", "enum": [ "slim body", "average body", "muscular body", "plussize body", "NONE" ] }, "ethnicity": { "type": "string", "enum": [ "caucasian", "black", "hispanic", "korean", "japanese", "chinese", "philippine", "asian", "indian", "arabic", "caribbean", "african", "NONE" ] }, "age": { "type": "string", "enum": [ "20 yo", "30 yo", "40 yo", "50 yo", "60 yo", "70 yo" ] }, "glasses": { "type": "string", "enum": [ "glasses", "NONE" ] }, "eye_color": { "type": "string", "enum": [ "blue eyes", "brown eyes", "green eyes", "gray eyes", "black eyes", "NONE" ] }, "hair_color": { "type": "string", "enum": [ "black hair", "brown hair", "blonde hair", "red hair", "grey hair", "white hair", "purple hair", "blue hair", "green hair", "pink hair", "dyed hair", "NONE" ] }, "hair_length": { "type": "string", "enum": [ "bald", "short hair", "medium hair", "long hair", "very long hair", "NONE" ] }, "hair_style": { "type": "string", "enum": [ "bald head", "balding top", "straight hair", "wavy hair", "curly hair", "afro", "dreadlocks", "cornrows", "slicked back", "comb over", "receding hairline", "undercut", "man bun", "mohawk", "crew cut", "faux hawk", "buzzcut", "NONE" ] }, "facial_hair": { "type": "string", "enum": [ "mustache", "beard", "goatee", "NONE" ] }, "is_bald": { "type": "string", "enum": [ "bald", "NONE" ] }, "headcover": { "type": "string", "enum": [ "with head cover", "NONE" ] }, "funny_face": { "type": "boolean" }, "wearing_sunglasses": { "type": "boolean" }, "wearing_hat": { "type": "boolean" }, "blurry": { "type": "boolean" }, "includes_multiple_people": { "type": "boolean" }, "full_body_image_or_longshot": { "type": "boolean" }, "selfie": { "type": "boolean" }, "low_resolution": { "type": "boolean" }, "low_quality": { "type": "boolean" } } } ``` #### POST /images/inspect ```bash curl -X POST "https://api.astria.ai/images/inspect" \ -H "Authorization: Bearer YOUR_API_KEY" \ -F "name=man" \ -F "file=@/path/to/your/image.jpg" ``` ```python import requests url = "https://api.astria.ai/images/inspect" headers = { "Authorization": "Bearer YOUR_API_KEY" } files = { "file": open("/path/to/your/image.jpg", "rb") } data = { "name": "man" } response = requests.post(url, headers=headers, files=files, data=data) print(response.json()) ``` ```javascript const axios = require('axios'); const FormData = require('form-data'); const fs = require('fs'); const url = "https://api.astria.ai/images/inspect"; const form = new FormData(); form.append("name", "man"); form.append("file", fs.createReadStream("/path/to/your/image.jpg")); axios.post(url, form, { headers: { "Authorization": `Bearer YOUR_API_KEY`, ...form.getHeaders() } }) .then(response => { console.log(response.data); }) .catch(error => { console.error(error); }); ``` #### Response ```json { "age": "20 yo", "blurry": false, "ethnicity": "caucasian", "eye_color": "brown eyes", "facial_hair": "", "full_body_image_or_longshot": false, "funny_face": false, "glasses": "", "hair_color": "brunette", "hair_length": "medium hair", "hair_style": "wavy hair", "includes_multiple_people": false, "is_bald": "", "name": "woman", "selfie": true, "wearing_hat": false, "wearing_sunglasses": false } ``` ### Example implementation The images inspect API is meant to be used by the client-side of your app. The /images/inspect needs to be proxied by your server to avoid exposing your API key to the client. The example below implements two behaviors 1. `createWarning` notifies the user about an attribute is true such as `funny_face` or `wearing_sunglasses`. The function expects a form input element containing the class `name` selected by the user. 2. `aggregateCharacteristics` aggregates the most common values for each key in the characteristics object and sets the `characteristicsInputTarget` value to the aggregated characteristics. ```javascript async inspect(previewEl, file) { const form = document.getElementById('new_tune'); const formValues = Object.fromEntries(new FormData(form)); const name = formValues['tune[name]']; const csrfToken = document.querySelector("[name='csrf-token']").content; const formData = new FormData(); formData.append('authenticity_token', csrfToken); formData.append('name', name); // Check if file is an image and readable if (file.type.startsWith('image/')) { try { const resizedFile = await this.resizeImage(file); formData.append('file', resizedFile || file); } catch (error) { console.warn('Image resizing failed, uploading original file:', error); formData.append('file', file); } } else { formData.append('file', file); } const response = await fetch('/images/inspect', { method: 'POST', body: formData, }); const data = await response.json(); if (!data['name']) { this.createWarning(previewEl, `Could not detect image`); } // Iterate over hash and add warning messages for each true value Object.keys(data).forEach((key) => { if (key === 'name') { if (data[key] === '') { this.createWarning(previewEl, `Could not detect ${name} in the image`); } else if (data[key] && data[key] !== name) { this.createWarning(previewEl, `Could not detect ${name} in the image (2)`); } } else if (data[key] === true) { const warning = capitalizeFirstLetter(key.replace(/_/g, " ")); this.createWarning(previewEl, warning); } }); this.characteristics.push(data); this.aggregateCharacteristics(); previewEl.querySelector('.loading').classList.add('d-none'); previewEl.querySelector('.remove-btn').classList.remove('d-none'); } // Helper function to resize the image async resizeImage(file) { return new Promise((resolve, reject) => { const img = new Image(); const reader = new FileReader(); reader.onload = (e) => { img.onload = () => { const canvas = document.createElement('canvas'); const maxDimension = 512; // Set max dimension for resizing let width = img.width; let height = img.height; if (width <= maxDimension && height <= maxDimension) { console.log(`Image is already smaller than ${maxDimension}x${maxDimension}`) resolve(file); return; } // Calculate new dimensions while maintaining aspect ratio if (width > height) { if (width > maxDimension) { height = Math.round(height * maxDimension / width); width = maxDimension; } } else { if (height > maxDimension) { width = Math.round(width * maxDimension / height); height = maxDimension; } } console.log(`Resizing image to ${width}x${height} from ${img.width}x${img.height}`) canvas.width = width; canvas.height = height; const ctx = canvas.getContext('2d'); ctx.drawImage(img, 0, 0, width, height); canvas.toBlob((blob) => { resolve(blob ? new File([blob], file.name, { type: file.type }) : null); }, file.type, 0.9); // Adjust quality if needed }; img.onerror = reject; img.src = e.target.result; }; reader.onerror = reject; reader.readAsDataURL(file); }); } aggregateCharacteristics() { const aggregated = {}; // Iterate over this.characteristics and select value which is more common into this.aggregatedCharacteristics // use only the characeteristics that are string this.characteristics.forEach((characteristic) => { Object.keys(characteristic).forEach((key) => { if (typeof characteristic[key] === 'string') { if (aggregated[key]) { aggregated[key].push(characteristic[key]); } else { aggregated[key] = [characteristic[key]]; } } }); }); console.log('aggregated', aggregated); const commonValues = {}; // find most common value for each key and set aggregatedCharacteristics to that value Object.keys(aggregated).forEach((key) => { const values = aggregated[key]; // only set the value if a high enough percentage was detected for a value // console.log(key, 'values.length', values.length, this.characteristics.length, aggregated[key]); if (values.length < this.characteristics.length / 2) { console.log('Not enough values for', key); return } // Now aggregate and find the most common value const mostCommonValue = values.sort((a, b) => values.filter(v => v === a).length - values.filter(v => v === b).length ).pop(); commonValues[key] = mostCommonValue; }); console.log('commonValues', commonValues); this.characteristicsInputTarget.value = JSON.stringify(commonValues); } ``` --- # List all packs Source: https://docs.astria.ai/docs/api/pack/list/ ### Parameters #### `public=true` (optional) Filters only public packs that are not disabled. By default, no filtering is applied. #### `listed=true` (optional) Filters packs that unlisted\_at is set to null. ### Returns An array of packs owned by the authenticated user or gallery packs for `GET /gallery/packs` #### `GET /packs` or `GET /gallery/packs` ```bash curl -X GET -H "Authorization: Bearer $API_KEY" https://api.astria.ai/packs # or curl -X GET -H "Authorization: Bearer $API_KEY" https://api.astria.ai/gallery/packs ``` ```javascript const headers = { Authorization: `Bearer ${API_KEY}` } fetch('https://api.astria.ai/packs', { headers: headers }); // or fetch('https://api.astria.ai/gallery/packs', { headers: headers }); ``` ```python import requests headers = {'Authorization': f'Bearer {API_KEY}'} requests.get('https://api.astria.ai/packs') # or requests.get('https://api.astria.ai/gallery/packs') ``` #### Response See the [original billing example](https://docs.astria.ai/docs/api/pack/list/); use [current pricing](https://www.astria.ai/pricing) for rates. --- # List all prompts Source: https://docs.astria.ai/docs/api/prompt/list/ ### Parameters #### `before_id` (optional, preferred) Keyset-pagination cursor. Returns prompts with `id` lower than the given value, following the default newest-first (id-descending) order. To fetch the next page, pass the `id` of the last prompt on the current page, and repeat until an empty page is returned, e.g. `GET /prompts?limit=120&before_id=12345`. Prefer this over `offset`: its cost stays constant no matter how deep you page. #### `limit` (optional) Page size. Default: 20. Maximum: 120. #### `offset` (optional) Starting offset for the list of prompts. Default: 0. Suitable for shallow paging only — offsets above 10,000 return HTTP 400 with an error message pointing to `before_id`. ### Returns An array of prompts owned by the authenticated user. If used as a nested resource (in the url), will return prompts owned by the tune. #### GET /tunes/:id/prompts ```bash curl -X GET -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/1/prompts ``` ```javascript const headers = { Authorization: `Bearer ${API_KEY}` } fetch('https://api.astria.ai/tunes/1/prompts', { headers: headers }); ``` ```python import requests headers = {'Authorization': f'Bearer {API_KEY}'} requests.get('https://api.astria.ai/tunes/1/prompts', headers=headers) ``` #### Response ```json [ { "id": 1, "callback": "https://optional-callback-url.com/to-your-service-when-ready?user_id=your_internal_user_id&transaction_id=internal_transaction_id", "text": "a painting of ohwx man in the style of Van Gogh", "negative_prompt": "old, blemish, wrinkles, mole", "cfg_scale": null, "steps": null, "seed": null, "trained_at": null, "started_training_at": null, "created_at": "2022-10-06T16:12:54.505Z", "updated_at": "2022-10-06T16:12:54.505Z", "tune_id": 1, "url": "http://api.astria.ai/tunes/1/prompts/1.json" } ] ``` --- # List all tunes Source: https://docs.astria.ai/docs/api/tune/list/ ### Parameters #### `before_id` (optional, preferred) Keyset-pagination cursor. Returns tunes with `id` lower than the given value, following the default newest-first (id-descending) order. To fetch the next page, pass the `id` of the last tune on the current page, and repeat until an empty page is returned, e.g. `GET /tunes?limit=120&before_id=12345`. Prefer this over `offset`: its cost stays constant no matter how deep you page. #### `limit` (optional) Page size. Default: 20. Maximum: 120. #### `offset` (optional) Starting offset for the list of tunes. Default: 0. Suitable for shallow paging only — API requests with offset above 200 return HTTP 400 with an error message pointing to `before_id`. ### Returns An array of all fine-tunes owned by the authenticated user #### GET /tunes ```bash curl -X GET -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes ``` ```javascript const headers = { Authorization: `Bearer ${API_KEY}` } fetch('https://api.astria.ai/tunes', { headers: headers }); ``` ```python import requests headers={'Authorization': f'Bearer {API_KEY}'} requests.get('https://api.astria.ai/tunes', headers=headers) ``` #### Response ```json [ { "id": 1, "title": "John Doe", "name": "woman", "token": "ohwx", "base_tune_id": null, "args": null, "steps": null, "face_crop": null, "ckpt_url": "https://sdbooth2-production.s3.amazonaws.com/mock", "ckpt_urls": [ "https://sdbooth2-production.s3.amazonaws.com/mock" ], "trained_at": "2023-10-02T14:32:40.363Z", "started_training_at": "2023-10-02T14:32:05.229Z", "expires_at": "2023-11-01T14:32:40.363Z", "created_at": "2023-10-02T14:32:05.067Z", "branch": "sdxl1", "model_type": "lora", "updated_at": "2023-10-02T14:32:40.363Z", "url": "https://www.astria.ai/tunes/788416.json", "orig_images": [ "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock" ] }, { "id": 775459, "title": "Marry Jane", "name": null, "is_api": false, "token": "ohwx", "base_tune_id": null, "args": null, "steps": null, "face_crop": null, "ckpt_url": "https://sdbooth2-production.s3.amazonaws.com/mock", "ckpt_urls": [ "https://sdbooth2-production.s3.amazonaws.com/mock" ], "trained_at": "2023-09-23T16:07:49.137Z", "started_training_at": "2023-09-23T16:07:37.334Z", "expires_at": "2023-10-23T16:07:49.137Z", "created_at": "2023-09-23T16:07:36.606Z", "branch": "sdxl1", "model_type": "lora", "updated_at": "2023-09-23T16:07:49.138Z", "url": "https://www.astria.ai/tunes/775459.json", "orig_images": [ "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock" ] } ] ``` --- # Retrieve a pack Source: https://docs.astria.ai/docs/api/pack/get/ ### Parameters ### Returns A pack object #### `GET /p/:id` ```bash curl -X GET -H "Authorization: Bearer $API_KEY" https://api.astria.ai/p/260 ``` ```javascript const headers = { Authorization: `Bearer ${API_KEY}` } fetch('https://api.astria.ai/p/260', { headers: headers }); ``` ```python import requests headers = {'Authorization': f'Bearer {API_KEY}'} # 260 is the ID for Corporate headshots as can be seen from https: requests.get('https://api.astria.ai/p/260') ``` #### Response See the [original billing example](https://docs.astria.ai/docs/api/pack/get/); use [current pricing](https://www.astria.ai/pricing) for rates. --- # Retrieve a prompt Source: https://docs.astria.ai/docs/api/prompt/retrieve/ ### Parameters No parameters ### Returns Returns the prompt object. #### GET /tunes/:tune\_id/prompts/:id ```bash curl -X GET -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/1/prompts/1 ``` ```javascript const headers = { Authorization: `Bearer ${API_KEY}` } fetch('https://api.astria.ai/tunes/1/prompts/1', { headers: headers }); ``` ```python import requests headers = { 'Authorization': f'Bearer {API_KEY}' } requests.get('https://api.astria.ai/tunes/1/prompts/1', headers=headers) ``` #### Response ```json { "id": 1, "callback": "https://optional-callback-url.com/to-your-service-when-ready", "text": "a painting of ohwx man in the style of Van Gogh", "negative_prompt": "old, blemish, wrinkles, mole", "cfg_scale": null, "steps": null, "seed": null, "trained_at": null, "started_training_at": null, "created_at": "2022-10-06T16:12:54.505Z", "updated_at": "2022-10-06T16:12:54.505Z", "tune_id": 1, "url": "http://api.astria.ai/tunes/1/prompts/1.json" } ``` --- # Retrieve a tune Source: https://docs.astria.ai/docs/api/tune/retrieve/ ### Parameters No parameters ### Returns Returns the Tune object. #### GET /tunes/:id ```bash curl -X GET -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/1 ``` ```javascript const headers = { Authorization: `Bearer ${API_KEY}` } fetch('https://api.astria.ai/tunes/1', { headers: headers }); ``` ```python import requests headers = {'Authorization': f'Bearer {API_KEY}'} requests.get('https://api.astria.ai/tunes/1') ``` #### Response ```json { "id": 1, "title": "John Doe", "name": "woman", "token": "ohwx", "base_tune_id": null, "args": null, "steps": null, "face_crop": null, "ckpt_url": "https://sdbooth2-production.s3.amazonaws.com/mock", "ckpt_urls": [ "https://sdbooth2-production.s3.amazonaws.com/mock" ], "trained_at": "2023-10-02T14:32:40.363Z", "started_training_at": "2023-10-02T14:32:05.229Z", "expires_at": "2023-11-01T14:32:40.363Z", "created_at": "2023-10-02T14:32:05.067Z", "branch": "sdxl1", "model_type": "lora", "updated_at": "2023-10-02T14:32:40.363Z", "url": "https://www.astria.ai/tunes/788416.json", "orig_images": [ "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock" ] } ``` --- # The pack object Source: https://docs.astria.ai/docs/api/pack/pack/ A pack is a collection of prompts. A pack also contains the configuration for fine-tuning such as using LoRA/FaceID/Checkpoint, base model and preset. info The [headshot-starter](https://github.com/astriaai/headshots-starter?tab=readme-ov-file#incoming-changes) open-source project now supports packs. ## Advantages 1. Test new prompts, ideas and themes on a bulk of models quickly to assure consistent high-quality results. 2. Move fast from the creative process of creating prompts to the deployment of new themes in production. 3. Avoid mismatch between hard-coded JSONs and the actual prompts. 4. Decouple the creative process from your code and avoid mirroring inference API details in your codebase. 5. Run user tests in production for new prompts and quickly iterate. 6. Aggregate likes for prompts to improve the quality of the generated images and tighten the feedback loop. 7. Sort packs by aggregated likes to present the best packs to the user. ## Example user flow 1. `GET /packs` Display a list of packs to the user. See [docs](https://docs.astria.ai/docs/api/pack/list/) 2. User selects a pack, a class name (man/woman) and training images 3. User selects image to upload. Images are inspected using the [Image Inspect API](https://docs.astria.ai/docs/api/images/inspect/) to warn against bad quality images and detect characteristics of the training images for templating with `characteristics`. 4. Call `POST /p/:id/tunes` with title, (training) images, class name and `characteristics` - to create a new fine-tune model using a pack. See [docs](https://docs.astria.ai/docs/api/pack/tunes/create/) 5. `GET /tunes/:id/prompts` to get a list of prompts and their status 6. `POST /prompts/:id/likes` to send feedback to the API. See [docs](https://docs.astria.ai/docs/api/like/create/) 7. Sort prompts by likes 8. Present packs sorted by aggregated likes ## Tips 1. Packs should include multiple classes, like `man` and `woman` or `cat` and `dog`. Astria will use the relevant filters when a new tune is created according to the tune class 2. Pack cover image is taken from the last prompt added to the pack. If you'd like to change the pack cover image, you can do so by removing the last prompt from the pack and adding it again. The pack cover image will be updated to the last prompt added to the pack. ## Getting started 1. Click your email in the header to access [my packs](https://www.astria.ai/packs), and create your first pack. ![create_pack.png](https://docs.astria.ai/assets/images/create_pack-54e08c82617726b7fff50eb99b6e9818.png) 2. Assign prompts to the pack from the [prompts tab](https://www.astria.ai/prompts).![assign_prompts.png](https://docs.astria.ai/assets/images/assign_prompts-b3a88dc38694dc14d78752a498d1f4ec.png) 3. Once a pack is assigned a new tag shows next to the prompt indicating that it is assigned to the pack. --- # The prompt object Source: https://docs.astria.ai/docs/api/prompt/prompt/ The prompt object is usually used as a nested resource of tune as prompts are generated using a fine-tune model. A prompt is a text that is used to generate images using a fine-tune model. The prompt object contains the generated images. --- # The tune object Source: https://docs.astria.ai/docs/api/tune/ Tune (or Fine-tune) represents a model that is created using training images to learn a new concept or subject. At its core a fine-tune is a neural-network weights file (usually weights 2GB) and contains the information of the trained images. A fine-tune model can be of various types (checkpoint or lora) and can be used to create prompts which will in turn generate images. --- # AI Interior Design Source: https://docs.astria.ai/docs/use-cases/room-redesign/ > Archived documentation, retained for reference. **Backend V1** Original room ![Original room](https://docs.astria.ai/assets/images/ai-interrior-redesign-input-af46f8947fdb4f40c831a3d387479d6f.jpeg) Generated using mlsd ![generated.png](https://docs.astria.ai/assets/images/ai-interrior-redesign-mlsd-c6d13d53f345055ace616d19399d62c8.jpg) Generated room using segroom + mlsd ![Generated room using segroom + mlsd](https://docs.astria.ai/assets/images/ai-interrior-redesign-segroom-8dc0b609de0ade09f903ca8a964a5eb0.jpeg) Generated room with window masking ![generated.png](https://docs.astria.ai/assets/images/ai-interrior-redesign-segroom-masking-b3b3c041cef3add1eedb0e7f609237a1.jpeg) Original sketch ![Original room](https://docs.astria.ai/assets/images/ai-interrior-redesign-input-sketch-6212a108e423e8896124e3f7ef9f51a4.png) Generated room with window masking ![generated.png](https://docs.astria.ai/assets/images/ai-interrior-redesign-input-sketch-mlsd-326ee922dbfeeb3edabdf1039ddf5f06.jpeg) See example prompts in the [photobooth](https://www.astria.ai/photobooth?controlnet=mlsd) page or in the [gallery](https://www.astria.ai/gallery?controlnet=mlsd). ## Overview AI Interrior design is the term for the process of generating images of a room with different furniture, wall colors, and decorations all while keeping the original room layout. This can be useful for interior designers, real estate agents, or when planning a renovation. ## How to use In order to preserve the room layout use either controlnet `mlsd` or `segroom`. Input images can be sketches, 3d models, or photos of the room. Depending on the input, you might need to adjust the strength and combination of controlnets as well as the prompts. Controlnet `segroom` is a specific controlnet that was trained to preserve the meaning of the room layout, such as walls and windows. Controlnet `mlsd` is a more general model, used with a pre-processor that produces straight lines and is suitable for architecture. `mlsd` is support for both SDXL and SD1.5 models while `segroom` is only available for SD1.5 models. Additionally you may decide to automatically mask out windows and doors by using the `--mask_prompt windows door --mask_invert` prompt. See more information on [prompt masking syntax](https://docs.astria.ai/docs/features/prompt-masking/). ### Example prompt ```text Clean scandinavian design living room, natural light, architecture magazine cover --mask_prompt windows door --mask_invert --controlnets mlsd segroom --controlnet_weights 0.5 1 num_images=1 negative_prompt=painted, sketch, deformed, easynegative seed= steps=30 cfg_scale= controlnet=lineart input_image_url=https://sdbooth2-production.s3.amazonaws.com/du6cwkp9nojx3ze66vv5nqi39jxo mask_image_url= denoising_strength=1 controlnet_conditioning_scale= controlnet_txt2img=false super_resolution=true inpaint_faces=false face_correct=false film_grain=false face_swap=false hires_fix=true backend_version=1 ar=1:1 scheduler=dpm++sde_karras color_grading= use_lpw=true w= h= ``` --- # Backend V1 Source: https://docs.astria.ai/docs/features/backend-v1/ > Archived documentation, retained for reference. Backend V1 is a complete rewrite of the original image inference and processing pipeline. Here are some of the improvements: 1. Hi-Res (aka Super-Resolution Details) for SDXL is available. 2. Faster. 3. Better handling of loading multiple LoRAs. 4. DPM++/Karras and DPM++SDE/Karras better results, especially with SDXL. 5. Civit results should be easy to reproduce. Features still pending implementation for Backend V1: 1. TCD sampler 2. FaceID with --faceid\_portrait 3. `--fix_bindi` info As this is still in BETA stages, we encourage you to report any issues you may encounter using the support email. ## Usage You can find the "Backend version" dropdown in the **Advanced** settings of the prompt. Alternatively you may select the default account version for the account in the [user account setting](https://www.astria.ai/users/edit#profile) For API usage provide the `backend_version` attribute in the Prompt payload. See: [Create Prompt](https://docs.astria.ai/docs/api/prompt/create/#backend_version-optional) for more details. ![Backend V1 advanced settings](https://docs.astria.ai/assets/images/backend_v1_settings-3d58f812ce883f7356ddd68be841ddcd.jpg) --- # ControlNet Source: https://docs.astria.ai/docs/use-cases/controlnet/ > Archived documentation, retained for reference. ## Preserve composition Input image ![source.png](https://docs.astria.ai/assets/images/controlnet-input-47a0e7bd790726c6290ff60c3102257f.jpeg) Output with Controlnet img2img tile ![generated.png](https://docs.astria.ai/assets/images/controlnet-output-8792495c9018de2ceb447bcce0a99495.jpg) Input image ![source.png](https://docs.astria.ai/assets/images/controlnet-input2-7026826b3c01ab8a035e9d247ae31097.jpeg) Output with Controlnet img2img pose ![generated.png](https://docs.astria.ai/assets/images/controlnet-output2-ca89564f598070101fb713b4a79ebe22.png) --- # Create a theme Source: https://docs.astria.ai/docs/api/themes/create/ > Archived documentation, retained for reference. Themes API leverages a LLM (Large Language Models) to generate a variety of prompts based on a theme. The theme can be a short description of a concept, a mood, or a style. The API call will create 10 prompts which will start processing and generating images. ### Parameters #### `theme` (required) Class name of the object to be inspected in the image. Currently only `man`, `woman` are supported. #### `tuen_ids` (requires) A list of tune ids to be used for the theme. For each tune, 10 prompts will be generated. #### `prompt_attributes` (optional) `num_images, w, h` can be overridden ### Returns Returns empty response with status code 201. The themes are created in the background and can be pulled using `GET /tunes/:id/prompts`. #### POST /themes ```bash curl -X POST "https://api.astria.ai/themes" \ -H "Authorization: Bearer YOUR_API_KEY" \ -F "theme[theme]=Corporate headshots" \ -F "theme[tune_ids][]=123" \ -F "theme[prompt_attributes][num_images]=123" ``` ```python import requests url = "https://api.astria.ai/themes" headers = { "Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json", "Accept": "application/json" } data = { "theme": "Corporate headshots", "tune_ids": [123], "prompt_attributes": { "num_images": 123 } } response = requests.post(url, headers=headers, data=data) print(response.json()) ``` ```javascript const axios = require('axios'); const FormData = require('form-data'); const fs = require('fs'); const url = "https://api.astria.ai/themes"; axios.post(url, { theme: "Corporate headshots", tune_ids: [123], prompt_attributes: { num_images: 123 } }, { headers: { "Authorization": `Bearer YOUR_API_KEY`, "Content-Type": "application/json", "Accept": "application/json" } }) .then(response => { console.log(response.data); }) .catch(error => { console.error(error); }); ``` #### Response 200 OK - empty body response --- # Face Swap Source: https://docs.astria.ai/docs/features/face-swap/ > Archived documentation, retained for reference. Without face-swap ![source.png](https://docs.astria.ai/assets/images/face-swap-before-bfce7608266c9946d32d7d3306a3b1b4.png) With face-swap ![generated.png](https://docs.astria.ai/assets/images/face-swap-after-d63dc108cb39d66fd732681de221198d.png) Face-swap uses training images to further enhance resemblance to the subject. It is recommended to use face-swap in conjunction with face-inpainting and super-resolution. --- # FaceID Source: https://docs.astria.ai/docs/features/faceid/ > Archived documentation, retained for reference. Input images ![source.png](https://docs.astria.ai/assets/images/ai-photoshoot-input-66e224267554a6902bfaa5e6103ae27f.jpg) [FaceID](https://www.astria.ai/gallery?text=faceid) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-output1-aa799eba49b320b187e29cec267a3258.jpg) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-output2-e44e20e05a5a6cffaba2d97ee9ac1947.jpg) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-output3-b4745421562fc7a28a7be8eb46f4853e.jpg) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-output4-8e3f3c0ebd0449c2adf1d12015229097.jpg) [FaceID portrait](https://www.astria.ai/gallery?text=faceid_portrait) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-portrait-output1-8c5ea4967162c608b58d6b56dcb60cce.jpg) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-portrait-output2-890a11d787c8827591af150362b1af05.jpg) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-portrait-output3-92853d1bee638e8a6785ae5961ef96b8.jpg) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-portrait-output4-4d41c4f7de56b0befb40e69b89bc0190.jpg) See example prompts in the [gallery](https://www.astria.ai/gallery?text=faceid) ## Overview FaceID is a model adapter allowing to generate image while preserving a person identity without fine-tuning. Input images can be as few as just one image. The adapter was trained on human faces and cannot be used for pets or other subjects. ## Usage 1. Generate a placeholder fine-tune dataset which will contain the person images. The fine-tune will not go through training and will be immediately ready. ![faceid-new-fine-tune.png](https://docs.astria.ai/assets/images/faceid-new-fine-tune-cc128107ac919832509e97a8ca45918b.png) 2. Generate the image using the FaceID adapter using a syntax similar to LoRA - `` ## Notes 1. Use `cfg_scale=3` 2. FaceID can work nicely together with [Face Swap](https://docs.astria.ai/docs/features/face-swap/) to improve similarity. 3. Disable Face-Swap in case your prompt is animation style. Additionally, make sure to select an animated model from the [gallery](https://www.astria.ai/gallery/tunes?title=pixar). 4. For fast generation use the [LCM schedulers](https://docs.astria.ai/docs/features/lcm/). 5. For realistic images, please consider enabling face-correct to improve facial features. 6. When creating a fine-tune: Base-model is only used as a default for UI generation but does not affect FaceID. 7. When creating a fine-tune: `steps` and `training_face_correct` are not used for FaceID. ## API FaceID is an adapter loaded on top of a base model. As such the inference needs to take place on a model from the gallery. The fine-tune's `trained_at` is set upon creation and there is no training time. As such no callback is needed. ### Step 1: Create fine-tune #### POST /tunes ```bash # With images as multipart/form-data # Hard coded tune id of Realistic Vision v5.1 from the gallery - https://www.astria.ai/gallery/tunes # https://www.astria.ai/gallery/tunes/690204/prompts curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes \ -F tune[title]="John Doe - UUID - 1234-6789-1234-56789" \ -F tune[name]=man \ -F tune[model_type]="faceid" \ -F tune[callback]="https://optional-callback-url.com/webhooks/astria?user_id=1&tune_id=1" \ -F tune[base_tune_id]=690204 \ -F "tune[images][]=@1.jpg" \ -F "tune[images][]=@2.jpg" \ -F "tune[images][]=@3.jpg" \ -F "tune[images][]=@4.jpg" # With image_urls as form-data curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes \ -F tune[title]="John Doe - UUID - 1234-6789-1234-56789" \ -F tune[name]=man \ -F tune[model_type]="faceid" \ -F tune[callback]="https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1" \ -F tune[base_tune_id]=690204 \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" # As JSON cat > data.json <<- EOM { "tune": { "title": "John Doe - UUID - 1234-6789-1234-56789", "name": "man", "model_type": "faceid", "callback": "https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1", "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ] } } EOM curl -X POST -H"Content-Type: application/json" -H "Authorization: Bearer $API_KEY" --data @data.json https://api.astria.ai/tunes ``` ```javascript // NodeJS 16 // With image_urls and fetch() // For NodeJS 18 - do NOT import the below as it is built-in import fetch from "node-fetch"; const API_KEY = 'sd_XXXXXX'; const DOMAIN = 'https://api.astria.ai'; function createTune() { let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY, 'Content-Type': 'application/json' }, body: JSON.stringify({ tune: { "title": 'John Doe - UUID - 1234-6789-1234-56789', // Hard coded tune id of Realistic Vision v5.1 from the gallery - https://www.astria.ai/gallery/tunes // https://www.astria.ai/gallery/tunes/690204/prompts "base_tune_id": 690204, "name": "man", "model_type": "faceid", "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ] } }) }; return fetch(DOMAIN + '/tunes', options) .then(r => r.json()) .then(r => console.log(r)) } createTune() /// With form-data, fetch() // For NodeJS 18 - do NOT import the two below as they are built-in import fetch from "node-fetch"; import FormData from 'form-data'; import fs from 'fs'; const API_KEY = 'sd_XXXX'; const DOMAIN = 'https://api.astria.ai'; function createTune() { let formData = new FormData(); formData.append('tune[title]', 'John Doe - UUID - 1234-6789-1234-56789'); formData.append('tune[model_type]', 'faceid'); // Hard coded tune id of Realistic Vision v5.1 from the gallery - https://www.astria.ai/gallery/tunes // https://www.astria.ai/gallery/tunes/690204/prompts formData.append('tune[base_tune_id]', 690204); formData.append('tune[name]', 'man'); // Load all JPGs from ./samples directory and append to FormData let files = fs.readdirSync('./samples'); files.forEach(file => { if(file.endsWith('.jpg')) { formData.append('tune[images][]', fs.createReadStream(`./samples/${file}`), file); } }); formData.append('tune[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1'); let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY }, body: formData }; return fetch(DOMAIN + '/tunes', options) .then(r => r.json()) .then(r => console.log(r)); } createTune(); ``` ```python import requests headers = {'Authorization': f'Bearer {API_KEY}'} def load_image(file_path): with open(file_path, "rb") as f: return f.read() # Assuming `tune.images` are already defined in your context # If not, you should define them before the below code image_data = load_image("assets/image.jpeg") data = { "tune[title]": "John Doe - UUID - 1234-6789-1234-56789", "tune[name]": "man", "tune[base_tune_id]": 690204, "tune[model_type]": "faceid", "tune[token]": "ohwx" } files = [] for image in tune.images: image_data = load_image(image) # Assuming image is a file path files.append(("tune[images][]", image_data)) API_URL = 'https://api.astria.ai/tunes' response = requests.post(API_URL, data=data, files=files, headers=headers) response.raise_for_status() ``` #### Response ```json [ { "id": 1, "title": "John Doe", "name": "woman", "token": "ohwx", "base_tune_id": null, "args": null, "steps": null, "face_crop": null, "training_face_correct": false, "ckpt_url": null, "ckpt_urls": [], "eta": "2023-10-02T14:32:40.363Z", "trained_at": "2023-10-02T14:32:40.363Z", "started_training_at": "2023-10-02T14:32:05.229Z", "expires_at": "2023-11-01T14:32:40.363Z", "created_at": "2023-10-02T14:32:05.067Z", "branch": "sd15", "model_type": "faceid", "updated_at": "2023-10-02T14:32:40.363Z", "url": "https://www.astria.ai/tunes/788416.json", "orig_images": [ "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock" ] }, { "id": 775459, "title": "Marry Jane", "name": null, "is_api": false, "token": "ohwx", "base_tune_id": null, "args": null, "steps": null, "face_crop": null, "training_face_correct": null, "ckpt_url": "https://sdbooth2-production.s3.amazonaws.com/mock", "ckpt_urls": [ "https://sdbooth2-production.s3.amazonaws.com/mock" ], "eta": "2023-09-23T16:07:49.137Z", "trained_at": "2023-09-23T16:07:49.137Z", "started_training_at": "2023-09-23T16:07:37.334Z", "expires_at": "2023-10-23T16:07:49.137Z", "created_at": "2023-09-23T16:07:36.606Z", "branch": "sd15", "model_type": "faceid", "updated_at": "2023-09-23T16:07:49.138Z", "url": "https://www.astria.ai/tunes/775459.json", "orig_images": [ "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock" ] } ] ``` ### Step 2: Create prompts #### POST /tunes/:id/prompts ```bash # Note the hard-coded 690204 which is the tune_id of Realistic Vision v5.1 from the gallery curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/690204/prompts \ -F prompt[text]=" woman trekking in the alps" \ -F prompt[negative_prompt]="" \ -F prompt[super_resolution]=true \ -F prompt[face_correct]=true \ -F prompt[face_swap]=true \ -F prompt[callback]="https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1" ``` ```javascript const fetch = require('node-fetch'); const FormData = require('form-data'); // Note the hard-coded 690204 which is the tune_id of Realistic Vision v5.1 from the gallery const API_URL = 'https://api.astria.ai/tunes/690204/prompts'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } const form = new FormData(); form.append('prompt[text]', ' woman trekking in the alps'); form.append('prompt[negative_prompt]', ''); form.append('prompt[super_resolution]', true); form.append('prompt[face_correct]', true); form.append('prompt[face_swap]', true); form.append('prompt[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1'); fetch(API_URL, { method: 'POST', headers: headers, body: form }).then(response => response.json()) ``` ```python import requests # Note the hard-coded 690204 which is the tune_id of Realistic Vision v5.1 from the gallery API_URL = 'https://api.astria.ai/tunes/690204/prompts' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } data = { 'prompt[text]': ' woman trekking in the alps', 'prompt[negative_prompt]': '', 'prompt[super_resolution]': True, 'prompt[face_correct]': True, 'prompt[face_swap]': True, 'prompt[callback]': 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1' } files = [] files.append((f"tune[prompts_attributes][{i}][input_image]", load_image(prompt['input_image']))) response = requests.post(API_URL, headers=headers, data=data) ``` --- # FAQ Source: https://docs.astria.ai/docs/use-cases/faq/ > Archived documentation, retained for reference. ## What image size should I upload? Is there a size limit to uploads? There is no hard limit on image uploads, the service does have some limits against JPEG bombs. A reasonable limit would be 3MB per image. The image size should be at least 512x512 pixels as models train on 512x512, but it's also a good idea to give some headroom for face-cropping, so possibly 1024x1024 or 2048x2048 could be a good target resize. Finally make sure compression level does not introduce artifacts to the uploaded images as the model is very sensitive to JPEG artifacts. ## Why isn't it free? For each model created using your training image, Astria creates an AI model using high-end GPU machines, and then serves those models to allow image generation. This process is expensive. ## I didn't get my images. Where can I see it? Check out your [tunes page](https://www.astria.ai/tunes) and click on the tune thumbnail to see your results. ## Can I get higher resolution pictures? Toggle the Super-resolution option in the prompt Advanced section. ## How do I download my images ? Go to [https://www.astria.ai/tunes](https://www.google.com/url?q=https://www.astria.ai/tunes&sa=D&source=editors&ust=1670415722347164&usg=AOvVaw33UrJ0mc31o6FeKglleCjz) and click on the tune thumbnail, hit download to save the pictures of each prompt. ## Generated images look weird. What happened? See [Fine-tuning guide](https://docs.astria.ai/docs/use-cases/finetuning-guide/) and the [AI Photohoot](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) guide for tips on how to get the best results. ## Can I download CKPT file? How do I use it? 1. Once the fine-tune is done processing, you should be able to see a TAR and CKPT buttons at the top of the fine-tune page. 2. You can use the CKPT with software like Automatic1111 and ComfyUI. ## I would like to use your API for my service or app, would you be able to handle the load? Astria is already powering some of the biggest companies and applications on the web and on the app-store using fine-tuning. All jobs are handled in parallel and there is no wait time or queuing. ## What happens to my model, training pictures and image generations? The entire fine-tune (including CKPT file, model, training pictures, generated images, prompts) is kept for 1 month, at the end of which it is all deleted. You can delete it at any time by clicking on the delete button in the fine-tunes page. ## Can I extend my model beyond 30 days? You can select to extend model storage in the [billing page](https://www.astria.ai/users/edit#billing) ## Can I train a model for more than one person? You can train a fine-tune using `class_name=couple` and get amazing results. Make sure that the couple appear in each of the training images together. In this case it might be harder for the model to create more variation and create long-shots and complex scenes. --- # Fine-tuning guide Source: https://docs.astria.ai/docs/use-cases/finetuning-guide/ > Archived documentation, retained for reference. **The guide for high quality avatars and AI photography** When building an app for avatars or professional AI photoshoot, you should consider two aspects 1. Similarity to the original subject 2. High quality generation which look professional ## Tracking subject similarity Make sure to test results on a person you closely know and can judge similarity. Create a list of candidate prompts and run them on 2-3 subjects. For each prompt ask the person to rate how many of the images look similar. Finally sum the ratios for each prompt and compare prompts. Improve the prompts or reconsider a different prompt if similarity is low. Alternatively, consider switching to a different base model to match ethnicity or avoid biased models. ## Fine-tuning training tips 1. Enable **Face-detection** in your [account settings page](https://www.astria.ai/users/edit#profile) - This will augment the training dataset with face-crops from the original training images. This should increase similarity to the subject but can also cause generations to be more toward close-ups instead of nice medium or long shots. In this case you might need to adjust the prompts to use emphasis weights. 2. **Base model** - Select plain SD15 for better resemblance. Select [Realistic Vision V5.1](https://www.astria.ai/gallery/tunes/690204/prompts) as a base model for nicer pictures. These models are good in realistic results. If you’d like to get generations that are more artistic - consider [Deliberate](https://www.astria.ai/gallery/tunes/538238/prompts) or Reliberate as a base model. For a more diverse ethnically and possibly better similarity to subjects, consider [Life Like Ethnicities](https://www.astria.ai/tunes/753832/prompts). Finally, we suggest checking training on [SDXL with text-embedding + LoRA](https://docs.astria.ai/docs/use-cases/sdxl-training/). ## Prompt generation tips 1. Set **width and height** to **512 x 640** - to match Instagram aspect ratio of 4:5 and also create good portraits. Avoid higher aspect ratios as you might get artifacts such as duplicate heads. 2. Enable **face-inpainting** - This is one of Astria’s unique features and allows creating long shots while avoiding deformed faces. 3. Weighted prompts - when a prompt has multiple keywords that collide and reduce similarity - use the parenthesis syntax for emphasis to increase similarity, such as `(ohwx man)` On the flip side, try to avoid weighted prompts altogether to preserve similarity to subject. 4. Use [Controlnet](https://docs.astria.ai/docs/use-cases/controlnet/) with input image to preserve composition and create unique long-shots. Enable `controlnet_txt2img` if you’d like to create more variation and draw more from the prompt. Increase denoising\_strength=1 --- # Latent Consistency Models Source: https://docs.astria.ai/docs/features/lcm/ > Archived documentation, retained for reference. DPM SDE Karras 30 steps 15 seconds for 8 images ![source.png](https://docs.astria.ai/assets/images/schedulers-dpm-sde-karras-ea7a472d4d98205a06688c86da6d0fc6.jpeg) LCM scheduler 6 steps 4 seconds for 8 images ![faceid-output.png](https://docs.astria.ai/assets/images/schedulers-lcm-235b5209584db4d786c70993abbbadfd.jpeg) ## Overview Latent Consistency Models (LCM) is a combination of a scheduler and a LoRA which allow generation within 5-6 steps thus reducing processing time significantly. ## Usage Select the `lcm` scheduler from the Schedulers dropdown. For API usage see [prompt API](https://docs.astria.ai/docs/api/prompt/create/#scheduler-optional). --- # Masked portraits Source: https://docs.astria.ai/docs/use-cases/masked-portraits/ > Archived documentation, retained for reference. Input image ![source.png](https://docs.astria.ai/assets/images/masking-input-1-30b69f2c9a58c8a1f23456828c28c466.jpg) Result using [RV v5.1](https://www.astria.ai/gallery/tunes/690204/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-1-9db772635af3ec249fd3e8ebe9822ed8.jpeg) using [Cartoon](https://www.astria.ai/gallery/tunes/636337/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-art-1-56a489f7b7e42006cd95a2c561849da8.jpeg) Using [Flat](https://www.astria.ai/gallery/tunes/623021/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-flat-1-7139aa49335d33554f1576849e08ae59.jpeg) Input image ![source.png](https://docs.astria.ai/assets/images/masking-input-2-7b8b5861fa50fc87b8e36b3153c879cd.jpg) Result using [RV v5.1](https://www.astria.ai/gallery/tunes/690204/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-2-4a8bc5cca3803cf82043236295dfbf89.jpeg) using [Cartoon](https://www.astria.ai/gallery/tunes/636337/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-art-2-887ab1b97bc8c2499f02a230e7125813.jpeg) Using [Flat](https://www.astria.ai/gallery/tunes/623021/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-flat-2-0f9ff603c09cf9b80380c61658572d21.jpeg) Input image ![source.png](https://docs.astria.ai/assets/images/masking-input-3-0589d66f0f53258a32e5de0ef482f29e.jpg) Result using [RV v5.1](https://www.astria.ai/gallery/tunes/690204/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-3-380476a5d8fbabf4c8a20ed4d92e2db7.jpeg) using [Cartoon](https://www.astria.ai/gallery/tunes/636337/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-art-3-bf4192f7ec3d086c3587d17d6d63e050.jpeg) Using [Flat](https://www.astria.ai/gallery/tunes/623021/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-flat-3-a546e7a92eb69b037149520ab9986a08.jpeg) Input image ![source.png](https://docs.astria.ai/assets/images/masking-input-4-b8bf227c6e5236b0d978f1ad0756d219.jpg) Result using [RV v5.1](https://www.astria.ai/gallery/tunes/690204/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-4-f91c19288661030447e4b4e7ac1a211c.jpeg) using [Cartoon](https://www.astria.ai/gallery/tunes/636337/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-art-4-b9dea5905f8c4e02ca9c77258b1c7058.jpeg) Using [Flat](https://www.astria.ai/gallery/tunes/623021/prompts) ![generated.png](https://docs.astria.ai/assets/images/masking-result-flat-4-36aa99d30a372ca38dbd7eea5bae29e7.jpeg) See example prompts in the [gallery](https://www.astria.ai/gallery?text=mask_prompt). ## Overview Use [prompt masking](https://docs.astria.ai/docs/features/prompt-masking/) feature to embed a person into a scene. This essentially allows you to create AI photography or avatars without fine-tuning. danger SDXL inpainting results are poor. Use SD15 models instead. See [pricing](https://www.astria.ai/pricing) for the cost of this feature. ## Example prompt ```text realistic digital painting, astronaut in a garden on a spring day, by martine johanna and simon stalenhag and chie yoshii and casey weldon and wlop, ornate, dynamic, particulate, rich colors, intricate, elegant, highly detailed, harpers bazaar art, fashion magazine, smooth, sharp focus, 8 k, octane rende --mask_prompt foreground --mask_negative clothes --mask_invert --mask_dilate -20 --hires_denoising_strength 0.2 num_images=1 negative_prompt=clay, text, watermark, padding, cropped, typography seed= steps=30 cfg_scale= controlnet=pose input_image_url=https://sdbooth2-production.s3.amazonaws.com/d6ff3soq5pok5tlbcanf599vkw06 mask_image_url= denoising_strength= controlnet_conditioning_scale= controlnet_txt2img=false super_resolution=true inpaint_faces=false face_correct=true film_grain=false face_swap=false hires_fix=true ar=1:1 scheduler=dpm++sde_karras color_grading= use_lpw=true w= h= ``` ### API usage --- # Multi-LoRA / Multi-Person for Flux Source: https://docs.astria.ai/docs/features/multiperson/ > Archived documentation, retained for reference. 2 people ![multiperson lora for two people](https://docs.astria.ai/assets/images/multiperson_two_people-f137ef20502e5f655ef688a1e4d566dc.jpg) 3 people ![multiperson lora for three people](https://docs.astria.ai/assets/images/multiperson_three_people-e93d9de5615c5921a8f3cdcc6747ca3b.jpg) 4 people ![multiperson lora for four people](https://docs.astria.ai/assets/images/multiperson_four_people-0568a2ddd41982f5d8a1a8a5f5449b10.jpg) See example prompts in the [gallery](https://www.astria.ai/gallery?branch=flux1&text=--multi) ## Overview Multi-person inference is a feature that allows you to generate images with multiple people in them. See [pricing](https://www.astria.ai/pricing) for the cost of this feature. ### Step 1: Training [Create a fine-tune](https://www.astria.ai/tunes/new) for each person. Models should be based on Flux1.dev and model type LoRA. ### Step 2 - Inference Write a simple prompt which references each LoRA and the trigger token and class name. --- # Outpainting Source: https://docs.astria.ai/docs/features/outpainting/ > Archived documentation, retained for reference. **For flux** ### Outpainting only Input ![outpainting for VTON](https://docs.astria.ai/assets/images/outpaint1-input-b5d163b9792b9504bdda840576fc9d8f.png) Outpainting only - no txt2img ![Outpainting VTON result](https://docs.astria.ai/assets/images/outpaint1-out-d2b73ae432b23ddc64a3e564581b87d8.jpg) ### txt2img + Outpainting Txt2img only 1:1 (1024x1024) ![outpainting for VTON](https://docs.astria.ai/assets/images/outpaint2-input-e7390c2fc24213b0acd02ec81e86e912.jpg) txt2img + outpaint to 9:16 ![Outpainting with txt2img result](https://docs.astria.ai/assets/images/outpaint2-out-9-16-467aae6e40fa6043ff6efecdda6190cb.jpg) txt2img + outpaint 16:9 ![outpainting for VTON](https://docs.astria.ai/assets/images/outpaint2-out-16-9-c61b36455bca3e5539434aaea55f9163.jpg) txt2img + outpaint 16:9 ![Outpainting with txt2img result](https://docs.astria.ai/assets/images/outpaint2-out-multi-person-d01b5eb044ee4469b18c3e234f27af8e.jpg) See outpaint examples in the [gallery](https://www.astria.ai/gallery?text=outpaint) ## Overview Outpainting is a technique used to extend an image to bring in more context and background into the initally generated subject of the image. Examples for usage could be taking a 1:1 aspect-ratio (1024x1024) and extending it into a 16:9 cinematic view, or taking a waist up portrait 1:1 and extending it into a full body portrait 9:16. One of the advantages of this is better preservation of the face identity, as well as the ability of the model on focusing on the background generation due to it being separated into a separate task. See [pricing](https://www.astria.ai/pricing) for the cost of this feature. ## Usage Below are the arguments that should be added at the end of the prompt text to enable outpainting: 1. `--outpaint` - The anchor for outpainting. Options: `top-left, top-right, bottom-left, bottom-right, center, top-center, bottom-center, left-center, right-center` 2. `--outpaint_height` 3. `--outpaint_width` 4. `--outpaint_prompt` - Optional In case you're interested in setting an input image and doing only outpainting, and skipping the txt2img/img2img step, make sure to set `denoising_strength: 0` ## Examples Outpaint only ```text --outpaint top-center --outpaint_width 864 --outpaint_height 1296 num_images=1 negative_prompt= seed= steps= cfg_scale= controlnet= input_image_url=https://sdbooth2-production.s3.amazonaws.com/qg202t8sqfn1olewuzdujgaq29si mask_image_url= denoising_strength=0 controlnet_conditioning_scale= controlnet_txt2img=false super_resolution=true inpaint_faces=false face_correct=false film_grain=false face_swap=false hires_fix=false backend_version=0 ar=1:1 scheduler=euler_a color_grading= use_lpw=true w=576 h=864 ``` txt2img + outpainting ```text ohwx woman, dressed in cozy peach sweater, shoulders up, depth of field --outpaint top-center --outpaint_width 1792 --outpaint_height 1024 --outpaint_prompt "fields" num_images=1 negative_prompt= seed= steps= cfg_scale= controlnet= input_image_url= mask_image_url= denoising_strength= controlnet_conditioning_scale= controlnet_txt2img=false super_resolution=false inpaint_faces=false face_correct=false film_grain=false face_swap=false hires_fix=false backend_version=0 ar=1:1 scheduler=euler_a color_grading= use_lpw=false w=1024 h=1024 ``` txt2img + multi-person outpainting ```text ohwx woman, dressed in cozy peach sweater, shoulders up, depth of field --outpaint left-center --outpaint_width 1792 --outpaint_height 1024 --outpaint_prompt "president Trump the middle of the field" num_images=1 negative_prompt= seed= steps= cfg_scale= controlnet= input_image_url= mask_image_url= denoising_strength= controlnet_conditioning_scale= controlnet_txt2img=false super_resolution=false inpaint_faces=false face_correct=false film_grain=false face_swap=false hires_fix=false backend_version=0 ar=1:1 scheduler=euler_a color_grading= use_lpw=false w=1024 h=1024 ``` ## API Usage #### POST /tunes/1504944/prompts ```bash # Note the hard-coded 1504944 which is the tune_id of Flux1.dev from the gallery # Outpaint only example curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/1504944/prompts \ -F prompt[text]="--outpaint top-center --outpaint_width 864 --outpaint_height 1296" \ -F prompt[denoising_strength]=0 \ -F prompt[input_image_url]="https://sdbooth2-production.s3.amazonaws.com/qg202t8sqfn1olewuzdujgaq29si" \ -F prompt[callback]="https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1" # txt2img + outpainting example curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/1504944/prompts \ -F prompt[text]=' ohwx woman, dressed in cozy peach sweater, shoulders up, depth of field --outpaint top-center --outpaint_width 1792 --outpaint_height 1024 --outpaint_prompt "fields"' \ -F prompt[w]=1024 \ -F prompt[h]=1024 \ -F prompt[callback]="https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1" ``` ```javascript const fetch = require('node-fetch'); const FormData = require('form-data'); // Note the hard-coded 1504944 which is the tune_id of Flux1.dev from the gallery const API_URL = 'https://api.astria.ai/tunes/1504944/prompts'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } const form = new FormData(); form.append('prompt[text]', '--outpaint top-center --outpaint_width 864 --outpaint_height 1296'); form.append('prompt[input_image_url]', 'https://sdbooth2-production.s3.amazonaws.com/qg202t8sqfn1olewuzdujgaq29si'); // Alternatively use prompt[input_image] // form.append('prompt[input_image]', fs.createReadStream(`./input-image.png`)); form.append('prompt[denoising_strength]', 0); form.append('prompt[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1'); fetch(API_URL, { method: 'POST', headers: headers, body: form }).then(response => response.json()) // txt2img + outpainting example const form = new FormData(); form.append('prompt[text]', ' ohwx woman, dressed in cozy peach sweater, shoulders up, depth of field --outpaint top-center --outpaint_width 1792 --outpaint_height 1024 --outpaint_prompt "fields"'); form.append('prompt[w]', 1024); form.append('prompt[h]', 1024); form.append('prompt[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1'); ``` ```python import requests # Note the hard-coded 1504944 which is the tune_id of Flux1.dev from the gallery API_URL = 'https://api.astria.ai/tunes/1504944/prompts' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } data = { 'prompt[text]': '--outpaint top-center --outpaint_width 864 --outpaint_height 1296', 'prompt[denoising_strength]': 0, 'prompt[callback]': 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1' } files = [] files.append((f"tune[prompts_attributes][{i}][input_image]", load_image(prompt['input_image']))) response = requests.post(API_URL, headers=headers, data=data) # txt2img + outpainting example data = { 'prompt[text]': ' ohwx woman, dressed in cozy peach sweater, shoulders up, depth of field --outpaint top-center --outpaint_width 1792 --outpaint_height 1024 --outpaint_prompt "fields"', 'prompt[w]': 1024, 'prompt[h]': 1024, 'prompt[callback]': 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1' } response = requests.post(API_URL, headers=headers, data=data) ``` --- # Product placement for Ecommerce Source: https://docs.astria.ai/docs/use-cases/product-shots/ > Archived documentation, retained for reference. ## Background replacement ## Overview Background replacement can be used for e-commerce product short, marketing campaigns and achieving compelling visuals. In this process the existing background is removed and a new background is generated using the prompt text. Using `relight` controlnet we can blend the product with the new background to achieve realistic results. Input image ![Input image of bottle of whiskey](https://docs.astria.ai/assets/images/product-input1-ade7c2a702cccd5e3b429fb746ad37f5.png) Output with controlnet relight ![Bottle of whiskey background replacement](https://docs.astria.ai/assets/images/product-placement-background-replacement-whiskey-out1-3af1e7835749d82838657b97cbc907ff.jpeg) Output with controlnet relight ![Bottle of whiskey background replacement](https://docs.astria.ai/assets/images/product-placement-background-replacement-whiskey-out2-5e923f067c8c7cd59ec1c81f49e9aebe.jpeg) Input image ![Input image of a shoe](https://docs.astria.ai/assets/images/product-input2-2ae76b239f75d4ca95e8575c1b720fcb.png) Output ![Shoe with background replacement of a forest](https://docs.astria.ai/assets/images/product-placement-background-replacement-shoe-out1-15a16a6f6fbe8311f810fb41e9817f8e.jpeg) Output ![Shoe with background replacement of snow](https://docs.astria.ai/assets/images/product-placement-background-replacement-shoe-out2-9db7f3cb72d61b6226f4790b38c3f798.jpeg) See example prompts in the [gallery](https://www.astria.ai/gallery?controlnet=relight). --- # Prompt Masking Source: https://docs.astria.ai/docs/features/prompt-masking/ > Archived documentation, retained for reference. Prompt masking uses a short text to create a mask from the input image. The mask can then be used to inpaint parts of the image. Use the below parameters as part of the prompts to use auto-masking Prompt masking can be used for [product shots](https://docs.astria.ai/docs/use-cases/product-shots/) or [Masked Portraits](https://docs.astria.ai/docs/use-cases/masked-portraits/). See example prompts in the [gallery](https://www.astria.ai/gallery?text=mask_prompt) danger SDXL inpainting results are poor. Use SD15 models instead. ### Syntax Below parameters should be used as text inside the prompt text #### `--mask_prompt` A short text like `foreground` or `face`, `head` or `person`. #### `--mask_negative` A space separated list of words that should not appear in the mask. e.g: `clothes hat shoes` #### `--mask_invert` Inverts the mask #### `--mask_dilate` Dilates the mask. Negative values will erode the mask. Use percentage values like `-20%` or `-20` if input image size is dynamic. #### `--hires_denoising_strength` Denoising strength for hires-fix. Use hi-res fix with prompt-masking to smooth the colors and blending. Range 0-1. Default: 0.4. ```text realistic digital painting, astronaut in a garden on a spring day, by martine johanna and simon stalenhag and chie yoshii and casey weldon and wlop, ornate, dynamic, particulate, rich colors, intricate, elegant, highly detailed, harpers bazaar art, fashion magazine, smooth, sharp focus, 8 k, octane rende --mask_prompt foreground --mask_negative clothes --mask_invert --mask_dilate -20 --hires_denoising_strength 0.1 num_images=1 negative_prompt=clay, text, watermark, padding, cropped, typography seed= steps=30 cfg_scale= controlnet=pose input_image_url=https://sdbooth2-production.s3.amazonaws.com/esfd53purhhcijhmzka4c364x6lb mask_image_url= denoising_strength= controlnet_conditioning_scale= controlnet_txt2img=false super_resolution=true inpaint_faces=false face_correct=true film_grain=false face_swap=false hires_fix=true ar=1:1 scheduler=dpm++sde_karras color_grading= use_lpw=true w= h= ``` --- # SDXL API usage Source: https://docs.astria.ai/docs/api/sdxl-api/ > Archived documentation, retained for reference. For general tips on SDXL training and inference, see [SDXL training](https://docs.astria.ai/docs/use-cases/sdxl-training/) Unlike SD15 checkpoint training, SDXL on Astria is trained as a LoRA+text-embedding. As such, inference is taking place a on a base line model such as SDXL 1.0 and `prompt.text` should specify the loaded lora such as `` - will load lora with id=123456 and strength=0.83 See [LoRA docs](https://docs.astria.ai/docs/features/lora/) on lora syntax With SDXL you cannot combine multiple LoRAs. Use any SDXL model from [the gallery](https://www.astria.ai/gallery/tunes?branch=sdxl1) to do inference. #### POST /tunes/:id/prompts ```bash # Note the hard-coded 666678 which is the tune_id of SDXL 1.0 from the gallery curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/666678/prompts \ -F prompt[text]=" a painting of ohwx man in the style of Van Gogh" \ -F prompt[negative_prompt]="old, blemish, wrin" \ -F prompt[super_resolution]=true \ -F prompt[face_correct]=true \ -F prompt[callback]="https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1" ``` ```javascript const fetch = require('node-fetch'); const FormData = require('form-data'); // Note the hard-coded 666678 which is the tune_id of SDXL 1.0 from the gallery const API_URL = 'https://api.astria.ai/tunes/666678/prompts'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } const form = new FormData(); form.append('prompt[text]', ' a painting of ohwx man in the style of Van Gogh'); form.append('prompt[negative_prompt]', 'old, blemish, wrin'); form.append('prompt[super_resolution]', true); form.append('prompt[face_correct]', true); form.append('prompt[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1'); fetch(API_URL, { method: 'POST', headers: headers, body: form }).then(response => response.json()) ``` ```python import requests # Note the hard-coded 666678 which is the tune_id of SDXL 1.0 from the gallery API_URL = 'https://api.astria.ai/tunes/666678/prompts' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } data = { 'prompt[text]': ' a painting of ohwx man in the style of Van Gogh', 'prompt[negative_prompt]': 'old, blemish, wrin', 'prompt[super_resolution]': True, 'prompt[face_correct]': True, 'prompt[callback]': 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1' } files = [] files.append((f"tune[prompts_attributes][{i}][input_image]", load_image(prompt['input_image']))) response = requests.post(API_URL, headers=headers, data=data) ``` --- # SDXL training Source: https://docs.astria.ai/docs/use-cases/sdxl-training/ > Archived documentation, retained for reference. ## Overview **Stable Diffusion XL** or **SDXL** is the 2nd gen image generation model that is tailored towards more photorealistic outputs with more detailed imagery and composition. SDXL can generate realistic faces, legible text within the images, and better image composition, all while using shorter and simpler prompts. info `LoRA + Text-embedding` is currently the only option for fine-tuning SDXL. Input training images![generated.png](https://docs.astria.ai/assets/images/ai-photoshoot-input-66e224267554a6902bfaa5e6103ae27f.jpg) Output images![generated.png](https://docs.astria.ai/assets/images/sdxl-output-e65457001cbfcde71dc935732d9b396c.jpg) ## Training tips Default token for SDXL should be `ohwx` and will be set automatically if none is specified ## Inference tips 1. Do not copy and paste prompts from SD15 2. Do not use textual-inversions such as `easynegative` or `badhands` from SD15 3. Consider activating face-swap and face-inpainting (which in turn requires super-resolution) - this is the biggest boost you can get to increase similarity to subject 4. Use clean small concise prompts - usually up to 15 words 5. Avoid long negatives - this will decrease similarity to subject. 6. Start with baseline SDXL 1.0 inference before going to other base models. Most custom SDXL models are biased and may reduce similarity. Models which we noticed that work okay are `ZavyChromaXL` and `ClearChromaXL` All above tips will help increase similarity to the original subject. ## Aspect ratios The below aspect ratios are recommended for SDXL inference since these were also used for the training. ```text aspect: width, height 0.5: 704, 1408 0.52: 704, 1344 0.57: 768, 1344 0.6: 768, 1280 0.68: 832, 1216 0.72: 832, 1152 0.78: 896, 1152 0.82: 896, 1088 0.88: 960, 1088 0.94: 960, 1024 1.0: 1024, 1024 1.07: 1024, 960 1.13: 1088, 960 1.21: 1088, 896 1.29: 1152, 896 1.38: 1152, 832 1.46: 1216, 832 1.67: 1280, 768 1.75: 1344, 768 1.91: 1344, 704 2.0: 1408, 704 2.09: 1472, 704 2.4: 1536, 640 2.5: 1600, 640 2.89: 1664, 576 3.0: 1728, 576 ``` ## API usage See here for [API usage](https://docs.astria.ai/docs/api/sdxl-api/) --- # Tiled upscale Source: https://docs.astria.ai/docs/features/tiled-upscale/ > Archived documentation, retained for reference. Super-Resolution ![Super resolution](https://docs.astria.ai/assets/images/sr-1-da25c1c7696cfedc9b739c41ca698428.jpeg) HiRes fix ![Hi-Res fix](https://docs.astria.ai/assets/images/hiresfix-1-e280a53d4326eab3aece7e4bdf04edec.jpeg) Tiled upscale V2 ![Tiled upscale V2](https://docs.astria.ai/assets/images/tiledupscale_v2-1-f7de0d054a93366994c69bf38b8c40d9.jpeg) Tiled upscale ![Tiled upscale](https://docs.astria.ai/assets/images/tiledupscale-1-77589f81054986904af10fe067b3c4cf.jpeg) Super-Resolution ![Super Resolution](https://docs.astria.ai/assets/images/sr-1-detail-86c0e083e452548a5dcfab21fc127b21.jpeg) HiRes fix ![HiRes fix](https://docs.astria.ai/assets/images/hiresfix-1-detail-cb3dfa592dc81c3758b233b0b0852742.jpeg) Tiled upscale v2 ![Tiled upscale V2](https://docs.astria.ai/assets/images/tiledupscale_v2-1-detail-96f797f76447b479a271f3f796bbc94c.jpeg) Tiled upscale ![Tiled upscale](data:image/jpeg;base64,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) See example prompts in the [gallery](https://astria.ai/gallery?text=tiled_upscale). ## Overview Tiled upscale iterates over tiles of the image to upscale them individually while using Tile controlnet to remove blur, add details to the generated image, improve skin tones and give a more realistic look. See example prompts in the [gallery](https://www.astria.ai/gallery?text=tiled_upscale) ## Usage To enable tiled upscale, add `--tiled_upscale_v2` to the prompt text, or `--tiled_upscale` for the legacy version. Tiled upscale V2 requires Backend V1. Customize parameters tiled upscale if needed with the following flags and the default values: 1. `--hires_cfg_scale 5.5` 2. `--resemblance 0.65` See [pricing](https://www.astria.ai/pricing) for the cost details. ## Tiled Upscale V2 Tiled upscale V2 uses SDXL tile controlnet which better preserves identity and similarity while adding details and skin tones, and helps avoid the oversmoothing and painterly look of the original image. ## Example ```text photograph of a woman, (smiling facial expression:1.1), textured skin, goosebumps, (blonde hair:1.2), (unbuttoned white shirt:1.2), distressed boyfriend jeans, long sleeves, (candlelight,chiaroscuro:1.1), cowboy shot, dark and mysterious cave with unique rock formations and hidden wonders, perfect eyes, Porta 160 color, shot on ARRI ALEXA 65, bokeh, sharp focus on subject, shot by Don McCullin --tiled_upscale_v2 num_images=2 negative_prompt=nude, nsfw, (CyberRealistic_Negative-neg:0.8), (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation seed=43 steps=30 cfg_scale= controlnet= input_image_url= mask_image_url= denoising_strength= controlnet_conditioning_scale= controlnet_txt2img=false super_resolution=true inpaint_faces=false face_correct=false film_grain=false face_swap=false hires_fix=false ar=1:1 scheduler=dpm++sde_karras color_grading= use_lpw=true w=512 h=768 ``` --- # Toonify Source: https://docs.astria.ai/docs/use-cases/toonify/ > Archived documentation, retained for reference. Input image ![source.png](https://docs.astria.ai/assets/images/toonify-input-66c7836358b6b2985ca6a595275d8448.png) Output Controlnet tile, model: Disney Pixar ![generated.png](https://docs.astria.ai/assets/images/toonify-output-d1468b1833dfd31e77a91d483951331f.png) --- # Upscale Source: https://docs.astria.ai/docs/use-cases/upscale/ > Archived documentation, retained for reference. Low-res input ![upscale-input](https://docs.astria.ai/assets/images/upscale-input-179b70105f9adc67180b6789f7f56553.jpg) [Upscaled result](https://www.astria.ai/gallery?text=only_upscale) ![upscale-result](https://docs.astria.ai/assets/images/upscale-result-3a748db52d0105807252262fcdfc5d05.jpg) Low-res input [Upscaled result](https://www.astria.ai/gallery?text=only_upscale) Low-res input ![upscale-input](https://docs.astria.ai/assets/images/upscale-lenna-in-bbba5c1db5c20f8aa84f4f7ce7f37763.png) [Upscaled result](https://www.astria.ai/gallery?text=only_upscale) ![upscale-result](https://docs.astria.ai/assets/images/upscale-lenna-out2-3c908eb514c4734ff3df76c0c72b777c.jpg) Low-res input [Upscaled result](https://www.astria.ai/gallery?text=only_upscale) See example prompts in the [gallery](https://www.astria.ai/gallery?text=only_upscale). Also check out [free upscaling tool page](https://www.astria.ai/upscale). ## Overview Upscaling uses model and text guidance to add details to original image. Depending on the `denoising_strength` the model can hallucinate more details shifting away from the original image. For applying tiled upscaling straight after image generation see [tiled upscaling](https://docs.astria.ai/docs/features/tiled-upscale/). ## Usage 1. In the [prompts](https://www.astria.ai/prompts) generate page - Set an input image in the Controlnet/img2img section. 2. Add a text prompt ending with `--only_upscale` 3. Select a model from the dropdown that matches the style and content of the input image. ## Example Example prompt for the result above ```text masterpiece, best quality, highres, --only_upscale num_images=1 negative_prompt=(worst quality, low quality, normal quality:2) JuggernautNegative-neg seed= steps=16 cfg_scale=6.0 controlnet= input_image_url=https://sdbooth2-production.s3.amazonaws.com/cvrx6g5wx5tbiuz9ivoah1j19why mask_image_url= denoising_strength=0.1 controlnet_conditioning_scale= controlnet_txt2img=false super_resolution=false inpaint_faces=false face_correct=false film_grain=false face_swap=false hires_fix=false backend_version=1 prompt_expansion=undefined ar=1:1 scheduler=euler_a color_grading= use_lpw=false w= h= ``` #### POST /tunes/:id/prompts ```bash # Note the hard-coded 690204 which is the tune_id of Realistic Vision v5.1 from the gallery curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/690204/prompts \ -F prompt[text]=very detailed, masterpiece, intricate details, UHD, 8K --only_upscale " \ -F prompt[negative_prompt]="lowres, medium quality, lack of details, bad quality, worst quality, blur, blurry, pixelated, jpeg artifacts" \ -F prompt[callback]="https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1" ``` ```javascript const fetch = require('node-fetch'); const FormData = require('form-data'); // Note the hard-coded 690204 which is the tune_id of Realistic Vision v5.1 from the gallery const API_URL = 'https://api.astria.ai/tunes/690204/prompts'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } const form = new FormData(); form.append('prompt[text]', 'very detailed, masterpiece, intricate details, UHD, 8K --only_upscale '); form.append('prompt[negative_prompt]', 'lowres, medium quality, lack of details, bad quality, worst quality, blur, blurry, pixelated, jpeg artifacts'); form.append('prompt[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1'); fetch(API_URL, { method: 'POST', headers: headers, body: form }).then(response => response.json()) ``` ```python import requests # Note the hard-coded 690204 which is the tune_id of Realistic Vision v5.1 from the gallery API_URL = 'https://api.astria.ai/tunes/690204/prompts' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } data = { 'prompt[text]': 'very detailed, masterpiece, intricate details, UHD, 8K --only_upscale ', 'prompt[negative_prompt]': 'lowres, medium quality, lack of details, bad quality, worst quality, blur, blurry, pixelated, jpeg artifacts', 'prompt[callback]': 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1' } files = [] files.append((f"tune[prompts_attributes][{i}][input_image]", load_image(prompt['input_image']))) response = requests.post(API_URL, headers=headers, data=data) ``` --- # Virtual Try-on for Flux Source: https://docs.astria.ai/docs/use-cases/virtual-try-on/ > Archived documentation, retained for reference. Garment / Clothing - with a model - Recommended ![Person + Garment](https://docs.astria.ai/assets/images/virtual-try-on-dress-input-01891a790108ec2fc5ab6e1ca4809dd4.webp) Generated images ![Person + Garment](https://docs.astria.ai/assets/images/virtual-try-on-dress-out-dcfc60e716f8bd64827d0a0d3dc553b7.jpg) Garment / Clothing - with a model - Recommended ![Person + Garment](https://docs.astria.ai/assets/images/virtual-try-on-swimming-suit-input-dd1e6b021ca5a09f1cfb799f343b0573.jpg) Generated images ![Person + Garment](https://docs.astria.ai/assets/images/virtual-try-on-swimming-suit-out-1a1ceacd85728ad510287e56b3facc10.jpg) Sports virtual try-on ![Person + Garment](https://docs.astria.ai/assets/images/virtual-try-on-flux-lakers-input-67a27371a263fbe3db870ed8dc4f2595.jpg) Generated images ![Person + Garment](https://docs.astria.ai/assets/images/virtual-try-on-flux-lakers-out-1a4642818495a74bc7ea2376378e345b.jpg) Garment / Clothing - Flat lay - must indicate "flat lay" in title - not recommended ![Garment / Clothing](https://docs.astria.ai/assets/images/virtual-try-on-flux-input-381bcf5a6e209dc2095cec56d8173f85.webp) Generated images ![Person + Garment](https://docs.astria.ai/assets/images/virtual-try-on-flux-out-d9d405f9d7042cb695036f5b130b8588.jpg) More examples in the [gallery](https://www.astria.ai/gallery?text=faceid&branch=flux1) ## Overview Virtual try-on is the term for the process of generating images of a person wearing a specific garment or clothing item. This can be useful for e-commerce websites, fashion designers, or even for personal use. See [pricing](https://www.astria.ai/pricing) for the cost of this feature. ## Usage To use Astria's virtual try-on feature, you need to create: 1. A [faceid](https://docs.astria.ai/docs/features/faceid/) fine-tune of the garment you want to try on. The faceid class name should be `clothing`, `shirt` or `pants`, `coat` or `swimming suit`. Class name is used to identify the garment in the image and remove the original model in case a reference image with a fashion model is used. 2. Optional: A model of the person you want to dress up ideally through [fine-tuning](https://docs.astria.ai/docs/use-cases/flux-finetuning/) ## Garment reference image When creating a `faceid` of the garment, two types of images are possible for use: 1. Reference image with a human model. This is recommended as it allows the virtual try-on AI model to infer relative position to the body and get a better understanding of the garment. 2. Flat lay image - a plain image of the garment. In this case, image title must include the wording `flat lay` in the faceid tune description. ![Virtual try on step 1 create reference](https://docs.astria.ai/assets/images/virtual-try-on-step-1-create-faceid-c58b84825c379657c3d7b1fb3869c507.jpeg) ## Tips for inference 1. For the virtual try-on to work, generated image or input image must show elbows or lower part body. Use `waist up` or `long shot` in your prompt. 2. To reference the garment use the `` syntax, where `xxxxxx` is the faceid of the garment. 3. If a face of a specific model is desired, use a fine-tuned [LoRA fine-tuned](https://docs.astria.ai/docs/use-cases/flux-finetuning/). 4. Use a short prompt to describe the background, pose and composition. For example `woman wearing a black shirt and jeans, fashion editorial plain white background`. 5. In case a reference image to the garment includes a fashion model, a better alternative to this, could be to use the input image as a reference pose as well as img2img to bring some of the background features into the image. See above example for dress and respective prompt below. 6. Use 9:16 aspect-ratio i.e: `768x1280` width x height for generating the image. 7. Use controlnet pose, preferably preprocessed stick figure. Set `controlnet conditioning scale` to a low 0.3-0.5 and 8. Turn on super resolution, super resolution details and inpaint faces for better results. 9. Avoid occlusions covering the garment in the generated image such as hands or a bag - The garment should be clearly visible. ## Examples Dress example prompt: ```text front view of ohwx woman wearing dress, fashion editorial plain, white background --control_guidance_end 0.5 num_images=1 negative_prompt= seed= steps= cfg_scale= controlnet=pose input_image_url=https://sdbooth2-production.s3.amazonaws.com/lttvv8rz1olcf28cah48igiydwqi mask_image_url= denoising_strength= controlnet_conditioning_scale=0.5 controlnet_txt2img=false super_resolution=true inpaint_faces=true face_correct=false film_grain=false face_swap=false hires_fix=true backend_version=0 ar=1:1 scheduler=dpm++2m_karras color_grading= use_lpw=true w=768 h=1280 ``` Guns and roses flat lay t-shirt example prompt: ```text closeup view of ohwx woman wearing Guns And Roses shirt, fashion editorial plain, white background --control_guidance_end 0.5 num_images=1 negative_prompt= seed= steps= cfg_scale= controlnet= input_image_url= mask_image_url= denoising_strength= controlnet_conditioning_scale=0.5 controlnet_txt2img=true super_resolution=true inpaint_faces=true face_correct=false film_grain=false face_swap=false hires_fix=true backend_version=0 ar=1:1 scheduler=dpm++2m_karras color_grading= use_lpw=true w=768 h=1280 ``` --- # AI Product Photography vs a Packshot Studio: The Hybrid Workflow Source: https://www.astria.ai/articles/ai-product-photography-vs-studio/ "AI or studio?" is the wrong production question for a fashion brand. It forces a choice between two systems that are good at different forms of evidence. If you first need the broader definition, inputs, and quality-control process, start with the [AI product photography guide](https://www.astria.ai/articles/ai-product-photography/). A physical packshot establishes what the customer will receive. An AI production system turns that approved truth into more scenes, casts, crops, formats, and campaign treatments. The strongest workflow assigns each system the work it can defend. **Upfront disclosure:** this article is published by Astria and recommends Astria for controlled derivative production. It does not recommend replacing every physical capture with generated imagery. **Reviewed September 1, 2026.** GoPackshot is used as the representative managed studio because its public service description spans logistics, physical fashion capture, retouching, QA, and asset delivery. Its operational claims are vendor-reported. ## The short answer | Requirement | Physical packshot studio | AI production workspace | Hybrid decision | | --- | --- | --- | --- | | Exact product color and material evidence | Strongest | Depends on the reference and review | Capture and approve physically | | Labels, hardware and construction details | Strongest | Can drift or invent | Keep physical detail views | | Marketplace main image | Established controlled workflow | Rules and risk vary by channel | Use approved photography as source of truth | | Ten casting or background variations | Expensive to reshoot | Strong once direction is approved | Generate derivatives from approved product references | | Last-minute crop or format change | Retouch or reshoot | Template-level change | Use AI for compliant derivatives where appropriate | | Campaign exploration | Requires crew, set and reshoots | Fast breadth, requires curation | Explore in AI; physically capture only what still needs proof | | Repeatability across a collection | Shot list and studio discipline | Reusable templates and references | Store both the capture standard and creative template | | Accountability for physical inventory | Studio service | Outside AI scope | Assign explicitly to studio or brand operations | The dividing line is not “real versus fake.” It is **evidence versus interpretation**. ## What a managed packshot studio actually buys you A good packshot studio is an operating system around the camera. GoPackshot’s [public services page](https://gopackshot.com/services) describes product logistics, calibrated packshot photography, model and ghost-mannequin work, flat lays, video and 360 content, retouching, quality control, and delivery into client systems. That breadth matters. The difficult parts of ecommerce capture often happen before and after the shutter: - the correct SKU must arrive, be checked, steamed, styled, and matched to the brief; - color and material need a controlled reference; - collars, linings, labels, closures, and hidden construction may need separate views; - filenames, crops, aspect ratios, backgrounds, and channel rules must survive export; - rejected images need an owner and a correction path. GoPackshot publishes figures for color tolerances, peak capacity, studio setups, logistics, and integrations. Those are **vendor-reported claims**. A buyer should verify the exact service level for its category and region rather than treating the website as a contract. The core advantage is accountability for physical truth. The weakness is the cost and delay of creating every possible derivative with people, samples, sets, and repeated post-production. ## What AI product photography actually buys you An AI production workspace is strongest after the product has an approved visual identity. In Astria, the brand can store product references, casting, poses, lighting, backgrounds, crops, and output treatments in a reusable workspace rather than rebuilding them image by image. That makes it useful for: - extending one approved product into on-model and editorial treatments; - testing casting, styling, location, and composition before committing to a physical campaign; - producing consistent social crops, banners, launch frames, and localized variations; - applying one approved direction across a collection or new colorway; - creating motion or campaign derivatives where exact product proof is not the sole job. The trade-off is review. Fine hardware, directional prints, transparent fabric, tiny text, seams, and garment construction can drift. An attractive image is not automatically a faithful product image. The brand still needs a source of truth and a rejection standard. If all you have is a low-resolution marketplace thumbnail, AI does not create missing evidence. It estimates it. ## The hybrid workflow ### 1\. Capture a canonical physical reference set Do not start by photographing the old full shot list. Start by asking which views prove the product and which are merely repeated presentation. For an apparel SKU, the canonical set may include: - clean front and back views; - a color and material reference under controlled light; - neckline, closure, label, trim, print, and texture details; - an interior or hidden view needed for a [ghost-mannequin composite](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/); - scale or construction references for structured accessories and footwear. This set is the product record. It should be versioned, approved, and retained even when downstream campaign imagery is generated. ### 2\. Lock the product standard before creative production The ecommerce lead or product owner approves color, proportions, construction, visible branding, and channel eligibility. Record what cannot change. A usable acceptance brief does not say “make it accurate.” It says: - logo width and placement must match the front reference; - the zipper has one pull, in brushed silver; - the stripe sequence cannot be mirrored; - the hem ends at a named body point; - the fabric is opaque and has no invented sheen; - the marketplace main image remains the approved physical file. Those statements become review criteria for every generated derivative. ### 3\. Build one reusable creative treatment In the Astria workspace, create the casting, pose, lighting, scene, crop, and output format once. Test it on the hardest products, not the easiest basics. The first review set should include directional prints, reflective hardware, loose drape, sheer panels, asymmetry, and any product whose visual identity depends on small construction details. Easy garments conceal workflow failures. When the treatment passes, save it as the collection template. This is the value of a production system: the direction survives the first image. ### 4\. Separate product QA from creative approval Run two reviews, in this order. **Product QA:** Does the generated garment or object match the approved physical reference? Check silhouette, scale, color family, pattern direction, seams, closures, labels, hardware, transparency, and any claim-bearing detail. **Creative approval:** Does the image fit the campaign? Check casting, pose, styling, lighting, crop, hierarchy, brand tone, and channel format. An image that fails product QA does not reach the creative review, however strong the art direction is. ### 5\. Publish from a channel-aware asset kit Do not export one folder called `final-final`. Build explicit asset classes: - physical source-of-truth packshots; - marketplace main and alternate images; - Shopify gallery images, video, or [3D product media](https://www.astria.ai/articles/shopify-product-media-guide/); - approved generated on-model and campaign derivatives; - social, email, paid-media, and motion crops; - rejected outputs retained only where needed for audit or learning. For Amazon apparel, use the [marketplace-specific image checklist](https://www.astria.ai/articles/amazon-product-images-apparel/) and recheck the current category rules. A creative image being persuasive does not make it compliant. ## Which products need more physical evidence? The risk rises when a customer could reasonably complain that the image promised a different object. | Product characteristic | Physical-reference requirement | AI use after approval | | --- | --- | --- | | Solid-color cotton basic | Standard front/back plus color reference | Broad, with normal review | | Directional print or embroidery | High-resolution placement and detail views | Use only with line-by-line pattern QA | | Sheer, lace or translucent fabric | Controlled material, layering and edge references | Limit until opacity and construction pass | | Reflective jewelry or hardware | Macro detail, color and geometry references | Good for scenes; inspect reflections and shape closely | | Structured bag or shoe | Multi-angle construction, sole/interior and dimension references | Strong candidate for multiple campaign views | | Regulated, technical or performance claim | Evidence for every visible claim-bearing feature | Avoid invented demonstrations or unsupported effects | This is also why [flat lay to on-model](https://www.astria.ai/articles/flat-lay-to-on-model/) is not a magic conversion. The source view may not contain the evidence needed to reconstruct the back, interior, drape, or fit. ## How to compare cost without fooling yourself The studio quote and AI credit price are not comparable units. Compare the cost of an **approved, published asset**. For the studio side, include: - sample logistics and product handling; - steaming, styling, set, model, photographer, and equipment; - capture, retouching, naming, QA, revisions, and integration; - reshoots caused by a changed crop, background, casting, or launch brief. For the AI side, include: - reference preparation and workspace setup; - template design, generation, review, rejects, and corrections; - human product QA and creative approval; - integration and export work; - the physical reference capture that made the workflow defensible. Then add the change test: approve ten products, change the background and crop, add a colorway, and calculate the operations required by each workflow. The economics often appear after approval, not on the first image. For a fuller worksheet, use [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/). ## A twelve-SKU pilot brief Do not migrate a collection on a portfolio demo. Run a bounded pilot. Choose twelve SKUs: - three easy basics; - three directional prints or graphics; - two reflective or hardware-heavy products; - two soft or sheer garments; - two structured products such as shoes or bags. Require the studio to deliver the canonical reference set and the AI workflow to deliver the same derivative asset kit for every SKU. Track: 1. time from sample availability to approved publication; 2. total operator and reviewer time; 3. first-pass approval rate; 4. product-accuracy failure reasons; 5. cost per approved published asset; 6. time and cost to apply one post-approval change; 7. whether the final files reach the PIM, DAM, Shopify, Amazon, or campaign channel correctly. The winning workflow may differ by product class. That is a useful result, not a failed pilot. ## When to choose one system without the other Choose a managed studio alone when the entire job is compliant catalog truth, volume is predictable, and the brand does not need a wide derivative campaign system. Choose an AI workflow with an existing in-house reference library when products have already been captured well, the immediate bottleneck is creative variation, and the team can enforce product QA. Choose the hybrid when new products arrive continuously and the same approved direction must extend across catalog, lookbook, campaign, social, and motion. The studio becomes the evidence engine; Astria becomes the derivative production system. If the question is specifically interactive viewing rather than campaign production, use the [3D packshot service comparison](https://www.astria.ai/articles/best-3d-packshot-services-ecommerce/) instead. ## Frequently asked questions ### Can AI product photography replace a packshot studio? Not for every job. A calibrated physical studio remains the stronger source of truth for exact color, construction, labels, hardware, fit evidence, and marketplace-compliant main images. AI is strongest when an approved product reference already exists and the brand needs repeatable on-model, campaign, social, localization, or motion derivatives. ### What should a fashion brand always photograph physically? Photograph any view used to prove the product: the primary catalog image, true color and material reference, construction details, labels, closures, scale, and any marketplace-required view. High-risk reflective, sheer, patterned, or technically constructed products also deserve stronger physical reference coverage. ### How does a hybrid product photography workflow work? The studio captures a compact canonical reference set. The brand approves color, geometry, construction, and channel compliance. Those approved references enter a controlled AI workspace where reusable casting, pose, lighting, background, crop, and format templates create derivatives. Human reviewers compare every output back to the source before publishing. ### How should brands compare AI and studio costs? Compare total cost per approved published asset, not the shutter click or generation price. Include logistics, samples, styling, capture, retouching, model and location fees, setup, prompting, review, rejected outputs, revisions, integration, and the cost of changing a treatment after approval. ### Can an AI-generated image be an Amazon apparel main image? A seller must follow the current requirements for the exact Amazon category and marketplace. Operationally, keep an approved physical photograph as the main-image source of truth and use generated imagery only where it is permitted, accurate, and clearly supports rather than contradicts the listing. ## Sources and methodology The managed-studio description uses [GoPackshot’s first-party services page](https://gopackshot.com/services). Platform and marketplace boundaries use Shopify’s [product media guidance](https://help.shopify.com/en/manual/products/product-media) and the repository’s separately sourced Amazon apparel guide. Astria workflow statements describe Astria’s own product and are therefore first-party claims. We do not treat vendor capacity, efficiency, or commercial-outcome claims as independent evidence; buyers should validate them with their own products and written service levels. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Aiuta (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-aiuta-fashion-ai/ Aiuta is an enterprise virtual try-on company first. Its shopper experience is deployed at recognizable retailers, and Aiuta Studio is a second product that returns finished product photography after human QA. If you are already buying try-on from Aiuta, the studio looks like an easy consolidation. It is worth understanding what you would be consolidating into. **The short answer:** Astria is the clear overall winner for fashion brands producing and directing their own imagery. Choose Aiuta when shopper-facing try-on is the primary purchase and catalog imagery delivered as a service is a welcome extra. ## How we evaluated Astria and Aiuta We used six B2B buying questions: who controls the creative decision, how long a change takes, coverage across a season's channels, casting and asset depth, how collaborators participate, and what the commercial relationship looks like for a brand rather than a retail platform team. ## Quick verdict: Astria vs Aiuta | Decision factor | Aiuta | Astria | Winner | | --- | --- | --- | --- | | Primary product | Enterprise virtual try-on; Studio is a separate service | A fashion production workspace, end to end | **Astria** | | Who controls the creative decision | Submitted as a request; outputs return after vendor QA | The brand's team edits and approves the treatment directly | **Astria** | | Turnaround on a change | Another 24–48 hour delivery cycle | Edit the treatment and re-run immediately | **Astria** | | Professional fashion production | Product photography with backgrounds, lighting, and flat-lay conversion | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Brand standards applied by the vendor's pipeline | Build and reuse casting, styling, scene, crop, lighting, and format as one approved treatment | **Astria** | | Casting and assets | Diverse AI models offered by the platform | Models across ages and body types, including children and extended-size casting, plus locations and poses | **Astria** | | Category coverage | Clothing, footwear, and similar product categories | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial templates | **Astria** | | Campaign and editorial work | Not the studio's focus in the material reviewed | Campaign, editorial, and social treatments from the same approved setup | **Astria** | | Motion | Not publicly verified for Studio in the material reviewed | Multiple current video options animate approved stills in the same workflow | **Astria** | | Team production | Integration through SDK or API | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Delivery throughput bounded by the QA cycle | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Not published; enterprise engagement | Brand pricing scoped to usage — pay for what you create | **Astria** | | Shopper-facing try-on at retail scale | Deployed at major retailers, with SDK and API options | Not attempted | **Aiuta** | | Best fit | Enterprise retailers buying try-on, with catalog imagery as an add-on | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A strong try-on vendor with a service attached | The stronger platform for professional fashion production | **Astria** | Reviewed August 9, 2026; no controlled image-quality benchmark was run. ## Aiuta: try-on infrastructure, with a studio attached Aiuta's core business is shopper-facing virtual try-on for enterprise retail. Shoppers upload a photo and see themselves in an item in seconds, building single looks or complete outfits. It deploys via SDK or API and is designed to slot into existing ecommerce infrastructure with minimal engineering lift. The customer list is the strongest part of the story: ASOS, About You, Alice + Olivia, adidas Originals, Namshi, HEMCO, Tanya Taylor, and Leset. Aiuta Studio is the second product and works differently from most tools in this comparison. A brand supplies basic product images; the platform returns studio-grade outputs with custom backgrounds and lighting, including flat-lay conversion from model shots. Two details define the experience: every output passes human QA before delivery, with a stated 95% first-pass acceptance rate, and turnaround is quoted at 24–48 hours. That QA guarantee is a real differentiator against pure self-serve generators, and it is also the shape of a service. You submit, you wait, you receive. Neither Aiuta page we reviewed publishes pricing for Studio. ## Astria: a professional fashion-production environment Astria puts the creative decision inside the brand. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—is stored, editable, and reusable by the people who own the brand's look. The immediate consequence is iteration speed. A creative director who wants a warmer background, a tighter crop, or different casting changes the treatment and re-runs; there is no submission and no return cycle. The second consequence is coverage: templates span beauty, jewelry, footwear, sports, luxury, and editorial alongside apparel, and the same direction extends into campaign frames, social crops, and motion. ## Public proof: retail deployment versus production adoption Aiuta's evidence is enterprise retail try-on, and it is genuinely impressive at that: recognizable global retailers running its shopper experience. We found no named customer references specifically for Aiuta Studio in the material reviewed, and no published performance metrics on the shopper-experience page beyond capability claims. Astria's public materials show a production base among fashion brands and the professionals who create for them: Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Vendor-published in both cases. The relevant question is which body of evidence matches the job you are buying for. ## A realistic workflow comparison: the consolidation temptation Consider a retailer already running Aiuta try-on across its apparel catalog. The ecommerce director likes the vendor, the integration is stable, and Studio promises catalog imagery from the same contract. Consolidating suppliers is a legitimately good instinct. Here is how the season plays out. Product images go in; polished outputs come back within 24–48 hours, QA-checked, and the acceptance rate is high. Then the creative director sees the first batch and wants the crop 15% wider and the light cooler. That is a new submission and another cycle. Two weeks later a campaign brief lands that needs editorial framing rather than catalog framing, and that is outside what the studio product is aimed at. The lookbook needs a different answer again. The consolidation saved a vendor relationship and did not save the production. With Astria, the crop change is an edit to the approved treatment, applied instantly across everything already produced and everything still to come. The campaign frames and lookbook come from the same treatment rather than a different supplier. And the try-on integration—if the retailer wants it—stays exactly where it is, doing the job it is genuinely good at. ## The differences that matter most ### Enterprise shopper-facing try-on — winner: Aiuta This is Aiuta's genuine edge and the one concession that matters here. Rendering a specific shopper into a garment in seconds, deployed at ASOS and adidas Originals scale, with SDK and API integration paths, is hard infrastructure that Astria does not build. Retailers whose bottleneck is fit confidence at the point of purchase should evaluate Aiuta on those merits and not expect a production tool to substitute. ### Who holds the creative decision — winner: Astria A QA-checked delivery pipeline is dependable precisely because it is standardized, and standardization is the opposite of art direction. In Astria the treatment is the brand's object: editable by the brand's creative director, visible to the brand's team, and applied on the brand's schedule. ### Iteration speed — winner: Astria A 24–48 hour turnaround is excellent for a service and slow for a decision. Creative work converges through rapid, cheap iteration; a delivery cycle makes each iteration a scheduled event. This is the single biggest practical difference between buying finished images and running production. ### Coverage of the season — winner: Astria Aiuta Studio is aimed at product photography. A season also needs lookbook, campaign, editorial, social, and motion, all sharing one visual language. Astria produces those from a single approved direction, which is both fewer suppliers and a more coherent result. ### Casting depth — winner: Astria Aiuta advertises diverse AI models intended to reflect a brand's customers, which is the right ambition. Astria's reusable library goes further into production requirements: ages and body types, including children and extended-size casting, alongside locations, poses, and brand references that persist across seasons. ### Collaboration and review — winner: Astria Aiuta's team surface is technical integration. Astria's is creative review: private brand workspaces, a multiplayer Board with named cursors, and live voice over the actual canvas, with the photographer, in-house studio, and agency all inside the same workspace. ### Collection scale and commercial fit — winner: Astria Studio throughput is bounded by the QA and delivery cycle, and no pricing is published, so a brand cannot model a season before entering a sales process. Astria treats the collection as the unit of work—import the SKU set from Shopify or Google Drive, run it against the approved setup—with brand pricing scoped to usage: pay for what you create. No claim is made here that Astria is cheaper. The point is that a delivery cycle you cannot price is hard to plan a season around; model a full collection, including revisions, before comparing. ### Compounding production value — winner: Astria After a year with a delivery service, a brand has a large set of finished files. After a year with Astria, it has the files plus the casting, references, templates, and approvals that produced them—and the next season starts from those. ## Choose Aiuta if... - Shopper-facing virtual try-on at enterprise retail scale is the primary purchase. - You want catalog imagery delivered as a QA-checked service rather than produced in-house. - A 24–48 hour turnaround fits your production calendar. - SDK or API integration into an existing ecommerce stack is a requirement. ## Choose Astria if... - Your creative director should be able to change direction without a delivery cycle. - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - Casting depth across ages and body types is a production requirement. - Your photographer, studio, and agency need to work in the same workspace. - You want the creative recipe at the end of the season, not only the files. ## The bottom line For fashion brands producing their own imagery, Astria is the stronger choice: the creative decision stays with the people who own it, and iteration costs minutes rather than days. Aiuta is a serious enterprise try-on vendor, and its studio service is a reasonable convenience for retailers who want finished catalog files—just not a substitute for owning production. Consolidating vendors is sound procurement. Consolidating your art direction into a delivery queue is not. ## What to test before choosing either platform Run five representative SKUs through both, then deliberately change the creative direction after the first batch is approved. Measure elapsed time for that change in each path — that number is the comparison. Ask Aiuta for Studio-specific customer references and published pricing. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, check what happens when a campaign or lookbook brief arrives mid-season. ## Frequently asked questions ### Is Aiuta an Astria alternative? Partly. Aiuta is primarily enterprise virtual try-on infrastructure, with Aiuta Studio as a separate service that returns finished product imagery after human QA. Astria is a workspace where a brand's own team directs and runs production. ### Should we buy catalog imagery from our try-on vendor? Only if finished files are what you want. Aiuta Studio is delivered through SDK or API on a 24–48 hour cycle with QA. That is dependable and it is not the same as your creative team iterating on a look in real time. ### How fast can a creative director change direction? In Astria, immediately: edit the approved treatment and re-run. With a QA-delivered service, a change means a new submission and another turnaround cycle. ### Which covers lookbook, campaign, and editorial work? Astria. Aiuta Studio focuses on product photography for catalog use. Astria carries one approved direction across catalog, lookbook, campaign, editorial, social, and motion. ### Is Astria better than Aiuta for fashion brands? For producing and directing a collection's imagery, yes. Enterprise retailers whose primary purchase is shopper-facing try-on, with catalog imagery as a convenient add-on, should evaluate Aiuta on the try-on merits. For adjacent decisions, see how Astria compares with [DRESSX](https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/), [Pixelz](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/), and [FASHN](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 9, 2026. Key sources include Aiuta's [Studio page](https://www.aiuta.com/aiuta-studio) describing its workflow, human QA step, first-pass acceptance rate, and 24–48 hour turnaround, and its [shopper experience page](https://www.aiuta.com/shopper-experience) listing named retail customers and integration options, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. We did not find public pricing for either Aiuta product in this review. Product scope, turnaround commitments, integrations, and commercial terms can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Ayna (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/ Ayna is the most complete listing-automation competitor in AI fashion imagery: flat-lay to on-model photos, short product videos, AI-written titles and descriptions, and a direct push to Shopify and marketplaces. Astria plays a different game for professional teams: it turns art direction into a reusable production system covering casting, styling, review, motion, and handoff across a whole collection. **The short answer:** Astria is the clear overall winner for fashion brands building a durable visual production capability. Choose Ayna when the job is automating routine marketplace listing imagery and copy, and a self-serve start matters more than owning the creative direction. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Ayna We used six B2B buying questions: product fidelity on real garments, fashion-specific creative control, whether approved work can be reused, collection and channel coverage, correction and collaboration workflow, and commercial fit at production scale. ## Quick verdict: Astria vs Ayna | Decision factor | Ayna | Astria | Winner | | --- | --- | --- | --- | | Professional fashion production | Standardized marketplace and catalog listings generated from flat-lays | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Menus of AI models, scene presets, poses, and accessories per shot | Build and reuse casting, styling, scene, crop, lighting, and format direction | **Astria** | | Casting and assets | Attribute-built AI models; custom models on higher plans | Models across ages and body types, locations, poses, and reusable references | **Astria** | | Template breadth | Scene presets and marketplace export presets for apparel listings | Fashion-specific categories including apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Refinement and corrections | Capped regenerations per image and an edit queue measured in working days | Face-inpainting and refinement layer inside the production workflow | **Astria** | | Motion | 10-second catalog videos, metered per second of generation | Multiple current video options animate approved stills in the same workflow | **Astria** | | Team production | One to ten seats depending on plan | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Bulk runs output marketplace variations, listing by listing | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | [See current Ayna pricing](https://www.getayna.com/pricing) | Brand pricing scoped to usage — pay for what you create | **Astria** | | Best fit | Marketplace sellers and catalog teams automating routine listing imagery and copy | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | The fast lane for routine marketplace listings | The stronger platform for professional fashion production | **Astria** | Reviewed August 1, 2026; no controlled image-quality benchmark was run. ## Ayna: listing automation from a single flat-lay Ayna positions itself as an AI photoshoot platform for fashion ecommerce brands. Upload a flat-lay, mannequin, or existing on-model photo, pick a model from an attribute-built library, choose a background, pose, and accessories, and Ayna generates the on-model shot—then rounds out the listing with a short product video, AI-written titles and descriptions, and a direct push to Shopify. Marketplace tooling is a real strength: compliant crops, ghost mannequins, platform-specific ratios, and bulk runs across large SKU batches. Ayna's own materials are candid about the job it does: it replaces the studio, the videographer, and the copywriter for routine catalog and marketplace work. That is a precise description of both the value and the boundary. The model library is deep—Ayna advertises over 1,000 attribute-built AI models, with brand-locked custom models on higher plans—and the categories skew toward marketplace-driven segments such as kidswear, ethnic wear, and plus-size. ## Astria: a professional fashion-production environment Astria approaches the same category from the brand's side rather than the marketplace's. It gives fashion teams a workspace where products, casting, references, templates, and outputs live together, so an approved look becomes a reusable recipe rather than a one-off generation. Templates cover fashion-specific categories beyond standard apparel PDPs—beauty, jewelry, footwear, sports, luxury, and editorial—and brand references keep results anchored to the brand rather than a generic AI aesthetic. Teams operate the approved workflow without becoming prompt experts, and creative decisions survive personnel changes because they are encoded in the workspace. ## Public proof: marketplace throughput versus professional production Ayna's case-study library is genuine adoption evidence, concentrated in marketplace-first fashion: [customer stories](https://www.getayna.com/resources/case-studies) describe Zucchini scaling catalog throughput on Myntra, Aditya Birla Fashion cutting per-SKU photoshoot spend, and D2C labels launching without a physical shoot. Ayna also reports 3,000+ brands and over a million images generated as self-reported platform data. These are vendor-written accounts rather than independent benchmarks, but they show real listing-volume adoption, including at enterprise brand houses. Astria's public materials show a professional production base: recognizable brands including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work on the platform. This is also vendor-published evidence, but the signal points toward brand-directed production—lookbooks, campaigns, and social—rather than listing automation alone. ## A realistic workflow comparison: a seasonal drop Imagine a brand launching 60 garments needing four PDP images per SKU, a small lookbook, paid-social variations, and a late colorway addition. With Ayna, the team uploads flat-lays, picks models and scene presets, and generates listings quickly—including marketplace variations and AI-written copy pushed straight to the store. Use [Ayna pricing](https://www.getayna.com/pricing) to budget for the full shot list and additional attempts. Each listing is also an independent generation: the lookbook, the social variants, and the late colorway each start from menu choices again, and image corrections route through an edit queue measured in working days. With Astria, the team defines casting, styling, lighting, and format once, approves the treatment, and runs the collection against it. The late colorway inherits the approved setup automatically. Campaign crops and video extend the same direction instead of restarting it, and reviewers work on the same board rather than trading screenshots. ## The differences that matter most ### Creative direction and repeatability — winner: Astria Ayna's menus—models, scenes, poses, accessories—make each listing fast, and custom templates on higher plans add some reuse. But the unit of work remains the individual listing, and a menu of presets is not the same as encoding a brand's casting, styling, and art direction. Astria makes the approved direction itself the reusable asset: it persists across SKUs, channels, and contributors, and compounds over a season. ### Natural-looking people and corrections — winner: Astria Ayna advertises fashion-grade realism and photoreal models, and its marketplace acceptance evidence suggests the routine case works. Its correction workflow, however, is metered: plans include one to three regenerations per image, and photoshoot edits carry a one-to-three-working-day turnaround depending on tier. Astria treats refinement as part of the workflow: a dedicated face-inpainting and refinement pass improves facial detail, skin, hair, and texture inside the production loop, without consuming a capped correction budget or waiting on a queue. ### Stills, motion, and formats — winner: Astria Ayna's video product generates 10-second catalog and reel clips, metered per second of generation and capped per plan. That covers PDP motion. Astria offers a choice of current video-generation options so an approved still can be animated without leaving the workspace, alongside campaign, editorial, and social formats drawn from the same source assets. ### Listing automation and marketplace publishing — winner: Ayna This is Ayna's genuine edge, and the one concession that matters: no other tool in this comparison turns a flat-lay into a complete, publishable listing—image, video, title, description, marketplace-compliant crops—and pushes it to Shopify in one flow, from a self-serve plan a team can start on today. It is a listing-operations advantage, not a creative-production verdict: the same pipeline that automates the routine listing standardizes it, and the brand's creative direction lives outside the tool. ### Collection scale and commercial fit — winner: Astria Ayna's bulk runs produce marketplace variations at volume, but the commercial model meters the work: yearly image allowances, per-image credits, capped regenerations, and seat limits by plan. At collection scale the bill and the caps track every SKU, colorway, channel, and retry. Astria treats the collection as the unit of work—the SKU set imports from Shopify or Google Drive, the approved setup runs across it—and brand pricing is scoped to usage: pay for what you create, sized to the production engagement. A per-image allowance is not a production budget; model a full collection, including rework, before comparing costs. ### Collaborative production and compounding value — winner: Astria Ayna offers team seats—one on the entry plan, up to ten on the highest published tier. Astria combines private brand workspaces with a multiplayer Board: teammates see named cursors, talk over the actual canvas with live voice, and resolve looks together. And the work accumulates: products, avatars, templates, approvals, and outputs carry from the collection into campaigns, video, and social, so the photographer, in-house studio, and social agency share one source of truth instead of a folder of exported listings. ## Choose Ayna if... - Your catalog lives on marketplaces and you need routine listing imagery, video, and copy automated end to end. - A self-serve start with published per-seat plans matters more than owning the creative direction. - Your inputs are flat-lays or mannequin shots and the standard on-model treatment is acceptable. - Listing enrichment and direct Shopify publishing are the core of the job. ## Choose Astria if... - One approved creative direction must carry across a collection and its channels. - A creative director or photographer needs to install a workflow other people can reuse. - Casting, styling, and scene decisions must survive team changes and late colorways. - Review should happen on a shared canvas with voice, not through exported files. - Video, campaign, and social work should extend the collection, not restart it. ## The bottom line For professional fashion brands, Astria is the stronger choice: it converts art direction into infrastructure that compounds across the season. Ayna is a capable, honestly positioned listing-automation platform—and the right answer when the entire job is routine marketplace content. Do not decide from hero images. Test the second SKU, the difficult garment, and whether a teammate can reproduce the approved look without help. ## What to test before choosing either platform Run five representative SKUs—including one sheer, knit, or layered garment—through both products. Require PDP, detail, and campaign crops, then add a colorway after approval. Count the credits, regenerations, and queue days the round trip actually consumes. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask a second operator to reproduce the approved direction from scratch. That last test separates listing generators from production systems. ## Frequently asked questions ### Is Ayna an Astria alternative? Yes, for AI on-model fashion imagery. They diverge quickly: Ayna automates routine marketplace listings, while Astria runs a reusable, brand-directed production workflow across a collection and its channels. ### How do Astria and Ayna pricing models differ? Ayna publishes per-seat plans with yearly image allowances and per-image credits, so the real budget tracks generation volume and regenerations. Astria's brand pricing is scoped to usage—model a full collection, not a single listing, before comparing costs. ### Can both turn flat lays into on-model photos? Yes. Ayna generates on-model shots from flat-lay or mannequin photos with menu-picked models and scenes. Astria places product photos into reusable templates that carry an approved creative direction. Test difficult garments such as sheer, knit, or layered pieces on both. ### Which is better for marketplace listings versus brand campaigns? Ayna is built for routine marketplace listings: compliant crops, platform ratios, AI copy, and Shopify publishing. Astria is stronger when one approved creative direction must carry across a whole collection, campaigns, video, and social. ### Is Astria better than Ayna for fashion brands? For professional brands producing on-brand imagery across a collection and multiple channels, yes. Marketplace sellers who mainly need routine listing imagery and copy automated may find Ayna sufficient. For adjacent decisions, see how Astria compares with [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/) and [Caimera](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 1, 2026. Key sources include Ayna's homepage and product pages for photography, models, video, and listings, its [pricing page](https://www.getayna.com/pricing) with plan allowances and credit rates, and its case-study library, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, plan limits, model libraries, and workflow support can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Botika (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-botika-fashion-ai/ Botika offers a quick way to turn existing product photos into standardized on-model images. Astria goes much further: it gives fashion teams the art direction, references, templates, asset range, finishing, motion, and collaborative workspace required to build professional visual production around a brand. **The short answer:** Astria is the clear overall winner for fashion brands. Choose Botika only when the requirement is a narrow, preset-led PDP transformation and speed matters more than distinctive creative direction. Choose Astria when the images must look intentional, on-brand, and reusable across a collection. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Botika We used six B2B buying questions: product fidelity, fashion-specific creative control, reuse of an approved setup, collection and channel coverage, correction workflow, and commercial predictability. ## Quick verdict: Astria vs Botika | Decision factor | Botika | Astria | Winner | | --- | --- | --- | --- | | Professional fashion production | Standardized product-photo transformation | Brand-specific production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Choose from prepared models, poses, and backgrounds | Build and reuse casting, styling, scene, crop, lighting, and format direction | **Astria** | | Casting and assets | Curated self-serve choices; custom options are plan-dependent | Models across ages and body types, locations, poses, and reusable references | **Astria** | | Template breadth | On-model, flat-lay, mannequin, and video transformations | Beauty, jewelry, apparel, footwear, sports, luxury, editorial, and campaign templates | **Astria** | | Natural-looking people | Manual human correction after generation on eligible plans | A face-inpainting and refinement layer improves facial detail inside the production workflow | **Astria** | | Motion | A guided fashion-video product | Multiple current video options can animate approved stills in the same workflow | **Astria** | | Team production | Team access is advertised on the Advanced plan | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Compounding value | Primarily completes product-photo transformations | SKUs, avatars, templates, approvals, and outputs carry from collection to campaign, video, and social | **Astria** | | Collection scale | Guided flow processes a batch, one preset transformation at a time | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Self-serve credit tiers; per-image credits accumulate across every SKU, variant, and channel | Brand pricing scoped to usage and the production engagement | **Astria** | | Best fit | Ecommerce teams that need fast, standardized on-model transformations from existing product photos | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A quick fix for a standardized PDP transformation | The stronger platform for professional fashion production | **Astria** | Reviewed August 1, 2026; no controlled image-quality benchmark was run. ## Botika: generic preset transformations Botika transforms clothing photography into new on-model results through separate [on-model](https://botika.com/products/on-model), flat-lay, mannequin, and fashion-video flows. Users choose a Botika model and background, generate, review, and bulk-download results. Its onboarding guide documents corrections. This is convenient, but convenience is the product's ceiling: selecting generic options is not the same as directing styling, casting, garment behavior, lighting, and composition for a fashion brand. ## Astria: a professional fashion-production environment Astria's broader fashion and ecommerce workflow moves from reusable templates and product inputs to ecommerce, lookbook, social, and campaign assets at scale. Templates move creative decisions upstream: define the treatment once, approve it, and reuse it across a drop. The templates gallery provides fashion-specific starting points, while brand references keep the result from collapsing into a generic AI aesthetic. Teams can operate the approved workflow without becoming prompt experts. ## Public proof: Botika's ecommerce cases versus broader professional adoption at Astria Botika's [case-study library](https://botika.com/resources/case-studies) includes named examples from Jordache, Get Dressed Collective, BLVCK, Nil+Mon, and JUAN & ME. These are vendor-written first-party accounts rather than independent benchmarks, but they show real ecommerce adoption of Botika's transformation workflow. Astria's homepage and fashion materials show a broader production base: recognizable brands including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and professional photographers building client workflows on Astria. This is still vendor-published evidence rather than an independent benchmark, but the signal is materially broader: Astria is used by brands and the professionals who create for them, not only as an image-transformation app. ## A realistic workflow comparison: a 60-SKU drop Imagine a brand has 60 new garments and needs four PDP images per SKU, a small lookbook, and social variations. The campaign should use the same casting direction and visual language, but the team expects late colorway changes. With Botika, the team prepares supported product photography, chooses models, poses, and backgrounds, and generates. Its flat-lay flow can combine garments and accept front and back views; eligible plans include corrections. The workflow processes the queue, but every new creative requirement remains bounded by the available presets. Botika's help documentation describes beta pose limits, while a newer guide documents back views. Confirm current behavior with representative garments. With Astria, the team establishes the casting, styling, scene, lighting, crop, and format once, then applies that direction across products. A late colorway, campaign crop, or video can reuse the approved setup. For a 60-SKU collection, that is the more capable production model. ## The differences that matter most ### Fashion direction — winner: Astria Botika standardizes the job: upload, choose prepared options, and generate. That is practical for routine PDP refreshes, but it can also produce a generic template result—familiar AI polish, weak garment behavior, or styling, poses, and lighting that are not specific to the brand. Paid correction can address marked defects; it does not replace fashion art direction. Astria keeps fashion context in the workflow. Teams can draw from models—including children and extended-size casting—locations, poses, and other references. Templates extend beyond basic apparel PDPs into beauty, jewelry, footwear, sports, luxury, and editorial treatments. ### Natural-looking people — winner: Astria Botika's answer to synthetic-looking people is a manual service: on eligible plans, users flag an output and a human corrects or retouches it. That turns realism into a post-production queue, with turnaround and consistency dependent on manual intervention. Astria handles the problem as a professional technology layer. Its face-inpainting and refinement pass rebuilds facial detail, skin, hair, eyes, and texture—especially in full-body and wider fashion frames where generic generation can produce an obvious AI look. Human creative review still matters, but the finishing layer operates inside a repeatable production workflow rather than being outsourced as a correction request. ### Stills, motion, and existing brand material — winner: Astria Both products offer fashion video. Astria provides a choice of current video-generation options, allowing a team to animate an approved still without rebuilding the job elsewhere. Astria's Describe tool can turn a rights-cleared reference image into usable creative direction while accounting for product and model references already loaded. Its Agent can help reconstruct an existing brand treatment as a reusable AI setup. For a team with campaign material it already owns, that shortens the path from “make something like this” to a repeatable template. We could not verify an equivalent reference-to-workflow feature in Botika's public material reviewed for this article. ### Collaborative production — winner: Astria Botika advertises team collaboration on its Advanced plan. Astria combines private brand workspaces with a multiplayer Board: teammates can arrange references, products, templates, and results on the same canvas, see named cursors, and start a live voice conversation over the work—closer to a creative review huddle than a file handoff. That distinction matters when art direction, merchandising, and production need to resolve a look together. ### Compounding production value — winner: Astria Astria becomes more valuable as the team uses it. Bring the full SKU set into one workspace from Shopify or Google Drive, create reusable avatars and templates, review together on the multiplayer Board with chat and voice, and run the collection. The approved products, casting, direction, and outputs can then move into campaigns and editorials, video, and social content. That keeps a photographer or freelancer, the in-house studio, and the social media agency on the same assets and approvals. Each stage builds on the last instead of duplicating uploads, recreating briefs, or restarting production in another tool. ### Commercial model — winner: Astria Botika publishes self-serve subscription tiers, which makes the first purchase simple. But those tiers meter per-image credits, so the bill grows with every SKU, variant, channel crop, and regeneration—and plan-dependent corrections sit on top. Astria's brand pricing is scoped to usage and the production engagement, and the approved setup is reused rather than re-prompted, so the commercial conversation matches how a brand actually produces. A per-image price is not a production budget—model a full collection, including rework, before comparing costs. ## Choose Botika only if... - You need a standardized on-model PDP transformation from existing product photos. - You are willing to trade category-specific fashion direction for generic preset choices. - A correction queue is sufficient; you do not need to own a reusable creative workflow. - Published credit pricing matters more than a distinctive, cross-channel visual system. ## Choose Astria if... - You need one visual system to cover catalog, lookbook, campaign, and social variations. - A creative director, photographer, educator, or studio will define a workflow that other people should reuse. - The brand needs its casting, styling, scene, crop, and campaign direction to carry across a collection. - Your briefs span different casting needs, category templates, stills, and video. - Creative review should happen with the team on the shared canvas, not through exported screenshots. - The durable deliverable should include the creative recipe, not only the exported images. ## The bottom line For professional fashion brands, Astria is the clear choice. Botika handles a narrow product-photo transformation; Astria provides the fashion direction, breadth, finishing, motion, and team workflow needed to produce the collection and its channels. Do not decide from a polished hero image. The revealing test is the second SKU, the late colorway, and whether another team member can reproduce the approved direction. ## What to test before choosing either platform Run the same five representative SKUs through both products. Require PDP, detail, and campaign crops; add a colorway after approval. Have a senior fashion creative review the outputs blind for garment fidelity, drape, anatomy, styling, lighting, brand fit, and visible AI artifacts. Then ask a second operator to reproduce the approved direction. The final step reveals the difference between generating individual images and operating a repeatable workflow. ## Frequently asked questions ### Is Botika an Astria alternative? Yes, for on-model fashion imagery. They become less interchangeable when the job expands into reusable multi-format production or brand-specific direction. ### Which is easier for a small ecommerce team? Botika's guided flow may be easier for one narrow task. Astria reduces repeated work across products and channels. ### Can both create images from flat lays? Both accept product-photo inputs; Botika provides a dedicated flat-lay flow, while Astria places product photos into a reusable template. Test back views, layers, texture, and fit. ### Which is better for producing a full collection? Astria is designed around taking an approved brand direction across products and content formats, which makes it the stronger fit when the collection—not an individual replacement image—is the unit of work. Botika can push a batch of products through its guided flow, but every image stays bounded by the same preset model, pose, and background choices. ### Is Astria better than Botika for fashion brands? Yes, when the requirement is professional, brand-specific production rather than standardized PDP replacement. Astria offers the broader creative environment and the higher operational ceiling. Buyers should still test difficult garments for fidelity and collection-wide consistency. For adjacent decisions, see how Astria compares with [Rawshot](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/), [Caimera](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/), and [Ayna](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/) — or read the [Botika alternatives](https://www.astria.ai/articles/botika-alternatives/) roundup. Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 1, 2026. Key sources include Botika's product overview, on-model workflow, case studies, pricing, and help center, plus Astria's fashion offering, template gallery, workspace documentation, and video documentation. Pricing, plan limits, model libraries, and workflow support can change. Confirm current terms and test representative source images before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Browzwear (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-browzwear-fashion-ai/ Browzwear and Astria are adjacent, not interchangeable, and any honest comparison has to start there. Browzwear is 30 years of 3D apparel design and development: fabric physics, virtual twins, fit validation, tech packs, fewer physical samples. Astria is a production system for the brand's imagery. The real question buyers ask is narrower and more useful than "which one"—it is whether an existing 3D investment can also produce the catalog and campaign. **The short answer:** for producing on-brand imagery across a collection, Astria is the clear winner, and that is the decision this page addresses. For designing and validating garments before manufacture, Browzwear is the right tool and Astria does not compete with it. Plenty of brands should run both. ## An Astria example: dress photo to packshot Compare the original dress with the generated image, especially the neckline, seams, and hem. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot This shows an Astria result. The other service was not tested with this same photo. [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) ## How we evaluated Astria and Browzwear Because these products sit at different points in the calendar, we evaluated them against one shared question: what does it take to get the season's imagery produced? That means looking at inputs required, specialist skills, category and channel coverage, collaboration, collection-scale behavior, and commercial shape — while stating plainly where Browzwear's real value lies, which is upstream. ## Quick verdict: Astria vs Browzwear | Decision factor | Browzwear | Astria | Winner | | --- | --- | --- | --- | | Where it sits in the calendar | Design and development, before manufacturing | Production of the brand's imagery, before and during selling | **Astria** for imagery | | What it needs as input | A digital twin per style: patterns, graded fits, scanned or curated fabrics | Product photography the brand already has | **Astria** | | Who operates it | Technical designers, pattern makers, and 3D specialists | A brand's creative and ecommerce team, or its photographer or agency | **Astria** | | Professional fashion production | Technical and ecommerce assets derived from 3D styles | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Materials, avatars, and rendering configured per style | Build and reuse casting, styling, scene, crop, lighting, and format as one approved treatment | **Astria** | | Casting and assets | Customizable 3D avatars for fit and grading | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Category coverage | Apparel, with deep garment construction focus | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial templates | **Astria** | | Campaign and editorial imagery | Not the software's purpose | Campaign, editorial, and social treatments from the same approved setup | **Astria** | | Motion | Not the software's purpose in the material reviewed | Multiple current video options animate approved stills in the same workflow | **Astria** | | Collection scale for imagery | Bounded by how many styles have production-ready digital twins | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for imagery | Licensing plus 3D specialist headcount; pricing not published | Brand pricing scoped to usage — pay for what you create | **Astria** | | Sampling, fit, and manufacturing | True-to-life fabric physics, tension mapping, grading, tech packs | Not attempted | **Browzwear** | | Best fit | Design and product development teams reducing samples and getting fit right | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | Depends on the job | | Overall, for producing imagery | A capable by-product of a design pipeline | The stronger platform for professional fashion production | **Astria** | Reviewed August 6, 2026; no controlled image-quality benchmark was run. ## Browzwear: getting the garment right before it exists Browzwear's platform is built around a serious idea: if a virtual garment behaves like the physical one, most of product development can happen before anything is cut. VStitcher creates what the company calls accurate virtual twins of physical garments, combining AI-driven fit intelligence with true-to-life 3D simulation. Designers work across 2D drafting and 3D draping, fit on customizable avatars, grade across sizes, use tension and pressure mapping, and export tech packs with detailed specifications. Fabric Analyzer measures real fabric properties so the simulation is grounded in the actual material. Stylezone handles collection collaboration; the Open Platform handles integrations. The claimed outcomes are the ones a product development director cares about: 50–80% fewer physical samples, 95% first-time-right samples, double the development speed, 35% total cost reduction. The company cites over 1,000 apparel companies and 10 million digital garments created, with brands including Nike, Puma, Under Armour, Target, Walmart, and VF Corporation. These are vendor-published figures rather than independent benchmarks, but the adoption base in this space is not in question. Browzwear also states that the platform generates technical and ecommerce assets. That is true, and it is the sentence that brings buyers to this comparison. ## Astria: a professional fashion-production environment Astria starts where Browzwear's job ends. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—becomes the reusable unit that produces the season's imagery. The inputs are the difference. Astria works from the product photography a brand already has, not from a digital twin built to manufacturing tolerances. Templates span beauty, jewelry, footwear, sports, luxury, and editorial alongside apparel, and the same approved direction extends into campaign frames, social crops, and motion. The people operating it are the brand's creative and ecommerce team, or the photographer or agency they work with—not a 3D department. ## Public proof: development adoption versus production adoption Browzwear's proof is deep and industrial: a thousand-plus apparel companies, household-name brands, and three decades of digital product creation. Read it for what it demonstrates—that 3D design and development works at scale in real supply chains. Astria's public materials show a production base among brands and the professionals who create for them, including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Also vendor-published. Neither set of evidence transfers to the other's domain, which is the honest summary of this whole comparison. ## A realistic workflow comparison: the brand with 3D already in the building Consider a sportswear brand that adopted 3D development two years ago. Roughly half its styles have digital twins. The ecommerce team asks the obvious question: can we skip the photoshoot? Rendering from Browzwear is possible, and for the styles that have production-ready twins it can look excellent, because the garment geometry and fabric behavior are real rather than inferred. But the path has costs that do not appear on a license: only half the line qualifies, so the other half needs a different solution anyway; each render needs a 3D artist to build the scene, light it, and produce the frames; the avatars are fit avatars rather than cast models with a brand's point of view; and a request for a warmer campaign crop is a rendering job, not an edit. Producing 400 SKUs across four channels this way is a studio operation staffed with specialists. With Astria, the same team works from the product photography it already produces. Casting, styling, lighting, crop, and format get approved once, the SKU set imports from Shopify or Google Drive, and the collection runs against that treatment. The styles with digital twins and the ones without are handled identically. Campaign and social extend the same direction, and motion animates stills that already passed review. The two are not in conflict. The 3D investment keeps paying for itself upstream—fewer samples, better fit, faster development—while imagery production stops depending on which styles happen to have twins. ## The differences that matter most ### Digital product creation, fit, and sampling — winner: Browzwear This is Browzwear's genuine edge and the one concession that matters, and it is a large one. True-to-life fabric physics, tension and pressure mapping, size grading, and tech pack generation solve problems that no image-generation product addresses, including Astria. If your objective is fewer physical samples, better first-fit rates, or a shorter development calendar, Browzwear is the answer and this comparison should not talk you out of it. ### What it takes to produce one image — winner: Astria Producing imagery from 3D requires a production-ready digital twin, a specialist to render it, and a scene built per treatment. Producing imagery in Astria requires a product photo and an approved template. For a design team with twins for every style and 3D artists on staff, the first path is viable. For everyone else, the prerequisite is the obstacle. ### Coverage of the channels that sell — winner: Astria Browzwear's ecommerce output is a by-product of a development pipeline: accurate, technical, and centered on the garment. Lookbooks, campaign frames, editorial treatments, social-native crops, and motion are not what the software is for. Astria treats those as expressions of one approved direction, which is what a season actually requires. ### Casting and creative direction — winner: Astria A fit avatar exists to test how a garment sits across sizes. A cast model exists to express who the brand is talking to. Astria's reusable library covers ages and body types, including children and extended-size casting, alongside locations, poses, and brand references—and it holds styling and composition decisions, not just body geometry. ### Collaboration on the creative decision — winner: Astria Browzwear's collaboration is built for collections in development, which is the right shape for its users. Astria's multiplayer Board is built for the creative review: named cursors, live voice, and the actual production canvas shared by the photographer, the in-house studio, and the agency. ### Collection scale for imagery — winner: Astria Browzwear's imagery scale is bounded by digital twin coverage; a style without a production-ready twin cannot be rendered, and building one is development work, not marketing work. Astria imports the whole SKU set and runs it against the approved setup regardless of how each style was developed. ### Commercial shape — winner: Astria Browzwear does not publish pricing in the material reviewed, and the real cost of imagery through a 3D pipeline is not the license—it is the specialist headcount that operates it. Astria's brand pricing is scoped to usage: pay for what you create. This article makes no claim about which is cheaper overall; it notes that the imagery cost in a 3D pipeline is mostly people, and asks you to model a full collection, including the styles without twins, before comparing. ## Choose Browzwear if... - Your objective is fewer physical samples, better first-fit rates, or a faster development calendar. - Garment construction, fabric behavior, grading, and tech packs are the work. - You have or want technical design and 3D specialist capability in-house. - Imagery is a welcome by-product rather than the goal. ## Choose Astria if... - The goal is producing the season's catalog, lookbook, campaign, social, and motion assets. - You want to work from product photography rather than build a digital twin per style. - Your creative team, photographer, or agency should operate the workflow — not a 3D department. - One approved creative direction must carry across the collection and its channels. - Review should happen on a shared canvas with voice, not through rendered files. ## The bottom line These two products belong in different parts of the year, and the most common right answer is both. For designing garments, validating fit, and cutting samples, Browzwear is a mature and well-proven investment. For producing the imagery that sells the finished collection, Astria is the stronger choice: it works from what the brand already has, covers every channel from one approved direction, and does not make marketing output depend on the state of the 3D pipeline. ## What to test before choosing either platform If you already run 3D, take five styles—ideally a mix with and without production-ready twins—and produce a full channel set for each: PDP, detail, campaign, and social. Track two numbers honestly: elapsed time and specialist hours. Then change the creative direction after approval and measure the cost of that change in each path. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask what happens to the styles that will never get a digital twin. ## Frequently asked questions ### Is Browzwear an Astria alternative? No — they are adjacent, not interchangeable. Browzwear is 3D design and development software for getting a garment right before manufacturing. Astria produces the brand's catalog, lookbook, campaign, and social imagery. Many brands run both. ### Can we use our 3D garments instead of a photoshoot? You can render from them, but it is a specialist path: every style needs a digital twin built and a 3D artist to light and render it. Astria produces marketing imagery from product photography without per-style CAD work. ### Which is better for reducing physical samples? Browzwear, decisively. Fabric physics, tension and pressure mapping, grading across sizes, and tech pack output address sampling and fit — problems no image-generation tool solves. ### Which is better for catalog and campaign imagery? Astria. It turns one approved creative direction into catalog, lookbook, campaign, social, and motion assets across a collection, without a 3D pipeline or specialist headcount. ### Is Astria better than Browzwear for fashion brands? For producing the brand's imagery, yes, and that is the decision this article addresses. For designing and validating garments before manufacture, Browzwear is the right tool and Astria does not compete with it. For adjacent decisions, see how Astria compares with [Looklet](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/), [Graswald](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/), and [Pixelz](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 6, 2026. Key sources include Browzwear's homepage with its stated platform outcomes and customer base, and its [VStitcher product page](https://browzwear.com/products/v-stitcher) describing virtual twins, fabric physics, fit tools, and tech pack output, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. We did not find public pricing for Browzwear in this review. Product scope, licensing, integrations, and workflow support can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Caimera (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/ Caimera is the strongest self-serve competitor in AI fashion imagery: fast editorial and catalog generation, transparent credit pricing, and real ad-performance case studies. Astria goes further for professional teams: it turns art direction into a reusable production system covering casting, styling, review, motion, and handoff across a whole collection. **The short answer:** Astria is the clear overall winner for fashion brands building a durable visual production capability. Choose Caimera when the job is fast self-serve product and ad imagery and you want to start without a sales conversation. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Caimera We used six B2B buying questions: product fidelity on difficult garments, fashion-specific creative control, whether approved work can be reused, collection and channel coverage, collaboration and correction workflow, and commercial predictability. ## Quick verdict: Astria vs Caimera | Decision factor | Caimera | Astria | Winner | | --- | --- | --- | --- | | Professional fashion production | High-volume editorial, catalog, and ad imagery | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Templates, a campaign ideas moodboard, and reference-inspired generation | Build and reuse casting, styling, scene, crop, lighting, and format direction | **Astria** | | Casting and assets | A pool of AI fashion models; exclusive models and digital twins on Enterprise | Models across ages and body types, locations, poses, and reusable references | **Astria** | | Template breadth | A large general template library for product and campaign visuals | Fashion-specific categories including apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Product fidelity tools | Click swaps preserve garments from on-model bases; Fluid handles flat-lay transformations | Face-inpainting and refinement layer plus reusable references inside the production workflow | **Astria** | | Motion | VooDoo video agent generates short fashion videos | Multiple current video options animate approved stills in the same workflow | **Astria** | | Team production | Team collaboration from the Starter plan; Photoshop requests on higher tiers | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Bulk flows generate images at volume, one independent generation at a time | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Free tier and per-user credit plans; per-image credits accumulate across a collection | Brand pricing scoped to usage and the production engagement | **Astria** | | Best fit | Ecommerce and growth teams producing fast self-serve product and ad imagery | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | The strongest self-serve option for fast product and ad imagery | The stronger platform for professional fashion production | **Astria** | Reviewed August 2, 2026; no controlled image-quality benchmark was run. ## Caimera: fast self-serve fashion imagery Caimera positions itself as an AI photo studio for ecommerce brands. Its Fluid tool transforms a single product shot into editorial-style imagery, while its Click tool swaps models and backgrounds on existing on-model or mannequin photos. A bulk catalog flow converts flat lays to on-model shots at volume, and the company advertises upscale quality suitable for billboard use. The workflow is deliberately prompt-light: upload a product, pick a template or describe a scene, generate, and refine with editing tools. A campaign ideas tool can read a brand's website or Pinterest board and propose moodboards. Caimera's own help documentation notes that Fluid works best when garment accuracy is not the top priority and recommends Click for delicate fabrics, textures, and sheer materials—an honest boundary that matters for demanding fashion work. ## Astria: a professional fashion-production environment Astria approaches the same category from the opposite direction. Instead of optimizing a single transformation, it gives fashion teams a workspace where products, casting, references, templates, and outputs live together. An approved look becomes a reusable recipe rather than a one-off generation. Templates cover fashion-specific categories beyond standard apparel PDPs, and brand references keep results anchored to the brand rather than a generic AI aesthetic. Teams can operate the approved workflow without becoming prompt experts, and creative decisions survive personnel changes because they are encoded in the workspace, not in someone's chat history. ## Public proof: two credible adoption stories Caimera's case-study library is genuinely useful evidence: [Adore Me](https://www.caimera.ai/case-study/adoreme), [BlissClub](https://www.caimera.ai/case-study/blissclub), [Floafers](https://www.caimera.ai/case-study/floafers), [Ioni Swim](https://www.caimera.ai/case-study/ioniswim), and Maggie the Label describe measurable outcomes—higher click-through rates, lower CPAs, and large production-cost reductions. These are vendor-written accounts rather than independent benchmarks, but they show real commercial adoption, particularly for advertising imagery. Astria's public materials show a broader professional base: recognizable brands including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work on the platform. This is also vendor-published evidence, but the signal points toward professional production use rather than purely self-serve image generation. ## A realistic workflow comparison: a seasonal drop Imagine a brand launching 60 garments needing four PDP images per SKU, a small lookbook, paid-social variations, and a late colorway addition. With Caimera, the team uploads products, runs the bulk catalog flow, uses templates and the campaign ideas tool for social variants, and generates VooDoo videos for ads. Credit consumption is predictable and the queue moves fast. Each image, however, is largely an independent generation: changing the colorway or extending the campaign means re-entering prompts and re-checking consistency manually. With Astria, the team defines casting, styling, lighting, and format once, approves the treatment, and runs the collection against it. The late colorway inherits the approved setup automatically. Campaign crops and video extend the same direction instead of restarting it, and reviewers work on the same board rather than trading screenshots. ## The differences that matter most ### Creative direction and repeatability — winner: Astria Caimera's templates and moodboard tool accelerate ideation, but the unit of work remains the individual image. Astria makes the approved direction itself the reusable asset: casting, styling, scene, crop, and format persist across SKUs, channels, and contributors. That distinction compounds over a season. ### Product fidelity and realism — winner: Astria Both platforms take garment accuracy seriously, and Caimera's Click tool preserves garments well when starting from fitted product photography. But Caimera's documentation concedes Fluid's limits on intricate and sheer garments, and realism depends on which tool fits the input. Astria adds a dedicated face-inpainting and refinement pass that improves facial detail, skin, hair, and texture inside the workflow—a finishing layer rather than a manual correction request. ### Stills, motion, and formats — winner: Astria Caimera's VooDoo agent produces short fashion videos, which covers many ad needs. Astria offers a choice of current video-generation options so an approved still can be animated without leaving the workspace, alongside campaign, editorial, and social formats drawn from the same source assets. ### Collaborative production — winner: Astria Caimera supports team collaboration from its Starter plan and human Photoshop requests on higher tiers. Astria combines private brand workspaces with a multiplayer Board: teammates see named cursors, talk over the actual canvas, and resolve looks together—closer to a studio review than a ticketing queue. ### Self-serve onboarding — winner: Caimera This is Caimera's genuine edge, and the one concession that matters: self-serve onboarding mean a team can start the same day, without a sales conversation. It is an onboarding advantage, not a production-cost verdict—those plans meter per-image credits, so at collection scale the bill tracks how many generations, and regenerations, the work actually takes. ### Collection scale and commercial fit — winner: Astria Caimera's bulk packshot flows produce images at volume, but each output remains an independent generation, and per-image credits accumulate with every SKU, colorway, channel, and retry. Astria treats the collection as the unit of work: the SKU set imports from Shopify or Google Drive, the approved setup runs across it, and brand pricing is scoped to usage and the production engagement rather than metered image by image. A per-image price is not a production budget—model a full collection, including rework, before comparing costs. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate: the photographer, in-house studio, and social agency work from the same source of truth. In Caimera, value accumulates mostly as a library of generated images. Both models deliver ROI; only one builds an owned creative capability. ## Choose Caimera if... - You need product or ad imagery quickly, without a sales cycle. - Published per-seat pricing and credit rollover matter for budgeting. - Your inputs are already well-shot product photos suited to swap or bulk flows. - You want API access, DAM integrations, or bulk packshots. - Ad-creative velocity matters more than owning a repeatable brand production system. ## Choose Astria if... - One approved creative direction must carry across a collection and its channels. - A creative director or photographer needs to install a workflow other people can reuse. - Casting, styling, and scene decisions must survive team changes and late colorways. - Review should happen on a shared canvas with voice, not through exported files. - Video and social should extend the campaign, not restart it. ## The bottom line For professional fashion brands, Astria is the stronger choice: it converts art direction into infrastructure. Caimera is a capable, honestly positioned tool for fast self-serve product and ad imagery—and the right answer when that is the entire job. Do not decide from hero images. Test the second SKU, the difficult garment, and whether a teammate can reproduce the approved look without help. ## What to test before choosing either platform Run five representative SKUs—including one sheer, knit, or layered garment—through both products. Require PDP, detail, and campaign crops, then add a colorway after approval. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask a second operator to reproduce the approved direction from scratch. That last test separates image generators from production systems. ## Frequently asked questions ### Is Caimera an Astria alternative? Yes, for AI-generated fashion and product imagery. They diverge when the job expands from generating individual images into running a repeatable, brand-directed production workflow across a collection. ### Which is easier to budget, Astria or Caimera? Caimera's published per-user plans make the first month easy to estimate, but per-image credits mean the real budget tracks generation volume and rework. Astria's brand pricing is scoped to the production, so model a full collection—not a single image—before comparing costs. ### Can both turn flat lays into on-model photos? Yes. Caimera offers dedicated flat-lay-to-on-model and swap tools, while Astria places product photos into reusable templates. Test difficult garments such as sheer, knit, or layered pieces before committing. ### Which is better for ads versus a full collection? Caimera is a fast way to produce individual product and ad images on self-serve plans. Astria is stronger when one approved creative direction must carry across a whole collection, campaigns, video, and social. ### Is Astria better than Caimera for fashion brands? For professional brands producing on-brand imagery across a collection and multiple channels, yes. Buyers who mainly need fast self-serve product imagery may find Caimera sufficient. For adjacent decisions, see how Astria compares with [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/), [Ayna](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/), and [Rawshot](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 2, 2026. Key sources include Caimera's product pages for its editorial and swap tools, its pricing and credit documentation, its help center articles on tool selection and campaign ideas, and its case-study library, plus Astria's fashion offering, template gallery, workspace documentation, and video documentation. Pricing, plan limits, model libraries, and workflow support can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs DRESSX (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/ DRESSX and Astria both put clothes on people with AI, and there the overlap ends. DRESSX builds the experience a shopper has at the moment of decision: a fitting room on the product page, an AI stylist, an interactive mirror in the store. Astria builds what fills that page in the first place—the catalog imagery, the lookbook, the campaign, the social cutdowns. **The short answer:** for producing a brand's imagery, Astria is the clear winner, and that is the decision this page addresses. For adding a shopper-facing fitting room to a product page or a store, DRESSX is the specialist and Astria does not compete for it. A brand with budget for both is not making a mistake. ## How we evaluated Astria and DRESSX These products sit on opposite sides of the product page, so we evaluated them against one shared question: what does a brand need in order to publish and merchandise a season? That means asking who each product serves, what it produces, what it measures, and where the creative direction lives — while stating plainly where DRESSX's real value sits, which is at the point of purchase. ## Quick verdict: Astria vs DRESSX | Decision factor | DRESSX | Astria | Winner | | --- | --- | --- | --- | | What it produces | A shopper experience: try-on, stylist, in-store mirror | The brand's imagery: catalog, lookbook, campaign, social, motion | **Astria** for content | | Who buys it | Ecommerce and retail experience owners | Creative directors, studios, and ecommerce production teams | **Astria** for production | | Professional fashion production | Not the suite's purpose in the material reviewed | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Rendering a shopper or model into a garment at request time | Build and reuse casting, styling, scene, crop, lighting, and format as one approved treatment | **Astria** | | Casting and assets | Shopper's own likeness or supplied model imagery | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Category coverage | Apparel try-on, with looks and mix-and-match | Fashion-specific templates for apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Campaign and editorial imagery | Not attempted | Campaign, editorial, and social treatments from the same approved setup | **Astria** | | Motion | Not attempted in the material reviewed | Multiple current video options animate approved stills in the same workflow | **Astria** | | Team production | Integration and merchandising configuration | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Scales with catalog coverage on the storefront | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Enterprise integration, sales-led; pricing not published | Brand pricing scoped to usage — pay for what you create | **Astria** | | Conversion, returns, and fit confidence | Purpose-built for it, with published claims | Not attempted | **DRESSX** | | Best fit | Retailers adding a fitting-room experience to the storefront or store | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | Depends on the job | | Overall, for producing imagery | Not a content production tool | The stronger platform for professional fashion production | **Astria** | Reviewed August 7, 2026; no controlled image-quality benchmark was run. ## DRESSX: the experience at the point of decision DRESSX has been in digital fashion since 2020 and has converged on a clear enterprise proposition: an AI suite for fashion and luxury brands, sold as modules. Virtual try-on is the flagship, answering the question the company puts at the center of its pitch—whether an item will actually look good on the shopper. It handles single items, full looks, and mix-and-match, integrates through an API, a white-label option, or a Shopify plugin, and the company advertises very fast integration. Around it sit adjacent products: an AI customer assistant, an AI stylist for retail, and DRESSX Mirror, a human-sized interactive screen for stores, pop-ups, window displays, and events. Named implementations include Victoria Beckham and Farfetch. The published outcome claims are aimed squarely at ecommerce leadership: a 40% reduction in return rate, a 3.2× conversion lift, 68% higher engagement, and up to 75% fit certainty. These are vendor-published figures without disclosed methodology, and a buyer should ask how they were measured and over what baseline. But they are the right metrics for what DRESSX sells, and they are not metrics a content production tool should be claiming. ## Astria: a professional fashion-production environment Astria's job starts earlier in the funnel and further back in the calendar. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—becomes the reusable unit that produces the season. That covers the assets a try-on experience cannot: the PDP images a shopper sees before deciding to engage, the lookbook that frames the collection, the campaign that drives traffic, the social cutdowns, and the motion. Templates span beauty, jewelry, footwear, sports, luxury, and editorial alongside apparel, and the same approved direction runs across the whole SKU set imported from Shopify or Google Drive. ## Public proof: storefront metrics versus production adoption DRESSX publishes brand implementations and conversion-oriented statistics. Read them as evidence that the experience layer gets deployed by serious retailers, and probe the methodology in a demo. Astria's public materials show a production base among brands and the professionals who create for them: Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Also vendor-published. Neither company's evidence speaks to the other's domain, which is the accurate summary of this comparison. ## A realistic workflow comparison: what the shopper actually sees Trace a single customer journey for a spring dress. She sees a paid-social video, clicks through to a campaign landing page, browses the collection grid, opens the product page, scrolls the four PDP images and a detail shot, then taps "try it on." DRESSX owns the last step of that journey and does it well. Everything before it—the social video, the campaign frame, the collection grid, the five PDP images—is content the brand had to produce. If the answer to producing it was a photoshoot, DRESSX has not changed the brand's production economics at all; it has improved the conversion rate on assets that still cost what they always cost. With Astria, that entire upstream chain comes from one approved treatment: the campaign frame, the grid, the PDP set, the detail crop, and the social video all derive from the same casting, styling, and direction, run across the collection. The try-on button at the end is a complementary purchase, not an alternative one. ## The differences that matter most ### Shopper-facing try-on, fit confidence, and returns — winner: DRESSX This is DRESSX's genuine edge and the one concession that matters. Rendering a specific shopper into a specific garment, at request time, on the product page or an in-store mirror, is a hard problem with direct commercial consequences—and Astria does not attempt it. Brands whose live problem is return rates or fit hesitation should evaluate DRESSX on that basis, ask hard questions about how the published lift figures were measured, and not expect a content production tool to substitute. ### Producing what the shopper sees first — winner: Astria A fitting room improves the last click. It does not make the campaign, the grid, or the PDP set. Astria produces all of them from a single approved direction, which is why the collection reads as one shoot rather than a series of separately commissioned jobs. ### Where the creative direction lives — winner: Astria DRESSX renders at request time against whatever imagery and product data the brand supplies; the brand's creative direction is an input it consumes rather than an asset it holds. In Astria the direction is the stored, reusable thing: casting, styling, scene, crop, lighting, and format, editable by the brand's own team and applied to everything downstream. ### Collaboration and review — winner: Astria DRESSX's team surface is integration and merchandising configuration, which is appropriate for an experience layer. Astria's is creative review: private brand workspaces, a multiplayer Board with named cursors, and live voice over the actual canvas, shared by the photographer, in-house studio, and agency. ### Collection scale and commercial fit — winner: Astria DRESSX scales with catalog coverage on the storefront, and its commercial model is an enterprise integration rather than published pricing. Astria treats the collection as the unit of work—import the SKU set, run it against the approved setup, extend into campaign and motion—with brand pricing scoped to usage: pay for what you create. The two are not substitutes on cost, and this article makes no claim that one is cheaper; they buy different things. ### Compounding production value — winner: Astria Every season, Astria's workspace holds more: products, avatars, templates, approvals, outputs. A try-on integration does not compound in the same way—it performs the same function on whatever catalog it is pointed at, which is exactly what it should do. ## Choose DRESSX if... - Return rates, fit hesitation, or PDP conversion are the problem you are funding. - You want a fitting-room experience on the storefront, in an app, or on an in-store screen. - An AI stylist or customer assistant fits your retail experience roadmap. - Your content production is already solved. ## Choose Astria if... - You need to produce the catalog, lookbook, campaign, social, and motion assets for a collection. - One approved creative direction must carry across every channel. - A creative director, photographer, or agency needs to install a workflow other people can reuse. - Review should happen on a shared canvas with voice, not through exported files. - Your production cost, not your conversion rate, is the constraint. ## The bottom line If content production is the bottleneck, Astria is the answer and try-on will not help. If content is handled and conversion is the bottleneck, DRESSX is the answer and a production system will not help. Most brands discover, in order, that they have the first problem — because the fitting room is only as good as the collection page that got the shopper to it. ## What to test before choosing either platform Map your funnel and count the assets. For every product page you plan to add try-on to, list the campaign, grid, PDP, detail, and social assets it depends on, and note who produces them today and at what cost. Then run five representative SKUs through Astria for that full channel set, and add a colorway after approval. If you are also evaluating DRESSX, ask for the methodology behind its published conversion and returns figures, and for a live implementation you can browse as a shopper. ## Frequently asked questions ### Is DRESSX an Astria alternative? No — they serve different jobs. DRESSX is a shopper-facing experience layer: virtual try-on, an AI stylist, and an in-store mirror. Astria produces the brand's catalog, lookbook, campaign, and social imagery. A brand can reasonably buy both. ### Does virtual try-on replace product photography? No. Try-on renders a shopper into a garment at the moment of decision. It does not produce the PDP images, lookbook, campaign frames, or social assets that bring the shopper to that page in the first place. ### Which owns conversion and returns? DRESSX targets those metrics directly and publishes claims for return reduction and conversion lift. Astria is upstream of them: it produces the imagery a brand merchandises with, across every channel. ### Which is better for producing a season's imagery? Astria. One approved creative direction produces catalog, lookbook, campaign, social, and motion across the collection. Content production is not what the DRESSX suite is built for. ### Is Astria better than DRESSX for fashion brands? For producing the brand's imagery, yes, and that is the decision this article addresses. For adding a fitting-room experience to a product page or a store, DRESSX is the specialist and Astria does not compete with it. For adjacent decisions, see how Astria compares with [FASHN](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/), [Tellos](https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/), and [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 7, 2026. Key sources include DRESSX's homepage describing its AI suite, its [B2B virtual try-on page](https://dressx.com/b2b/virtual-try-on) with integration options and published outcome claims, and its [Mirror product page](https://dressx.com/b2b/mirror), plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. We did not find public pricing for the DRESSX B2B suite in this review. Product scope, integrations, published metrics, and commercial terms can change. Confirm current terms and request methodology for any performance claim before making a decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Emilia AI Studio (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-emilia-fashion-ai/ Emilia AI Studio turns a phone snap of a product into a studio-grade image in about forty seconds, and it does that across six product categories: general goods, jewelry, clothing, food and drink, furniture, and beauty. Built by a digital agency and available in English, Hebrew, and Spanish, it is a capable and genuinely broad ecommerce tool. Clothing is one of its six categories. For Astria, fashion is the whole product. **The short answer:** Astria is the clear overall winner for fashion brands producing a collection and its channels. Choose Emilia when your range spans several product types, your volumes are modest, and a free tier with a strong hands-on editor is what you need. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Emilia AI Studio We used six B2B buying questions: how much of the product is about fashion specifically, whether casting can scale with a collection, what the plan structure limits, channel coverage across a season, how the meter behaves at catalog volume, and what evidence supports the vendor's claims. ## Quick verdict: Astria vs Emilia AI Studio | Decision factor | Emilia AI Studio | Astria | Winner | | --- | --- | --- | --- | | Category focus | Clothing is one of six categories, alongside food, furniture, and beauty | Built for fashion, end to end | **Astria** | | Professional fashion production | Studio-grade product shots: packshot, with presenter, lifestyle scene | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Style presets and campaign kits, selected per generation | Approved treatment — casting, styling, scene, crop, lighting, format — stored and reused | **Astria** | | Casting | Custom presenters capped by plan: 1, 2, 5, 8, or 12 | Reusable casting library across ages and body types, including children and extended sizes | **Astria** | | Reusable brand assets | Custom styles capped by plan, and image storage capped too | Products, casting, locations, poses, and references accumulate without plan entitlements | **Astria** | | What "lookbook" means | Up to three variations of one product by angle or focal point | An editorial set expressing the collection's direction | **Astria** | | Campaign and editorial work | Ready-made four-image campaign kits per category | Campaign, editorial, and social treatments from the same approved setup | **Astria** | | Motion | Video generation not verified in the material reviewed | Multiple current video options animate approved stills in the same workflow | **Astria** | | Catalog import | CSV upload and a WooCommerce plugin | SKU import from Shopify or Google Drive into a brand workspace | **Astria** | | Team production | Single-account tool in the material reviewed | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Credits charged by resolution; storage capped per plan | The full SKU set runs against one approved setup | **Astria** | | Commercial model for brands | Monthly credit tiers plus one-time packs, with entitlements attached | Brand pricing scoped to usage — pay for what you create | **Astria** | | Public customer proof | Aggregate statistics; no named brands or case studies found | Named fashion brands plus studios and photographers running client work | **Astria** | | Hands-on image editing | Region-select editing with reference drop, filters, expand, background removal, upscaling | Face-inpainting and refinement inside the production workflow | **Emilia AI Studio** | | Best fit | Small sellers with mixed product ranges | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A broad, well-made ecommerce product studio | The stronger platform for professional fashion production | **Astria** | Reviewed August 14, 2026; no controlled image-quality benchmark was run. ## Emilia AI Studio: breadth across the shelf Emilia's promise is compact and well executed: upload a product photo, pick a style, get campaign-ready images in seconds. Three output types anchor the experience—clean packshot, with presenter, and lifestyle scene—and six category libraries supply the style presets. Campaign kits produce ready-made sets of four images, applied all at once or chosen individually. The editor is the part that stands out. Users draw on an image to mark a region, then describe the change or drop a reference image; there are move and remove operations, a proper filter set spanning instant film, teal and orange, high-key and low-key, bleach bypass, rim light, noir and more, plus background removal, simple and advanced expand, aspect-ratio resizing across eight ratios, and AI super-resolution up to 4K. That is more genuine editing craft than most self-serve generators in this category offer. Practical touches are sensible too: a WooCommerce plugin and CSV upload for catalog import, bulk upload, a true free tier, and an interface available in English, Hebrew, and Spanish. The company describes itself as built by a digital agency, and it reports 150,000+ images generated, 5,000+ active businesses, 42+ countries, and around forty seconds per image. Two things a fashion buyer should read carefully. The first is what "Lookbook" means here: up to three variations of one product from different angles or focal points. Useful, and not what a fashion team means by a lookbook. The second is the plan structure, discussed below. ## Astria: a professional fashion-production environment Astria narrows to fashion and spends the depth there. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—is the reusable unit that produces a season. That means casting is a library rather than an entitlement, references accumulate rather than expiring against a storage cap, and templates encode the conventions of apparel, beauty, jewelry, footwear, sports, luxury, and editorial work as production disciplines. Campaign frames, editorial spreads, social crops, and motion all derive from the same approved direction, and review happens on a multiplayer Board with named cursors and live voice, with external photographers and agencies inside the same workspace. ## Public proof: aggregate figures versus named brands Emilia publishes volume statistics—images generated, active businesses, countries, average generation time. Those describe reach, and in the material we reviewed we found no named customer, case study, or testimonial. That is common for a self-serve product at this stage; it also means a buyer has nothing specific to verify. Astria's public materials name fashion brands including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Vendor-published too, and at least checkable by looking. ## A realistic workflow comparison: the day you need a third model Consider a womenswear label with 60 styles going live, needing four images per style at 4K, plus campaign assets and social crops. The team evaluates Emilia against that complete brief. The first week goes well. The editor is pleasant, packshots and lifestyle scenes look good, and the WooCommerce import saves real time. Then three constraints arrive together. Casting: the brand wants three recurring models for different segments of the line, and Creator allows five custom presenters, which is workable—but Starter would have allowed two, and a brand that grows into more distinct casting needs runs into a ceiling that is a billing conversation rather than a creative one. Credits: use [current Emilia pricing](https://ai.emilia.digital/en/pricing) to budget for every required image and rejected attempt. Storage: the plan caps stored images, so a season's output has to be managed against a ceiling. Then the campaign brief lands. Campaign kits give four ready-made images per category, which is a starting point rather than a brand's campaign. A real lookbook is not three angle variations. Motion for social is not something we could verify the product does at all. With Astria, casting is a library the brand builds and keeps, the 60 styles run against one approved treatment, the campaign and editorial frames come from that same direction, and motion animates stills that already passed review. Nothing in the season is rationed by a plan tier. ## The differences that matter most ### Hands-on image editing — winner: Emilia AI Studio This is Emilia's genuine edge and the one concession that matters here. Region-select editing with a reference-image drop, a real filter set, expand, background removal, and super-resolution gives an operator direct manual control over a finished frame, and several larger competitors in this category offer less. If your work is a modest number of images that each need individual attention, that toolkit is a real advantage. ### Fashion depth versus shelf breadth — winner: Astria A product serving food, furniture, and apparel optimizes for what they share: a clean subject, a pleasing background, a consistent light. Fashion's hard parts are not shared—drape on a knit, how a sleeve breaks, casting that matches a brand's customer, a crop convention held across a collection. Emilia's jewelry and beauty categories are style presets for those product types; Astria's are production disciplines with their own casting, lighting, and framing logic. ### Casting that scales with the collection — winner: Astria This is the sharpest structural difference. Emilia rations custom presenters by plan—one, two, five, eight, or twelve. For a seller shooting one product line that is generous. For a fashion brand, casting is not a feature allowance: a womenswear line, a kids range, and an extended-size offer are three casting requirements before anyone discusses campaigns. Astria treats casting as a reusable library, alongside locations, poses, and brand references that accumulate season over season. ### What survives between sessions — winner: Astria Style presets and campaign kits are chosen per generation; custom styles are capped and stored images have a ceiling. Astria stores the approved treatment itself, so the tenth SKU is produced the same way as the first by someone who was not there for the decision. ### Coverage of the season — winner: Astria Three angle variations are not a lookbook, and a four-image campaign kit is not a campaign. Editorial framing, social-native crops, and motion are separate disciplines, and Astria produces them as expressions of one approved direction rather than as presets applied to a product shot. ### Collaboration — winner: Astria Emilia reads as a single-account tool in the material reviewed, with subscription, credits, and referrals oriented around one operator. Astria's brand workspaces and multiplayer Board—named cursors, live voice over the production canvas—exist because fashion work passes between a stylist, an ecommerce lead, a photographer, and often an agency. ### Collection scale and commercial fit — winner: Astria Check [Emilia pricing](https://ai.emilia.digital/en/pricing) and [Astria pricing](https://www.astria.ai/pricing) for current rates and plan features. Model a full collection, including rework, collaborators, and every channel, before comparing costs. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate in a workspace shared by the whole production, and next season starts from them. In Emilia, what accumulates is a stored image library — against a cap, and without the casting and direction that produced it. ## Choose Emilia AI Studio if... - Your range spans several product types — apparel plus homeware, food, or beauty. - You want a strong manual editor for individually crafted frames. - Volumes are modest and a free tier or one-time credit pack suits your buying. - A Hebrew or Spanish interface, or a WooCommerce plugin, matters to your setup. ## Choose Astria if... - Fashion is the business, not one category among six. - Casting must scale with the collection rather than sit in a plan entitlement. - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - Your photographer, stylist, or agency should work inside the brand workspace. - You want a real lookbook and a real campaign, not angle variations and preset kits. ## The bottom line For fashion brands, Astria is the stronger choice: it is built for how a collection is cast, directed, and produced, and nothing about the season is rationed by a plan tier. Emilia AI Studio is a well-made, genuinely broad ecommerce product studio with an unusually good editor — a sensible pick for a small seller whose shelf holds more than clothes. ## What to test before choosing either platform Count your casting needs first — how many recurring models does the line actually require? — and check that number against any per-plan presenter cap before you shortlist. Then price your real annual volume at the resolution you ship, including a realistic rejection rate. Run five representative styles through both, require PDP, detail, campaign, and social outputs, and add a colorway after approval. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask each vendor for a fashion customer you can contact. ## Frequently asked questions ### Is Emilia AI Studio an Astria alternative? For single product shots, yes. Emilia is a general ecommerce product-photography studio where clothing is one of six categories alongside food, furniture, and beauty. Astria is built only for fashion production across a collection. ### How many AI models can each platform hold? Emilia caps custom presenters by plan — 1 on Free, 2 on Starter, and up to 12 on its highest published tier. Astria treats casting as a reusable library rather than a plan entitlement, which matters when a collection needs consistent models. ### Is Emilia's Lookbook the same as a fashion lookbook? No. Emilia's Lookbook generates up to three variations of one product from different angles or focal points. A fashion lookbook is an editorial set expressing a collection's direction, which is what Astria produces. ### How does the credit model behave at collection scale? Check [Emilia pricing](https://ai.emilia.digital/en/pricing) for current credit rates and allowances, and [Astria pricing](https://www.astria.ai/pricing) for Astria rates. Include the resolution you need and rejected attempts in the collection budget. ### Is Astria better than Emilia AI Studio for fashion brands? For brands producing a collection across channels, yes: fashion-specific depth, uncapped casting and references, campaign and editorial work, and motion. Small sellers with mixed product ranges may find Emilia's breadth and free tier a good fit. For adjacent decisions, see how Astria compares with [Stylic](https://www.astria.ai/articles/astria-vs-stylic-fashion-ai/), [Lumoo](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/), and [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 14, 2026. Key sources include Emilia AI Studio's homepage and studio interface describing its three output types, six category libraries, campaign kits, and editor operations, its [about page](https://ai.emilia.digital/en/about) with company background and published statistics, and its [pricing page](https://ai.emilia.digital/en/pricing) covering credit tiers, per-resolution credit costs, and the custom presenter, custom style, and storage limits on each plan, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, credit costs, plan entitlements, and product scope can change, and we could not verify video generation in the material reviewed. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Astria vs FashionLab (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-fashionlab-fashion-ai/ FashionLab has done something none of the other tools in this comparison have tried: it has put a talent marketplace next to the generator. Brands batch-produce on-model imagery through a five-step flow, and they can also hire AI creative directors, prompt engineers, retouchers, and 3D artists inside the same platform. Astria's answer to the same underlying problem is different—make the creative direction itself reusable, so the brand needs less external help each season. **The short answer:** Astria is the clear overall winner for fashion brands producing a collection across its channels. Choose FashionLab if you want to hire AI creative talent and buy retouching alongside the tooling. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and FashionLab We used six B2B buying questions: how the brand gets to a repeatable result, whether creative direction is owned or hired, casting and category depth, channel coverage, how the account structure and meter behave across a catalog, and what evidence supports each vendor. ## Quick verdict: Astria vs FashionLab | Decision factor | FashionLab | Astria | Winner | | --- | --- | --- | --- | | How you get a repeatable result | Configure the batch flow; hire marketplace talent for the harder parts | Approve a treatment once and reuse it across the collection | **Astria** | | Professional fashion production | Batch on-model imagery from garment references | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Selected per batch: model, pose, background, styling | Approved treatment — casting, styling, scene, crop, lighting, format — stored and reused | **Astria** | | Casting and assets | Free AI model library, custom brand models, licensed digital twins, kids and size-inclusive casting | Models across ages and body types, including children and extended-size casting, plus locations, poses, and brand references | **Astria** | | Category coverage | Apparel and editorial projects | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial templates | **Astria** | | Natural-looking people | Professional retouching offered as a paid service | Face-inpainting and refinement layer inside the production workflow | **Astria** | | Motion | Not offered in the material reviewed | Multiple current video options animate approved stills in the same workflow | **Astria** | | Account structure | Entry plan covers one brand and one project; more are paid add-ons | Private brand workspaces without per-project gating | **Astria** | | Collaboration | Two seats included; extra seats billed monthly | Multiplayer canvas with named cursors and live voice over the actual looks | **Astria** | | Collection scale | Batch generation metered in tokens per generation | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Base plan plus per-seat, per-project, and per-token charges | Brand pricing scoped to usage — pay for what you create | **Astria** | | Public proof | One named testimonial; "top Scandinavian brands" | Named fashion brands plus studios and photographers running client work | **Astria** | | Access to AI creative talent | Marketplace for creative directors, prompt engineers, retouchers, and 3D artists | Not offered | **FashionLab** | | Best fit | Brands wanting tooling plus hired creative talent in one place | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A generator with a talent layer attached | The stronger platform for professional fashion production | **Astria** | Reviewed August 13, 2026; no controlled image-quality benchmark was run. ## FashionLab: generation plus a talent layer FashionLab positions itself for brand and marketing teams producing on-brand AI content, and its core flow is a clean five-step batch: select garment references as flat lay or on-body, assign models, choose pose, choose background, then generate dozens of looks with consistent models and backgrounds. Generation times are quoted around ninety seconds, and the company is unusually candid about disclosing the underlying generation model it uses. Its casting options are broader than most small competitors: a free library of diverse AI models, custom brand-specific models, and the option to hire authorized digital twins of real models and influencers. Its published use cases include kids fashion—explicitly framed around avoiding the complexity of photoshoots with minors—and size inclusivity, both of which are real production needs that many tools in this category ignore. Credit where it is due: that is a thoughtful use-case set. The genuinely novel part is the Creative Marketplace. Brands can hire specialized talent for creative direction, prompting, retouching, and project management; creators can find paid projects from brands. A separate in-built professional retouching service handles polished finishing at scale. No other product in this comparison bundles a labour market with the software. Check the vendor’s current pricing page for brand, project, seat, and token terms. Published proof is early: one named testimonial from a brand and marketing manager at Bruun Steengade, plus a reference to top Scandinavian brands. ## Astria: a professional fashion-production environment Astria's answer to the same problem is to make outside expertise less necessary each season. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—is stored so the next person runs it rather than rebuilding it. That extends across categories and channels. Templates cover beauty, jewelry, footwear, sports, luxury, and editorial as distinct disciplines; the casting library spans ages and body types, including children and extended-size casting, alongside locations, poses, and brand references; and campaign, editorial, social, and motion all derive from the same direction. When a brand does bring in an outside photographer or art director, that person works inside the workspace and leaves the treatment behind. ## Public proof: an early reference base versus named brands FashionLab's public evidence is a single named testimonial and a regional claim. That is honest for a young product and it gives a buyer little to verify. Its marketplace, if it reaches liquidity, may become its strongest proof—brands returning to hire talent is a hard signal to fake. Astria's public materials name fashion brands including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Vendor-published, and broader across brand size and contributor type. ## A realistic workflow comparison: buying expertise versus keeping it Consider a brand with three lines—womenswear, a kids range, and a small accessories capsule—producing roughly 200 SKUs a season. On FashionLab, the batch flow handles the womenswear and kids imagery well, and the kids use case is a real strength. Where the model shows its shape is in the account structure and the economics of expertise. Check the current pricing page for the brand, project, collaborator, and generation capacity required by the full collection, including rejected frames. If the brand does not have a strong prompt-fluent operator, the marketplace answer is to hire one—which works, and which means the brand's creative consistency is a contractor relationship rather than a stored asset. On Astria, each line gets a brand workspace, the treatment is approved once per line, and the 200 SKUs import from Shopify or Google Drive and run against it. The accessories capsule uses jewelry templates rather than being forced through an apparel flow. Motion for social comes from the same approved stills. When the brand hires an outside art director, that person works in the workspace and the direction stays after the invoice is paid. ## The differences that matter most ### A marketplace for AI creative talent — winner: FashionLab This is FashionLab's genuine edge and the one concession that matters here. Hiring vetted creative directors, prompt engineers, retouchers, and 3D artists from inside the platform—plus a professional retouching service for polished finishing—solves a real problem: most brands adopting AI production do not yet have the skills in-house. Astria does not offer a labour marketplace. Brands that know they need people as much as software should weigh this seriously. ### Owning the direction versus renting it — winner: Astria The marketplace solves the skills gap by supplying a person. Astria solves it by making the output of that person durable: once a treatment is approved, running it does not require the expert. Both are legitimate strategies; only one of them costs less next season than it did this one. ### Category coverage — winner: Astria FashionLab's flow is built around garments on models, with editorial projects as a use case. A jewelry capsule, a beauty extension, or footwear macro work each carry conventions an apparel flow does not encode. Astria treats them as separate template categories. ### Casting — winner: Astria, narrowly This is closer than most rows in this comparison, and FashionLab deserves credit: a free model library, custom brand models, licensed digital twins, and explicit kids and size-inclusive use cases is a strong set. Astria's advantage is that casting sits alongside locations, poses, and brand references as reusable production assets that persist across seasons, rather than being selected per batch. ### Motion — winner: Astria We found no video capability in FashionLab's material reviewed. Astria animates approved stills with a choice of current video options inside the same workflow, so social motion carries the campaign's casting and styling. ### Account structure and collaboration — winner: Astria One brand, one project, and two seats on the entry plan, with paid add-ons for more of each, is a structure that charges for the shape of a real fashion business—multiple lines, several collaborators, a partner agency. Astria's brand workspaces and multiplayer Board with named cursors and live voice are built for that shape rather than metering it. ### Collection scale and commercial fit — winner: Astria Use the vendor’s current pricing page and [Astria pricing](https://www.astria.ai/pricing) to budget for the same collection. Include the required brands, collaborators, projects, output settings, and rework before comparing total production costs. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate in a workspace shared by the whole production, and the next season starts from them. On FashionLab, what accumulates is a library of generated looks and, if you used the marketplace, a relationship with a freelancer. ## Choose FashionLab if... - You need to hire AI creative talent as much as you need software. - A professional retouching service inside the platform is valuable to you. - Your work centres on batch on-model apparel imagery, including kids and size-inclusive casting. - A single brand and project fits your business, or the add-on costs are acceptable. ## Choose Astria if... - You want creative direction to become a brand asset rather than a hired service. - Multiple lines or brands need their own workspaces without per-project charges. - Your categories extend into beauty, jewelry, footwear, sports, or luxury. - Social motion should carry the same casting and styling as the campaign. - Review should happen on a shared canvas with voice, with your external partners inside it. ## The bottom line For fashion brands producing a full season, Astria is the stronger choice: broader categories, motion, workspaces that match how brands are actually organised, and a model where the expertise you pay for stays with you. FashionLab has built the most interesting answer to the skills gap in this category, and a brand that knows it needs people as well as tooling should take its marketplace seriously. The question to settle before choosing: do you want to hire the expertise, or keep it? ## What to test before choosing either platform Price the whole shape of your business, not the entry plan: count brands, projects, seats, and a realistic annual generation volume including rejected frames. Run five representative SKUs through both, spanning at least two categories and including a kids or extended-size product if relevant, then add a colorway after approval. Ask for social motion derived from an approved still. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask what remains yours if you stop paying. ## Frequently asked questions ### Is FashionLab an Astria alternative? Yes, for generating on-model fashion imagery in batches. The distinguishing part of FashionLab is its creative marketplace and retouching service; Astria's is producing a whole collection across every channel from one approved treatment. ### What is FashionLab's strongest feature? Its creative marketplace. Brands can hire AI creative directors, prompt engineers, retouchers, and 3D artists, and an in-built professional retouching service handles polished finishing at scale. ### How does FashionLab's pricing scale to a catalog? Check the current vendor pricing page for plan scope, collaborators, projects, and generation rates. Use [Astria pricing](https://www.astria.ai/pricing) for Astria rates, then budget for the full collection including rework. ### Do both handle kids and size-inclusive casting? Yes — both address them explicitly, and it is a genuine strength of FashionLab's use-case set. Astria additionally carries locations, poses, and brand references as reusable production assets alongside casting. ### Is Astria better than FashionLab for fashion brands? For producing a collection across channels, yes: broader categories, motion, multi-brand workspaces, and usage-scoped pricing. Brands that want to hire AI creative talent alongside the tooling should look closely at FashionLab's marketplace. For adjacent decisions, see how Astria compares with [Pixelz](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/), [Stoodio](https://www.astria.ai/articles/astria-vs-stoodio-fashion-ai/), and [Lookgen](https://www.astria.ai/articles/astria-vs-lookgen-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 13, 2026. Key sources include FashionLab's homepage describing its five-step batch flow, model library and digital-twin options, published use cases, creative marketplace, and retouching service, and its pricing page covering the Basic plan's brand, project, and seat limits, add-on costs, and token package rates, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, token rates, plan limits, marketplace scope, and product features can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Astria vs FASHN (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/ FASHN is one of the most technically credible names in AI fashion imagery: strong virtual try-on, model swap, product-to-model, and a set of endpoints that other software products build on top of. Astria is not competing for that job. It is a production workspace where a fashion team encodes its own creative direction and runs it across a collection, its channels, and its collaborators. **The short answer:** Astria is the clear overall winner for fashion brands producing their own catalog, lookbook, campaign, and social assets. Choose FASHN when the requirement is virtual try-on itself—especially embedded inside a product you are building. ## How we evaluated Astria and FASHN We used six B2B buying questions: what the product treats as the unit of work, how creative direction is specified and reused, casting and asset depth, collaboration and review, channel and format coverage, and what it takes to run a whole collection rather than a single image. ## Quick verdict: Astria vs FASHN | Decision factor | FASHN | Astria | Winner | | --- | --- | --- | --- | | Professional fashion production | Individual image operations: try-on, model swap, product-to-model | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Controls per generation, plus a face reference to anchor a look | Build and reuse casting, styling, scene, crop, lighting, and format as one approved treatment | **Astria** | | Casting and assets | AI models generated by attributes; custom face references on the top plan | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Category coverage | Apparel and accessory try-on and on-model imagery | Fashion-specific templates for apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Natural-looking people | Face swap and editing tools applied per image | Face-inpainting and refinement layer inside the production workflow | **Astria** | | Motion | Short one-click clips; 1080p video from the mid plan | Multiple current video options animate approved stills in the same workflow | **Astria** | | Team production | Seat-capped plans, from two to ten members | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Tool-by-tool in the app; catalog automation means building against the endpoints | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Per-seat plans with monthly and daily credit caps; every generation spends credits | Brand pricing scoped to usage — pay for what you create | **Astria** | | Best fit | Product teams embedding try-on, and creators needing individual image operations | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | The strongest packaged try-on capability | The stronger platform for professional fashion production | **Astria** | Reviewed August 3, 2026; no controlled image-quality benchmark was run. ## FASHN: a capability, packaged two ways FASHN describes itself around a single clear promise: realistic images of your clothes, worn by anyone. It ships that promise in two forms. The app gives brands, agencies, and creators a set of tools—product to model, model swap, AI model creation, background removal, editing, reframing, 4K upscaling, face swap, and short video. The endpoint suite offers the same capabilities to other software: try-on at up to 4K, product to model, model swap, image to video, and more, with published latency ranges and language SDKs. The customer signal is worth reading closely. On the app side, FASHN publishes testimonials from fashion-adjacent businesses and marketplaces. On the endpoint side, the logos are largely other AI products and tools rather than fashion brands. That is not a criticism—it is an accurate picture of what FASHN sells best. It is a capability that other people build products and workflows around, and it is good enough at that job to have become infrastructure for a chunk of this category. FASHN also does real work on consistency. A face reference can anchor a specific look across generations, and model creation exposes control over age, body type, expression, and styling. A brand that needs the same face on 200 PDPs has a supported path. ## Astria: a professional fashion-production environment Astria's unit of work is not the image; it is the treatment. Products, casting, references, templates, and outputs live together in a brand workspace, so an approved look becomes something a team runs rather than something one operator recreates. The practical consequence is what carries forward. A face reference keeps a face consistent; an Astria template keeps the casting, styling, scene, crop, lighting, and format consistent, then applies that whole treatment to the next SKU, the next colorway, and the next channel. Templates span beauty, jewelry, footwear, sports, luxury, and editorial work, and Astria's Describe tool can turn a rights-cleared reference into usable creative direction while accounting for products and models already loaded. ## Public proof: developer adoption versus brand production FASHN's published proof points to genuine traction in its own lane: named app testimonials from fashion businesses and a roster of software products building on its endpoints, alongside open-source model releases that earned it technical credibility. These are vendor-published signals rather than independent benchmarks. Astria's public materials show a production base among brands and the professionals who create for them: recognizable names including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, plus studios and photographers running client work. Also vendor-published, and also an adoption signal rather than a quality proof—but it points at a different buyer. FASHN's strongest evidence is that developers ship with it. Astria's is that fashion teams produce with it. ## A realistic workflow comparison: an agency running three client brands Consider a small creative agency producing seasonal content for three fashion clients: catalog imagery for one, a lookbook and campaign for another, social-first content for the third. Different casting, different visual language, different approvers. With FASHN, the agency works tool by tool. Product to model for the catalog shots, model swap where a client supplies existing on-model photography, a face reference per client to hold casting steady, then editing and reframing for channel variants. It works, and for individual assets it works quickly. But each client's visual language lives in the operator's head and in a set of remembered settings; seats are capped by plan; and automating the catalog client's volume means building against the endpoints, which is an engineering project, not a production feature. With Astria, each client gets a private brand workspace. The agency defines the treatment once per client, approves it, and runs the collection against it. Client approvers join the multiplayer Board and review the actual canvas with voice rather than receiving a folder of exports. When the catalog client adds a colorway in week six, it inherits the approved setup instead of restarting it—and when the agency hands the workflow back to the client's in-house team, the recipe goes with it. ## The differences that matter most ### Unit of work: operation versus treatment — winner: Astria FASHN is organized around operations. You pick a tool, supply an input, get an output. That is the right shape for try-on, and it is a poor shape for a season, because nothing above the individual image is durable. Astria is organized around the treatment: the approved combination of casting, styling, scene, crop, and format that a brand wants repeated. Once that exists, running the next SKU is an execution step rather than a fresh creative decision. ### Virtual try-on as an embeddable capability — winner: FASHN This is FASHN's genuine edge and the one concession that matters here. If the requirement is try-on—shopper-facing, inside your own product, at 4K, with documented latency and language SDKs—FASHN is built for exactly that and Astria is not. Teams whose roadmap item is "add try-on to our app" should choose FASHN and stop reading. That is a real and well-executed job; it is simply a different job from producing a brand's campaign and catalog imagery. ### Collaboration, review, and handoff — winner: Astria FASHN's plans define collaboration by seat count—two, five, or ten members depending on tier. Astria treats review as part of production: private brand workspaces, a multiplayer Board with named cursors, and live voice over the actual looks. For work that passes between a photographer, an in-house studio, and an agency, the question is not how many logins exist but whether the next person inherits the decisions. ### Collection scale — winner: Astria Scaling FASHN to a full catalog is possible and well documented, but the path runs through its endpoints: someone writes the integration, manages the queue, and reassembles the outputs. That is a reasonable trade for a product team and an unreasonable one for a brand's creative department. Astria imports the SKU set from Shopify or Google Drive and runs it against the approved setup as a normal operation. ### Commercial model for brands — winner: Astria FASHN's app plans are per-seat with monthly and daily credit allowances, and credits are consumed by every generation, including the frames a creative director rejects. That is a sensible model for individual operations and an awkward one for a collection push, where rework is the norm and seats multiply across contributors. Astria's brand pricing is scoped to usage—pay for what you create, sized to the production engagement. A per-credit rate is not a production budget; model a full collection, including rework, before comparing costs. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate, and each stage of the season keeps what the last one approved. With FASHN, what accumulates is a library of finished images plus, if you built one, an integration. Both have value. Only one of them is the brand's creative capability. ## Choose FASHN if... - Virtual try-on is the actual requirement, especially embedded in a product you are building. - You need individual image operations—model swap, product to model, reframe, upscale—on demand. - Your team is comfortable assembling a workflow from tools and endpoints. - Documented latency, SDKs, and per-operation control matter more than a shared production canvas. ## Choose Astria if... - One approved creative direction must carry across a collection and its channels. - A creative director, photographer, or agency needs to install a workflow other people can reuse. - Casting, styling, and scene decisions must survive team changes, colorways, and client handoffs. - Review should happen on a shared canvas with voice, not through exported files. - Campaign, editorial, video, and social should extend the collection, not restart it. ## The bottom line For fashion brands and the studios that serve them, Astria is the stronger choice: it converts art direction into a system the whole team can run. FASHN is the better answer to a narrower and genuinely different question—how to put clothes on a person, reliably, wherever you need that capability to live. Do not decide from a single try-on demo. Test the second SKU, the client handoff, and whether someone other than the operator can reproduce the approved look. ## What to test before choosing either platform Run five representative SKUs—including one sheer, knit, or layered garment—through both products. Require PDP, detail, and campaign crops, then add a colorway after approval. Count the credits each round trip consumes, rejected frames included, and check how many seats the work really needs. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask a second operator—or the client—to reproduce the approved direction from scratch. ## Frequently asked questions ### Is FASHN an Astria alternative? Partly. FASHN is strongest as virtual try-on and image capability, offered both as an app and as endpoints other products build on. Astria is a production workspace where a fashion team runs its own creative direction across a collection. ### Which is better for virtual try-on specifically? FASHN. Try-on is its core competency and it is packaged so other products can embed it. Astria is built for producing brand-directed campaign, lookbook, and catalog imagery rather than shopper-facing try-on. ### Can both keep the same model across a catalog? Yes. FASHN anchors a look with a face reference across generations. Astria keeps casting alongside styling, scene, crop, and format in a reusable template, so the whole treatment carries, not just the face. ### Which scales better to a full collection? Astria. The SKU set imports from Shopify or Google Drive and runs against one approved setup. Reaching comparable scale with FASHN generally means building it yourself against its endpoints. ### Is Astria better than FASHN for fashion brands? For brand teams producing on-brand imagery across a collection and its channels, yes. Teams whose actual requirement is embeddable try-on inside another product should choose FASHN. For adjacent decisions, see how Astria compares with [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/), [Caimera](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/), [Ayna](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/), and [Rawshot](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 3, 2026. Key sources include FASHN's homepage, its [app product page](https://fashn.ai/products/app), its [endpoint product page](https://fashn.ai/products/api), and its [pricing page](https://fashn.ai/pricing) covering plan tiers, seat limits, and credit allowances, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, plan limits, model libraries, and workflow support can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Fermat AI: Pricing & Fashion Review (2026) Source: https://www.astria.ai/articles/astria-vs-fermat-fashion-ai/ Fermat brings design exploration and photography into the same fashion toolbox. That makes it a real contender for both the designer changing a silhouette and the team preparing product imagery. A useful comparison with Astria has to test the production handoff, not assume Fermat stops when the design is approved. **The short answer:** shortlist Fermat when design tools and enterprise requirements are central to the purchase. Evaluate Astria alongside it when the brief is a consistent collection of fashion images. This review concerns Fermat at fermat.app. ## Where to check current pricing For current rates, credit allowances, billing terms, and plan features, use [Fermat pricing](https://www.fermat.app/pricing) and [Astria pricing](https://www.astria.ai/pricing). Compare the same collection, output settings, additional attempts, review time, and approved deliverables. Use the [cost-per-SKU worksheet](https://www.astria.ai/articles/product-photography-cost-per-sku/) with the rates from those pages. ## Quick verdict: Astria vs Fermat | Buying question | What to evaluate | | --- | --- | | Are designers developing silhouettes, prints, and materials? | Start with Fermat's design toolbox | | Are approved products becoming a fashion image set? | Compare both on the complete channel brief | | Are team security controls a purchasing requirement? | Request Fermat's enterprise documents and Astria's corresponding terms | | Must the approved direction survive a handoff? | Have a second operator extend the set in both tools | | Is catalog automation required? | Compare the available API workflow and commercial terms | ## Fermat review: design tools with photography overlap Fermat's [homepage](https://www.fermat.app/) describes sketch rendering, design changes, material work, and virtual models. It also describes bulk automation and a shared workspace that preserves iterations and decisions. That is more than a folder of exported design files. The [photography workflow](https://www.fermat.app/use-cases/photography) explicitly covers ecommerce and campaign imagery, garment extraction, model visualisation, background and pose changes, and exports up to 4K. That puts it in direct consideration for a photography brief as well as a design brief. Our assessment: the breadth is valuable when design and content share inputs. The test is how much setup and correction survives between tools. Can a reviewer trace an image back to the approved garment reference? Can another operator reproduce the treatment? A long tool list answers neither question on its own. ## Enterprise security and IP questions Fermat publishes SOC 2 Type II, private-model, content-ownership, and SSO/on-premise claims on its [site](https://www.fermat.app/). Treat these as vendor statements and request the current documents and exact plan scope during procurement. This review has not audited the certification or deployment options. Ask both suppliers how uploaded brand assets are handled, who can access shared work, and how exports and deletion work. Obtain answers for the service and plan you would actually buy. A homepage badge is a starting point for that conversation, not its conclusion. ## Astria: evaluating the final image set Astria's [fashion offering](https://www.astria.ai/ecommerce) uses product references and templates for lookbook, social, and campaign imagery. Its [API documentation](https://docs.astria.ai/docs/api/tune/create/) is available for teams assessing programmatic production. The reason to include it in the pilot is the final deliverable: an approved treatment that the team can carry across the collection. The [AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/) explains the sequence we recommend. Test the same sequence in Fermat rather than assuming a design-led product cannot support it. ## A realistic workflow comparison: two departments, one collection Imagine that design already uses Fermat and ecommerce is choosing how to produce 250 style-colourways at four images each. The existing design assets are useful inputs. They are not automatically accurate records of the manufactured garments. Choose five approved products and supply the same front, back, and detail references to both pilots. Require a neutral product image and a directed campaign frame. Change a colourway after approval, then ask a different operator to finish the set. Record which steps need manual repetition and which references can be reused. If Fermat already clears the quality and handoff test, account for the cost of introducing another tool. If Astria reduces correction or setup work, quantify that saving against the same deliverables. Existing adoption is neither a reason to stop evaluating nor a reason to replace a working process. ## Choose Fermat if... - Design development is a substantial part of the job. - The same team needs to move from design references into photography work. - Its documented enterprise options warrant a procurement review. ## Choose Astria if... - The immediate brief is a fashion image set from approved products. - You want to evaluate template-led production alongside a broader design toolbox. - Your technical team wants to test the documented generation API. ## What to test before choosing either platform Review the garments at full size and the collection as a contact sheet. Count approved images, manual correction time, and the effort of repeating the treatment after a handoff. Then calculate [cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/), including people as well as credits. For a neighbouring design option, read [Astria vs StyTrix](https://www.astria.ai/articles/astria-vs-stytrix-fashion-ai/). The broader shortlist is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/). ## Frequently asked questions ### Is Fermat only a fashion design tool? No. Its published photography workflow also covers ecommerce and campaign imagery. Evaluate how well that workflow preserves your approved products and creative direction. ### Does Fermat have team and automation options? Yes. Its published plans include team access and enterprise automation options. Match the required features to the quoted plan before comparing costs. ### Is Astria better than Fermat for fashion brands? The answer depends on the brief. Compare the same garments, channel outputs, review standard, and operator handoff. This documentation review does not establish an image-quality winner. ## Sources and methodology Reviewed September 5, 2026. This is an Astria-authored review of public product documentation and pricing, not an independent customer review or a controlled image-quality benchmark. The recommendations are our assessment of workflow fit; vendor examples do not establish results for your garments. Primary sources checked: [Fermat homepage](https://www.fermat.app/), [photography use case](https://www.fermat.app/use-cases/photography), [pricing](https://www.fermat.app/pricing), [Astria fashion offering](https://www.astria.ai/ecommerce), and [Astria API documentation](https://docs.astria.ai/docs/api/tune/create/). Pricing, plan entitlements, security claims, and product scope can change. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Astria vs Figma Weave (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/ Figma Weave—the product formerly known as Weavy, acquired by Figma in October 2025—is one of the most capable AI creative environments available: a node-based canvas where many current models sit alongside professional editing tools, inside the design platform creative teams already live in. It is also general-purpose by design. Astria is the opposite bet: a system that already knows what a fashion collection is. **The short answer:** Astria is the clear overall winner for fashion brands producing catalog, lookbook, campaign, and social imagery. Choose Figma Weave when your team's work spans many kinds of visual output and you want a flexible canvas to build workflows on. ## How we evaluated Astria and Figma Weave We used six B2B buying questions: how much fashion knowledge the product supplies versus how much you must build, what it takes to produce the first usable PDP set, coverage across a season's channels, who on the team can operate it, how a full collection runs, and what accumulates after the work is done. ## Quick verdict: Astria vs Figma Weave | Decision factor | Figma Weave | Astria | Winner | | --- | --- | --- | --- | | Fashion domain knowledge | General-purpose canvas; fashion logic is something you build | Built around garments, casting, SKUs, and fashion channels | **Astria** | | Time to a usable PDP set | Design the node graph, then generate | Pick a fashion template, load products, approve, run | **Astria** | | Creative direction | Encoded in a workflow graph the author maintains | Approved treatment stored as a reusable template in the brand workspace | **Astria** | | Casting and assets | Generated per workflow; no fashion casting library | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Category coverage | Any visual output, none of it fashion-specific | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial templates | **Astria** | | Garment fidelity workflow | Depends entirely on how you assemble the graph | Product references and refinement built into the production path | **Astria** | | Motion | Video models available on the canvas | Multiple current video options animate approved stills in the same workflow | **Astria** | | Who can operate it | Workflow authors build; teammates use a simplified UI they generate | Brand and ecommerce teams run approved treatments directly | **Astria** | | Collection scale | Run your workflow at volume once it exists | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model | Credit-based consumption within Figma's plans | Brand pricing scoped to usage — pay for what you create | **Astria** | | Model breadth and editing craft | Many current models plus professional editing on one canvas | Broad choice across current image and video options inside a fashion workflow | **Figma Weave** | | Best fit | Design and creative teams producing many kinds of visual work | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | The most flexible general creative canvas | The stronger platform for professional fashion production | **Astria** | Reviewed August 8, 2026; no controlled image-quality benchmark was run. ## Figma Weave: a canvas for building creative pipelines Figma acquired Weavy on October 30, 2025 and relaunched it as Figma Weave. The product's premise, in Figma's own framing, is that the first prompt is a creative starting point rather than a final destination—so generation and hands-on craft belong in the same place. In practice that means a node-based canvas where outputs from many current models can be combined, branched, and refined, with professional editing operations—inpaint, outpaint, relight, upscale, crop, masking, color work—applied as part of the graph rather than in a separate tool. Model coverage is deliberately broad across image, video, and 3D. Workflows are reusable: a author builds the graph once, and Figma Weave can generate a simplified interface so teammates can run it without touching the nodes. Since 2026 a set of Weave tools and community workflows has been reachable directly inside Figma Design. It is genuinely excellent software. It is also, by intent, a general creative environment—the published examples span ad generation, product photography, illustration, storyboarding, and 3D rendering. Nothing in it is specifically about fashion. ## Astria: a professional fashion-production environment Astria makes the opposite trade. Instead of a canvas that can express any workflow, it supplies a workflow that already understands fashion: products, casting, references, templates, and outputs in a brand workspace, with the approved treatment—casting, styling, scene, crop, lighting, format—as the reusable unit. The practical difference is what you start with. A fashion template for footwear, jewelry, beauty, or editorial work already encodes the conventions of that category. A casting library already spans ages and body types, including children and extended-size casting. The SKU set imports from Shopify or Google Drive rather than being wired up. Astria's Describe tool can extract usable direction from a rights-cleared reference while accounting for products and models already loaded. None of that is a node graph anyone had to author. ## Public proof: creative breadth versus fashion production Figma Weave's evidence is its adoption across creative disciplines—independent creatives, startups, and enterprise teams including architects, VFX artists, marketers, and designers—and the credibility of the Figma platform around it. That is a strong signal for general creative production and says nothing specific about fashion catalogs. Astria's public materials show a production base among fashion brands and the professionals who create for them: Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Vendor-published, and narrow on purpose. ## A realistic workflow comparison: who builds the pipeline Consider a brand with 200 SKUs, a small creative team, and a design lead who is fluent in Figma. The Figma Weave path is attractive because the design lead can build exactly what the brand wants: a graph that takes a product photo, places it on a generated model, applies the brand's lighting treatment, produces four crops, and upscales. Once built, the team can run it behind a simplified interface. The costs are real but easy to overlook. Someone has to design that graph and keep it working; the fashion judgment inside it—how this brand casts, how a knit should fall, what crop convention the PDP uses—is knowledge the graph author supplies, not knowledge the tool contributes; and every new category, a jewelry capsule, say, is a new graph. When the design lead leaves, the pipeline becomes an artifact nobody fully understands. The Astria path starts further along. The team picks a fashion template appropriate to the category, loads the products, sets casting and styling, approves the treatment, and runs the SKU set imported from Shopify or Google Drive. What the brand maintains is a creative decision, not a piece of software. When someone new arrives, they inherit an approved treatment rather than a graph. ## The differences that matter most ### Supplied knowledge versus built knowledge — winner: Astria This is the whole comparison. A node canvas is infinitely expressive and completely uninformed: it will happily build a beautiful pipeline that gets a garment's drape wrong, because nothing in it knows what drape is. Astria's templates, casting library, and product-reference handling exist because fashion has conventions worth encoding once. Flexibility is valuable when your outputs are unpredictable; supplied domain knowledge is valuable when they are not. ### Model breadth and editing craft on one canvas — winner: Figma Weave This is Figma Weave's genuine edge and the one concession that matters here. Combining many current models with professional editing operations—relight, inpaint, matte work, compositing—inside a single node graph, reachable from the design environment a team already uses, is a real capability advantage for creative work in general. Teams whose output spans ads, illustration, motion design, and product imagery will get more from that breadth than from any fashion-specific system, and they should choose accordingly. ### Who can run it without help — winner: Astria Figma Weave's answer is thoughtful: workflow authors can publish a simplified UI so teammates run the graph without editing nodes. But that still divides the team into people who can change the workflow and people who cannot. In Astria, changing the crop convention or the casting is an edit to the treatment, made by the person who owns the creative decision. ### Coverage of a fashion season — winner: Astria A season is catalog, detail, lookbook, campaign, editorial, social, and motion, produced consistently across a collection. Figma Weave can produce every one of those as an individual asset. Astria produces them as expressions of one approved direction, which is what makes them look like a collection rather than a set of executions. ### Collection scale — winner: Astria Both scale, differently. Figma Weave scales a workflow once the workflow exists. Astria scales a creative decision: import the SKU set, run it against the approved treatment, extend into campaign and motion. The distinction matters most when the direction changes mid-season—an edit to a template versus a revision to a graph. ### Commercial model for brands — winner: Astria Figma Weave consumption sits inside Figma's credit and plan structure, which is designed for a design organization rather than for a collection's production budget. Astria's brand pricing is scoped to usage—pay for what you create, sized to the production. No claim is made here that Astria is cheaper; the point is that credits inside a design-seat subscription are a hard thing to map onto 200 SKUs across four channels. Model a full collection, including rework, before comparing. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate in a workspace shared by the photographer, the studio, and the agency. In Figma Weave, what accumulates is a library of workflows—valuable, and dependent on the people who authored them. ## Choose Figma Weave if... - Your team produces many kinds of visual work, not primarily fashion collections. - You want access to many current models with professional editing on one canvas. - You have someone who enjoys building and maintaining node-based pipelines. - Deep integration with Figma Design is a meaningful advantage for your workflow. ## Choose Astria if... - You are producing a fashion collection and want the fashion knowledge supplied, not built. - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - Casting across ages and body types, and category-specific templates, matter to your work. - The people who own the creative decision should be able to change it themselves. - Review should happen on a shared canvas with voice, alongside your photographer and agency. ## The bottom line For fashion brands, Astria is the stronger choice: it starts where a node graph would need to be built, and it keeps the creative decision editable by the person who made it. Figma Weave is exceptional general-purpose creative software, and for a design team with broad output it may well be the better investment—just not the faster route to a season's catalog. ## What to test before choosing either platform Time the first usable PDP set on both, honestly, including build time for any workflow. Then change the creative direction after approval and time that too. Add a second category—jewelry, footwear, beauty—and see what each requires. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask whether the person who owns the brand's look can make a change without the person who built the pipeline. ## Frequently asked questions ### Is Figma Weave an Astria alternative? For general creative production, it is a powerful option. For fashion, it is a different kind of tool: a node-based canvas you assemble workflows on, with no fashion domain built in. Astria ships the fashion production system rather than the toolkit for building one. ### Can a fashion team build a catalog workflow in Figma Weave? Yes, and some will want to. It means designing the node graph yourself: casting, garment handling, crops, formats, and batching are all things you construct rather than things the product already understands. ### Which handles a full collection more directly? Astria. The SKU set imports from Shopify or Google Drive and runs against one approved treatment. Figma Weave scales by running a workflow you built, which is powerful but starts with building it. ### What is Figma Weave's real advantage? Breadth and craft: many current AI models plus professional editing on one canvas, inside the design environment creative teams already use. For general campaign and design production that combination is hard to beat. ### Is Astria better than Figma Weave for fashion brands? For producing a fashion collection's imagery, yes: it is built around casting, garments, SKUs, and channels. Design teams producing broad creative work across many asset types should evaluate Figma Weave for that wider job. For adjacent decisions, see how Astria compares with [Rawshot](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/), [Graswald](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/), and [Tolstoy](https://www.astria.ai/articles/astria-vs-tolstoy-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 8, 2026. Key sources include Figma's [Weave solution page](https://www.figma.com/solutions/figma-ai-tool-weave/), the [Figma Weave product site](https://weave.figma.com/), and Figma's [acquisition announcement](https://www.figma.com/blog/welcome-weavy-to-figma/) dated October 30, 2025, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Model availability, workflow features, integration scope, and credit terms can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs FLORA (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-flora-fashion-ai/ FLORA is the most credible creative-canvas competitor in this comparison set, and the only one with a purpose-built fashion module: sketch to render, colorways and fabrics, on-model and campaign imagery, batch across a line. It is also a general creative platform with ambitions to be the next Adobe, and that shows in where its strengths and its evidence sit. Astria is narrower: a production system for fashion collections and the people who make them. **The short answer:** Astria is the clear overall winner for fashion brands producing an approved collection across its channels. Choose FLORA when the work is upstream—design exploration, colorway and print development, and creative ideation across many models. ## How we evaluated Astria and FLORA We used six B2B buying questions: where each product is strongest in the calendar, how deep the fashion-specific assets go, what evidence exists in fashion specifically, how collaborators inside and outside the company get access, how a full collection runs, and how the commercial model behaves as the team grows. ## Quick verdict: Astria vs FLORA | Decision factor | FLORA | Astria | Winner | | --- | --- | --- | --- | | Center of gravity | Design-stage ideation, extending into catalog work | Production of an approved collection across its channels | **Astria** | | Fashion-brand proof | Marquee references in design and entertainment; fashion page credits individual designers | Named fashion brands plus studios and photographers running client work | **Astria** | | Creative direction | Chained workflows on a canvas, saved for reuse | Approved treatment stored as a reusable template in the brand workspace | **Astria** | | Casting and assets | On-model styling across poses and colorways | Models across ages and body types, including children and extended-size casting, plus locations and poses | **Astria** | | Category coverage | Apparel-centric fashion studio | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial templates | **Astria** | | Design exploration | Sketch to render, colorways, fabric and print replacement | Not the product's focus | **FLORA** | | Motion | Broad video model access on the canvas | Multiple current video options animate approved stills in the same workflow | **Astria** | | Collaborator access | Per-seat licensing; self-serve tiers cap at eight seats | Brand workspaces open to the photographer, in-house studio, and agency | **Astria** | | Review experience | Real-time team iteration on the canvas | Multiplayer canvas with named cursors and live voice over the actual looks | **Astria** | | Collection scale | Batch one approved look across SKUs on the canvas | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Per-seat subscription plus metered usage credits | Brand pricing scoped to usage — pay for what you create | **Astria** | | Best fit | Design teams and agencies exploring and developing creative | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A strong creative canvas with a fashion module | The stronger platform for professional fashion production | **Astria** | Reviewed August 8, 2026; no controlled image-quality benchmark was run. ## FLORA: a creative canvas that reaches into fashion FLORA describes itself as the AI-powered canvas for designers, brand teams, and agencies, and it has built a genuine fashion solution on top of that foundation. The pitch for fashion teams is end to end: from a sketch to a render, through colorways and fabrics, to on-model and campaign imagery, with batch application of one approved look across every SKU. It splits the experience into a Fashion Studio for everyday work and a Canvas for deeper ideation where models and steps chain together into reusable workflows. The platform breadth is real: FLORA advertises access to 50+ models spanning image, video, and multimodal reasoning under one subscription, with recognizable video models available for cinematic work. It has raised [current pricing](https://www.astria.ai/pricing) million and its published references include Pentagram, Lionsgate, Base Design, and Hebbia. That reference list is the most useful signal for a fashion buyer. Pentagram is a design consultancy; Lionsgate is entertainment; Hebbia is enterprise software. They are impressive names and they attest to FLORA's quality as a creative environment. What the fashion solution page offers instead of brand case studies is credits to individual designers. For a brand evaluating who has actually run a season on the platform, that is a thinner evidence base than the platform's overall profile suggests. ## Astria: a professional fashion-production environment Astria is deliberately less ambitious in scope and more specific in depth. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—is the reusable unit that produces the season. Depth shows in the assets. The casting library spans ages and body types, including children and extended-size casting, alongside locations, poses, and brand references that accumulate season over season. Templates treat beauty, jewelry, footwear, sports, luxury, and editorial as distinct fashion categories rather than variations on apparel. And the collaboration model assumes the work crosses company boundaries, because in fashion it almost always does. ## Public proof: creative credibility versus fashion production FLORA's evidence establishes that serious creative organizations choose it. That matters, and it is not the same as evidence that fashion brands produce collections on it. Astria's public materials show a production base among fashion brands and the professionals who create for them: Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Both sets are vendor-published adoption signals rather than independent benchmarks. The difference is which industry they come from. ## A realistic workflow comparison: the season, from sketch to shipped Consider a contemporary label developing a 120-piece resort collection. The design team explores silhouettes and prints in the autumn; the production team needs catalog, lookbook, campaign, and social assets in the spring; a freelance photographer sets the visual direction; an agency handles paid social. FLORA is genuinely strong in the autumn half. Sketch-to-render, colorway exploration, and print and pattern replacement are exactly the design-stage problems a creative team has, and having 50+ models on one canvas makes exploration cheap. The spring half is where the shape of the product starts to matter. Batch across SKUs exists, but the casting depth is shallower, category templates outside apparel are thinner, and—most practically—every additional collaborator is a seat. Check [FLORA pricing](https://flora.ai/pricing) for current seat and usage terms when adding the photographer, agency collaborators, and merchandiser. Astria inverts the emphasis. It contributes little to the autumn exploration, and it is built for the spring: the approved treatment runs across the 120 pieces imported from Shopify or Google Drive, campaign and social extend the same direction, motion animates approved stills, and the photographer and agency work inside the brand workspace rather than against a seat count. ## The differences that matter most ### Design-stage ideation — winner: FLORA This is FLORA's genuine edge and the one concession that matters here. Sketch to render, colorway and fabric exploration, print and pattern replacement, and very broad model access make it a strong environment for developing what a collection should look like before it exists. Astria does not compete for that work. Design teams whose central problem is exploration should weigh this heavily. ### Fashion-specific depth — winner: Astria Beyond apparel, fashion production is beauty, jewelry, footwear, sports, luxury, and editorial, each with its own conventions of lighting, crop, and styling. Astria treats those as first-class templates. Casting depth matters just as much: age range, body type, children's and extended-size casting are production requirements, not preferences, and a general on-model capability does not cover them. ### Who gets to be in the room — winner: Astria This is where per-seat licensing quietly shapes the work. Fashion production is collaborative across company lines—photographer, in-house studio, agency, merchandiser—and a model that charges per seat and caps self-serve teams at eight makes each additional participant a cost decision. Astria's brand workspaces are built for that cast of collaborators, with a multiplayer Board, named cursors, and live voice over the actual looks. ### Evidence in your own industry — winner: Astria Both companies publish first-party proof. FLORA's strongest names come from design and entertainment; Astria's come from fashion brands and the studios that serve them. Neither is an independent benchmark, and a buyer should discount both accordingly—but when the question is "has anyone run a fashion season on this," the relevant evidence is the fashion evidence. ### Collection scale — winner: Astria FLORA batches one approved look across SKUs, which is the right idea. Astria imports the SKU set directly from Shopify or Google Drive and runs the approved treatment across it, then extends the same direction into campaign, editorial, social, and motion. The difference is how much of the season comes out of one decision. ### Commercial model for brands — winner: Astria FLORA's shape is a creative-suite subscription: per seat, per month, with metered usage credits layered on and enterprise beyond the eight-seat ceiling. That is coherent for a design department with stable headcount, and awkward for a production that expands and contracts around a season and pulls in outside collaborators. Astria's brand pricing is scoped to usage—pay for what you create. This article makes no claim that Astria is cheaper; it observes that seats plus credits is not a production budget. Model a full collection, including every collaborator who needs access, before comparing. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate in a workspace the whole production shares, and next season starts from them. In FLORA, saved workflows accumulate on the canvas—useful, and tied to the seats that can open them. ## Choose FLORA if... - Design-stage exploration is the work: silhouettes, colorways, prints, fabrics. - You want very broad model access, including video, under one subscription. - Your team is a design studio or agency with stable seat count. - Creative ideation matters more to you than running a collection to publication. ## Choose Astria if... - You are producing an approved collection across catalog, lookbook, campaign, social, and motion. - Casting depth across ages and body types is a production requirement. - Categories beyond apparel — beauty, jewelry, footwear, sports, luxury, editorial — are part of the line. - Your photographer, studio, and agency all need to be in the workspace. - You want fashion-brand evidence, not general creative credibility. ## The bottom line These two are complementary more often than they are rivals, and the split is the calendar. FLORA is a strong place to decide what the collection should look like. Astria is the stronger place to produce it once that is decided—deeper fashion assets, no seat gate on the people who make the work, and evidence from the industry you are actually in. ## What to test before choosing either platform Split the test by stage. For exploration, try a print and colorway development pass on both. For production, run five representative SKUs—including one non-apparel item if your line has them—through a full channel set, then add a colorway after approval. Count the seats the real production needs, external collaborators included, and price that honestly. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask each vendor for a fashion brand reference you can speak to. ## Frequently asked questions ### Is FLORA an Astria alternative? Yes, with different centers of gravity. FLORA is a broad creative canvas strongest at design-stage exploration — sketch to render, colorways, prints — that also reaches into catalog work. Astria is a production system built around collections, casting, review, and handoff. ### Which has stronger fashion-brand proof? Astria. FLORA's marquee references are design and entertainment names such as Pentagram and Lionsgate, and its fashion page credits individual designers. Astria's materials name fashion brands including Ronny Kobo, Gant, Lee Cooper, and Nine West. ### How does per-seat pricing affect a production team? FLORA prices per seat with usage credits on top, and its self-serve tiers cap at eight seats. When a photographer, in-house team, and agency all need access, seats become a gate on collaboration. Astria's brand pricing is scoped to usage. ### Which is better for exploring a new collection's design? FLORA. Sketch-to-render, colorway exploration, and print or pattern replacement are genuine design-stage strengths, backed by very broad model access under one subscription. ### Is Astria better than FLORA for fashion brands? For producing an approved collection across its channels, yes: deeper casting, category templates, unrestricted collaborator access, and usage-scoped pricing. For upstream design exploration, FLORA is the stronger tool. For adjacent decisions, see how Astria compares with [Figma Weave](https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/), [Graswald](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/), and [Browzwear](https://www.astria.ai/articles/astria-vs-browzwear-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 8, 2026. Key sources include FLORA's [fashion solution page](https://flora.ai/solution-fashion) describing Fashion Studio, Canvas, and batch application across SKUs, its homepage and published references, and its [pricing page](https://flora.ai/pricing) covering per-seat tiers, seat caps, and included usage, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, seat limits, model availability, and workflow features can change, and launch offers may expire. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Graswald (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/ Graswald is the most direct positional competitor Astria has: an AI production studio for fashion brands, where you configure brand standards once—avatars, lighting, poses—and generate across the catalog. The thesis is close enough that the comparison has to be made on execution rather than category. Three things separate them: who is allowed to adopt the product, how far the work extends past apparel PDPs, and whether the people outside your company can join the production. **The short answer:** Astria is the clear overall winner for fashion brands, with broader category coverage, external collaboration and handoff, and no enterprise procurement cycle. Choose Graswald if your priority is retailer-compliant wholesale localisation at enterprise scale. ## An Astria example: dress photo to packshot Compare the original dress with the generated image, especially the neckline, seams, and hem. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot This shows an Astria result. The other service was not tested with this same photo. [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) ## How we evaluated Astria and Graswald We used six B2B buying questions: who can actually adopt the product, how creative direction is specified and reused, category and channel coverage, how collaborators inside and outside the company participate, what evidence each vendor publishes, and how the commercial model behaves for a brand. ## Quick verdict: Astria vs Graswald | Decision factor | Graswald | Astria | Winner | | --- | --- | --- | --- | | Who can adopt it | Enterprise fashion brands via demo and sales conversation | Brands, studios, photographers, and educators producing fashion work | **Astria** | | Professional fashion production | On-brand ecommerce and wholesale product imagery at catalog volume | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Brand standards configured once: avatars, lighting, poses, presets | Build and reuse casting, styling, scene, crop, lighting, and format as one approved treatment | **Astria** | | Category coverage | Apparel and product imagery in the material reviewed | Fashion-specific templates for apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Casting and assets | Custom brand avatars built with the vendor | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Natural-looking people | Avatar quality plus resolution upscaling | Face-inpainting and refinement layer inside the production workflow | **Astria** | | Motion | Video listed among supported formats | Multiple current video options animate approved stills in the same workflow | **Astria** | | External collaborators | Internal teams and vendor studio partnerships | Photographer, in-house studio, and agency share one workspace, references, and approvals | **Astria** | | Review experience | Built-in collaboration and review tools | Multiplayer canvas with named cursors and live voice over the actual looks | **Astria** | | Collection scale | Batch generation, including CSV-driven runs | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Not published; sales-led engagement | Brand pricing scoped to usage — pay for what you create | **Astria** | | Best fit | Enterprise brands localising wholesale imagery to retailer specifications | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A strong enterprise catalog engine | The stronger platform for professional fashion production | **Astria** | Reviewed August 5, 2026; no controlled image-quality benchmark was run. ## Graswald: enterprise brand standards, configured once Graswald describes itself as the AI production studio for fashion brands, and the workflow matches: a team establishes brand standards—custom avatars, lighting, studio scene, poses—then generates across the catalog, including batch runs driven by CSV upload. Supported output spans PDP on-model imagery, packshots, lookbooks, lifestyle content, and video, with 4K resolution and PIM/DAM integrations. The customer evidence is genuinely strong for this category. The [VAUDE case study](https://www.graswald.ai/case-study/vaude-produces-on-model-imagery-in-hours) reports 50% lower content production costs and 30% more content published live, with the brand generating from flat-lays and factory sample photos on two custom avatars that replicate its studio scene digitally—work the internal team now runs in-house. The [PUMA case study](https://www.graswald.ai/case-study/puma-uses-graswald-ai-to-localise-product-imagery-in-the-us-driving-relevance-and-conversion) describes something more unusual: adapting central global creative to individual US retailer specifications for wholesale, so partners receive imagery that meets their guidelines without a round of rework. Two caveats a buyer should carry into a demo. The PUMA story, unlike the VAUDE one, publishes no quantitative outcomes. And the homepage headline figures—90% cost reduction, 10x faster, 100% brand consistency—are vendor absolutes. "100% brand consistency" is not a testable claim; your own difficult garments are. ## Astria: a professional fashion-production environment Astria shares the configure-once thesis and diverges on who the system is for. Products, casting, references, templates, and outputs live in a brand workspace that a creative director, an in-house team, an outside photographer, or an agency can all work in. The approved treatment—casting, styling, scene, crop, lighting, format—is the reusable unit, and it is designed to be handed over rather than maintained by one party. Coverage is the other divergence. Astria's templates span beauty, jewelry, footwear, sports, luxury, and editorial work alongside apparel, so a brand with an accessories line or a beauty extension is not routing those categories elsewhere. Astria's Describe tool can turn a rights-cleared reference into usable creative direction while accounting for the products and models already loaded, which is a faster path from "match our last campaign" to a template than commissioning a new avatar build. ## Public proof: enterprise logos versus production breadth Graswald publishes named enterprise stories—VAUDE with real numbers, PUMA on wholesale localisation—plus a strategic partnership with Orendt Studios. That is a serious set of references for large-brand catalog work, and it is vendor-published rather than independently verified. Astria's public materials show a production base among brands and the professionals who create for them: Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work on the platform. Also vendor-published. The distinction worth noting is the shape of the base: Graswald's evidence concentrates in large enterprises with internal content teams, Astria's spans brands and the external creatives they hire. ## A realistic workflow comparison: an external creative director installing a workflow Consider a mid-size brand whose visual direction comes from an outside photographer it has used for six seasons. The brand wants to produce most of its catalog and campaign imagery with AI, but it wants that photographer's eye encoded in the result, and it wants its own two-person ecommerce team running the work by spring. With Graswald, the path runs through the vendor: an enterprise conversation, then a collaboration to build custom avatars and replicate the studio scene and lighting digitally. That produces a strong result—VAUDE's story describes exactly this and the outcome was good—but the brand's photographer is not naturally a participant in it, and the configuration lives in an enterprise engagement rather than in a workspace the brand's creative partner can operate. With Astria, the photographer works directly in the brand's workspace: sets the casting, builds the templates, uses Describe to pull direction out of a rights-cleared reference from a previous season, and reviews with the ecommerce team on the multiplayer Board with live voice. When the season starts, the two-person team runs the approved treatment across the SKU set imported from Shopify or Google Drive. The photographer's judgment is now the brand's asset, and the handoff happened inside the tool rather than as a document. ## The differences that matter most ### Who is allowed to adopt it — winner: Astria Graswald's go-to-market is demo-led and enterprise-shaped; we found no published pricing in the material reviewed. That is a reasonable choice for a company selling into PUMA, and it is a real barrier for the brand that wants to start next week, and for the studio or photographer who wants to build a practice on the tool. Astria is used by brands and by the creatives who serve them, which changes who can put the system in place. ### Enterprise wholesale localisation and PIM/DAM depth — winner: Graswald This is Graswald's genuine edge and the one concession that matters here. Producing imagery that satisfies each retail partner's format, dimension, and background specifications—out of the box, from one central creative—is a specific and unglamorous problem that large wholesale brands actually have, and Graswald has a named enterprise reference for solving it. Paired with PIM/DAM integration, that makes it a strong fit for brands whose content pipeline is an operations function. It is a distribution-compliance advantage rather than a creative-direction verdict. ### Category coverage — winner: Astria Graswald's published work centers on apparel and product imagery. Astria's template library covers beauty, jewelry, footwear, sports, luxury, and editorial treatments as first-class categories. For a brand whose line includes accessories, fragrance, or a jewelry capsule, that is the difference between one production system and several. ### Collaboration, external partners, and handoff — winner: Astria Graswald lists built-in collaboration and review tools, and for an internal content team that may be sufficient. Astria's model assumes the work crosses company boundaries: private brand workspaces that an external photographer or agency can be invited into, a multiplayer Board with named cursors, and live voice over the actual canvas. The test is not whether reviewers can comment; it is whether the person who defined the look and the person who will run it next season can be in the same room. ### Collection scale — winner: Astria Both products batch. Graswald's published route includes CSV-driven runs, which works and requires someone to maintain the spreadsheet. Astria imports the SKU set directly from Shopify or Google Drive and runs it against the approved setup, then extends the same treatment into campaign, editorial, social, and motion. Scale is not just how many images come out; it is how many separate jobs the season requires. ### Commercial model for brands — winner: Astria Graswald does not publish pricing, so a brand cannot model cost before entering a sales process. Astria's brand pricing is scoped to usage—pay for what you create, sized to the production engagement. Neither approach is inherently cheaper, and this article makes no such claim: the difference is that one can be estimated against a real collection before you commit, and the other cannot. Model a full collection, including rework and the channels beyond the catalog, before comparing. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate in a workspace shared by the photographer, the in-house studio, and the agency. In Graswald, the configured brand setup is a real asset too—but it is one the vendor helped build and the internal team maintains, which is a narrower base to compound on. ## Choose Graswald if... - You are an enterprise brand and wholesale localisation to retailer specifications is a live problem. - Deep PIM/DAM integration is a requirement of your content operations. - Your content team is internal and does not need outside creatives inside the tool. - Apparel and product imagery for ecommerce is the bulk of the work. ## Choose Astria if... - You want to start without an enterprise procurement cycle. - An outside photographer, studio, or agency should be able to build and hand off the workflow. - Your line spans categories beyond apparel—beauty, jewelry, footwear, sports, luxury, editorial. - Review should happen on a shared canvas with voice, not through exported files. - Campaign, editorial, social, and motion should extend the collection, not restart it. ## The bottom line For most fashion brands, Astria is the stronger choice: the same configure-once thesis, but open to the creatives who define the brand's look, broader across categories, and built so the workflow can be handed over. Graswald is a capable enterprise catalog engine with a genuinely distinctive answer to wholesale localisation. These two deserve a real bake-off. Run the same difficult garment through both, and pay attention to who is in the room when the look gets approved. ## What to test before choosing either platform Run five representative SKUs—including one sheer, knit, or layered garment, and one from a non-apparel category if you have one—through both products. Require PDP, detail, campaign, and social crops, then add a colorway after approval. Test the vendor absolutes: put your hardest fabric against a "100% brand consistency" claim. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, invite your external photographer or agency into the workflow and ask them to reproduce the approved direction. ## Frequently asked questions ### Is Graswald an Astria alternative? Yes — it is the closest positional competitor in this category. Both configure a brand setup once and generate at catalog scale. They differ on who can adopt them, how far coverage extends beyond apparel PDPs, and how external collaborators join the work. ### Which suits a brand without an enterprise procurement cycle? Astria. Graswald's go-to-market is demo-led and aimed at enterprise fashion brands, with no published pricing in the material reviewed. Astria is used by brands alongside the studios, photographers, and educators who produce for them. ### Can an external photographer or agency build the workflow? In Astria, yes: an outside creative can define casting, styling, and templates in a brand workspace, review with the team on a multiplayer canvas with live voice, then hand the running workflow to in-house staff. ### What about categories beyond apparel? Astria's template library covers beauty, jewelry, apparel, footwear, sports, luxury, and editorial work. Graswald's published material centers on apparel and product imagery for ecommerce and wholesale. ### Is Astria better than Graswald for fashion brands? For most brands, yes: broader category coverage, external collaboration and handoff, and no enterprise procurement cycle. Large brands whose priority is retailer-compliant wholesale localisation with deep PIM/DAM integration should evaluate Graswald on that basis. For adjacent decisions, see how Astria compares with [Looklet](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/), [Caimera](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/), [Ayna](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/), and [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 5, 2026. Key sources include Graswald's homepage and its stated production workflow, its [VAUDE case study](https://www.graswald.ai/case-study/vaude-produces-on-model-imagery-in-hours), and its [PUMA case study](https://www.graswald.ai/case-study/puma-uses-graswald-ai-to-localise-product-imagery-in-the-us-driving-relevance-and-conversion), plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. We did not find a public pricing page for Graswald in this review. Pricing, plan limits, model libraries, integrations, and workflow support can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Higgsfield (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-higgsfield-fashion-ai/ Higgsfield is the first product in this comparison set that can credibly answer a fashion brief with both a polished still and a serious filmmaking stack. Its own Soul model is explicitly fashion-aware; Product Photography and Marketing Studio cover on-model images, product shots, ads, marketplace assets, and UGC; Canvas connects references and generations on one board; Cinema Studio adds camera, lens, movement, multishot, and VFX controls across a large model catalog. This is not a lightweight social generator. It is also not a fashion production system in the same sense as Astria. Higgsfield organizes a very broad creative suite around models, formats, and projects. Astria organizes production around a brand, its product catalog, casting, references, and an approved treatment that has to survive across the season. **The short answer:** choose Astria when the unit of work is a fashion collection. Choose Higgsfield when the unit of work is a film, ad, or stream of cross-category marketing assets and access to the broadest creative toolset matters more than a fashion-specific operating model. ## How we evaluated Astria and Higgsfield We used seven B2B buying questions: what each platform treats as the reusable unit, how fashion products and casting persist, how much of a season it covers, the depth of still and motion control, how a team works together, what the meter does at production scale, and what public evidence supports the claims. We reviewed product and help-center material from both vendors; no controlled output-quality benchmark was run. ## Quick verdict: Astria vs Higgsfield | Decision factor | Higgsfield | Astria | Winner | | --- | --- | --- | --- | | Core product | Broad AI-native creative suite for images, video, audio, ads, UGC, effects, and agentic workflows | Fashion production system organized around a brand and its season | Depends on the job | | Reusable unit | Projects, products, avatars, assets, templates, and connected Canvas nodes | Brand workspace plus an approved treatment — casting, styling, scene, crop, lighting, format | **Astria** for fashion production | | Product imagery | Studio, lifestyle, on-model, detail, multi-angle, variant, and batch catalog tools | Product-led ecommerce, lookbook, campaign, editorial, social, and motion | Tie | | Fashion specialization | Soul is fashion-aware; Fashion Factory, AI Stylist, try-on, product-photo, and editorial modes | Fashion-specific templates and workflows across apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Casting consistency | Soul ID, preset and custom avatars, AI Cast, and reusable faces | Trained casting library kept with products, locations, poses, and brand references | Tie | | Cinematic video | Multiple current video models, Cinema Studio, multishot, camera and lens controls, VFX, native audio | Multiple current video options animate approved fashion stills and artboards in the same workspace | **Higgsfield** | | Model choice | 50+ image, video, and audio models advertised on Enterprise, plus Higgsfield's own models | Curated image and video options inside the fashion workflow | **Higgsfield** | | Collection ingestion | Product link or image upload; reusable product library and advertised batch catalog generation | SKU import from Shopify or Google Drive into the brand workspace | **Astria** | | Collaboration | Shared Canvas and business workspaces; live editing, saved versions, pooled credits, shared assets | Multiplayer Board with named cursors and live voice inside private brand workspaces | Tie | | Creative-tool integrations | Native plugins advertised for Premiere Pro, After Effects, Photoshop, DaVinci Resolve, and Figma; MCP and CLI | Public REST API and fine-tuning, with generation and templates in the web workspace | **Higgsfield** for creative tools | | Enterprise controls | SSO, roles, spend controls, dedicated capacity, no-training terms, indemnification, and support at upper tiers | Private workspaces and production collaboration; public API available without an enterprise gate | **Higgsfield** for procurement depth | | Commercial model | Subscription credits, add-on packs, and selected web-only Unlimited access; costs vary by model and settings | Brand pricing scoped to usage — pay for what you create | Depends on volume and workflow | | Public production proof | Named studio and director stories spanning Lacoste, Dolce & Gabbana, Madonna, and hybrid commercial work | Named fashion brands plus studios and photographers running client work | **Higgsfield** for film proof; tie overall | | Fast first exploration | Free access to a limited model set, large preset library, many single-purpose apps | Setup is oriented around a brand, its products, and reusable production | **Higgsfield** | | Best fit | Filmmakers, agencies, creators, and marketing teams producing varied image and video formats | Fashion brands and studios producing a coherent collection across channels | Depends on the buyer | | Overall for collection-scale fashion | Capable across the required outputs, but broad by design | The narrower and stronger operating model for a fashion season | **Astria** | Reviewed September 5, 2026; no controlled image-quality benchmark was run. ## Higgsfield: a broad creative suite with real fashion and film depth Higgsfield has grown past the one-feature category. Its Product Photography workflow starts from one or several product photos and advertises studio, lifestyle, on-model, detail, multi-angle, colorway, batch, 4K upscale, and channel-resizing outputs. Marketing Studio keeps products in a reusable library, can import them from a site or social link, and applies templates across product shots, UGC, ads, marketplace images, posters, and motion graphics. Products and avatars persist, and layered poster elements remain editable. The image side is not merely utilitarian. Soul 2.0 is positioned as a fashion-aware foundation model with reference images, moodboards, color transfer, editorial composition, and Soul ID for consistent characters. AI Stylist lets a creator change a look by category — outerwear, base, pants, footwear, accessories — then choose pose and background presets. That makes Higgsfield unusually credible at both fashion aesthetics and fast content variation. Motion is where the distance opens. Canvas connects prompts, references, images, and video models into a node-based pipeline on a shared board. Cinema Studio adds the language of production — cameras, lenses, focal lengths, movement, cast, locations, and multishot sequences — rather than reducing video to “animate this image.” Higgsfield also aggregates current third-party models and advertises native connections to the editing applications where filmmakers finish the work. That breadth is Higgsfield's advantage and the main buying caution. Product Photography, Marketing Studio, Canvas, Cinema Studio, Soul, the Apps catalog, and Supercomputer are several different surfaces. A capable team can connect them into a pipeline. A fashion brand should test whether the exact product, casting, and art-direction decisions it approved are recalled as one reusable production setup, or whether its operator is responsible for rebuilding that context across tools. ## Astria: the collection is the operating model Astria starts with a narrower question: how does a brand produce one season so that the catalog, lookbook, campaign, editorial frames, social crops, and motion all look as though the same team directed them? Products, casting, locations, poses, references, templates, and outputs live in one brand workspace. The reusable object is the approved treatment — casting, styling, scene, crop, lighting, and format together — so it can be run against the next SKU and recalled next season. The SKU set imports from Shopify or Google Drive rather than being assembled asset by asset. That is also where Astria spends its category depth. Templates encode different conventions for apparel, beauty, jewelry, footwear, sports, luxury, and editorial work. Casting spans ages and body types including children and extended sizes. A campaign frame and a PDP are different outputs from the same direction, not unrelated generations that happen to use the same product. Astria's Board is an infinite production canvas with multiplayer cursors and live voice. A photographer or agency can build the workflow with the client inside the same brand workspace, then leave the approved template behind. The public API and fine-tuning system are available without requiring an enterprise contract. ## Public proof: Higgsfield deserves to be taken seriously Higgsfield publishes better production evidence than most general AI suites. In its first-party case study, Istanbul studio Sinecera describes using Cinema Studio, AI Cast, Soul ID, AI Locations, and Multishot in an approval-gated workflow, with an approved still before motion. The same story names a full-AI Lacoste × SuperStep film and a hybrid production model where several artists work inside one project. A separate first-party profile of director Sasha Kasiuha describes using Soul 2.0 and Cinema Studio for selected AI-assisted B-roll around Dolce & Gabbana and Madonna projects. The most persuasive point is the modesty of the use case: generative work extends an authored visual world instead of pretending that access to a model replaces direction or post-production. Astria's public materials name fashion brands including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers producing client work. Those are vendor-published references too. Higgsfield has the stronger published film-production detail; Astria's evidence is closer to the collection-production decision this article addresses. ## A realistic workflow comparison: one drop, two operating models Consider a fashion brand launching 80 styles. It needs five ecommerce frames per style, a 24-image lookbook, eight campaign hero images, and twelve short social clips. A creative director has approved the casting, warm directional light, a low camera position, and a particular balance between clean PDPs and slightly imperfect editorial movement. ### Running it in Higgsfield The team can get surprisingly far. Product Photography supplies the catalog shot types and batch claim. Marketing Studio turns products into ad and social formats. Soul creates the editorial keyframes. Canvas connects references to still and video branches. Cinema Studio gives the motion team more model choice and more explicit camera language than Astria offers, and the Adobe, DaVinci, and Figma integrations reduce export friction. The critical test happens after the first ten styles. The creative director asks for the light to move cooler, keeps the same casting, and changes only the campaign crop. The buyer should watch whether those decisions update one reusable fashion treatment that can be run across every remaining SKU, or whether an operator has to update several products, templates, nodes, or projects. Higgsfield has the components; the question is how much production logic the brand must supply. The meter should be tested at the same time. Higgsfield charges credits according to the chosen model, resolution, and duration. Individual subscription credits expire at renewal, while add-on credits expire after their stated period. Selected Unlimited access applies only to manual generation on the main website: Canvas, MCP, CLI, and Supercomputer always spend credits. A workflow designed around Canvas or automation must therefore be costed in credit mode even when a pricing card advertises Unlimited models. ### Running it in Astria The brand imports the SKU set, keeps the products and trained casting inside its workspace, and runs the approved treatment across the catalog. The cooler-light revision changes that treatment; it does not start a separate product-photo job. Lookbook and campaign frames derive from the same product, casting, and visual references, and approved artboards carry those decisions into motion. The photographer, ecommerce lead, and agency review together on the Board. Astria has fewer filmmaking surfaces and less model choice. It does not replace a dedicated finishing stack. What it removes is the work of turning a broad set of generation tools into a fashion production system every season. ## The differences that matter most ### Cinematic control and model breadth — winner: Higgsfield This is Higgsfield's decisive edge. A large current model catalog, Cinema Studio, camera and lens language, multishot generation, VFX presets, audio-capable models, and integrations with editing software make it the more capable environment for filmmakers. A fashion film whose central problem is the shot rather than the catalog should be tested in Higgsfield first. ### Fashion-specific production — winner: Astria Higgsfield knows fashion aesthetically. Astria models fashion operationally. The difference appears after approval: whether the casting, garment references, scene, crop, light, and channel format become one production asset that can be applied to the next SKU and recalled next season. For a brand, that repeatability matters more than the number of models behind a generation button. ### Product and channel breadth — tie Both platforms now cover far more than on-model apparel. Higgsfield is broader across general products and marketing formats, with packshots, UGC, posters, ads, marketplace assets, and motion graphics. Astria is deeper across fashion disciplines and connects ecommerce, lookbook, campaign, editorial, social, and motion around one collection. The correct winner depends on whether “breadth” means more media formats or more of the fashion season. ### Collaboration — tie, with different strengths Higgsfield's Canvas is a real shared production surface, not a token team folder: prompts, models, outputs, and versions live together, and business plans pool credits and assets. Astria's Board adds named cursors and live voice inside the brand workspace, with photographers and external agencies treated as participants in the production. Test the people who actually cross company boundaries, not just two coworkers on the same account. ### Enterprise procurement and finishing integrations — winner: Higgsfield Higgsfield publishes a fuller enterprise checklist: SSO, roles, spend controls, dedicated capacity, contractual no-training terms, indemnification, and named creative-tool plugins. Some protections are explicitly reserved for Enterprise, so buyers should read the tier boundary carefully. Astria's advantage is different: its public API and fine-tuning are not held behind an enterprise sales process. ### Pricing clarity at workflow level — no universal winner Higgsfield shows a generation's credit cost before it runs, which is good practice. The complication is that the rate changes with the model and settings, and web-only Unlimited access does not cover Canvas or automated workflows. Astria's brand model is usage-scoped rather than a flat monthly credit allowance. Neither structure is automatically cheaper. Price one real drop, including rejected frames, upscales, every motion attempt, and the tools used to make them — not the cheapest still on the pricing page. ### Fast creative exploration — winner: Higgsfield Higgsfield offers a limited free route, a wide preset library, single-purpose apps, and many models to try before a team commits to one production method. That makes it the faster environment for exploring a concept, a camera language, or an unfamiliar content format. Astria expects the brand to invest in a reusable setup, which pays back across a season rather than in the first ten minutes. ## Choose Higgsfield if... - Cinematic video, camera control, multishot work, VFX, or audio are central to the brief. - Your agency produces fashion alongside beauty, food, technology, UGC, posters, and general advertising. - You want many image and video models inside one subscription and one interface. - Native paths into Premiere Pro, After Effects, Photoshop, DaVinci Resolve, or Figma matter. - Enterprise security controls, dedicated capacity, and contract-backed terms are procurement requirements. ## Choose Astria if... - The unit of work is a collection rather than an individual film or ad. - One approved creative direction must carry across ecommerce, lookbook, campaign, social, and motion. - Products, casting, locations, poses, and references should become a library the brand keeps. - Your photographer, stylist, ecommerce lead, and agency should produce inside the brand workspace. - You need public API and fine-tuning access without an enterprise negotiation. ## The bottom line Higgsfield is the stronger broad creative suite and the clear winner for cinematic controls, model choice, finishing integrations, and fast cross-format exploration. It may also be the better purchase for an agency whose clients span many categories and whose output is dominated by ads and video. Astria is our recommendation for a fashion brand producing a full collection. Its advantage is not access to a better generic model; it is that the products, casting, art direction, review, and output channels are arranged as one repeatable production system. Higgsfield gives a capable team more instruments. Astria gives a fashion team the score it has to play all season. ## What to test before choosing either platform Build a pilot around ten difficult products: directional prints, fine jewelry hardware, sheer fabric, reflective footwear, and at least two colorways. Require PDP, detail, lookbook, campaign, and motion outputs. After the first five products are approved, change only the light and crop, then count every operation required to carry the change through the remaining products and formats. Price all accepted and rejected generations at the actual resolution and duration you will ship. Finally, have a senior creative review the outputs blind for product fidelity, drape, anatomy, casting consistency, editability, and brand fit, and ask both vendors for a customer running a comparable seasonal volume. ## Frequently asked questions ### Is Higgsfield an Astria alternative? Yes, for AI fashion stills, on-model product imagery, campaign frames, and motion. The products diverge in emphasis: Higgsfield is a broad AI media suite with many image and video models, while Astria is a fashion production system organized around a brand, its products, casting, and reusable creative direction. ### Which is better for AI fashion video? Higgsfield has the broader filmmaking stack, with multiple current video models, Cinema Studio, camera and lens controls, multishot tools, VFX, and native integrations with editing software. Astria is the better fit when motion must extend the same product, casting, and direction already approved for a fashion collection. ### Which is better for a fashion ecommerce catalog? Astria is the stronger fit for a collection-scale catalog because products, casting, references, and approved templates persist in one brand workspace and the SKU set can be imported. Higgsfield is faster to explore and offers capable product, on-model, marketplace, and batch tools across many categories. ### How does Higgsfield pricing work? Higgsfield combines subscriptions, credits, optional credit packs, and selected web-only Unlimited model access. Generation cost varies by model, resolution, and duration. Subscription credits reset and do not roll over for individual plans, and Canvas, MCP, CLI, and Supercomputer generations always consume credits even when a model is Unlimited on the main website. ### Is Astria better than Higgsfield for fashion brands? For a brand producing a full collection across ecommerce, lookbook, campaign, and social, Astria is our recommendation. For a filmmaker, agency, or performance-marketing team that values a large model catalog, cinematic controls, VFX, UGC formats, and editing integrations more than a fashion-specific operating model, Higgsfield may be the better purchase. For adjacent decisions, see how Astria compares with [Pletor](https://www.astria.ai/articles/astria-vs-pletor-fashion-ai/), [FLORA](https://www.astria.ai/articles/astria-vs-flora-fashion-ai/), and [Figma Weave](https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and the motion workflow is covered in the [AI fashion film guide](https://www.astria.ai/articles/ai-fashion-film-guide/). ## Sources and methodology This article compares public product information available on September 5, 2026. Key Higgsfield sources include its [AI Product Photography](https://higgsfield.ai/ai-product-photography) page covering studio, lifestyle, on-model, multi-angle, variant, batch, upscale, and resize outputs; [Marketing Studio](https://higgsfield.ai/marketing-studio-intro) covering reusable products and avatars, templates, UGC, ads, marketplace assets, posters, and motion; [Canvas](https://higgsfield.ai/canvas-intro) covering connected nodes, team editing, version history, and model access; [Soul 2.0](https://higgsfield.ai/soul-intro) covering fashion-aware image generation, references, moodboards, and Soul ID; its help-center explanations of [plans](https://higgsfield.ai/creator-hub/help-center/plans/how-do-higgsfield-plans-work), [credits](https://higgsfield.ai/creator-hub/help-center/credits/how-credits-work), and [business tiers](https://higgsfield.ai/creator-hub/help-center/business/team-and-business-higgsfield); its [Enterprise](https://higgsfield.ai/enterprise) page covering model breadth, controls, commercial terms, and integrations; and its first-party production stories about [Sinecera](https://higgsfield.ai/creator-hub/case-studies/sinecera-hybrid-production-higgsfield) and [Sasha Kasiuha](https://higgsfield.ai/creator-hub/case-studies/madonna-and-dolce-gabbana-with-sasha-kasiuha). Astria sources include its [platform tour](https://www.astria.ai/academy/watch/astria-tour/) covering workspaces, templates, models, products, and the Board; [fashion-film workflow](https://www.astria.ai/academy/watch/artboard-2/) covering approved artboards and product references; [template gallery](https://www.astria.ai/gallery/packs?ws=291); and [API documentation](https://docs.astria.ai/docs/api/overview/). All performance, user-count, customer, savings, and product-fidelity statements from either vendor are first-party claims, not independent benchmarks. Model availability, credit rates, plan entitlements, and product scope change frequently. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Lookgen (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-lookgen-fashion-ai/ Lookgen has picked a sharper target than most of this category: not a single garment on a model, but a complete styled look—items combined, accessories added, angles varied, then published straight into a PIM. It was built with professional stylists, and its Dutch and European retail references say the approach lands. Astria's scope is wider: producing everything a collection needs, from one approved creative direction. **The short answer:** Astria is the clear overall winner for fashion brands producing a collection across its channels. Choose Lookgen when styled outfit merchandising, pushed automatically into your PIM, is the core of the job. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Lookgen We used six B2B buying questions: what the product treats as the unit of work, how a brand gets access, casting and category depth, channel coverage across a season, how collaborators review creative, and how the commercial model behaves for a collection. ## Quick verdict: Astria vs Lookgen | Decision factor | Lookgen | Astria | Winner | | --- | --- | --- | --- | | Unit of work | A styled look, published to the catalog | An approved treatment applied across the collection and its channels | **Astria** | | How you get access | Booked demo; no published pricing | Direct access for brands, studios, photographers, and educators | **Astria** | | Professional fashion production | Styled on-model looks with accessories and angle variants | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Stylist expertise built into the product; configured per look | Approved treatment — casting, styling, scene, crop, lighting, format — stored and reused | **Astria** | | Casting and assets | AI models plus exclusive brand-only avatars | Models across ages and body types, including children and extended-size casting, plus locations and poses | **Astria** | | Category coverage | Apparel looks, with accessories as components | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial as full template categories | **Astria** | | Natural-looking people | Photography-grade output claims | Face-inpainting and refinement layer inside the production workflow | **Astria** | | Motion | Not offered in the material reviewed | Multiple current video options animate approved stills in the same workflow | **Astria** | | Campaign and editorial work | Scenery and mood changes around a look | Campaign, editorial, and social treatments from the same approved setup | **Astria** | | Review experience | Stylist-led creation and adjustment | Multiplayer canvas with named cursors and live voice over the actual looks | **Astria** | | Collection scale | Look generation at volume, published through PIM | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Not published; demo-led engagement | Brand pricing scoped to usage — pay for what you create | **Astria** | | Outfit styling and PIM/DAM publishing | Purpose-built: combine items, add accessories, publish automatically | Not the product's focus | **Lookgen** | | Best fit | Retailers merchandising styled combinations into a catalog | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A focused look-merchandising engine | The stronger platform for professional fashion production | **Astria** | Reviewed August 11, 2026; no controlled image-quality benchmark was run. ## Lookgen: the look as the product Lookgen's three-step flow is deliberately unglamorous: upload items in whatever form you have them—flat lay, hanger shot, ghost mannequin, straight off the rack—pick a virtual model, and generate. From there a stylist adjusts poses, swaps accessories, and fine-tunes, then extends the look to more camera angles and different sceneries before publishing. Two things distinguish it. First, the emphasis on complete outfits rather than single products: accessories can be added in one click, from the brand's own collection or generated, and the customer testimonials repeatedly describe showing shoppers styling options rather than individual garments. Lookgen says it was built in collaboration with professional stylists so that expertise sits inside the product, and the framing rings true. Second, the publishing path: Lookgen connects to a PIM and DAM to import, code, and publish automatically, which for a European multi-brand retailer is often the part that decides whether a tool gets used at all. Its published proof is a wall of named-person testimonials—WE Fashion, NAADAM, Esqualo, The English Hatter, Suitable, Arthur & Willemijn—which is a more checkable form of evidence than aggregate statistics, even though it remains vendor-published. Access is through a booked demo; no pricing is published. ## Astria: a professional fashion-production environment Astria's unit of work is not the look but the treatment behind it. Products, casting, references, templates, and outputs live in a brand workspace, and the approved combination of casting, styling, scene, crop, lighting, and format becomes the reusable thing. That is a superset of look-building in practice. Astria produces the styled on-model shots, and it also produces the campaign frames, editorial spreads, social crops, and motion from the same direction. Categories go beyond apparel plus accessories into beauty, jewelry, footwear, sports, and luxury as full template disciplines, and the casting library spans ages and body types including children and extended sizes. ## Public proof: named voices versus named brands Lookgen's testimonials name individuals at recognizable European retailers, which makes them unusually verifiable for vendor content—you can look those people up. What they attest to is enthusiasm and early results in look creation, not measured production outcomes. Astria's public materials name fashion brands—Ronny Kobo, Nununu, Gant, Lee Cooper, Nine West—alongside studios and photographers running client work. Also vendor-published. Both should be read as adoption signals. ## A realistic workflow comparison: styling a season versus producing one Consider a menswear retailer with 300 SKUs that merchandises by outfit: a jacket shown with the right shirt, trousers, and shoes, so shoppers buy three items instead of one. Lookgen is well aimed at this. A stylist builds the looks, swaps accessories, generates angle variants, and the results publish into the PIM without a manual handoff. For catalog merchandising of styled combinations, that is a tight, purpose-built loop, and the retailer's ecommerce team gets exactly the assets it needs. The gaps appear at the edges of the season. The spring campaign needs imagery with a point of view rather than a scenery swap. Social needs motion. The accessories line has a jewelry capsule that needs macro treatment, not one-click placement. A new hire needs to produce looks consistent with what the stylist established, and the direction lives in the stylist's choices rather than in a stored treatment. And the brand's freelance art director cannot join the work without a demo and a contract. With Astria, the styled looks come from an approved treatment that also produces the campaign, the editorial frames, the social crops, and the motion. Jewelry and beauty have their own template categories. The art director works inside the brand workspace, reviewing on the multiplayer Board with live voice, and what the stylist decided is stored rather than remembered. ## The differences that matter most ### Outfit styling and PIM publishing — winner: Lookgen This is Lookgen's genuine edge and the one concession that matters here. Building complete looks with one-click accessories, then importing, coding, and publishing automatically through a PIM and DAM, is a specific and valuable capability that Astria does not match. Retailers whose merchandising model is styled combinations, and whose content operation is PIM-driven, should evaluate Lookgen seriously on exactly that. ### Coverage of the whole season — winner: Astria Changing scenery around a look produces context; it does not produce a campaign. Lookbook, editorial, social-native crops, and motion are separate disciplines with their own conventions, and Astria treats them as expressions of one approved direction rather than as variations on a catalog shot. ### Direction that is stored, not styled each time — winner: Astria Having stylist expertise inside a product is genuinely valuable, and it is generic expertise: it knows how outfits work, not how this brand's outfits work. Astria stores the brand's own approved treatment so the next operator inherits a decision rather than a set of good defaults. ### Casting and category depth — winner: Astria Brand-only avatars are a real feature and a narrower one than a casting library spanning ages, body types, children's and extended-size representation, locations, and poses. Likewise, accessories as components differ from jewelry and beauty as production categories with their own lighting and framing conventions. ### Access without a sales cycle — winner: Astria Lookgen's route in is a demo, and no pricing is published, which means a brand cannot validate the tool on its own products before entering a conversation. Astria is used directly by brands and by the studios, photographers, and educators who serve them. ### Collaboration and review — winner: Astria Lookgen's model centers on a stylist creating and adjusting. Astria's multiplayer Board assumes a group decision: named cursors, live voice, and the production canvas shared with the photographer, the in-house team, and the agency. ### Collection scale and commercial fit — winner: Astria Both generate at volume. Astria's difference is where the collection enters and how far it travels: the SKU set imports from Shopify or Google Drive, runs against the approved treatment, and extends into every channel. With no published pricing on Lookgen's side, a brand also cannot model a season in advance; Astria's brand pricing is scoped to usage—pay for what you create. This article makes no claim about which is cheaper. Model a full collection, including campaign and social, before comparing. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate in a workspace the whole production shares. In Lookgen, the durable output is a catalog full of well-styled looks — genuinely useful, and it does not make next season's campaign easier to produce. ## Choose Lookgen if... - Your merchandising model is styled outfits rather than single products. - PIM and DAM publishing automation is a hard requirement of your content operation. - A stylist-led, zero-prompt flow suits how your team works. - Catalog looks are the bulk of your content need. ## Choose Astria if... - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - Your line includes categories beyond apparel and accessories. - Casting depth across ages and body types is a production requirement. - External art directors, photographers, or agencies should work inside the workspace. - You want to validate the workflow on your own products before committing. ## The bottom line For fashion brands producing a full season, Astria is the stronger choice: it stores the brand's direction rather than restyling per look, and it covers the channels a collection actually needs. Lookgen is a well-focused product with a real specialism, and for a retailer merchandising styled combinations into a PIM it may be the more direct answer to that specific job. ## What to test before choosing either platform Build five looks in both, then ask for the campaign frame, the social crop, and a short clip derived from the same styling. Add an accessory category with different framing needs — jewelry or eyewear — and see what each requires. Check how a second person reproduces the first person's styling. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask Lookgen for pricing before the demo, so you can model a season. ## Frequently asked questions ### Is Lookgen an Astria alternative? Yes, for generating styled on-model looks and publishing them to a catalog. They diverge beyond that: Lookgen is focused on look-building and PIM publishing, while Astria produces catalog, lookbook, campaign, social, and motion from one approved direction. ### What is Lookgen best at? Composing complete outfits. It was built with professional stylists, adds accessories with one click, and pushes finished looks into a PIM or DAM automatically — a strong fit for retailers merchandising styled combinations. ### Can a brand try either without a sales call? Lookgen's published route is a booked demo, with no public pricing in the material reviewed. Astria is used directly by brands and by the studios and photographers producing for them. ### Which covers campaign, editorial, and video? Astria. Lookgen offers editorial-style campaign imagery by changing scenery around a look. Astria treats campaign, editorial, social, and motion as expressions of the same approved treatment. ### Is Astria better than Lookgen for fashion brands? For producing a collection across every channel, yes: broader categories, deeper casting, live collaborative review, and no demo gate. Retailers whose central need is styled outfit merchandising pushed into a PIM should evaluate Lookgen on that basis. For adjacent decisions, see how Astria compares with [Looklet](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/), [Graswald](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/), and [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 11, 2026. Key sources include Lookgen's homepage describing its three-step flow, virtual models and brand-only avatars, accessory placement, camera angles, PIM and DAM automation, editorial scenery options, and its published customer testimonials, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. We did not find public pricing for Lookgen in this review. Product scope, integrations, and commercial terms can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Looklet (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/ Looklet is the incumbent in this category, and it earned that position honestly: digitized real models, a mature styling pipeline, and enterprise retailers running it across multiple studios. It is also the comparison where the difference is structural rather than stylistic. Looklet is a capture-to-delivery system that begins with photographing your garments. Astria is a production system that begins with the product photography you already have. **The short answer:** Astria is the clear overall winner for fashion brands producing on-brand imagery across a collection and its channels. Choose Looklet if you are a large retailer that wants to install and run an in-house capture studio for very high catalog throughput. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Looklet We used six B2B buying questions: what infrastructure the workflow requires before the first image, how creative direction is specified and reused, channel and format coverage, casting and asset depth, collaboration and review, and how the commercial model behaves for a brand rather than a retail group. ## Quick verdict: Astria vs Looklet | Decision factor | Looklet | Astria | Winner | | --- | --- | --- | --- | | What it takes to start | Garments shot in a Looklet studio; enterprise tier includes studio hardware | Existing product photography, uploaded into a brand workspace | **Astria** | | Professional fashion production | On-model catalog imagery with shop-the-look metadata | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Digital styling of captured garments onto digitized models | Build and reuse casting, styling, scene, crop, lighting, and format as one approved treatment | **Astria** | | Casting and assets | Digitized model portfolio with extensive expressions and body movements | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Category coverage | Apparel catalog production | Fashion-specific templates for apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Campaign and editorial work | Not the product's purpose in the material reviewed | Campaign, editorial, and social treatments from the same approved setup | **Astria** | | Motion | Not publicly verified in the material reviewed | Multiple current video options animate approved stills in the same workflow | **Astria** | | Team production | Studio operator roles; multiplayer review not publicly verified | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Throughput depends on studio capture capacity and processing queue | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | [See current Looklet pricing](https://www.looklet.com/pricing/) | Brand pricing scoped to usage — pay for what you create | **Astria** | | Best fit | Retail groups running in-house capture studios at very high catalog volume | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | The mature choice for industrial catalog capture | The stronger platform for professional fashion production | **Astria** | Reviewed August 4, 2026; no controlled image-quality benchmark was run. ## Looklet: digital styling on top of a capture operation Looklet has been doing this since 2009, and its architecture reflects that lineage. The workflow starts in a studio: garments are shot in a Looklet setup, with the company describing up to 100 items per day handled by one or two operators. Captured items are processed with automated quality control and a human check, with turnaround stated in hours. Only then does the styling step happen, in software where a team composes looks on digitized models—mixing garments shot at different times without reshooting—and exports with metadata for the storefront. The model library is the part competitors cannot easily replicate. Looklet's digitized models are real people captured with a large range of facial expressions and body movements, alongside a smaller set of AI-generated models. For a retailer with licensing sensitivities, digitized real models are a meaningfully different proposition from synthetic faces. The customer evidence is correspondingly serious. Looklet's [Saks Fifth Avenue case study](https://www.looklet.com/case-study/saks-fifth-avenue/) describes deployment across five studios, single-operator shoots replacing eight-person crews, and looks live within 6–24 hours. It is vendor-written, but the operational detail is specific enough to be checkable, and it describes exactly the buyer Looklet is built for. ## Astria: a professional fashion-production environment Astria removes the capture step from the equation. Products, casting, references, templates, and outputs live in a brand workspace, and the inputs are the product photographs a brand already has. An approved look becomes a reusable treatment rather than a styling session repeated per item. The consequence is reach. Templates span beauty, jewelry, footwear, sports, luxury, and editorial work rather than catalog PDPs alone, and brand references keep results anchored to the brand's visual language. The same approved treatment extends into campaign frames, social crops, and motion, so the season's assets come from one direction instead of several disconnected production tracks. ## Public proof: retail operations versus brand production Looklet's proof is enterprise retail: Saks Fifth Avenue, Holt Renfrew, and Stockmann, with outcomes framed around staffing, turnaround, and cost per look. That is credible evidence of industrial catalog production and it should be read as such. Astria's public materials show a production base among brands and the professionals who create for them, including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Both sets are vendor-published adoption signals rather than independent benchmarks. They describe different jobs: Looklet's customers are running a photography operation more efficiently; Astria's are producing a brand's creative output. ## A realistic workflow comparison: a brand deciding what to install Consider a growing contemporary brand with roughly 500 SKUs a year. It needs catalog imagery, a seasonal lookbook, campaign assets, and social content, and it currently books a photographer twice a season. Choosing Looklet means choosing an operating model. Garments must reach a studio and be shot there; the enterprise tier bundles the hardware to make that possible in-house. Once that pipeline exists, catalog throughput is genuinely excellent and the per-look styling economics improve with volume. But the brand has taken on a capture operation—space, equipment, sample logistics, and operators—to solve what was, for it, a creative problem rather than a throughput problem. The lookbook, campaign, and social work still need a separate answer. Choosing Astria means the product photos the brand already produces become the input. Casting, styling, lighting, crop, and format get defined once and approved; the SKU set imports from Shopify or Google Drive and runs against that treatment. The lookbook and campaign extend the same direction, motion animates stills that already passed review, and there is no sample-to-studio logistics chain in the middle. ## The differences that matter most ### The prerequisite — winner: Astria This is the decisive difference and it comes before any image quality discussion. Looklet's workflow assumes a capture step you operate; Astria's assumes product photography you already have. For a retail group amortizing five studios across tens of thousands of SKUs, Looklet's assumption is sound. For most brands, installing a capture operation to produce catalog imagery is a larger commitment than the problem warrants. ### Enterprise catalog throughput with digitized real models — winner: Looklet This is Looklet's genuine edge and the one concession that matters here. Nothing else in this comparison combines a licensed portfolio of digitized real people, hundreds of expressions and body movements, quality-controlled processing, and same-day delivery at retailer volume. If your requirement is thousands of consistent catalog looks per season with real-model likeness rights already handled, Looklet is built for precisely that and Astria is not competing for it. It is a throughput and licensing advantage, not a creative-direction verdict. ### Coverage beyond the catalog — winner: Astria Looklet's output is on-model catalog imagery, complete with shop-the-look metadata and out-of-stock handling. Those are the right features for a product grid. They are not campaign frames, editorial spreads, jewelry or beauty treatments, or social-native crops. Astria treats all of those as expressions of one approved direction, which is why a brand can run the season from a single setup instead of routing each channel to a different supplier. ### Creative direction and reuse — winner: Astria Styling a captured garment onto a digitized model is a compositional decision made per look. It is efficient and repeatable, but the reusable unit is the model and the template, not the brand's point of view. Astria encodes casting, styling, scene, crop, lighting, and format as an approved treatment that another operator can run without being told what the brand wants. ### Collaboration and handoff — winner: Astria Looklet's collaboration model centers on studio operators and production roles; we could not verify multiplayer creative review in the material reviewed. Astria combines private brand workspaces with a multiplayer Board: named cursors, live voice over the actual canvas, and review on the looks themselves. When merchandising, art direction, and an outside agency need to agree before a drop, that is where the time goes. ### Collection scale and commercial fit — winner: Astria Looklet's scale is capture throughput, and it is bounded by studio capacity and the processing queue. Astria's scale is the collection as a unit of work: import the SKUs, run them against the approved setup, extend into campaign and motion. For current commercial terms, see [Looklet pricing](https://www.looklet.com/pricing/) and [Astria pricing](https://www.astria.ai/pricing). A per-image bundle rate is not a production budget; model a full collection, including rework and the channels beyond the catalog, before comparing costs. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate, and the photographer, in-house studio, and social agency work from one source of truth. In Looklet, what accumulates is a library of captured items and digitized looks—valuable, but tied to the pipeline that produced them. ## Choose Looklet if... - You are a retailer or retail group with catalog volume that justifies an in-house capture studio. - Licensed digitized real models, with likeness rights handled, are a requirement. - Same-day catalog turnaround at very high volume is the core operational goal. - Shop-the-look metadata and out-of-stock handling matter more than campaign and editorial coverage. ## Choose Astria if... - You want production imagery from the product photos you already have, with no capture operation to install. - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - A creative director, photographer, or studio needs to install a workflow other people can reuse. - Review should happen on a shared canvas with voice, not through exported files. - Your channel mix is broader than the product grid. ## The bottom line For fashion brands, Astria is the stronger choice: it produces the collection and its channels from assets the brand already owns, and it keeps the creative direction reusable. Looklet remains the mature answer to a different question—how a large retailer industrializes catalog photography without losing consistency. Do not decide from a sample look. Decide from what each option asks you to operate. ## What to test before choosing either platform Run five representative SKUs through both paths, including one difficult garment. For Looklet, price the full operating model, not just the images: sample logistics, studio time or hardware, and operator staffing. For Astria, start from the product photos you already have and require PDP, detail, campaign, and social crops, then add a colorway after approval. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask a second operator to reproduce the approved direction from scratch. ## Frequently asked questions ### Is Looklet an Astria alternative? Only partly. Looklet is a capture-to-delivery system: garments are photographed in a Looklet studio, then styled digitally onto digitized models. Astria produces brand-directed imagery from product photos you already have, with no capture operation to install. ### Does Looklet require a physical studio? Its enterprise offering includes a fully equipped Looklet studio and describes shooting items in that studio before digital styling. That is an operational commitment Astria does not ask for, because Astria starts from existing product photography. ### Which covers campaign, editorial, and video work? Astria. Looklet is optimized for on-model catalog imagery with shop-the-look metadata. Astria carries one approved creative direction across catalog, lookbook, campaign, editorial, social, and motion. ### How do Astria and Looklet pricing compare? Looklet publishes Virtual Studio image bundles and sells its enterprise tier through sales, including studio hardware. Astria's brand pricing is scoped to usage. Model a full collection, including rework, before comparing costs. ### Is Astria better than Looklet for fashion brands? For brands producing on-brand imagery across a collection and multiple channels, yes. Large retailers already running in-house capture studios who need very high catalog throughput with licensed digitized models should evaluate Looklet on that basis. For adjacent decisions, see how Astria compares with [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/), [Caimera](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/), [Ayna](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/), [Rawshot](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/), [FASHN](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/), and [Tellos](https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 4, 2026. Key sources include Looklet's homepage, its product workflow page describing studio capture and the Look Creator styling software, its [pricing page](https://www.looklet.com/pricing/) covering Virtual Studio bundles and the enterprise offering, and its [Saks Fifth Avenue case study](https://www.looklet.com/case-study/saks-fifth-avenue/), plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, plan limits, model portfolios, and workflow support can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Astria vs Lumoo: Pricing & AI Fashion Review (2026) Source: https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/ Lumoo Air starts with a product photo and turns it into a connected set of visual outputs. That is a useful proposition for a small team: fewer handoffs between creating, editing, and packaging an asset. The question is how that workflow behaves when the brief becomes a collection. **The short answer:** evaluate Lumoo for its connected editing workflow and Astria for a template-led fashion production. Neither an entry price nor a credit allowance settles the comparison. Count the images, try-ons, and videos your brief actually requires. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## Where to check current pricing For current rates, credit allowances, billing terms, and plan features, use [Lumoo pricing](https://lumoo.com/products/air) and [Astria pricing](https://www.astria.ai/pricing). Compare the same collection, output settings, additional attempts, review time, and approved deliverables. Use the [cost-per-SKU worksheet](https://www.astria.ai/articles/product-photography-cost-per-sku/) with the rates from those pages. ## Quick verdict: Astria vs Lumoo | Buying question | What to evaluate | | --- | --- | | Do you need to generate, edit, and crop in one flow? | Try Lumoo's connected workspace | | Do you want a fashion template as the production starting point? | Try Astria with your product references | | Does the brief mix stills, try-ons, and video? | Model the actual mix, rather than the headline image allowance | | Will collaborators work in the tool? | Run a real approval handoff in both | | Can one treatment carry the collection? | Test a second look and a late colourway change | ## Lumoo review: one image, many outputs Lumoo's [product page](https://lumoo.com/products/air) shows a source image branching into model shots, detail views, and video. It lists cropping, colour controls, template packages, and live collaboration; Basic and Pro also list Shopify integration. The page names brands including GANT, Gina Tricot, Holebrook, NEUW, and Nikben. Those are vendor-published references, not independent evidence of image quality. Our assessment: the appeal is keeping adjacent tasks in one place. If the team currently passes each image between a generator, an editor, and a crop tool, test whether a connected flow reduces that work. The public feature list alone does not establish how much time it will save or how reliably casting carries from one item to the next. A generated back view needs particular care. A single front photo cannot document unseen construction. Supply the actual back and detail references when they matter; reject plausible inventions. The preparation checklist in [flat lay to on-model](https://www.astria.ai/articles/flat-lay-to-on-model/) applies here too. ## Astria: a fashion production starting point Astria publishes [fashion templates and product-photo workflows](https://www.astria.ai/ecommerce). Its [lookbook gallery](https://www.astria.ai/gallery/workspaces) lets you inspect sets of images rather than isolated demonstrations. For the comparison, give both tools the same collection references and approved hero image. Require the next look to preserve casting, colour, and garment details while changing the outfit. The useful question is what the team can reproduce, not which homepage has the more ambitious promise. See the [AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/) for that approval sequence. ## A realistic workflow comparison: when one image becomes 150 Suppose 150 style-colourways each need four approved images: 600 deliverables. Use [Lumoo pricing](https://lumoo.com/products/air) to budget for that count plus additional attempts, try-ons, and clips. A larger plan may be appropriate; exceeding the base allowance does not make the workflow unsuitable. Keep a simple ledger during the pilot: operation, credits consumed, human review minutes, correction work, and whether the output shipped. Use [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/) to turn that into a useful budget. The same accounting applies to Astria. Ask for a price against the identical output list, and include preparation and review. This review does not establish which platform is cheaper for your collection. ## Choose Lumoo if... - You want to test generation and editing in one connected flow. - Your recurring mix of outputs fits the available credit plans. - A live workflow shared by your team is central to the pilot. ## Choose Astria if... - You want to begin with a fashion template and a collection of product references. - Your evaluation centres on a coherent lookbook or campaign set. - You want to inspect published collection examples before building the pilot. ## What to test before choosing either platform Choose five garments with different review problems. Include a pattern, layering, and a construction detail that cannot be inferred from the front. Produce a full look and detail view for each, then ask a colleague to extend the approved treatment to a new item. Compare accepted images and review time, not just render time. For a multi-vendor shortlist, see [Lumoo alternatives](https://www.astria.ai/articles/lumoo-alternatives/). For adjacent options, see [Astria vs WearView](https://www.astria.ai/articles/astria-vs-wearview-fashion-ai/) and the [AI fashion photography platform shortlist](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/). ## Frequently asked questions ### Is Lumoo an Astria alternative? Yes, for overlapping fashion-image work. Test Lumoo if a connected generation-and-editing flow fits your team, and Astria if fashion templates are your preferred starting point. ### Does Lumoo Pro include images, videos, and try-ons separately? No. The pricing page presents approximate alternative outputs from one credit pool. A mixed workload must be budgeted against the operations actually used. ### Is Lumoo suitable for a whole collection? Test the collection workflow rather than deciding from the base allowance alone. Budget additional attempts and review time, then compare a suitable volume plan with the alternatives. ## Sources and methodology Reviewed September 5, 2026. This is an Astria-authored review of public product documentation and pricing, not an independent customer review or a controlled image-quality benchmark. The recommendations are our assessment of workflow fit; vendor examples do not establish results for your garments. Primary sources checked: [Lumoo Air product and plan page](https://lumoo.com/products/air), [Astria fashion offering](https://www.astria.ai/ecommerce), and [Astria lookbook gallery](https://www.astria.ai/gallery/workspaces). Prices, estimated output counts, and product scope can change. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Astria vs Pixelz (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/ Pixelz is not another AI image generator, and treating it as one misses the decision. It is a technology-enabled production company: you send product images, expert retouchers and automated steps finish them, and files come back on a guaranteed turnaround. Astria is the opposite arrangement—a production system your own team operates, where the brand's creative direction becomes a reusable asset instead of a specification held by a supplier. **The short answer:** Astria is the clear overall winner for fashion brands that want to own a repeatable production capability. Choose Pixelz when you specifically want expert human retouching delivered on an SLA and have no internal team to run production. ## An Astria example: dress photo to packshot Compare the original dress with the generated image, especially the neckline, seams, and hem. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot This shows an Astria result. The other service was not tested with this same photo. [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) ## How we evaluated Astria and Pixelz We used six B2B buying questions: where the creative direction lives, what a change costs once work is in flight, coverage across catalog and campaign formats, how the total commercial picture assembles, what the team owns at the end, and how each option behaves across a full collection. ## Quick verdict: Astria vs Pixelz | Decision factor | Pixelz | Astria | Winner | | --- | --- | --- | --- | | Operating model | Managed service: submit assets, receive finished files | A production system your team operates in a brand workspace | **Astria** | | Where creative direction lives | A specification maintained with the supplier | An approved treatment stored and reused in your workspace | **Astria** | | Professional fashion production | Retouching, editorial finishing, and AI content delivered as services | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Turnaround on a change | Revised spec, re-ordered assets, back through the queue | Edit the treatment and re-run the collection | **Astria** | | Casting and assets | AI models and digital twins offered as a service engagement | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Category coverage | Ecommerce product imagery, plus editorial and campaign retouching | Fashion-specific templates for apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Motion | Video post-production and image-to-video as services | Multiple current video options animate approved stills in the same workflow | **Astria** | | Team production | Review, markup, and order tracking on the vendor's platform | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Daily throughput bounded by plan and the production queue | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Monthly platform subscription plus per-image charges, module fees, and onboarding services | Brand pricing scoped to usage — pay for what you create | **Astria** | | What you own afterwards | The finished files | The finished files plus the reusable creative recipe | **Astria** | | Best fit | Retailers outsourcing high-volume finishing with no internal production team | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A mature outsourced finishing operation | The stronger platform for professional fashion production | **Astria** | Reviewed August 5, 2026; no controlled image-quality benchmark was run. ## Pixelz: outsourced finishing, industrialised Pixelz describes itself as one place to scale ecommerce content, and the offering is broad: PDP image retouching, editorial and campaign retouching, video post-production, image-to-video, AI-generated content, and AI models and digital twins. What holds it together is the delivery model. Pixelz's own terms describe the standard service as human-assisted hybrid editing—expert retouchers combined with automated AI-driven steps—which is an honest description of a service rather than a product. The operational apparatus is real and mature. Customers get a cloud platform for review, markup, order tracking, and dashboards; the Professional plan publishes next-morning turnaround with a three-hour expedite option and capacity around 200 images a day; the Enterprise plan offers turnaround from three hours with custom capacity. Editorial work is quoted at 48 hours standard, video at 24. Brand logos on the site include large retailers such as Zalando, Mango, ASICS, and Bestseller. Check [Pixelz pricing](https://www.pixelz.com/pricing/) for current subscription, per-image, add-on, and service terms. Use [Astria pricing](https://www.astria.ai/pricing) for Astria rates. Include every required service in the production budget. ## Astria: a professional fashion-production environment Astria puts the same capability inside the brand. Products, casting, references, templates, and outputs live in a workspace the team controls, and the approved treatment—casting, styling, scene, crop, lighting, format—is stored as a reusable recipe rather than a brief sent to a supplier. That changes what a season looks like. Templates cover beauty, jewelry, footwear, sports, luxury, and editorial work alongside apparel, so campaign and catalog needs come from one setup. Motion extends the same approved stills rather than becoming a separate order. And review happens on a multiplayer Board with named cursors and live voice, where the people making the creative decision are looking at the same canvas rather than annotating returned files. ## Public proof: service references versus production adoption Pixelz's evidence is enterprise retail scale: recognizable logos and a published case study with luxury retailer Luisa Via Roma. As with every vendor in this comparison, these are first-party materials rather than independent benchmarks. They do establish that large operations trust Pixelz with volume, which is the relevant proof for a service business. Astria's public materials show a production base among brands and the professionals who create for them—Ronny Kobo, Nununu, Gant, Lee Cooper, Nine West—alongside studios and photographers running client work. Also vendor-published. The two point at different buyers: Pixelz's customer has outsourced production and wants it run well; Astria's customer wants production capability in the building. ## A realistic workflow comparison: the mid-season direction change Consider a brand three weeks into a 400-SKU season. The catalog imagery is half delivered when the creative director decides the crop is wrong: the products need more headroom and a warmer background, across everything shipped so far and everything still to come. With a managed service, this is a commercial event. The specification is revised with the supplier, the delivered assets are re-ordered for correction, and the remaining queue is updated. The turnaround is contractually good—next morning on the Professional tier—but the volume doubles for the affected batch, per-image charges apply again, and the brand's own team spends the week coordinating rather than creating. The decision was made in-house; the execution of it was not. With Astria, the change is an edit. The approved treatment is updated once, the affected SKUs re-run against it, and the same change automatically applies to everything still to come—including the campaign frames and social crops derived from the same direction. The cost is generation, not re-procurement, and the correction happens in the room where the decision was made. ## The differences that matter most ### Expert human retouching on a guaranteed turnaround — winner: Pixelz This is Pixelz's genuine edge and the one concession that matters here. Skilled retouchers working to a documented specification with a contractual delivery time is a service Astria does not offer. For brands with complex finishing standards, legacy imagery to correct, or no internal production capacity at all, that is a real answer, and the published SLAs and expedite options make it a dependable one. It is a service-delivery advantage rather than a production-capability verdict—you are buying finished files, and that remains true after the hundredth order. ### Where the creative direction lives — winner: Astria With a service, the brand's visual standard becomes a specification the supplier maintains. It works well, and it is not portable: leaving means rebuilding it, and changing it means renegotiating scope. In Astria the standard is a template in the brand's own workspace, editable by the brand's own people, and it carries into every channel that starts from it. ### Coverage across catalog and campaign — winner: Astria Pixelz covers a lot: PDP retouching, editorial finishing, video post, AI content. But each is a service line with its own quote, turnaround, and intake. Astria produces catalog, lookbook, campaign, social, and motion from a single approved treatment, which is why the season reads as one shoot rather than four workstreams that were separately briefed. ### Collaboration and review — winner: Astria Pixelz's platform offers review, markup, and order tracking, which is exactly right for approving work someone else produced. Astria's multiplayer Board is for producing it together: named cursors, live voice, and the actual production canvas, with the photographer, the in-house studio, and the agency in the same workspace. ### Collection scale — winner: Astria Pixelz's scale is throughput through a production queue, bounded by plan capacity and turnaround windows. Astria's scale is the collection as the unit of work: import the SKU set from Shopify or Google Drive, run it against the approved setup, extend it into campaign and motion. No intake form, no queue position, no per-asset ticket. ### Commercial model for brands — winner: Astria Pixelz's per-image rate is transparent, and it is only one component: the monthly platform subscription, module add-ons, and onboarding services stack on top, and every revision is billable volume. Astria's brand pricing is scoped to usage—pay for what you create, sized to the production engagement. This article makes no claim that Astria is cheaper; the point is that a per-image rate plus a subscription plus onboarding is not a production budget. Model a full collection, including the mid-season change, before comparing costs. ### Compounding production value — winner: Astria This is the whole argument in one line. After a year with a managed service, a brand has a large archive of finished images and a supplier relationship. After a year with Astria, it has the images plus the casting, references, templates, and approvals that produced them—and the next season starts from those rather than from a purchase order. ## Choose Pixelz if... - You need expert human retouching to a complex standard, with a contractual turnaround. - You have no internal production team and do not intend to build one. - Legacy image correction at volume is a significant part of the work. - Buying finished files is the outcome you want, and owning the workflow is not. ## Choose Astria if... - You want production capability inside the brand rather than a supplier relationship. - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - Mid-season creative changes should be an edit, not a re-order. - A creative director, photographer, or agency needs to install a workflow other people can reuse. - Review should happen on a shared canvas with voice, not on returned files. ## The bottom line For fashion brands, Astria is the stronger choice: it converts art direction into infrastructure the brand owns, and it makes the next season cheaper in effort rather than in invoices. Pixelz is a mature, well-run outsourced finishing operation—and the right answer when buying finished files, rather than building capability, is genuinely the goal. The honest test is not image quality. It is what you have at the end of the year. ## What to test before choosing either platform Take five representative SKUs and run them through both paths, then deliberately change the creative direction after approval. Measure what the change costs in each: money, elapsed time, and your own team's hours. Price the full commercial picture for the service option—subscription, per-image charges, add-on modules, onboarding—not just the headline rate. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask what you would keep if you switched vendors tomorrow. ## Frequently asked questions ### Is Pixelz an Astria alternative? They solve the same business problem in opposite ways. Pixelz is a managed service: you send assets and receive finished files on an SLA. Astria is a production system your team operates, where the creative direction and the outputs stay in-house. ### Does Pixelz still require a photoshoot? Its core business is post-production on images you supply, though it also offers AI-generated content, AI models, and digital twins as services. Astria produces imagery from the product photography you already have, inside a workflow your team runs. ### Which is more predictable to budget? Pixelz publishes per-image rates, but the total stacks a monthly platform subscription, per-image charges, optional module fees, and onboarding services. Astria's brand pricing is scoped to usage. Model a full collection, including rework, before comparing costs. ### What happens when the creative direction changes mid-season? With a managed service, a direction change means revising a specification and re-ordering affected assets through the queue. In Astria, you edit the approved treatment and re-run the collection against it. ### Is Astria better than Pixelz for fashion brands? For brands that want to own a repeatable production capability, yes. Brands that specifically want expert human retouching delivered on a guaranteed turnaround, with no internal team to run it, should evaluate Pixelz for that service. For adjacent decisions, see how Astria compares with [Looklet](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/), [Graswald](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/), and [Caimera](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 5, 2026. Key sources include Pixelz's homepage and services overview, its [pricing page](https://www.pixelz.com/pricing/) covering plan subscriptions, per-image rates, turnaround times, add-on modules, and professional services, and its published customer material, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, plan limits, service scope, and turnaround commitments can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Astria vs Pletor (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-pletor-fashion-ai/ Pletor calls itself AI creative infrastructure for marketing teams, and the description is accurate. It orchestrates models, brand context, and marketing data to produce performance ads, UGC, and campaign versions at the pace a paid-social calendar demands. Astria sits one step upstream, producing the collection imagery a fashion brand's marketing is built from. **The short answer:** Astria is the clear overall winner for fashion brands producing catalog, lookbook, campaign, and social imagery. Choose Pletor when your bottleneck is ad variant volume—many versions of a campaign across formats, markets, and languages. ## How we evaluated Astria and Pletor We used six B2B buying questions: which stage of the funnel each product serves, how much fashion knowledge it supplies, casting and category depth, how collaborators review creative, how a full collection runs, and what the commercial model assumes about the work. ## Quick verdict: Astria vs Pletor | Decision factor | Pletor | Astria | Winner | | --- | --- | --- | --- | | Where it sits | Downstream: ad and campaign versioning for marketing teams | Upstream: producing the collection imagery marketing uses | **Astria** | | Industry focus | General marketing across industries | Built for fashion | **Astria** | | Professional fashion production | Product imagery generated from catalogs as one use case | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Brand context supplied to an orchestration system | Approved treatment — casting, styling, scene, crop, lighting, format — stored and reused | **Astria** | | Casting and assets | Generated per output; no fashion casting library | Models across ages and body types, including children and extended-size casting, plus locations and poses | **Astria** | | Category coverage | Marketing creative for any product category | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial templates | **Astria** | | Garment fidelity | Not a stated design focus | Product references and refinement inside the production path | **Astria** | | Motion | Video and UGC output within credit allowances | Multiple current video options animate approved stills in the same workflow | **Astria** | | Review experience | Marketing workflow and versioning | Multiplayer canvas with named cursors and live voice over the actual looks | **Astria** | | Collection scale | Credit allowances sized for ad output | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | [See current Pletor pricing](https://www.pletor.ai/pricing) | Brand pricing scoped to usage — pay for what you create | **Astria** | | Ad variant production at campaign pace | Purpose-built: one campaign into every version it needs | Not the product's focus | **Pletor** | | Best fit | Marketing and growth teams producing high ad variant volume | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A capable ad-production engine | The stronger platform for professional fashion production | **Astria** | Reviewed August 10, 2026; no controlled image-quality benchmark was run. ## Pletor: an engine for campaign versions Pletor's framing is deliberate: production infrastructure, not a generative toy. It orchestrates models—Flux, Seedance, Kling, Veo, Sora, and language models among them, with new ones added continuously—alongside brand context and marketing data, so a team can produce product imagery from a catalog, performance ads at campaign pace, and AI-generated UGC. The clearest statement of intent is its promise to turn one campaign into every version it needs to be: languages, formats, markets, seasons. Use [Pletor pricing](https://www.pletor.ai/pricing) for current plans, credit allowances, rollover, and team access. For Astria rates and terms, see [Astria pricing](https://www.astria.ai/pricing). Its published references—an agency, a creative director, a global consumer electronics brand—describe marketing organizations rather than fashion brands. That is consistent with the positioning. Pletor is not claiming to be a fashion product, and a fair comparison should not pretend otherwise. ## Astria: a professional fashion-production environment Astria produces the source material. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—is the reusable unit that generates the collection's imagery. That includes fashion knowledge a general marketing engine has no reason to carry: category templates for beauty, jewelry, footwear, sports, luxury, and editorial work; a casting library spanning ages and body types, including children and extended-size casting; product references and a refinement pass aimed at how garments and faces actually read. Campaign frames and social crops extend the same approved direction, and motion animates stills the brand already signed off. ## Public proof: marketing adoption versus fashion production Pletor's evidence points at marketing teams and agencies, with a named agency case study and a consumer electronics reference. Read as evidence for ad production infrastructure, that is reasonable and on-point. Astria's public materials show a production base among fashion brands and the professionals who create for them: Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Vendor-published in both cases, from different industries. ## A realistic workflow comparison: where the campaign starts Consider a fashion brand launching a 90-piece spring collection with a significant paid-social budget. The growth team needs dozens of ad variants across three markets and two languages. The ecommerce team needs PDP sets. The brand team needs a lookbook and campaign imagery. Pletor is excellent at one third of this, and it is the third that comes last. Versioning a campaign across formats, markets, and languages is precisely its design goal, and its credit pricing makes high variant counts affordable. But every one of those variants derives from source creative: a cast model wearing the garment, shot in a treatment the brand approved. If that source does not exist, no amount of versioning infrastructure creates it, and generic product imagery generated from a catalog is not the same as a campaign image a brand would run. Astria produces the source. Casting, styling, lighting, crop, and format get approved once and applied across the 90 pieces imported from Shopify or Google Drive; the lookbook and campaign frames come from the same treatment; motion extends approved stills. The growth team then has something worth versioning—and can version it in Pletor, or anywhere else, without conflict. ## The differences that matter most ### Ad variant production at campaign pace — winner: Pletor This is Pletor's genuine edge and the one concession that matters here. Producing many versions of an approved campaign across languages, formats, markets, and seasons is a real operational problem for growth teams, and Pletor is built for it with pricing and rollover that suit bursty demand. Astria is not competing for that job, and a brand with a large paid-social operation may well want both. ### Fashion knowledge — winner: Astria A general marketing engine has no opinion about drape, casting conventions, or how a jewelry macro differs from an apparel PDP, because it serves electronics and services too. Astria's templates and casting library exist because those conventions are worth encoding once. When the output is a garment on a person, supplied domain knowledge beats general flexibility. ### Casting depth — winner: Astria Pletor generates people as part of an output. Astria treats casting as a production asset: a reusable library across ages and body types, including children and extended-size casting, that persists between seasons and stays consistent across a collection. ### Where the creative decision lives — winner: Astria Feeding brand context into an orchestration system produces outputs that are approximately on-brand. Storing an approved treatment produces outputs that are consistent because consistency is the object being stored, not an instruction repeated per asset. ### Review and creative collaboration — winner: Astria Pletor's collaboration model is generous on access—unlimited team invites—and oriented toward marketing workflow. Astria's multiplayer Board is oriented toward the creative decision: named cursors, live voice, and the production canvas shared with the photographer, in-house studio, and agency. ### Collection scale — winner: Astria Pletor's credits are sized around ad output; Studio's 15,000 monthly credits translate to a few thousand images, which is generous for campaign variants and a different shape from a catalog run. Astria imports the whole SKU set and runs it against the approved treatment, then extends into campaign, editorial, social, and motion. ### Commercial model for brands — winner: Astria Credit tiers with rollover are a sensible fit for campaign bursts and an awkward one for a season's production, where volume is predictable but revision-heavy and the unit that matters is a finished collection rather than a variant count. Astria's brand pricing is scoped to usage—pay for what you create. No claim is made here that Astria is cheaper; the two meters measure different work. Model a full collection, including rework, before comparing. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate, and next season starts from them. In Pletor, what accumulates is a library of campaign variants — valuable while the campaign runs, and superseded when the next one starts. ## Choose Pletor if... - Your bottleneck is ad variant volume across formats, markets, and languages. - You are a marketing or growth team rather than a production team. - Published credit pricing with rollover suits your bursty demand. - Your source creative already exists and needs multiplying, not making. ## Choose Astria if... - You need to produce the collection imagery that campaigns are built from. - Fashion-specific casting, categories, and garment handling matter. - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - Your photographer, studio, and agency should work in the same brand workspace. - Review should happen on a shared canvas with voice, not in a marketing workflow tool. ## The bottom line These two are complementary far more than they are competitive, and the honest advice is to be clear about which problem you have. If you cannot produce enough ad variants, Pletor solves that well. If your ads are constrained by not having enough good source imagery of your own collection, a versioning engine will not help — and Astria is the stronger choice. ## What to test before choosing either platform Ask one question first: do we lack variants, or do we lack source creative? Then test accordingly. For Astria, run five representative SKUs through a full channel set — PDP, detail, campaign, social — and add a colorway after approval. For Pletor, take one approved campaign image and count how many usable market and format versions come out of it. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. ## Frequently asked questions ### Is Pletor an Astria alternative? Only partly. Pletor is creative infrastructure for marketing teams — performance ads, UGC, and campaign versioning across markets and formats. Astria is a fashion production system for catalog, lookbook, campaign, and social imagery. ### Is Pletor built for fashion? No. It serves marketing teams across industries, and its published customer references span agencies and consumer electronics. Fashion is a use case rather than the design center. ### Which is better for paid-social ad volume? Pletor. Turning one campaign into every version it needs — languages, formats, markets, seasons — at campaign pace is exactly what it is built for, and it publishes credit-based pricing to match. ### Which produces the assets the ads are built from? Astria. Casting, styling, and product imagery for the collection come first; ad versioning is downstream of them. Astria produces that source material from one approved creative direction. ### Is Astria better than Pletor for fashion brands? For producing a fashion collection's imagery, yes. Marketing teams whose bottleneck is ad variant volume rather than source imagery should evaluate Pletor for that job. For adjacent decisions, see how Astria compares with [Tolstoy](https://www.astria.ai/articles/astria-vs-tolstoy-fashion-ai/), [FLORA](https://www.astria.ai/articles/astria-vs-flora-fashion-ai/), and [Figma Weave](https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 10, 2026. Key sources include Pletor's homepage describing its orchestration approach, supported models, and marketing use cases, its published customer references, and its [pricing page](https://www.pletor.ai/pricing) covering credit tiers, rollover, and team access, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, credit allowances, model availability, and product scope can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Astria vs Rawshot (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/ Rawshot makes a sharp promise: fashion AI for people who make clothes, not people who engineer prompts. No prompt box, no studio, no samples—just menus of models, lenses, lighting, and backgrounds, plus a provenance stack that labels every output as AI-generated. Astria answers a different question: not "how do I get an image without learning prompts," but "how does my brand's creative direction become a system my whole team can run." **The short answer:** Astria is the clear overall winner for fashion brands producing a collection and its channels. Choose Rawshot when you need occasional images with zero learning curve and no setup, and AI-disclosure labelling is a hard requirement. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Rawshot We used six B2B buying questions: how creative direction gets specified, whether an approved look survives the next SKU, casting and asset depth, collaboration and review, channel and format coverage, and what a full collection actually costs to run. ## Quick verdict: Astria vs Rawshot | Decision factor | Rawshot | Astria | Winner | | --- | --- | --- | --- | | Professional fashion production | Menu-driven image generation for individual products | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Pick from preset models, lenses, framings, lighting, and backgrounds | Build and reuse casting, styling, scene, crop, lighting, and format direction | **Astria** | | Casting and assets | A library of synthetic models configured by body attributes | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Category coverage | Broad preset menus applied to apparel and product shots | Fashion-specific templates for apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Natural-looking people | Regenerate with different preset choices until acceptable | Face-inpainting and refinement layer inside the production workflow | **Astria** | | Motion | Short scene-builder clips, metered per second | Multiple current video options animate approved stills in the same workflow | **Astria** | | Team production | Not publicly verified in the material reviewed | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Token-metered generation, image by image | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Monthly token allowances; a mid-size drop can exceed a plan before revisions | Brand pricing scoped to usage — pay for what you create | **Astria** | | Public customer proof | Not found on the public site in the material reviewed | Named brands including Ronny Kobo, Gant, Lee Cooper, and Nine West | **Astria** | | Best fit | Solo designers and marketplace sellers needing occasional images fast | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | The easiest on-ramp to an AI fashion image | The stronger platform for professional fashion production | **Astria** | Reviewed August 2, 2026; no controlled image-quality benchmark was run. ## Rawshot: removing the prompt, one menu at a time Rawshot positions itself as an AI photo studio for fashion, built explicitly for garment people rather than prompt engineers. The interface replaces the text box with controls: lens choices from 35mm to 135mm, framings from full body to close-up, lighting from studio softbox to golden hour, a large background library, and a catalog of synthetic models configured across body attributes covering womenswear, menswear, plus-size, kidswear, and infant. Two things deserve credit. First, Rawshot does support repeatability within its own vocabulary: the product describes reusing the same face, styling logic, and framing across a large assortment so a catalog stays coherent. Second, and more distinctive, Rawshot publishes a provenance and disclosure stack—C2PA signing, visible and cryptographic watermarking, and stated alignment with EU AI Act Article 50, California SB 942, and GDPR. For a brand whose legal team has AI-labelling obligations on the roadmap, that is a concrete, checkable capability rather than a marketing line. The site itself is the other signal. Rawshot's public surface runs to thousands of programmatically generated pages—one per feature, solution, and comparison permutation. It is an effective way to capture search demand. It is not evidence of production adoption, and we did not find named customer case studies or testimonials on the public site in the material reviewed. ## Astria: a professional fashion-production environment Astria starts from the brand rather than the interface. Products, casting, references, templates, and outputs live together in a workspace, so an approved look becomes a reusable recipe rather than a configuration a single operator remembers. That difference shows up in what a team can encode. Templates cover fashion-specific categories well beyond standard apparel PDPs—beauty, jewelry, footwear, sports, luxury, and editorial—and brand references anchor results to the brand's own visual language rather than a generic AI aesthetic. Astria's Describe tool can extract usable creative direction from a rights-cleared reference while accounting for the products and models already loaded, which shortens the path from "make something like our last campaign" to a template the team can run. ## Public proof: named production versus search surface Astria's public materials show a professional production base: recognizable brands including Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work on the platform. This is vendor-published evidence rather than an independent benchmark, and it should be read as an adoption signal, not a quality proof. Rawshot's public evidence is different in kind. The company publishes extensive category and comparison content and clear technical specifications, but in the material we reviewed on August 23, 2026, we could not find a named customer story, testimonial, or case study. Absence of published proof is not proof of absence—Rawshot may well have customers who prefer not to be named—but a buyer evaluating a production commitment has less to check. ## A realistic workflow comparison: a knitwear capsule in three colorways Consider a knitwear label with 24 styles, each in three colorways, needing PDP and detail shots, a small lookbook, and paid-social crops. With Rawshot, an operator configures the first look—model, lens, framing, lighting, background—and generates. The result arrives in well under a minute, labelled and cleared for commercial use. Reusing that face and framing across the assortment is supported, so the catalog can stay coherent. Use [Rawshot pricing](https://rawshot.ai/pricing) to budget for the full collection, model creation, and rejected attempts. With Astria, the team defines casting, styling, lighting, crop, and format once, approves the treatment, and runs the collection against it. Colorway two and three inherit the approved setup rather than repeating the configuration. The lookbook and social crops extend the same direction instead of restarting it, and the reviewers work on the shared board rather than passing exports around. ## The differences that matter most ### Menu choice versus art direction — winner: Astria This is the heart of the comparison. Rawshot's menus are broad and genuinely well organized, and for a designer who has never written a prompt they remove a real barrier. But a menu is a fixed vocabulary. It can describe a 50mm lens and golden-hour light; it cannot describe why this brand shoots its knitwear cropped at the collarbone with the sleeve pushed back, or that its casting skews older than the category norm. Astria lets a creative team encode those decisions—casting, styling, pose, composition, references—and then hand the result to someone else to run. ### Casting and reusable assets — winner: Astria A library of configurable synthetic models is useful and covers a lot of ground, including body types and age ranges. What it does not produce is an asset the brand owns and reuses as its own: a specific cast, a specific location, a specific reference set that recurs season after season. Astria's reusable library spans ages and body types, including children and extended-size casting, alongside locations, poses, and other production references that accumulate in the brand's workspace. ### Compliance, provenance, and disclosure — winner: Rawshot This is Rawshot's genuine edge and the one concession that matters here. Publishing C2PA signing, visible and cryptographic watermarking, and explicit alignment with EU AI Act Article 50 and California SB 942 is more than most of this category documents, and for a brand with disclosure obligations it removes a procurement question early. It is a compliance advantage rather than a production verdict: labelling an image correctly is a different problem from directing it, and buyers with those obligations should confirm current scope directly with both vendors. ### Collaboration and handoff — winner: Astria Rawshot's published material describes a browser interface and an API; we could not verify multiplayer review, shared workspaces, or team roles in the material reviewed. Astria combines private brand workspaces with a multiplayer Board: teammates see named cursors, talk over the actual canvas with live voice, and resolve looks together. When the photographer, the in-house studio, and the social agency all touch the same drop, that difference decides whether work is handed over or handed off. ### Collection scale and commercial fit — winner: Astria Rawshot's token model is transparent and easy to start—which is exactly why the arithmetic matters. Tokens are consumed by every generation, including the ones a creative director rejects, and monthly allowances are sized for steady trickles rather than collection pushes. Astria treats the collection as the unit of work: the SKU set imports from Shopify or Google Drive, the approved setup runs across it, and brand pricing is scoped to usage—pay for what you create, sized to the production. A per-image token price is not a production budget; model a full collection, including rework, before comparing costs. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate: each stage of the season keeps what the last one approved, and the same source of truth serves the photographer, the studio, and the agency. In Rawshot, what accumulates is a folder of generated images and an operator's memory of which settings produced them. ## Choose Rawshot if... - You need occasional fashion images fast, with no setup and no prompt learning curve. - AI-disclosure labelling and content provenance are hard requirements today. - You are a solo designer, marketplace seller, or small label without a creative team to coordinate. - Preset models, lenses, and backgrounds cover your visual range. ## Choose Astria if... - One approved creative direction must carry across a collection and its channels. - A creative director or photographer needs to install a workflow other people can reuse. - Casting, styling, and scene decisions must survive team changes, colorways, and late additions. - Review should happen on a shared canvas with voice, not through exported files. - Video, campaign, and social work should extend the collection, not restart it. ## The bottom line For professional fashion brands, Astria is the stronger choice: it turns art direction into infrastructure that compounds across a season. Rawshot is the easiest on-ramp in this category and an honest one about what it is—a fast, well-labelled image generator for people who do not want to learn prompts. Do not decide from a hero image. Test the second colorway, the difficult garment, and whether a teammate can reproduce the approved look without help. ## What to test before choosing either platform Run five representative styles—including one sheer, knit, or layered piece—through both products. Require PDP, detail, and campaign crops, then add a colorway after approval. Count what the round trip actually consumes: tokens or credits spent, including rejected frames. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask a second operator to reproduce the approved direction from scratch. That last test separates image generators from production systems. ## Frequently asked questions ### Is Rawshot an Astria alternative? Yes, for generating AI fashion images without writing prompts. They diverge when the job moves from producing images to running a brand's creative direction across a collection, its channels, and its collaborators. ### Does a no-prompt interface mean better fashion results? It means faster results with less setup, which is a real benefit. But menus of models, lenses, and backgrounds remove prompt craft without encoding a brand's point of view. Choosing from a menu is not the same as directing a shoot. ### Which handles a full catalog more predictably? Astria. It imports the SKU set from Shopify or Google Drive and runs it against one approved setup. Rawshot meters generation in tokens, so a mid-size drop can exhaust a monthly plan before revisions begin. ### What public customer proof does each publish? Astria's materials name brands including Ronny Kobo, Gant, Lee Cooper, and Nine West. We did not find named customer case studies or testimonials on Rawshot's public site in the material reviewed in August 2026. ### Is Astria better than Rawshot for fashion brands? For professional brands producing on-brand imagery across a collection and multiple channels, yes. Solo designers and marketplace sellers who need occasional images with no learning curve may find Rawshot sufficient. For adjacent decisions, see how Astria compares with [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/), [Caimera](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/), and [Ayna](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 2, 2026. Key sources include Rawshot's homepage, its [AI fashion photography solution page](https://rawshot.ai/solution/ai-fashion-photography), and its [pricing page](https://rawshot.ai/pricing) covering plan tiers and token costs, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, plan limits, model libraries, compliance scope, and workflow support can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Rewarx Studio (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-rewarx-fashion-ai/ Rewarx Studio takes a phone snapshot of a product and returns a 4K commercial image, and it does that across more than twenty industries — electronics, cosmetics, jewelry, home decor, beverages, footwear, eyewear, pet supplies, hand tools. It is a genuinely wide, well-built ecommerce production suite, with eight named tools and marketplace-formatted output for Amazon, Shopify, Etsy, and eBay. Fashion and apparel is one of its verticals. For Astria, fashion is the whole product. **The short answer:** Astria is the clear overall winner for fashion brands producing a collection and the channels around it. Choose Rewarx Studio when your catalog spans several product types, your work is mostly single-product listing images, and marketplace-ready output matters more than art direction. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Rewarx Studio We used six B2B buying questions: how much of the product is about fashion specifically, whether casting and creative direction survive between sessions, what the plan tier decides for you, how much of a season each platform covers, how the meter behaves at catalog volume, and what evidence supports the vendor's claims. ## Quick verdict: Astria vs Rewarx Studio | Decision factor | Rewarx Studio | Astria | Winner | | --- | --- | --- | --- | | Category focus | Fashion is one of 20+ industries, alongside hand tools, pet supplies, and beverages | Built for fashion, end to end | **Astria** | | Professional fashion production | Studio renders, ghost mannequin, on-model shots, listing sets | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Style and scene selections made per generation; custom style training is an Agency-tier entitlement | Approved treatment — casting, styling, scene, crop, lighting, format — stored and reused | **Astria** | | Casting | Model archetypes plus a Lookalike reference that holds one face across a production | Reusable casting library across ages and body types, including children and extended sizes | **Astria** | | Reusable brand assets | Cloud asset storage, unlimited only on the top tier | Products, casting, locations, poses, and references accumulate without plan entitlements | **Astria** | | What "lookbook" means | Standardizing one lighting setup across a catalog | An editorial set expressing the collection's direction | **Astria** | | Campaign and editorial work | Commercial ad posters with typography over a product focal point | Campaign, editorial, and social treatments from the same approved setup | **Astria** | | Motion | Cinematic video clips from a static shot | Multiple current video options animate approved stills in the same workflow | **Astria** | | Catalog import | Marketplace and store integrations: Shopify, Amazon, Etsy, eBay | SKU import from Shopify or Google Drive into a brand workspace | **Astria** | | Team production | Multi-user account access listed on the Agency tier | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | [See current Rewarx pricing](https://www.rewarx.com/#pricing) | The full SKU set runs against one approved setup | **Astria** | | Commercial model for brands | Monthly credit tiers with entitlements attached to each level | Brand pricing scoped to usage — pay for what you create | **Astria** | | Public customer proof | Aggregate figures and first-name testimonials; no named brands found | Named fashion brands plus studios and photographers running client work | **Astria** | | Ready-made ecommerce output types | Ghost mannequin, group shot, mockup, product page sets, ad poster, marketplace formatting | Fashion production outputs, without marketplace listing templates | **Rewarx Studio** | | Best fit | Marketplace sellers producing listing images across a mixed catalog | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A broad, capable ecommerce product-photography suite | The stronger platform for professional fashion production | **Astria** | Reviewed August 23, 2026; no controlled image-quality benchmark was run. ## Rewarx Studio: breadth across the catalog Rewarx Limited, based in San Francisco, positions Rewarx Studio as an AI production suite for commerce content. The promise is compact: upload a product photo, choose a scene or model, export a 4K asset. Eight tools anchor the workspace — AI Photography Studio, Lookalike Creator, Model Studio, Ghost Mannequin, Mockup Generator, Group Shot Studio, Product Page Builder, and Commercial Ad Poster — plus a Virtual Model Try-on tool and a REST API. The apparel-specific work is real. Ghost Mannequin fills a neckline and removes the mannequin. The company describes a Geometry-Lock approach it says preserves a product's dimensions and logo placement, and a texture engine intended to hold stitching and material grain. Model Studio offers archetypes across ethnicities, ages, and body types, and its Lookalike technology can carry one face and body across a production — a meaningful capability, and one a fashion buyer should test rather than assume. The commercial plumbing is sensible too: assets formatted for Amazon, Shopify, Etsy, and eBay, a Shopify app, batch generation, exports up to 4096 pixels, stated commercial ownership of generated assets, and self-serve onboarding. The company also states that uploads are not used to train public models. Two things a fashion buyer should read closely. The first is what the fashion page means by standardizing a lookbook: applying the same studio lighting setup consistently across a large catalog. That is useful, and it is catalog standardization rather than what a fashion team means by a lookbook. The second is the plan structure, discussed below. ## Astria: a professional fashion-production environment Astria narrows to fashion and spends the depth there. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment — casting, styling, scene, crop, lighting, format — is the reusable unit that produces a season. Casting is a library rather than an entitlement, covering ages and body types including children and extended sizes, alongside locations, poses, and brand references that accumulate season over season. Templates encode apparel, beauty, jewelry, footwear, sports, luxury, and editorial work as production disciplines with their own casting, lighting, and framing logic. Campaign frames, editorial spreads, social crops, and motion all derive from the same approved direction, and review happens on a shared production canvas with named cursors and live voice, with external photographers and agencies inside the same workspace. ## Public proof: aggregate figures versus named brands Rewarx publishes headline statistics — an 85% reduction against studio and retouching fees, a 34% average conversion increase, and a 10x time-to-market improvement — alongside a 5.0 G2 rating. Its testimonials carry first names and a last initial with generic role labels, and we found no named customer brand or case study in the material reviewed. The reach figures are also inconsistent between pages: the homepage invites you to join "5M+ Sellers and Creators" while the about page says "10,000+ brands." Both are vendor-published; a buyer has nothing specific to verify. Astria's public materials name fashion and retail brands including Gant, Nine West, Lee Cooper, Nununu, Renuar, Fox, and Terminal X, alongside studios and photographers running client work. Vendor-published too, and at least checkable by looking. ## A realistic workflow comparison: the month the drop lands Consider a contemporary womenswear label with 60 styles going live: four images per style at 4K, a campaign set, social crops, and a handful of short clips for Reels. The team evaluates Rewarx’s batch tools against that complete brief. The first week goes well. Ghost mannequin on the knitwear is fast and clean, background removal saves the offshore clipping-path budget, and the Shopify export is genuinely convenient. Then three ceilings arrive at once. Budget for all required images, clips, and rejected attempts using [current Rewarx pricing](https://www.rewarx.com/#pricing). Check capacity for the complete drop before choosing a plan. Collaboration: the stylist wants to review with the ecommerce lead and the freelance photographer. Multi-user account access is an Agency-tier entitlement, so a workflow question becomes a billing question. Direction: the brand wants its own recurring look held across the season. Custom style training also sits on the Agency tier. Until then, direction is re-selected per generation by whoever is at the keyboard. Then the campaign brief lands. An ad poster puts typography over a product focal point, which is a listing asset rather than a campaign. A real lookbook is not one lighting setup applied consistently. Include the social calendar alongside the catalog when checking [current Rewarx pricing](https://www.rewarx.com/#pricing). With Astria, casting is a library the brand builds and keeps, the 60 styles run against one approved treatment, the campaign and editorial frames come from that same direction, motion animates stills that already passed review, and the photographer is in the workspace rather than outside the account. Nothing in the season is rationed by a plan tier. ## The differences that matter most ### Ready-made ecommerce output types — winner: Rewarx Studio This is Rewarx's genuine edge and the one concession that matters here. Ghost mannequin, group shots, pattern mockups, eight-image product page sets, and ad posters, all exporting into Amazon, Etsy, eBay, and Shopify formats, cover a marketplace seller's asset list without anyone having to decide what a listing should look like. If your job is to put correct, consistent, well-lit images against many SKUs across a mixed catalog, that toolkit is a real advantage, and self-serve onboarding supports an initial evaluation. Check [Rewarx pricing](https://www.rewarx.com/#pricing) for current trial terms. ### Fashion depth versus catalog breadth — winner: Astria A product serving hand tools, beverages, eyewear, and apparel optimizes for what they share: a clean subject, a pleasing background, consistent light. Fashion's hard parts are not shared — drape on a knit, how a sleeve breaks, casting that matches a brand's customer, a crop convention held across a collection. Rewarx's twenty verticals are material-physics and lighting profiles for those product types; Astria's categories are production disciplines. ### Direction that survives the session — winner: Astria Style and scene are chosen per generation in Rewarx, and custom style training is an entitlement on the top tier. Astria stores the approved treatment itself, so the tenth SKU is produced the same way as the first by someone who was not there for the decision. That is the difference between a tool that repeats an operation and a system that repeats a decision. ### Casting that scales with the collection — winner: Astria Rewarx's Lookalike reference holding one face across a production is a real capability, and it solves the single-model consistency problem well. A fashion brand's casting problem is usually plural: a womenswear line, a kids range, and an extended-size offer are three casting requirements before anyone discusses campaigns. Astria treats casting as a reusable library spanning ages and body types, sitting alongside locations, poses, and brand references that carry into the next season. ### Coverage of the season — winner: Astria One lighting setup across 500 SKUs is not a lookbook, and typography over a product shot is not a campaign. Editorial framing, social-native crops, and motion are separate disciplines. Astria produces them as expressions of one approved direction rather than as separate tools applied to a product image, and motion is not metered twenty times heavier than a still. ### Collaboration — winner: Astria Rewarx's plan structure places multi-user account access, API access, and a white-label delivery portal on the Agency tier, which reads as an agency-reseller shape rather than a brand-team shape. Astria's brand workspaces and multiplayer Board — named cursors, live voice over the production canvas — exist because fashion work passes between a stylist, an ecommerce lead, a photographer, and often an agency, at every plan level. ### Collection scale and commercial fit — winner: Astria Check [Rewarx pricing](https://www.rewarx.com/#pricing) and [Astria pricing](https://www.astria.ai/pricing) for current rates and plan features. Model a full collection, including a realistic rejection rate, collaborators, and every channel, before comparing costs. ### Compounding production value — winner: Astria In Astria, products, casting, templates, approvals, and outputs accumulate in a workspace shared by the whole production, and next season starts from them. A photographer, the in-house studio, and a social agency work from the same references and approvals instead of re-uploading assets and rebuilding briefs. In Rewarx, what accumulates is a stored asset library — uncapped only at the top tier, and without the casting and direction that produced it. ## Choose Rewarx Studio if... - Your catalog spans several product types — apparel plus electronics, homeware, or beauty. - Most of your work is single-product listing images rather than art-directed campaigns. - Marketplace-formatted output for Amazon, Etsy, or eBay is part of the job. - Ghost mannequin and clean background replacement at volume is the specific bottleneck. - You want to try something today for very little and decide later. ## Choose Astria if... - Fashion is the business, not one vertical among twenty. - Casting must scale with the collection rather than sit in a plan entitlement. - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - Your photographer, stylist, or agency should work inside the brand workspace, not around a single account. - You want a real lookbook and a real campaign, not standardized lighting and a poster template. ## The bottom line For fashion brands, Astria is the stronger choice: it is built for how a collection is cast, directed, and produced, and nothing about the season is rationed by a plan tier. Rewarx Studio is a broad, capable ecommerce product-photography suite with unusually complete listing output — a sensible pick for a marketplace seller whose catalog holds more than clothes. ## What to test before choosing either platform Price your real annual volume at the resolution you actually ship, including motion and a realistic rejection rate, then check that number against the credit allowance on the tier you were planning to buy. Separately, list who needs to be in the account — stylist, ecommerce lead, photographer, agency — and check that against per-tier seat entitlements before you shortlist. Then run five representative styles through both, requiring PDP, detail, campaign, and social outputs, and add a colorway after approval to see what has to be redone. Have a senior creative review outputs blind for garment construction, drape, anatomy, category-appropriate styling, casting, and brand fit. Finally, ask each vendor for a named fashion customer you can contact. ## Frequently asked questions ### Is Rewarx Studio an Astria alternative? For single-product transformations, yes. Rewarx Studio is an AI product-photography suite covering more than twenty industries, where fashion is one vertical. Astria is built only for fashion production across a collection and its channels. ### How does Rewarx Studio's credit model behave at collection scale? Use [Rewarx pricing](https://www.rewarx.com/#pricing) for current output rates and allowances, and [Astria pricing](https://www.astria.ai/pricing) for Astria rates. Budget for the complete collection, including rejected images and motion. ### Can a fashion team collaborate inside Rewarx Studio? Check the current Rewarx pricing page for multi-user account access and plan terms. Astria puts brand workspaces and a shared production canvas in front of the whole team, including external photographers and agencies. ### Does Rewarx Studio produce a fashion lookbook? Its fashion page describes standardizing a lookbook as applying one studio lighting setup consistently across a catalog. That is catalog standardization. A fashion lookbook is an editorial set expressing a collection's direction, which is what Astria produces. ### Is Astria better than Rewarx Studio for fashion brands? For brands producing a collection across channels, yes: fashion-specific depth, casting and references that accumulate rather than sitting in a plan tier, campaign and editorial work, collaboration, and motion. Marketplace sellers with mixed catalogs may prefer Rewarx's breadth of ready-made listing outputs. For adjacent decisions, see how Astria compares with [Emilia AI Studio](https://www.astria.ai/articles/astria-vs-emilia-fashion-ai/) and [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/), or read the wider [platform round-up](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/). ## Sources and methodology This article compares public product information available on August 23, 2026. Key sources include the [Rewarx Studio homepage](https://www.rewarx.com/) describing its eight production tools, twenty-plus industry profiles, marketplace integrations, and published statistics; its [plans and pricing](https://www.rewarx.com/#pricing) covering the Starter, Growth, Scale, and Agency tiers, the credits included at each level, the per-output credit costs, and the entitlements attached to each tier; its [fashion and apparel use case](https://www.rewarx.com/use-cases/fashion-apparel-photography.html) covering ghost mannequin, catalog standardization, resolution, and commercial rights; its [AI Model Studio](https://www.rewarx.com/tools/model-studio.html) and [Virtual Model Try-on](https://www.rewarx.com/tools/virtual-model-try-on.html) pages covering archetypes, Lookalike consistency, and batching; and its [developer API](https://www.rewarx.com/tools/rewarx-api.html) page — plus Astria's [fashion and ecommerce offering](https://www.astria.ai/ecommerce), template gallery, and workspace documentation. Pricing, credit costs, plan entitlements, and product scope can change. Confirm current terms and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Astria vs Stoodio (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-stoodio-fashion-ai/ Stoodio is one of the few competitors in this set that is, like Astria, built exclusively for fashion—image generation, video, workflows, a model database, and asset management in one platform. Its distinguishing bet is legal: a partnership with ModelManagement.com giving access to a very large pool of consented, commercially licensed real-model digital twins. The question for a buyer is whether that licensing advantage outweighs a production model that runs through the vendor. **The short answer:** Astria is the clear overall winner for most fashion brands—broader categories, deeper casting, live collaborative review, and no onboarding engagement to get started. Choose Stoodio when licensed real-model likeness is the overriding requirement. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Stoodio We used six B2B buying questions: how a brand gets started and how dependent it stays on the vendor, casting depth and licensing, category and channel coverage, how collaborators review the work, what published evidence exists, and how a full collection runs. ## Quick verdict: Astria vs Stoodio | Decision factor | Stoodio | Astria | Winner | | --- | --- | --- | --- | | How you start | Booked demo plus guided onboarding with the vendor's team | Direct access for brands, studios, photographers, and educators | **Astria** | | Professional fashion production | Fashion-specific image, video, and workflow platform | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Chained prompts, models, edits, and exports as repeatable pipelines | Approved treatment — casting, styling, scene, crop, lighting, format — stored and reused | **Astria** | | Casting depth | Very large pool of licensed adult real-model twins | Models across ages and body types, including children and extended-size casting, plus locations and poses | **Astria** | | Category coverage | Ecommerce, campaign, social, and print for apparel | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial templates | **Astria** | | Natural-looking people | Photoreal output from licensed twins | Face-inpainting and refinement layer inside the production workflow | **Astria** | | Motion | Looping campaign video and social content from stills | Multiple current video options animate approved stills in the same workflow | **Astria** | | Review experience | Asset management: organize, review, and ship | Multiplayer canvas with named cursors and live voice over the actual looks | **Astria** | | External collaborators | Vendor-guided production partnership | Photographer, in-house studio, and agency share one workspace and approvals | **Astria** | | Collection scale | Repeatable pipelines run across products | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Not published; demo-led engagement | Brand pricing scoped to usage — pay for what you create | **Astria** | | Model licensing and consent | Consented real-model twins with worldwide, perpetual rights | Reusable casting library within the platform | **Stoodio** | | Best fit | Brands for whom licensed real-model likeness is a hard requirement | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A strong licensing-led fashion studio | The stronger platform for professional fashion production | **Astria** | Reviewed August 10, 2026; no controlled image-quality benchmark was run. ## Stoodio: licensing as the product strategy Stoodio's pitch is that it builds a brand's AI studio, and it is refreshingly specific about being fashion-only rather than a general image tool. The platform lists five integrated capabilities: image generation, video generation, workflows that chain prompts, models, edits, and exports into repeatable pipelines, a model database, and asset management. The model database is the strategy. As an official partner of ModelManagement.com, Stoodio offers access to over 100,000 consented digital twins with worldwide, perpetual commercial rights, and it is pointed about what that means—no composite faces, no grey zones. For a brand whose legal team is uneasy about synthetic likeness, or that operates in a market tightening around AI disclosure and personality rights, this is a materially different proposition from a library of invented faces. The company frames itself as a production partner rather than a tool, promising guided onboarding so a team integrates AI into real production from the first session. Its published evidence is a single detailed case study: a Berlin activewear brand reporting 40% lower cost per SKU, production cycles moving from three weeks to three days, and double the content output. There is no published pricing; the route in is a booked demo. ## Astria: a professional fashion-production environment Astria shares Stoodio's fashion focus and diverges on autonomy and breadth. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—is the reusable unit the brand's own people edit and run. Breadth shows in two places. Categories: templates treat beauty, jewelry, footwear, sports, luxury, and editorial as distinct fashion disciplines, not variations on apparel. And casting: alongside adult models across body types, the library covers children's and extended-size casting, which are production requirements a licensed adult-model roster does not address. ## Public proof: one deep story versus a broader base Stoodio's case study is genuinely useful—specific numbers, a named founder, a real brand—and it is one story from a small brand. That is honest early-stage evidence, and a buyer should read it as such rather than as a pattern. Astria's public materials show a production base among fashion brands and the professionals who create for them: Ronny Kobo, Nununu, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Also vendor-published, and broader across brand size and contributor type. ## A realistic workflow comparison: the legal review Consider a European brand whose counsel has flagged AI-generated faces as a risk. The marketing director wants AI production; legal wants documented consent and clear commercial rights for any human likeness used in advertising. This is Stoodio's scenario, and it answers it well. A licensed twin from a consented roster with worldwide perpetual rights is a document legal can read, and the onboarding engagement means someone from the vendor helps wire it into the brand's real production. If that constraint is genuinely binding, the rest of this comparison is secondary. Now change one detail: the brand also sells a children's line and a small jewelry capsule, and its campaign imagery is set by a freelance stylist who is not an employee. The licensed adult-model roster does not cover the children's line. Jewelry needs different lighting and macro conventions than activewear. And the stylist needs to be inside the workflow, reviewing looks with the team, not briefing a vendor. In Astria those are ordinary: children's and extended-size casting exist in the library, jewelry and beauty are first-class template categories, and the stylist joins the brand workspace and reviews on the multiplayer Board with live voice. ## The differences that matter most ### Model licensing and consent — winner: Stoodio This is Stoodio's genuine edge and the one concession that matters here. A roster of over 100,000 consented real-model digital twins, sourced through a named partner with worldwide perpetual commercial rights, is the cleanest answer to likeness risk in this comparison, and Astria does not match it. Brands operating under strict legal review, or in markets tightening around AI disclosure and personality rights, should weight this heavily and ask both vendors for written rights documentation. ### Getting started without an engagement — winner: Astria Stoodio's positioning as a production partner with guided onboarding is a real service, and it is also a dependency and a delay. Astria is used directly by brands and by the studios, photographers, and educators who serve them, which means a team can validate the workflow on its own products before committing to anything. ### Casting breadth beyond licensed adults — winner: Astria A licensed twin roster solves adult likeness elegantly and does not solve children's casting or extended-size representation, both of which are ordinary production requirements for brands with a full line. Astria's library covers ages and body types, including children and extended-size casting, alongside locations and poses. ### Category coverage — winner: Astria Stoodio's published channels—ecommerce, campaign, social, print—describe distribution rather than category. Astria's templates encode the conventions of beauty, jewelry, footwear, sports, luxury, and editorial work, which is what a brand with more than one product type actually needs. ### Review and external collaborators — winner: Astria Stoodio offers asset management: organize, review, and ship from one place. That is the right feature set for tracking output. Astria's multiplayer Board is for making the decision: named cursors, live voice, and the production canvas shared with the photographer, the in-house studio, and the agency. ### Collection scale and commercial fit — winner: Astria Both build repeatable pipelines. Astria's differ in where the collection enters: the SKU set imports from Shopify or Google Drive and runs against the approved treatment, then extends into campaign, editorial, social, and motion. On commercials, Stoodio publishes no pricing, so a brand cannot model a season before entering a sales conversation; Astria's brand pricing is scoped to usage—pay for what you create. This article makes no claim that Astria is cheaper; it notes that one option can be estimated against a real collection before commitment and the other cannot. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate in a workspace the whole production shares, and the next season starts from them. Stoodio's pipelines accumulate too—inside a partnership the brand does not fully operate alone. ## Choose Stoodio if... - Documented consent and commercial rights for human likeness are a hard legal requirement. - You want a vendor partner to help integrate AI into production from the first session. - Adult apparel is the bulk of your line. - A licensed real-model roster matters more to you than running the workflow independently. ## Choose Astria if... - You want to validate the workflow on your own products before any commitment. - Your line spans categories — beauty, jewelry, footwear, sports, luxury, editorial — and age ranges including children. - External stylists, photographers, or agencies should work inside the brand workspace. - Review should happen on a shared canvas with voice, not through an asset manager. - One approved creative direction must carry across the collection and every channel. ## The bottom line For most fashion brands, Astria is the stronger choice: broader categories, deeper casting, collaborative review with the people who actually set the look, and a workflow the brand runs itself. Stoodio has built the best licensing answer in this category, and for a brand where consent documentation is the binding constraint, that may be the only factor that matters. Ask both vendors for their rights documentation in writing. Then ask who runs the workflow after onboarding ends. ## What to test before choosing either platform Run five representative SKUs through both, spanning at least two categories if your line has them, and include a children's or extended-size product if relevant. Require PDP, detail, campaign, and social crops, then add a colorway after approval. Request written likeness-rights documentation from both. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, invite your external stylist or photographer into the workflow and see whether they can work in it. ## Frequently asked questions ### Is Stoodio an Astria alternative? Yes — it is a genuine fashion-specific competitor with image generation, video, workflows, a model database, and asset management. The main differences are its licensing-led model library, its demo-and-onboarding sales motion, and how much of the work you run yourself. ### What is Stoodio's strongest advantage? Consent and licensing. Through its ModelManagement.com partnership Stoodio offers a very large pool of commercially licensed real-model digital twins with worldwide, perpetual rights — a clean answer for brands with legal sensitivity about AI faces. ### Which can a brand start using on its own? Astria. Stoodio's route is a booked demo with guided onboarding and positions itself as a production partner. Astria is used directly by brands and by the studios, photographers, and educators producing for them. ### How does published proof compare? Stoodio publishes one detailed customer story with specific outcomes. Astria's materials name several fashion brands plus studios and photographers running client work. Both are vendor-published rather than independent. ### Is Astria better than Stoodio for fashion brands? For most brands, yes: broader category templates, deeper casting including children and extended sizes, live multiplayer review, and no onboarding engagement required. Brands whose overriding requirement is licensed real-model likeness should evaluate Stoodio on that basis. For adjacent decisions, see how Astria compares with [Looklet](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/), [Graswald](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/), and [Rawshot](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 10, 2026. Key sources include Stoodio's homepage describing its five platform capabilities, its ModelManagement.com partnership and licensed digital-twin roster, its stated channel coverage, and its published activewear case study, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. We did not find public pricing for Stoodio in this review. Roster size, rights terms, product scope, and commercial terms can change. Confirm current licensing documentation and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Stylic (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-stylic-fashion-ai/ Stylic is one of the cheapest ways to turn a product photo into an on-model image: upload, pick from a few hundred backgrounds and a few dozen models, download in about three minutes. It is aimed squarely at marketplace sellers—Shopify, Amazon, Meesho, and the Indian marketplace ecosystem—and it is priced accordingly. Astria answers a different brief, from brands and from the photographers and studios who produce for them. **The short answer:** Astria is the clear overall winner for fashion brands. Choose Stylic if you are an individual marketplace seller who needs inexpensive listing images and nothing beyond them. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Stylic We used six B2B buying questions: who the product is designed for, how creative direction is specified and whether it persists, casting and category depth, what evidence supports the vendor's claims, how a collection with revisions behaves, and what the team owns at the end. ## Quick verdict: Astria vs Stylic | Decision factor | Stylic | Astria | Winner | | --- | --- | --- | --- | | Intended buyer | Marketplace sellers and small D2C listings | Fashion brands, studios, photographers, and educators | **Astria** | | Professional fashion production | On-model listing images from a product photo | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Configured per photoshoot from background and model menus | Approved treatment — casting, styling, scene, crop, lighting, format — stored and reused | **Astria** | | Casting and assets | A few dozen platform AI models, plus custom models | Models across ages and body types, including children and extended-size casting, plus locations and poses | **Astria** | | Category coverage | Apparel listing photography | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial templates | **Astria** | | Natural-looking people | Regenerate until acceptable | Face-inpainting and refinement layer inside the production workflow | **Astria** | | Motion | Not offered in the material reviewed | Multiple current video options animate approved stills in the same workflow | **Astria** | | Campaign and lookbook work | Not the product's purpose | Campaign, editorial, and social treatments from the same approved setup | **Astria** | | Team production | Single-operator tool in the material reviewed | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Bulk and catalogue photoshoots, configured per run | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Low monthly tiers sized for listing volume | Brand pricing scoped to usage — pay for what you create | **Astria** | | Verifiable proof | Large vendor-published figures without stated methodology | Named fashion brands plus studios and photographers running client work | **Astria** | | Time to first image | Roughly three minutes end to end, with no setup | A production setup that pays back across a collection | **Stylic** | | Best fit | Individual sellers producing listing images cheaply | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A cheap listing-image generator | The stronger platform for professional fashion production | **Astria** | Reviewed August 11, 2026; no controlled image-quality benchmark was run. ## Stylic: three minutes to a listing image Stylic's promise is speed and price. The published workflow is three steps: upload a product image in any format, spend about a minute choosing from 500+ backgrounds and 30+ AI models, and download commercial-ready results roughly ninety seconds later. Features include single, multiple, and catalogue photoshoots, 4K and 8K output, brand watermarking, clothing colour change, custom AI models, and bulk creation. Check the vendor’s current pricing page for plans and enterprise terms. The intended audience is stated plainly: Shopify stores, Amazon sellers, Meesho brands, D2C fashion labels, fashion startups, and creative agencies. The pricing, the marketplace orientation, and the rupee-denominated savings figure all point the same way—this is a tool for sellers, not a system for brands. The published statistics deserve a sceptical read. Stylic cites 10,000+ fashion businesses in one place and 200+ fashion brands in another, alongside 2.4 million images generated, a rupee figure for photoshoot budget saved, and a claim that 94% of users replaced their photographer. No methodology accompanies any of them, and the two audience-size figures are hard to reconcile. None of this means the product is bad; it means a buyer should ask how each number was produced before treating it as evidence, and should weigh it against a company profile that is early-stage and light on named references. ## Astria: a professional fashion-production environment Astria's buyer list includes the people Stylic's headline claim positions against. Photographers, studios, and educators use Astria to install repeatable production workflows for brands—which is a useful signal about what the product is for. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—is stored and reused rather than reconfigured per shoot. Templates span beauty, jewelry, footwear, sports, luxury, and editorial as distinct categories, casting covers ages and body types including children and extended sizes, and the same direction extends into campaign frames, social crops, and motion. ## Public proof: numbers versus names Stylic's evidence is aggregate and self-reported: user counts, images generated, savings, and a replacement statistic, with marketplace names such as Myntra and Flipkart mentioned as context rather than as customers with published stories. Astria's public materials name fashion brands—Ronny Kobo, Nununu, Gant, Lee Cooper, Nine West—alongside studios and photographers running client work. Vendor-published too, and checkable in a different way: a named brand can be looked at, and a photographer can be asked. ## A realistic workflow comparison: the seller who becomes a brand Consider a label that started on marketplaces and is now launching its own site, with 70 styles, a stylist on retainer, and a founder who cares how the clothes are framed. The Stylic era made sense: cheap listing images, generated fast, good enough for a marketplace grid where every competitor's photo looks similar. What breaks is not image quality—it is everything around it. The stylist's point of view cannot be stored, so each shoot is reconfigured from menus and drifts. The site needs a lookbook and campaign imagery, which the tool is not aimed at. Motion is not on offer. And when the founder asks for a warmer, tighter crop across all 70 styles, that is 70 reconfigurations rather than one edit. With Astria, the stylist sets casting, styling, lighting, crop, and format once, the founder approves it, and the 70 styles run against that treatment imported from Shopify or Google Drive. The lookbook and campaign come from the same direction. The crop change is a single edit applied everywhere. And the stylist's judgment is now an asset the brand keeps. ## The differences that matter most ### Time to first image — winner: Stylic This is Stylic's genuine edge and the one concession that matters here. About three minutes from upload to download, with no setup and nothing to configure beyond two menus, is a genuinely low barrier — and for a seller listing a handful of products it may be all that is required. No production system competes with that on immediacy, and there is nothing wrong with buying the simple thing when the simple thing is sufficient. ### "Replacing the photographer" versus encoding the photographer — winner: Astria Stylic's headline statistic frames AI as a substitute for a professional. Astria's position is the opposite, and it is not merely rhetorical: photographers and studios are among Astria's users because the product lets them encode a look and hand it to a brand's team to run. That is the difference between removing craft from the process and making craft repeatable. Brands that have paid for good creative direction generally want to keep it. ### Direction that persists — winner: Astria Configuring a shoot from menus produces a result. Storing an approved treatment produces consistency, because the thing being reused is the decision rather than the operator's memory of it. Over 70 styles and three channels, that is the whole difference between a collection and a folder. ### Casting and category depth — winner: Astria A few dozen platform models and a large background library cover routine apparel listings. They do not cover a children's line, extended-size representation, or the lighting and macro conventions that jewelry and beauty require. Astria treats those as first-class. ### Channels beyond the listing — winner: Astria Lookbook, campaign, editorial, and motion are not in Stylic's scope. For a marketplace seller that is a sensible omission. For a brand building a site and a social presence, it means the tool solves the smallest part of the problem. ### Collaboration — winner: Astria Stylic reads as a single-operator tool in the material reviewed. Astria's brand workspaces and multiplayer Board—named cursors, live voice over the actual canvas—exist because fashion production involves a stylist, a founder, an ecommerce lead, and often an agency looking at the same look. ### Collection scale and commercial fit — winner: Astria Bulk and catalogue photoshoot modes handle volume, and the plans are sized for listing throughput rather than for a season with revisions. Astria treats the collection as the unit of work and prices to usage—pay for what you create. This article makes no claim that Astria is cheaper; at these tiers Stylic plainly costs less per month. The question is what the money buys: listing images, or a production capability that carries a brand's direction across a collection. ### Compounding production value — winner: Astria After a year on Stylic, a seller has a large set of listing images. After a year on Astria, a brand has the images plus the casting, references, templates, and approvals that produced them—and the stylist's eye is still in the building. ## Choose Stylic if... - You are an individual marketplace seller and listing images are the entire job. - Monthly cost is the primary constraint. - Preset backgrounds and platform models cover your visual range. - You do not need lookbook, campaign, motion, or team review. ## Choose Astria if... - You are building a brand rather than filling a marketplace grid. - A stylist's or photographer's direction should be captured and reused, not replaced. - Your line spans categories and age ranges beyond standard adult apparel. - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - Review should happen on a shared canvas with the people who own the look. ## The bottom line For fashion brands, Astria is the stronger choice by a wide margin: it captures creative direction instead of substituting for it, covers the channels a brand actually sells through, and keeps the collection consistent as it grows. Stylic is a cheap, fast listing-image tool, and for a marketplace seller that is a perfectly reasonable thing to buy. The moment you start caring how the clothes are framed, you have outgrown it. ## What to test before choosing either platform Run five representative styles through both, then add a colorway and change the crop convention after approval — count how many operations each change takes. Ask Stylic for the methodology behind its published statistics. Have a senior creative, or your stylist, review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask whether your creative partner can work inside the tool with you, or only hand you a brief. ## Frequently asked questions ### Is Stylic an Astria alternative? For marketplace listing images, yes. Stylic is a low-cost, three-step generator aimed at Shopify, Amazon, and Indian marketplace sellers. Astria is a production system for brands and the photographers and studios producing their collections. ### Does AI replace your photographer? That framing sells tools; it does not describe professional practice. Astria is used by photographers and studios to install repeatable workflows for brands. The durable value is the creative direction, which is a person's judgment encoded — not removed. ### How should I read Stylic's published statistics? Carefully. Figures such as user counts, images generated, and budget saved are vendor-published without methodology, and its two audience-size claims sit oddly together. Ask how each was measured before treating any of them as evidence. ### Which handles a full collection with revisions? Astria. The SKU set imports from Shopify or Google Drive and runs against one approved treatment, and a direction change is an edit. Stylic's flow is configured per photoshoot rather than stored as a reusable brand treatment. ### Is Astria better than Stylic for fashion brands? For brands producing on-brand imagery across a collection and its channels, yes. Individual marketplace sellers who need cheap listing images and nothing more may find Stylic sufficient. For adjacent decisions, see how Astria compares with [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/), [Rawshot](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/), and [Ayna](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 11, 2026. Key sources include Stylic's homepage describing its three-step workflow, model and background libraries, feature list, target audiences, and published statistics, and its pricing tiers, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, feature scope, library sizes, and published statistics can change. Confirm current terms, request methodology for any performance claim, and test representative garments before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs StyTrix: Pricing & Fashion Workflow Review (2026) Source: https://www.astria.ai/articles/astria-vs-stytrix-fashion-ai/ StyTrix and Astria overlap in fashion image generation, but the brief matters more than the overlap. A designer developing a garment needs room to change it. An ecommerce team photographing an approved garment needs it to stay the same. **The short answer:** shortlist StyTrix for design development and test Astria for the collection's final imagery. StyTrix's saved assets make the comparison closer than a simple “design tool versus production tool” label suggests. Check the linked pricing pages, then test the handoff from one approved look to the rest of the collection. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## Where to check current pricing For current rates, credit allowances, billing terms, and plan features, use [StyTrix pricing](https://www.stytrix.com/pricing) and [Astria pricing](https://www.astria.ai/pricing). Compare the same collection, output settings, additional attempts, review time, and approved deliverables. Use the [cost-per-SKU worksheet](https://www.astria.ai/articles/product-photography-cost-per-sku/) with the rates from those pages. ## Quick verdict: Astria vs StyTrix | Buying question | What to evaluate | | --- | --- | | Are you still changing the garment? | Start with StyTrix's design tools | | Are you producing imagery for approved products? | Test Astria against the complete channel brief | | Must the same casting and style recur? | Test saved references in both; do not assume StyTrix lacks them | | Is automation required? | Compare the required API access and plan terms | | Is one cheaper? | Price the same approved deliverables, including rework and labour | ## StyTrix review: the design room, digitised StyTrix documents sketch rendering, outfit assembly, image-to-sketch, split layers, and style training. Its guide also describes reusable model, fabric, and style libraries, plus shared-canvas comments. Those capabilities are described in the [StyTrix user guide](https://www.stytrix.com/guide). Our assessment: these tools are a sensible starting point when the expensive loop is trying ideas, discussing them, and changing the design. The production test comes later. A garment that looks persuasive in a proposal still needs to match the approved sample before it appears on a product page. ## Astria: producing imagery from approved products Astria's [fashion offering](https://www.astria.ai/ecommerce) centres on product-photo inputs and templates for lookbook, campaign, and social imagery. Its [public API documentation](https://docs.astria.ai/docs/api/tune/create/) provides a separate route for technical teams. The practical reason to evaluate it is repeatability. Approve a hero image, record the casting and treatment, then require the next operator to produce the next look. Our [AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/) sets out that production sequence. Whether a particular tool preserves the details well enough is something the pilot must establish. ## A realistic workflow comparison: the day the collection is approved Imagine a 60-style capsule requiring four images per style. Before production, decide which images are product records and which are editorial. A front view that changes a closure is a reject even if it makes a better campaign image. Run five representative styles first: a plain garment, a print, a layered look, a reflective trim, and the hardest silhouette. Keep a record of the inputs, settings, credits, manual edits, and approved outputs. Then add a colourway and hand the work to a second operator. That last step tells you whether the treatment can actually be reused. A subscription allowance is a budgeting constraint, not proof that the platform cannot handle a collection. Compare top-ups or a larger plan with the cost of moving the job elsewhere. Include the time it takes to prepare and review the assets in either system. ## Choose StyTrix if... - The brief starts with a sketch or an unfinished design. - The team needs to compare silhouettes, fabrics, and outfit combinations before approving a sample. - Its canvas and saved libraries fit how your designers review work. ## Choose Astria if... - Your inputs are approved products and your deliverables are publishable fashion images. - You want to build the lookbook production around an approved hero treatment. - You want to evaluate a documented API alongside the visual workflow. ## What to test before choosing either platform Require a product image, an editorial frame, and a repeatable second look from the same inputs. Check garment construction at full size and compare the whole set for casting and colour drift. Record approvals and rejects. Neither a rendered sketch nor a saved style is proof of garment fidelity. For a multi-vendor design shortlist, see [StyTrix alternatives](https://www.astria.ai/articles/stytrix-alternatives/). For a second head-to-head comparison, read [Astria vs Fermat](https://www.astria.ai/articles/astria-vs-fermat-fashion-ai/), or use [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/) for the category map. ## Frequently asked questions ### Is StyTrix an Astria alternative? There is overlap in fashion image generation. Evaluate StyTrix for design development and Astria for producing imagery from approved products. Test the complete brief before choosing. ### Does StyTrix save models and styles? Yes. Its user guide describes model, fabric, and style libraries. Test how those saved assets behave across your own collection rather than assuming each image starts from scratch. ### What should a StyTrix pricing comparison include? Include the required operations, additional attempts, seats, storage, and automation access. Compare the total against approved deliverables rather than raw image count. ## Sources and methodology Reviewed September 5, 2026. This is an Astria-authored review of public product documentation and pricing, not an independent customer review or a controlled image-quality benchmark. The recommendations are our assessment of workflow fit; vendor examples do not establish results for your garments. Primary sources checked: [StyTrix pricing](https://www.stytrix.com/pricing), [StyTrix user guide](https://www.stytrix.com/guide), [Astria fashion offering](https://www.astria.ai/ecommerce), and [Astria API documentation](https://docs.astria.ai/docs/api/tune/create/). Prices and entitlements can change; the pricing-page/guide discrepancy remains unresolved by the vendor. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Astria vs Tellos (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/ Tellos comes at AI fashion content from the storefront: product video that publishes to Shopify, TikTok, and Meta, photo generation alongside it, and a shoppable video layer with real conversion evidence behind it. Astria comes at it from the studio: a workspace where a brand's casting, styling, and art direction become a reusable system that produces the collection and everything downstream of it. **The short answer:** Astria is the clear overall winner for fashion brands building a durable visual production capability. Choose Tellos when the priority is getting shoppable video onto a Shopify storefront and social feeds quickly. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## How we evaluated Astria and Tellos We used six B2B buying questions: how creative direction is specified and reused, motion and format coverage, casting and asset depth, collaboration and review, what it takes to run a whole collection, and how the commercial model behaves as volume grows. ## Quick verdict: Astria vs Tellos | Decision factor | Tellos | Astria | Winner | | --- | --- | --- | --- | | Professional fashion production | Photo and video assets generated per product for store and social | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Brand-trained private models plus per-generation choices | Build and reuse casting, styling, scene, crop, lighting, and format as one approved treatment | **Astria** | | Casting and assets | Model swap across AI models supplied by the platform | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Category coverage | Ecommerce apparel and product content, with beauty examples | Fashion-specific templates for apparel, beauty, jewelry, footwear, sports, luxury, and editorial | **Astria** | | Natural-looking people | Fabric movement and fit emphasized in generated output | Face-inpainting and refinement layer inside the production workflow | **Astria** | | Motion | Video-first: clips optimized for PDP, Reels, TikTok, and Meta Ads | Multiple current video options animate stills the brand already approved | **Astria** | | Team production | Not publicly detailed in the material reviewed | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | Bulk runs turn a catalog into clips, asset by asset | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Monthly generation balance for studio; storefront video priced by traffic tier | Brand pricing scoped to usage — pay for what you create | **Astria** | | Best fit | Ecommerce teams adding product video to a Shopify storefront and social feeds | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | The fastest route to storefront and social video | The stronger platform for professional fashion production | **Astria** | Reviewed August 4, 2026; no controlled image-quality benchmark was run. ## Tellos: video-first content for the storefront Tellos positions itself as an AI fashion video and photo studio for ecommerce brands, with a blunt promise: no studios, no models, no production chaos. Upload flat-lay or mannequin shots, generate on-model images and video, and publish. Output is optimized for Shopify product pages, Instagram Reels, TikTok, and Meta Ads, and the Shopify integration is a few clicks rather than an engineering task. Two capabilities stand out. Tellos advertises private AI models trained on a brand's style, fits, and visual language, which is a serious answer to consistency rather than a menu of presets. And its heritage in shoppable video shows: the company sells a storefront video layer priced by monthly traffic, separate from its generation studio. That heritage also produces its best evidence. Tellos publishes a [customer story with Inglot](https://www.jointellos.com/brand-stories/inglot) reporting a 17% conversion-rate uplift and an 11% increase in average order value from shoppable video on the storefront. It is vendor-written and specific to that brand's implementation, but it is a commerce outcome rather than a production-speed claim, which makes it more useful than most case studies in this category. ## Astria: a professional fashion-production environment Astria's unit of work is the treatment rather than the asset. Products, casting, references, templates, and outputs live together in a brand workspace, so an approved look becomes something the team runs repeatedly instead of something one operator recreates each time. That shows up in coverage. Templates span beauty, jewelry, footwear, sports, luxury, and editorial work, not only standard apparel PDPs. Brand references keep results anchored to the brand rather than a generic AI aesthetic, and Astria's Describe tool can extract usable creative direction from a rights-cleared reference while accounting for the products and models already loaded. Motion is an extension of the same workspace: a still the brand has already approved can be animated with a choice of current video options, so the campaign's casting and styling survive into the clip. ## Public proof: commerce outcomes versus production adoption Tellos names brands including INGLOT, Crocs, Sereia, and Strongful, and its Inglot story quantifies storefront impact. This is first-party evidence, and it is strongest where Tellos is strongest: content that lives on a product page and moves a conversion metric. Astria's public materials show a production base among brands and the professionals who create for them, including Ronny Kobo, Gant, Lee Cooper, and Nine West, alongside studios and photographers running client work. Also vendor-published, and also an adoption signal rather than a quality proof. The two point at different rooms in the same building: Tellos at the storefront, Astria at the studio that feeds it. ## A realistic workflow comparison: a footwear drop with motion Consider a footwear brand launching 40 styles. Each needs PDP stills, a detail shot, a short clip for the product page, paid-social cutdowns, and a campaign image for the seasonal landing page. Two colorways arrive late. With Tellos, the team uploads product shots and generates on-model stills and clips, then publishes them to Shopify and social with formats already sized per channel. For the video job specifically this is efficient, and the storefront layer means the clips are not just assets but merchandising. What the workflow does not carry is the creative decision. The campaign image, the lookbook treatment, and the late colorways each start again from generation choices, and the brand's visual language lives in a trained model plus whatever the operator remembers. With Astria, the team establishes casting, styling, lighting, crop, and format once and approves it. The 40 styles run against that treatment; the late colorways inherit it; the campaign image and the social crops are extensions of the same direction rather than separate jobs. When the clip is generated, it animates a still the brand already signed off, which is why the motion looks like the campaign instead of adjacent to it. ## The differences that matter most ### Where the creative decision lives — winner: Astria Tellos's brand-trained models are a genuine consistency mechanism, and they deserve more credit than a preset menu. But a trained model captures a look, not a process. It cannot tell the next operator that this brand crops footwear tight at the ankle, casts older than the category, and never shoots on a white sweep. Astria encodes those decisions as a reusable treatment, which is what makes an approved direction survive a new hire, a new agency, or a new season. ### Shoppable storefront video — winner: Tellos This is Tellos's genuine edge and the one concession that matters here. A video layer that installs on Shopify, plays on the product page, and comes with a published conversion outcome is a merchandising capability, not just an asset pipeline. Astria produces the video; it does not sell the storefront surface that plays it. Brands whose actual goal is on-site video merchandising should evaluate Tellos on that basis—and note that it is priced by traffic tier, which is a different commercial shape from paying for production. ### Motion that matches the campaign — winner: Astria Both products make fashion video, and Tellos makes it faster to publish. The question is what the clip is derived from. In Tellos the video is generated as its own asset; in Astria it animates a still that already passed creative review, using a choice of current video options inside the same workspace. For a PDP loop that distinction rarely matters. For a campaign where the film and the stills must read as one shoot, it decides whether the work holds together. ### Collaboration, review, and handoff — winner: Astria We could not verify multiplayer review, shared brand workspaces, or role-based approvals in Tellos's public material reviewed for this article. Astria combines private brand workspaces with a multiplayer Board: named cursors, live voice over the actual canvas, and review that happens on the looks instead of in an email thread. When merchandising, art direction, and an outside agency need to agree, that is the difference between resolving a look and circulating a file. ### Collection scale and commercial fit — winner: Astria Tellos can turn a catalog into clips in bulk, but each asset is still an independent generation, and the commercial model reflects that: a monthly generation balance for the studio, and a separate storefront product priced by how much traffic the site receives. Neither meter tracks the thing a brand is actually buying, which is a finished collection. Astria treats the collection as the unit of work—the SKU set imports from Shopify or Google Drive and runs against the approved setup—and brand pricing is scoped to usage: pay for what you create. A monthly balance is not a production budget; model a full collection, including rework, before comparing costs. ### Compounding production value — winner: Astria In Astria, products, avatars, templates, approvals, and outputs accumulate across the season, and the photographer, the in-house studio, and the social agency work from the same source of truth. With Tellos, what accumulates is a library of published assets and a trained model. Both are useful. Only one of them is a workflow another person can pick up. ## Choose Tellos if... - Shoppable video on a Shopify storefront is the outcome you are buying. - Product video for TikTok, Reels, and Meta Ads is the bulk of your content need. - You want channel-sized outputs published without a separate editing step. - A conversion metric on the product page matters more than a campaign that reads as one shoot. ## Choose Astria if... - One approved creative direction must carry across a collection and its channels. - A creative director, photographer, or studio needs to install a workflow other people can reuse. - Casting, styling, and scene decisions must survive team changes, colorways, and late additions. - Review should happen on a shared canvas with voice, not through exported files. - Stills, campaign, editorial, and motion should all come from the same approved treatment. ## The bottom line For professional fashion brands, Astria is the stronger choice: it turns art direction into a system that produces the collection and everything downstream of it. Tellos is a well-aimed product for a narrower job—getting convincing video onto the storefront and the feed, with commerce evidence to back it. Do not decide from a single product clip. Test the second colorway, the campaign frame, and whether a teammate can reproduce the approved look without help. ## What to test before choosing either platform Run five representative styles through both products, including one with a difficult material—patent, mesh, or a layered upper. Require PDP stills, a detail crop, a product clip, and one campaign frame, then add a colorway after approval. Check whether the clip and the stills read as the same shoot. Have a senior creative review outputs blind for product fidelity, movement, anatomy, styling, and brand fit. Finally, ask a second operator to reproduce the approved direction from scratch. ## Frequently asked questions ### Is Tellos an Astria alternative? Yes, for AI fashion photo and video generation. They diverge on what happens around the asset: Tellos optimizes publishing video to the storefront and social, while Astria runs a brand's creative direction across a collection and every format it needs. ### Which is better for AI fashion video? Tellos is built video-first and publishes straight to Shopify, TikTok, and Meta. Astria carries an already-approved still into motion using a choice of current video options, so the campaign's casting and styling survive into the clip. ### Do brand-trained AI models solve consistency? They help with look, not with process. A trained model reproduces a visual style; it does not carry the casting, styling, crop, format, approvals, and handoff that keep a collection consistent across contributors and channels. ### How do Astria and Tellos pricing models compare? Tellos sells a monthly generation balance for its studio and prices shoppable video by storefront traffic tier. Astria's brand pricing is scoped to usage. Model a full collection, including rework, before comparing costs. ### Is Astria better than Tellos for fashion brands? For brands producing on-brand imagery and motion across a collection and its channels, yes. Brands whose main goal is adding shoppable video to a Shopify storefront should evaluate Tellos for that specific job. For adjacent decisions, see how Astria compares with [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/), [Caimera](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/), [Ayna](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/), [Rawshot](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/), and [FASHN](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 4, 2026. Key sources include Tellos's homepage, its AI video studio page, its [pricing page](https://www.jointellos.com/pricing) covering studio balances and traffic-tiered storefront plans, and its [Inglot brand story](https://www.jointellos.com/brand-stories/inglot), plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, plan limits, model libraries, and workflow support can change. Confirm current terms and test representative products before making a production decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs Tolstoy (2026): Which Is Better for Fashion Brands? Source: https://www.astria.ai/articles/astria-vs-tolstoy-fashion-ai/ Tolstoy sells a connected commerce stack: generate product content, make it shoppable, and help the shopper choose. Content generation is one module of three, and the platform serves fashion alongside beauty, skincare, and homeware. Astria does one thing—produce a fashion brand's imagery—and is built around the way fashion collections actually get made. **The short answer:** Astria is the clear overall winner for fashion brands producing a collection and its channels. Choose Tolstoy if you want content, shoppable publishing, and an AI shopping assistant from one Shopify-native vendor, and your content needs sit within its token allowances. ## From a fashion photo to a nine-second video The brief asks the model to walk in, pause with a hand on her hip, and walk past the camera. Press play to see the generated result. ![Starting image of a model in a navy pinstriped dress and sandals in a grey studio](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-first-frame.jpg) Starting image [Watch the generated video](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-720p-live-runway.mp4) AI video · 9 seconds · Seedance 2 Fast · no audio [View this generation](https://www.astria.ai/prompts/46608981)[Compare video models](https://www.astria.ai/articles/video-model-comparison-calibration) ## How we evaluated Astria and Tolstoy We used six B2B buying questions: how much of the product is actually about producing imagery, how category-specific the creative direction can be, channel and format coverage, collaboration and review, what a full collection costs under each meter, and what the brand owns at the end. ## Quick verdict: Astria vs Tolstoy | Decision factor | Tolstoy | Astria | Winner | | --- | --- | --- | --- | | Product focus | A commerce platform; content is one module of three | A fashion production system, end to end | **Astria** | | Category specificity | Serves fashion alongside beauty, skincare, home, and jewelry | Built for fashion, with category-specific templates | **Astria** | | Professional fashion production | On-brand product images and video for the storefront | Brand-directed production across ecommerce, lookbook, campaign, and social | **Astria** | | Creative direction | Generated from photos and brand guidelines per asset | Build and reuse casting, styling, scene, crop, lighting, and format as one approved treatment | **Astria** | | Casting and assets | On-model imagery generated per request | Models across ages and body types, locations, poses, and reusable brand references | **Astria** | | Template breadth | Product and lifestyle content for ecommerce | Apparel, beauty, jewelry, footwear, sports, luxury, and editorial templates | **Astria** | | Campaign and editorial work | Not the platform's focus in the material reviewed | Campaign, editorial, and social treatments from the same approved setup | **Astria** | | Motion | Shoppable video, published through its own player | Multiple current video options animate approved stills in the same workflow | **Astria** | | Team production | Merchandising and campaign configuration | Private workspaces and a multiplayer canvas with named cursors and live voice | **Astria** | | Collection scale | [See current Tolstoy pricing](https://www.gotolstoy.com/pricing) | The full SKU set imports from Shopify or Google Drive and runs against one approved setup | **Astria** | | Commercial model for brands | Three metered products: content tokens, player impressions with overage, assistant tokens | Brand pricing scoped to usage — pay for what you create | **Astria** | | Storefront merchandising and shopper assistance | Shoppable player and AI shopping assistant, with published lift figures | Not attempted | **Tolstoy** | | Best fit | Shopify brands buying content, shoppable widgets, and an assistant as one bundle | Fashion brands producing on-brand ecommerce, lookbook, campaign, and social assets across a collection | **Astria** | | Overall | A broad commerce stack with content attached | The stronger platform for professional fashion production | **Astria** | Reviewed August 7, 2026; no controlled image-quality benchmark was run. ## Tolstoy: three products around the product page Tolstoy's pitch is a sequence: create AI product content, make it shoppable, help shoppers choose. AI Studio generates on-brand product images and video from photos and brand guidelines. AI Player publishes shoppable video, galleries, and interactive widgets on the storefront. AI Shopper is an assistant offering recommendations, catalog guidance, and try-on. The platform is Shopify-native and widely adopted—the company cites over 5,000 Shopify brands—and it publishes commerce outcomes such as a 24% conversion lift at LSKD and a 15% AOV lift at Pantynova. Named customers include Culture Kings, Hampden, Skin Rocks, Laura Mercier, The Ordinary, Milk Makeup, Tarte, and KitchenAid. That customer list is the most informative thing on the site. Tolstoy is a general ecommerce content and merchandising platform that fashion brands use, not a fashion platform. Skincare, cosmetics, and kitchen appliances have real content needs, and a system designed to serve all of them well is necessarily designed around what they share—a product, a page, a shopper—rather than around casting, drape, fit, and collection logic. ## Astria: a professional fashion-production environment Astria narrows deliberately. Products, casting, references, templates, and outputs live in a brand workspace, and the approved treatment—casting, styling, scene, crop, lighting, format—is the reusable unit that produces the season. The narrowing is what buys the depth. Templates cover apparel, beauty, jewelry, footwear, sports, luxury, and editorial as distinct fashion categories rather than as generic product photography. The casting library spans ages and body types, including children and extended-size casting. Campaign frames, editorial spreads, social crops, and motion all derive from the same approved direction, and review happens on a multiplayer Board where the photographer, the in-house studio, and the agency work on the same canvas. ## Public proof: commerce lift versus production adoption Tolstoy's evidence is strong for what it sells: named brands, a large Shopify install base, and specific conversion and AOV figures. It is vendor-published and concentrated on merchandising outcomes rather than production quality, which is the right emphasis for a commerce platform. Astria's public materials show a production base among fashion brands and the professionals who create for them—Ronny Kobo, Nununu, Gant, Lee Cooper, Nine West—alongside studios and photographers running client work. Also vendor-published. Tolstoy proves that its widgets sell product; Astria's evidence is about who produces fashion imagery with it. ## A realistic workflow comparison: the bundle question Consider a fashion brand on Shopify with 400 SKUs and a two-person ecommerce team. It needs four PDP images per SKU, a lookbook, campaign assets, and social content, and it is attracted to buying one platform instead of three. A 400-SKU catalog at four images requires 1,600 images before rejected frames, colorways, or campaign assets. Check [Tolstoy pricing](https://www.gotolstoy.com/pricing) for current AI Studio, player, and assistant terms, and budget for each required service. The creative question matters more. Brand guidelines are an input Tolstoy consumes per asset; they are not the same as an approved treatment that fixes casting, crop, and styling across a collection. The lookbook and campaign work is outside what the content module is aimed at, so it still needs an answer. With Astria, the 400 SKUs import from Shopify and run against one approved treatment. The lookbook and campaign extend that direction rather than starting over, motion animates approved stills, and pricing is scoped to what the brand creates rather than to three separate meters. If the brand also wants a shoppable player and an AI assistant on the storefront, those remain available to buy—from Tolstoy or anyone else—without conflating them with production. ## The differences that matter most ### Depth versus breadth — winner: Astria A platform serving apparel, skincare, and small appliances optimizes for the common denominator. Fashion's hard parts—drape on a sheer fabric, casting that matches a brand's customer, a crop convention the brand has used for six seasons—are not common-denominator problems. Astria is narrower on purpose, and that is where the depth comes from. ### Storefront merchandising and shopper assistance — winner: Tolstoy This is Tolstoy's genuine edge and the one concession that matters here. A shoppable player with published conversion lift, plus an AI shopping assistant, plus Shopify-native distribution, is a coherent commerce surface that Astria does not offer. Brands whose bottleneck is on-site merchandising rather than content production should evaluate Tolstoy on that basis—and should ask for the methodology behind the published lift figures. ### Where the creative direction lives — winner: Astria Feeding brand guidelines into each generation is not the same as approving a treatment once and running it. The first produces assets that are roughly on-brand and individually variable; the second produces a collection that is consistent because consistency is the stored object, not an instruction repeated per asset. ### Campaign, editorial, and the rest of the season — winner: Astria Tolstoy's content focus is the storefront. A season also needs a lookbook, campaign frames, editorial treatments, and social-native crops, and those are not storefront assets. Astria produces them from the same approved direction, which is both cheaper in effort and more coherent in result. ### Collaboration and review — winner: Astria Tolstoy's team surface is merchandising and campaign configuration. Astria's is creative review: private brand workspaces, a multiplayer Board with named cursors, and live voice over the actual looks, with external photographers and agencies inside the same workspace. ### Collection scale and commercial fit — winner: Astria Three metered products with separate allowances make a collection hard to plan and easy to over-run: content tokens for images and video, impressions with overage for the player, tokens again for the assistant. Astria treats the collection as the unit of work and prices to usage—pay for what you create. This article makes no claim that Astria is cheaper; it observes that a bundle discount across three meters is not a production budget. Model a full collection, including rework and every channel, before comparing. ### Compounding production value — winner: Astria In Astria the workspace accumulates products, avatars, templates, approvals, and outputs, and the next season starts from them. In Tolstoy the durable asset is the merchandising surface on the storefront, which is valuable and does not make the next collection easier to produce. ## Choose Tolstoy if... - Your bottleneck is on-site merchandising rather than content production. - You want shoppable video, galleries, and an AI shopping assistant from one Shopify-native vendor. - Your catalog is mixed-category and general product content is sufficient. - Your monthly image and video needs sit comfortably within its token allowances. ## Choose Astria if... - You are producing a fashion collection and the imagery is the bottleneck. - One approved creative direction must carry across catalog, lookbook, campaign, social, and motion. - Category-specific fashion direction matters — apparel, beauty, jewelry, footwear, sports, luxury, editorial. - A creative director, photographer, or agency needs to install a workflow other people can reuse. - Review should happen on a shared canvas with voice, not through exported files. ## The bottom line For fashion brands, Astria is the stronger choice: it is built for how a collection is produced rather than for how a product page is merchandised. Tolstoy is a capable commerce platform, and its content module is a convenience within that platform rather than a reason to choose it over a production system. The two are not mutually exclusive. Just do not let a bundle discount decide how your collection gets made. ## What to test before choosing either platform Count your real annual volume first: SKUs times images, plus colorways, plus campaign and social assets, plus a realistic rejection rate. Check that number against the token allowances of any metered plan before you shortlist. Then run five representative SKUs through both, require PDP, detail, campaign, and social crops, and add a colorway after approval. Have a senior creative review outputs blind for garment fidelity, drape, anatomy, styling, and brand fit. Finally, ask a second operator to reproduce the approved direction from scratch. ## Frequently asked questions ### Is Tolstoy an Astria alternative? Partly. Tolstoy's AI Studio module generates product imagery, but Tolstoy is a commerce platform spanning content, shoppable widgets, and an AI shopping assistant across fashion, beauty, home, and jewelry. Astria is a fashion-specific production system. ### Is Tolstoy fashion-specific? No. Its published customers span beauty, skincare, and homeware alongside apparel. That breadth is a strength for a general ecommerce team and a limitation for a brand that needs category-specific fashion direction. ### How do the pricing models compare at catalog volume? Check [Tolstoy pricing](https://www.gotolstoy.com/pricing) and [Astria pricing](https://www.astria.ai/pricing) for current rates and allowances. Budget for the complete collection, rework, and any storefront services before comparing. ### Which is better for campaign and editorial work? Astria. Tolstoy's content module is aimed at product imagery and shoppable video for the storefront. Astria carries one approved direction into lookbook, campaign, editorial, social, and motion. ### Is Astria better than Tolstoy for fashion brands? For producing a fashion collection's imagery, yes. Shopify brands that want content, shoppable publishing, and an AI shopping assistant from a single vendor should evaluate Tolstoy as a commerce bundle rather than a production tool. For adjacent decisions, see how Astria compares with [Tellos](https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/), [DRESSX](https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/), and [Ayna](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/). Still narrowing the field? The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and how production actually runs is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). ## Sources and methodology This article compares public product information available on August 7, 2026. Key sources include Tolstoy's homepage describing AI Studio, AI Player, and AI Shopper with named customers and published lift figures, and its [pricing page](https://www.gotolstoy.com/pricing) covering bundle and per-product tiers, token allowances, and impression overage rates, plus Astria's fashion and ecommerce offering, template gallery, workspace documentation, and video documentation. Pricing, token allowances, product scope, and published metrics can change. Confirm current terms and request methodology for any performance claim before making a decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Astria vs WearView: Pricing & AI Fashion Review (2026) Source: https://www.astria.ai/articles/astria-vs-wearview-fashion-ai/ WearView starts with garment-to-model photography and extends into poses, video, and other fashion outputs. Astria offers another route into the same collection brief. The useful comparison is the number of approved images your team can deliver, at the resolution and consistency you need. **The short answer:** test WearView for garment-led image generation and Astria for template-led fashion production. Both advertise work beyond the product page. Compare a complete set before deciding that one tool covers the season better. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## Where to check current pricing For current rates, credit allowances, billing terms, and plan features, use [WearView pricing](https://www.wearview.co/pricing) and [Astria pricing](https://www.astria.ai/pricing). Compare the same collection, output settings, additional attempts, review time, and approved deliverables. Use the [cost-per-SKU worksheet](https://www.astria.ai/articles/product-photography-cost-per-sku/) with the rates from those pages. ## Quick verdict: Astria vs WearView | Buying question | What to evaluate | | --- | --- | | Are garment-to-model images the immediate need? | Start with WearView's published product workflow | | Does a fashion template fit the brief? | Test Astria with the same garments | | Are lookbooks and campaigns required? | Include both in the pilot; both vendors advertise these uses | | Do you need repeatable casting? | Compare the same person across several difficult looks | | Is the brief seasonal? | Price a heavy production month, including additional capacity and revisions | ## WearView review: more than a product-page tool WearView's [homepage](https://www.wearview.co/) lists virtual try-on, product-to-model, consistent models, pose control, ghost mannequin, model swap, and video. It explicitly presents lookbook and brand-campaign use cases. Include those outputs when comparing it with Astria. Our assessment: this is a relevant shortlist for an apparel team whose first task is putting real garments on models. A broader use-case list still needs testing. A lookbook requires a sequence and a consistent point of view; a set of attractive pose variations does not establish either. For the image-production requirements, use the [AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/). For the finished document, see [how to create a lookbook](https://www.astria.ai/articles/how-to-create-a-lookbook/). ## Astria: another route to the collection brief Astria publishes [fashion workflows](https://www.astria.ai/ecommerce) built around product photos and templates. Its [lookbook gallery](https://www.astria.ai/gallery/workspaces) provides collection examples to inspect. Give both tools the same hero reference and ask for the same deliverables. Keep product accuracy separate from aesthetic preference: a frame can win the mood review and fail the garment check. The pilot should show whether the team can preserve both across a collection. ## A realistic workflow comparison: the week the campaign brief lands Start with five representative garments and produce the required product views. Approve those against the references, then ask for a campaign frame with different lighting and a wider composition. Keep the casting and garment fixed while the scene changes. This is the revealing test. It exposes whether creative direction can change without altering a hem, fastening, print scale, or face. Repeat with a layered look and then a new colourway. Record the rejected attempts and any manual correction; do not count only the successful final click. Have the person who normally reviews your brand's imagery approve the results without platform labels. Then give the saved work to a colleague and ask them to extend it. A repeatable workflow should survive that handoff. Read [consistent AI fashion models](https://www.astria.ai/articles/consistent-ai-fashion-models/) for a focused casting test. ## Choose WearView if... - Garment-to-model work is the first requirement you want to test. - You can budget the actual operation and resolution mix against its plans. - Its product, lookbook, and campaign outputs pass the same garment review. ## Choose Astria if... - You want to begin with a fashion template and adapt it to approved products. - The evaluation centres on the whole lookbook or campaign treatment. - Its pilot produces the required consistency with acceptable review effort. ## What to test before choosing either platform Use an identical output list and deadline. Compare product fidelity, consistency across images, correction time, and accepted output cost. Neither the headline subscription price nor a vendor's generation-speed claim measures the time to an approved collection. For adjacent options, read [Astria vs Lumoo](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/) and [Astria vs Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/). The category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/). ## Frequently asked questions ### Does WearView produce lookbook and campaign images? Yes. WearView explicitly advertises both uses. Test a complete set for garment fidelity, casting, and creative direction rather than assuming the tool is limited to product pages. ### Where can I check WearView generation rates? Use [WearView pricing](https://www.wearview.co/pricing) for current rates for your intended workflow and resolution. ### Is Astria cheaper than WearView? This review does not establish that. Price identical approved deliverables, including additional attempts, corrections, human review, and any unused subscription capacity. ## Sources and methodology Reviewed September 5, 2026. This is an Astria-authored review of public product documentation and pricing, not an independent customer review or a controlled image-quality benchmark. The recommendations are our assessment of workflow fit; vendor examples do not establish results for your garments. Primary sources checked: [WearView pricing](https://www.wearview.co/pricing), [WearView homepage](https://www.wearview.co/), [Astria fashion offering](https://www.astria.ai/ecommerce), and [Astria lookbook gallery](https://www.astria.ai/gallery/workspaces). Prices, credit selectors, rollover conditions, and product scope can change. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Best 3D Packshot Services for Ecommerce (2026): What to Buy Source: https://www.astria.ai/articles/best-3d-packshot-services-ecommerce/ Searching for a 3D packshot service produces a category error. One service generates a rotation video from a flat lay. Another photographs your inventory on a turntable. A third sells the turntable. A fourth builds a real-time model. A fifth assumes that model already exists and optimizes it for the web. Those are five different inputs, contracts, and failure modes. **Upfront disclosure:** this comparison is published by Astria, and Astria is our recommendation when a fashion brand needs a fast rotation video and multi-angle stills from one flat-lay photo. Astria calls the workflow a “3D ghost packshot,” but the delivered rotation is an MP4—not a dimensionally accurate interactive 3D model. That distinction is central to the comparison. **Reviewed September 2, 2026.** Vendor capabilities below come from first-party public material. Claims about price, capacity, efficiency, or commercial outcomes are labeled as vendor-reported and should be tested in your own workflow. ## An Astria example: dress photo to packshot Compare the original dress with the generated image, especially the neckline, seams, and hem. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot This shows an Astria result. The other service was not tested with this same photo. [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) ## The shortlist by job | Purchase | Representative supplier | You provide | You receive | Best when | | --- | --- | --- | --- | --- | | **AI-generated fashion rotation** | **Astria** | One flat-lay product photo | 6-second 1080p rotation MP4 plus three angle stills | You need fast, repeatable PDP and social assets without shipping samples | | **Managed physical capture** | GoPackshot | Physical inventory, styling and channel brief | Approved stills, ghost mannequin, video or photographic 360 assets | You want a service accountable for logistics through delivery | | **Automated capture system** | Orbitvu | Products, operators, studio space and capital | Hardware/software workflow for stills, 360 spins and video | Product volume is steady enough to justify an in-house line | | **Automated capture plus photogrammetry options** | PhotoRobot | Products, cameras, operators and production design | Stills, spins, video and—on suitable workflows—photogrammetry-derived models | You need a configurable in-house capture system and will validate each output type | | **True 3D commerce platform** | Threekit | Product data and production-ready 3D assets or an asset-creation scope | Interactive 3D, configuration, renders and AR experiences | Products have options or configuration is part of the buying decision | | **3D optimization and distribution** | VNTANA | Existing CAD, design or scanned 3D files | Web-optimized 3D/AR assets and channel delivery | The bottleneck is publishing existing models, not photographing products | | **Custom model creation** | Shopify Partner | Multi-angle photos, measurements and technical drawings where available | A reviewed GLB for Shopify, with USDZ handled by the platform workflow | You need store-ready true 3D for a bounded set of products | This is not a ranking from one to seven. It is a routing table. An AI-generated rotation, a managed studio, and a true 3D platform solve different problems. ## 1\. Astria: the fast fashion rotation from one flat lay Astria is the most direct option here for a fashion team that has a flat-lay product photo and needs the product to move. The [Astria 3D ghost packshot workflow](https://www.astria.ai/w/3d-packshots) returns one six-second 1080p MP4 rotation plus front, side, and three-quarter stills. The assets are designed for product pages, Reels, TikTok, and ad creative. The operational difference is substantial: no turntable, model, studio booking, or sample shipment. Choose a locked template for the product class, upload the flat lay, and use the resulting rotation and stills across the collection. See [Astria pricing](https://www.astria.ai/pricing) for current packshot rates and billing terms. The trade-off is equally important. Astria infers views that the one input photograph does not show. The output is a generated video, not a photographed sequence and not a model containing geometry, materials, or verified dimensions. Review prints, logos, closures, hardware, hemlines, back construction, and product color against the real SKU before publication. **Choose Astria when:** the product is fashion, speed and no-shipping production matter, an MP4 rotation answers the channel need, and your team can approve the inferred views. Start with one difficult SKU before scaling highly structured or sheer products. **Do not choose Astria for this job when:** the shopper must freely control the camera, the deliverable must be GLB or USDZ, AR scale must be dimensionally validated, or a marketplace and product-risk policy requires calibrated physical evidence. ## 2\. GoPackshot: managed physical production GoPackshot is the closest match for a buyer who means, “Take our products and return ecommerce-ready assets.” Its [services page](https://gopackshot.com/services) describes packshot, model, ghost-mannequin and flat-lay photography; video and 360-degree content; product logistics; retouching; quality control; and delivery into brand systems. The important word is **managed**. You ship physical inventory and buy an operating service, not a self-serve 3D application. That makes GoPackshot a credible choice when color, garment construction, marketplace rules, product handling, and accountability matter more than owning the capture line. GoPackshot also publishes claims about color tolerances, daily capacity, studio count, integrations, and tracked logistics. Treat those as **vendor-reported claims**, then put the relevant ones into the contract or test brief. A public capacity number does not establish capacity for your garment mix, revision policy, or launch window. Its public service description includes 360-degree video, but does not describe the output as a freely navigable GLB or USDZ model. If you need true 3D, ask three blunt questions: 1. Is the deliverable an image sequence, video, web viewer, or model file? 2. Can the shopper move to an angle that was never photographed? 3. Do we receive geometry, materials, dimensions, and usage rights? If the answers are “sequence,” “no,” and “no,” you are buying a photographic spin. That can be an excellent asset. It is not a true 3D model. ## 3\. Orbitvu: bring automated capture in-house Orbitvu sells automated photography systems rather than operating as your outsourced packshot studio. Its [ecommerce product line](https://orbitvu.com/industry/ecommerce) covers stills, 360-degree images and video; its fashion positioning includes ghost mannequin, flat lay, on-figure work, and standardized footwear angles. This route changes the economics. Instead of paying a service per project or SKU, you own equipment, studio utilization, operators, styling, color management, maintenance, and workflow integration. The best candidate already has products moving through one facility and a team that can keep a capture recipe stable. Vendor language such as “web-ready content in minutes” is **vendor-reported** and should be separated into capture time, operator touch time, retouching time, rework, and publishing time. A fast turntable does not dress a mannequin, correct a crushed collar, approve color, or resolve a barcode mismatch. Choose Orbitvu when the problem is repeatable physical capture at sustained volume. Do not choose it because the word “360” appears near “3D.” Confirm the exact output your selected system produces. ## 4\. PhotoRobot: automated capture with a wider output menu PhotoRobot also sells hardware and workflow software for in-house or studio operators. Its [public output gallery](https://www.photorobot.com/gallery) explicitly separates still images, 360 spins, “3D spins,” photogrammetry 3D models, and 360 video. That taxonomy is useful because it exposes how different outputs are often grouped under one marketing label. For fashion, PhotoRobot sells [ghost-mannequin equipment](https://www.photorobot.com/ghost-mannequins) and describes workflows for stills, spins, and ecommerce 3D models. Those are **vendor-reported capabilities**. Ask to see an output from the same product class and material as yours: a matte sneaker is not evidence for patent leather; a rigid bag is not evidence for a soft dress. PhotoRobot is the more relevant shortlist entry when you want a configurable automated studio and may need several capture modes. The procurement risk is complexity: a system that can produce five asset types still needs five acceptance standards. ## 5\. Threekit: true 3D, configuration, and AR Threekit belongs in the shortlist only when the buying problem is interactive 3D rather than photography. Its [platform overview](https://community.threekit.com/learn/platform-overview) distinguishes virtual photography, WebGL-based interactive 3D, and augmented reality. Its platform can create glTF and USDZ variants for web AR and supports configurable products whose visual state changes with selected options. That is a materially different deliverable. The shopper can interact with geometry and materials, and the same product definition can support many configurations. It is especially relevant to customizable bags, footwear, jewelry, furniture, equipment, or other structured products where options and spatial understanding affect purchase. The hard part is the asset system behind the viewer. Before buying, scope who creates the base geometry, materials, variant logic, dimensions, and future revisions. Threekit publishes commercial performance statistics; treat every such number as a **vendor-reported result**, not a forecast for your store. For a single non-configurable garment, this can be more system than the buying question deserves. For a configurable product family, it may be the only category here that actually solves the problem. ## 6\. VNTANA: optimize and distribute models you already have VNTANA is best understood as a 3D asset pipeline. Its [3D commerce page](https://www.vntana.com/products/3d-commerce/) describes taking existing CAD or 3D files, optimizing them, and distributing web-ready 3D and AR assets to commerce channels. That is valuable when product development already produces usable 3D assets. It is not the same as shipping a garment to a photography studio. Ask whether model creation or cleanup is included in your scope, because “upload your 3D file” assumes the most expensive input may already exist. VNTANA publishes performance claims for 3D commerce; those are **vendor-reported**. Evaluate the platform on file reduction, visual fidelity after optimization, variant handling, viewer performance, publishing integrations, and the labor saved in your actual catalog. ## 7\. Shopify Partners: bounded custom model production For a Shopify merchant with a small, prioritized model list, the most direct procurement path may be a 3D-modeling partner rather than an enterprise platform. Shopify’s [partner guidance](https://help.shopify.com/en/manual/products/product-media/expert-3d-model) says a modeler may need multi-angle photographs, dimensions in millimeters, diagrams, and CAD files where available. It also provides a practical review checklist for shape, scale, materials, reflections, textures, edges, and unwanted gaps. Shopify says the delivered model should be a GLB, approximately 4 MB and no more than 15 MB under the guidance on that page. Recheck current limits before commissioning because platform requirements change. Your theme must also support the media type; see the [Shopify product media guide](https://www.astria.ai/articles/shopify-product-media-guide/) before approving the project. This route is strong when true 3D is needed for ten important products, not ten thousand speculative ones. ## The service-selection scorecard Do not ask each supplier for “a 3D packshot quote.” Send one matrix and require every cell to be answered. | Criterion | What a comparable answer includes | | --- | --- | | **Input** | Physical product, photos, measurements, CAD, existing model, or some combination | | **Output** | JPG/PNG, image sequence, MP4, hosted viewer, GLB, USDZ, source model | | **Interaction** | Fixed spin, arbitrary camera, zoom, configuration, animation, AR | | **Accuracy** | Color tolerance, dimensional tolerance, material reference, scale validation | | **Product fit** | Proof on your hardest material and construction—not a generic demo | | **Operations** | Logistics, styling, operators, retouching, QA, hosting, publishing | | **Revisions** | Included rounds, correction SLA, product-change process, version history | | **Ownership** | Source files, derivatives, viewer dependency, usage term, portability | | **Performance** | File weight, load behavior, mobile fallback, analytics events | | **Commercials** | Setup, per-SKU, per-view, hosting, integration, support, annual minimum | Then run the same five-product pilot with every finalist: a structured bag, a reflective item, a product with fine text or hardware, a soft garment, and a color-critical product. Score published assets, not sales demos. ## How Astria fits with physical capture and true 3D Astria should be scored on the asset it actually delivers: a fast AI-generated rotation video and angle stills from one fashion flat lay. It should not be scored as a true 3D modeling bureau. The strongest hybrid uses an approved packshot or well-lit flat lay as Astria’s input, reviews the inferred views against the physical SKU, and publishes the MP4 where rotation communicates shape or drape. Keep calibrated photography for exact color and construction evidence. Buy true 3D only when arbitrary viewing, configuration, or AR is the requirement. That gives Astria a clear place in the buying decision rather than pretending every “3D packshot” is the same file. For the full format distinction, read [3D packshots for ecommerce](https://www.astria.ai/articles/3d-packshots-ecommerce/). For the physical-to-generative operating handoff, continue to [AI product photography vs a packshot studio](https://www.astria.ai/articles/ai-product-photography-vs-studio/). ## Frequently asked questions ### What is the best 3D packshot service for ecommerce? There is no single best service because several different purchases share the name. Choose Astria for a fast AI-generated fashion rotation video and angle stills from one flat lay, a managed studio for approved physical photography and captured spins, capture equipment for a repeatable in-house studio, a model-creation partner for a true GLB or USDZ, and a 3D commerce platform for configuration, AR, or multi-channel delivery. ### Where does Astria fit among 3D packshot services? Astria is the fast AI-generated option for fashion. Its 3D ghost packshot workflow takes one flat-lay photo and returns a six-second 1080p rotation video plus three angle stills for social and product pages. It does not return an interactive GLB or USDZ model, and inferred views still require product-accuracy review. ### Does GoPackshot create true interactive 3D models? GoPackshot's public services page describes packshot photography, ghost mannequin, video, and 360-degree content within a managed physical production service. It does not present those outputs as freely navigable GLB or USDZ models. Ask for the exact deliverable, viewer, and file format rather than assuming that 360 means true 3D. ### Is a 360 spin the same as a 3D product model? No. A photographic 360 spin is a sequence of captured angles. A true 3D model contains geometry and materials, can usually be viewed from arbitrary angles, and may support augmented reality. Some vendors use the word “3D” for a spin effect, so inspect the exported file and interaction rather than the label. ### When should a brand buy automated 360 capture equipment? Buy equipment when physical products already flow through one location, the shot recipe is stable, utilization will be high, and the team can own styling, color management, maintenance, and quality control. Outsource when volume is irregular or operational ownership would sit with nobody. ### What should be included in a 3D packshot quote? A comparable quote should name the source inputs, output files, number of views or model variants, polygon and texture budgets, color and dimension tolerances, AR support, viewer or hosting fees, revision rounds, ownership, turnaround, and what happens when the physical product changes. ## Sources and methodology This comparison uses public first-party material from [Astria](https://www.astria.ai/w/3d-packshots), [GoPackshot](https://gopackshot.com/services), [Orbitvu](https://orbitvu.com/industry/ecommerce), [PhotoRobot](https://www.photorobot.com/gallery), [Threekit](https://community.threekit.com/learn/platform-overview), [VNTANA](https://www.vntana.com/products/3d-commerce/), and [Shopify](https://help.shopify.com/en/manual/products/product-media/expert-3d-model). We did not accept vendor conversion, capacity, or efficiency claims as independent evidence. Astria’s output, workflow, timing, and price statements are also first-party claims and are labeled accordingly. Availability, integrations, pricing, and technical limits change; confirm them in a written scope and test the hardest products in your catalog. [Create an Astria 3D ghost packshot](https://www.astria.ai/w/3d-packshots). --- # Best AI Fashion Photography Platforms (2026): A Buyer's Shortlist Source: https://www.astria.ai/articles/best-ai-fashion-photography-platforms/ Most "best AI fashion tools" lists rank products that do not compete with each other. A shopper-facing try-on widget, a managed retouching service, and a collection production system end up in the same numbered list, which helps nobody decide anything. **Upfront disclosure:** this list is published by Astria, and it recommends Astria for collection-scale fashion production. What follows is the category map we would give a buyer, including the cases where something else is the better purchase. **Reviewed September 7, 2026. Evidence status:** this is a source-reviewed category guide, not a matched hands-on benchmark of every platform. Vendor scope and pricing come from current public material and the linked comparisons. Product-fidelity winners are not claimed without a controlled test. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## What is the best AI fashion model generator for a clothing brand? The best choice depends on what must happen after the first image. Search results use “AI fashion model generator” for four different purchases: | Product type | What the buyer expects | Choose it when | Do not mistake it for | | --- | --- | --- | --- | | **One-off model generator** | Upload one garment and receive a few people wearing it | Speed and a low first-image cost matter most | A collection production system | | **Fashion production system** | Store products, casting, art direction, and repeatable outputs | The same treatment must survive a catalog, team, and campaign | A shopper fitting-room widget | | **Virtual try-on** | Put a garment on a chosen brand model or an individual shopper | The on-body visualization itself is the job | Measured fit or sizing evidence | | **Background and listing tool** | Clean, crop, relight, and format product photos | Marketplace consistency is the bottleneck | Full fashion art direction | For a few marketplace images, the simplest generator may be the best buy. For a clothing brand producing a season, test whether an approved model, garment treatment, crop, and lighting setup can be reused across hard products and changed without rebuilding the job. Run the test on directional prints, fine hardware, sheer fabric, asymmetry, and difficult drape. Count approved images, rejects, correction time, and the operations needed to add a new colorway. That result is more useful than a feature checklist or a vendor's best demo. The [AI product photography guide](https://www.astria.ai/articles/ai-product-photography/) provides the full input and QA workflow. ## First, work out which category you are buying | Category | The job | Buy it if | | --- | --- | --- | | **Production systems** | Run a whole collection across catalog, lookbook, campaign, social, and motion from one approved direction | You produce seasons, not one-off images | | **Virtual try-on** | Put a garment on a body — brand-side or shopper-side | Try-on specifically is the bottleneck | | **Listing tools** | Cheap on-model images for marketplace grids | You are a seller, and listings are the whole job | | **Enterprise and managed** | Delivered output, contractual SLAs, procurement-friendly | You need someone accountable, not a tool | | **Design-stage** | Ideation, 3D fit, pre-production visualisation | The work happens before there is a product to photograph | Nearly every disappointing purchase in this space is a category error rather than a product failure — a brand buying a listing tool and expecting a lookbook, or buying a design tool and expecting catalog throughput. ## Production systems For brands producing collections. The distinguishing question: can an approved creative direction be stored and re-run against new products, by more than one person? **Astria — our recommendation for collection-scale production.** A brand workspace holds products, casting, references, templates, and outputs, with the approved treatment — casting, styling, scene, crop, lighting, format — stored and reused rather than rebuilt per shoot. Category templates cover apparel, beauty, jewelry, footwear, sports, luxury, and editorial; casting spans ages and body types including children and extended sizes; the same direction extends into campaign frames, social crops, and motion. A multiplayer canvas with named cursors and live voice means the stylist, founder, ecommerce lead, and agency review the same work. Photographers and studios use it to install workflows for brands and keep the client relationship. **Trade-off:** it expects a setup investment. For a single listing image today, that is the wrong purchase. **Rawshot** — a dedicated AI fashion studio with substantial model, camera, and background libraries. Strong on breadth of presets. [Full comparison](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/). **Botika** — an established on-model generation platform with an ecommerce orientation and published case studies. [Full comparison](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/). **Caimera** — strong creator-led go-to-market and published case-study material. [Full comparison](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/). **Ayna** — positions around fashion photoshoot production with published pricing and case studies. [Full comparison](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/). **Lumoo** — a connected product-image workflow with editing, template packages, and live collaboration. [Lumoo pricing and review](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/). **Stoodio** — licensed digital twins of real models, with guided onboarding. Worth a look if licensed real-model likenesses are a requirement. [Full comparison](https://www.astria.ai/articles/astria-vs-stoodio-fashion-ai/). **Graswald** — brand avatars and product visualisation. [Full comparison](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/). **FashionLab** — a talent-marketplace model, pairing brands with creators. [Full comparison](https://www.astria.ai/articles/astria-vs-fashionlab-fashion-ai/). **Higgsfield** — a broad AI-native creative suite with serious fashion stills, product content, and cinematic video depth. Strongest when model choice, camera control, VFX, UGC, and editing-tool integrations matter; less fashion-specific in how a collection is organized. [Full comparison](https://www.astria.ai/articles/astria-vs-higgsfield-fashion-ai/). ## Virtual try-on Two different products share this name — see [virtual try-on for fashion brands](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) for why that distinction decides your purchase. **FASHN** — try-on and model generation aimed at brands and creative teams. [Full comparison](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/). **WearView** — garment-to-model generation with consistent models, pose control, and video; it also advertises lookbook and campaign imagery. [WearView pricing and review](https://www.astria.ai/articles/astria-vs-wearview-fashion-ai/). **Aiuta** — enterprise try-on with a QA-delivered studio service. Suited to buyers who want output delivered rather than a tool to operate. [Full comparison](https://www.astria.ai/articles/astria-vs-aiuta-fashion-ai/). **DRESSX** — shopper-facing try-on and digital fashion. A storefront experience, not a production tool. [Full comparison](https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/). **Looklet** — digitised models with a studio capture requirement. Established, and the capture step is the thing to evaluate. [Full comparison](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/). ## Listing tools Cheap, fast, and genuinely sufficient for marketplace sellers. Buy one if listings are the entire job. **Stylic** — three-step upload-to-download, low monthly tiers, marketplace-seller oriented. The fastest route to a first image on this page, and honestly better value than a production system for that specific need. [Full comparison](https://www.astria.ai/articles/astria-vs-stylic-fashion-ai/). **Lookgen** — styled outfit generation with PIM publishing. [Full comparison](https://www.astria.ai/articles/astria-vs-lookgen-fashion-ai/). **Emilia AI Studio** — a general ecommerce product studio where clothing is one of six categories, with an unusually good hands-on editor and a free tier. [Full comparison](https://www.astria.ai/articles/astria-vs-emilia-fashion-ai/). **Rewarx Studio** — an AI product-photography suite spanning 20+ industries, with ghost mannequin, group shots, mockups, and marketplace-formatted listing sets for Amazon, Etsy, and eBay. Metered in credits, with 4K at triple a standard frame. [Full comparison](https://www.astria.ai/articles/astria-vs-rewarx-fashion-ai/). ## Enterprise and managed services **Pixelz** — managed post-production at scale. If you want a service level rather than software, this is a different and legitimate answer to the same problem. [Full comparison](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/). **Browzwear** — 3D design and fit validation. Deep, established, and aimed at the design room rather than the catalog. [Full comparison](https://www.astria.ai/articles/astria-vs-browzwear-fashion-ai/). ## Design-stage and adjacent These are frequently mistaken for production tools. They are upstream or sideways of it. **FLORA** — design-stage ideation across a broad model canvas. [Full comparison](https://www.astria.ai/articles/astria-vs-flora-fashion-ai/). **StyTrix** — a design canvas with sketch, outfit, and image tools, plus saved model, fabric, and style libraries. [StyTrix pricing and review](https://www.astria.ai/articles/astria-vs-stytrix-fashion-ai/). **Figma Weave** — a general node-based AI canvas. Powerful and not fashion-specific. [Full comparison](https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/). **Fermat** — a fashion design toolbox that also covers photography and shared-workspace production, with enterprise options. [Fermat pricing and review](https://www.astria.ai/articles/astria-vs-fermat-fashion-ai/). **Pletor** — an ad-production engine for marketing teams. [Full comparison](https://www.astria.ai/articles/astria-vs-pletor-fashion-ai/). **Tellos** — AI fashion video and shoppable storefront content. [Full comparison](https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/). **Tolstoy** — a bundled commerce platform with an AI content module. [Full comparison](https://www.astria.ai/articles/astria-vs-tolstoy-fashion-ai/). ## How to run the evaluation Feature lists do not discriminate between these products; two tests do. **Test one — your hardest garments.** Ten pieces with directional prints, sheer panels, fine hardware, and complex drape. Not your bestsellers. A tool evaluated on easy garments tells you nothing about your catalog. **Test two — change your mind.** Get five products approved, then add a colorway and change the crop convention. Count the operations each platform needs. This is the single best predictor of running cost, and it is the test vendors do not design their demos around. Then ask the four questions in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/): can direction be stored and reused, what happens when you change your mind, can several people work on it, and what do you own at the end. ## Frequently asked questions ### What is the best AI fashion photography platform? It depends on which of five different jobs you are buying for. For brands producing a full collection across catalog, lookbook, campaign, and social, a production system is the right category and Astria is our recommendation. For a marketplace seller who needs cheap listing images, a listing tool is sufficient and cheaper. ### Are these tools interchangeable? No, and treating them as interchangeable is the most common buying error. A shopper-facing try-on widget, a managed retouching service, and a collection production system solve unrelated problems despite sharing the phrase "AI fashion". ### What should I test during an evaluation? Run your ten hardest garments, not your easiest. Then change something after approval — add a colorway or change the crop convention — and count the operations required. That second test predicts your real running cost better than any feature list. ### How much do AI fashion platforms cost? Listing tools commonly run low monthly subscriptions. Production platforms are typically usage-based. Enterprise and managed services are quoted per engagement. Compare on total cost per approved published image rather than headline price. ### Is this list independent? No. It is published by Astria and recommends Astria for collection-scale production. The category framing and the assessments of where other tools are the better buy are offered in good faith, and every entry links to a fuller head-to-head so you can check the reasoning. ### What is the best AI fashion model generator for a clothing brand? Choose by production job. A low-cost listing generator can be enough for a few marketplace images. A clothing brand producing a full collection needs repeatable product references, casting, art direction, review, and channel exports. Test the hardest garments and calculate cost per approved image before choosing. ## Sources and methodology Each entry summarises a longer head-to-head published on this site, written against public first-party product material and reviewed on the date shown in that article. Categories reflect how each vendor positions itself publicly. Product scope, pricing, and library sizes change frequently — confirm current terms with any vendor before deciding, and run the two tests above on your own garments. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Botika Alternatives (2026): What to Look at Instead, and Why Source: https://www.astria.ai/articles/botika-alternatives/ Botika is an established AI on-model photography platform with real ecommerce traction, and for a lot of catalogs it does the job it advertises. Brands typically start looking elsewhere for one of four specific reasons — and which reason applies determines which alternative is right. **Disclosure:** this page is published by Astria and recommends Astria for collection-scale production. Where something else is the better buy, that is said plainly. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## Diagnose the reason first | Why you are looking | What to shortlist | | --- | --- | | Direction drifts across the collection | A production system that stores an approved treatment | | You need lookbook, campaign, or motion — not just PDP images | A production system with channel and video coverage | | You sell jewelry, beauty, footwear, or children's ranges | A platform with genuine category depth | | More than one person needs to work on it | A platform with shared workspaces and review | | It costs more than the listings justify | A cheaper listing tool, not a bigger platform | That last row matters. If the honest answer is "this costs more than my marketplace listings are worth," moving to a more capable platform is the wrong direction — see the listing tools below. ## Production systems **Astria — our recommendation for brands producing collections.** The difference that matters against Botika is that the approved treatment — casting, styling, scene, crop, lighting, format — is stored and re-run rather than configured per photoshoot. Products, casting, references, templates, and outputs live in one brand workspace; templates cover beauty, jewelry, footwear, sports, luxury, and editorial as distinct categories; casting spans ages and body types including children and extended sizing; and the same direction carries into campaign frames, social crops, and motion. A multiplayer canvas with named cursors and live voice means the stylist, the founder, the ecommerce lead, and an outside agency review the same work rather than trading exports. **Where Botika may still suit you better:** if standard adult apparel PDP images are genuinely the whole job and you are happy with the current output, there is no prize for switching. [Full head-to-head](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/). **Rawshot** — a dedicated AI fashion studio with large model, camera, and background libraries. Worth a look if preset breadth is what you feel is missing. [Comparison](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/). **Caimera** — creator-led go-to-market with published case-study material. [Comparison](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/). **Ayna** — fashion photoshoot production with published pricing. [Comparison](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/). **Stoodio** — licensed digital twins of real models. The right shortlist if licensed real-model likenesses are a hard requirement for your brand. [Comparison](https://www.astria.ai/articles/astria-vs-stoodio-fashion-ai/). **Lumoo** — compact and focused, built around producing from a single image. [Comparison](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/). ## If try-on is the actual bottleneck **FASHN** — try-on and model generation for brands and creative teams. [Comparison](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/). **Aiuta** — enterprise try-on delivered as a QA'd service rather than a tool you operate. [Comparison](https://www.astria.ai/articles/astria-vs-aiuta-fashion-ai/). **Looklet** — digitised models, with a studio capture step to evaluate. [Comparison](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/). Before shortlisting any of these, check you are solving the right problem — [virtual try-on for fashion brands](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) explains why two unrelated products share that name. ## If cost is the reason Be honest about this rather than shopping upmarket. **Stylic** — three-step upload-to-download at low monthly tiers, aimed at marketplace sellers. Genuinely cheaper, and for a seller producing listing images and nothing else, sufficient. [Comparison](https://www.astria.ai/articles/astria-vs-stylic-fashion-ai/). **Lookgen** — styled outfit generation with PIM publishing. [Comparison](https://www.astria.ai/articles/astria-vs-lookgen-fashion-ai/). ## If you want output delivered rather than a tool **Pixelz** — managed post-production at scale, with service levels. A different model, and a legitimate answer if you would rather buy an outcome than run a process. [Comparison](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/). ## How to run the switch 1. **Don't switch mid-season.** A catalog with two treatments in it is worse than a catalog with one imperfect treatment. 2. **Evaluate in parallel.** Keep the current tool running. Take ten products — including your hardest — through the shortlist. 3. **Test the revision path.** Get five approved, then add a colorway and change the crop convention. Count operations. This is the test that predicts your running cost. 4. **Check category coverage** against what you actually sell, not against a demo reel. 5. **Ask what you keep.** Images only, or the casting, references, and templates that produced them? This determines whether next season starts from something or from nothing. 6. **Switch between seasons**, and rebuild the treatment deliberately rather than trying to replicate the old one exactly. The wider category map is in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/), and the production process itself in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). If your shortlist starts elsewhere, use [Lumoo alternatives](https://www.astria.ai/articles/lumoo-alternatives/) for product-photo and catalog workflows, or [StyTrix alternatives](https://www.astria.ai/articles/stytrix-alternatives/) to separate visual design, technical sampling, and photography decisions. ## Frequently asked questions ### What is the best Botika alternative? For brands producing a full collection across catalog, lookbook, campaign, and social, we recommend Astria. For marketplace sellers who only need cheap listing images, a listing tool such as Stylic is a simpler and cheaper answer. ### Why do brands look for a Botika alternative? The common reasons are wanting creative direction that persists across a collection rather than being reconfigured per shoot, needing categories beyond standard apparel, needing video, and needing more than one person to work on the same production. ### Should I switch tools mid-season? Rarely. Run a parallel evaluation on ten representative products while the current tool continues, and switch between seasons. Mid-season switching usually produces a catalog with two visibly different treatments in it. ### What should I check before switching? That your hardest garments convert acceptably, that a change of creative direction after approval is one operation rather than many, that the categories you sell are supported, and what you own at the end — images only, or the setup that produced them. ### Is this comparison impartial? No — it is published by Astria and recommends Astria for collection-scale production. The cases where another tool is the better fit are stated explicitly, and each alternative links to a full head-to-head so the reasoning can be checked. ## Sources and methodology Each alternative summarises a longer head-to-head published on this site, written against public first-party product material and reviewed on the date shown in that article. Product scope and pricing change frequently — confirm current terms with any vendor before switching. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Ghost Mannequin Alternatives: Which Workflow to Choose (2026) Source: https://www.astria.ai/articles/ghost-mannequin-alternative/ The best ghost mannequin alternative depends on what you want to stop doing. Dressing forms, waiting for neck composites, and showing clothes without a person are three different problems. They do not need the same replacement. **The short version:** use flat lay when you need a simpler product-only view, a live model when the worn garment needs to be documented, and AI on-model when you have approved product references and need more styling options. Keep ghost mannequin where its clean, body-shaped presentation still earns its place. **Upfront disclosure:** Astria publishes this guide and offers AI fashion imagery. The comparisons below are editorial production judgments, not results from a timed vendor benchmark. **Reviewed September 5, 2026.** Service descriptions were checked against first-party sources. No universal cost saving or conversion lift is assumed. ## An Astria example: dress photo to packshot Compare the original dress with the generated image, especially the neckline, seams, and hem. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot This shows an Astria result. The other service was not tested with this same photo. [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) ## Choose the alternative by the work it removes | Route | What changes in production | What the image gives you | What you still have to solve | | --- | --- | --- | --- | | Keep ghost mannequin | Dress a form, capture hidden areas, join the views | Isolated garment with body volume | Styling, accurate joins, and product review | | Outsource the composite | Move masking and joining to a retouching service | The same ghost mannequin format | Physical capture and approval remain with your team | | Flat lay | Arrange and photograph the garment on a surface | Unobstructed product shape, graphics, and details in a flat state | Body volume and worn appearance need other views | | Hanger photograph | Suspend the garment and photograph it | A hanging silhouette with no person | The hanger affects shoulders and supplies no torso volume | | Live-model photograph | Schedule a wearer, style the sample, shoot selected poses | Observed drape and proportions on that person | Sample fit, shoot coordination, and retouching | | AI on-model | Prepare references, choose a treatment, generate, review, correct | A generated worn presentation and styling options | Product fidelity, invented drape, and rejected outputs | These are workflow differences, not speed rankings. A heavily styled flat lay can consume more time than a familiar mannequin setup. An AI image that needs repeated correction can miss a deadline that a short model session would meet. If you already know you want to keep the hollow garment format, use the [ghost mannequin capture and compositing guide](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/). If you have chosen AI on-model and need source preparation, use [flat lay to on-model](https://www.astria.ai/articles/flat-lay-to-on-model/). ## When the bottleneck is editing, keep the format A queue of unfinished collars is a capacity problem. Changing the entire catalog to on-model imagery introduces casting, styling, and gallery decisions before it fixes that queue. Pixelz describes its [ghost mannequin service](https://www.pixelz.com/invisible-ghost-mannequin-service/) as combining multiple supplied product images in post-production, with manual, FTP, and API upload options. That is a concrete alternative to making every composite in-house. It still needs the source photographs. Before switching formats, ask a retouching provider to process a representative set using your existing crop, garment shape, and color standard. Compare revision time and accepted results with the current process. If the catalog presentation is already useful, moving the joining work may be enough. An AI tool that produces another hollow garment image is also preserving the format. Ask which visible areas come from supplied photographs and which are generated. A plausible invented lining is a different deliverable from a composite of the photographed lining. ## When you need simpler product-only capture Flat lay is the clearest alternative when chest or hip volume is not the point of the image. A graphic tee may need its print shown without folds crossing the design. A matching set may need both pieces visible together. There is no reason to create an invisible torso for every such view. [Shopify's clothing photography guide](https://www.shopify.com/blog/clothing-photography) describes flat lay as an overhead view on a surface and distinguishes it from model, mannequin, and lifestyle photography. It also recommends planning the angles and detail views needed for each product. A simpler setup does not eliminate the shot list. A hanger is another option when the suspended garment reads well and fits the brand's presentation. Judge the actual sample: if the shoulders collapse, the neckline stretches, or the body hangs like a narrow tube, the hanger has removed information you needed. Keeping the hanger visible can be more straightforward than retouching it out and reconstructing the area behind it. **Our recommendation:** choose these formats for their useful product view, then account for any missing view elsewhere in the gallery. Do not treat a single flat lay as an equivalent replacement for a front, back, and volume set. ## When a person is the missing information Live-model photography is the strongest choice when the buying question concerns the actual garment on a body: where a sleeve ends, how a skirt falls when standing, or what an open jacket reveals. The photograph records one sample, one wearer, one pose, and any styling adjustments. It does not prove how every size fits every shopper. Record the model's measurements and sample size, and avoid pinning that changes the feature you are trying to show. For a dress whose appeal is its drape, a short, controlled shoot may be a more useful investment than perfecting a hollow silhouette. Keep separate detail views for features that hair, hands, or styling obscure. This is a decision about what to document. A brand can still use ghost mannequin for the clean front view and a real wearer for the side profile or movement. There is no requirement to make every gallery slot use the same format. ## When AI on-model is a useful replacement AI on-model is worth testing when the missing asset is a styled presentation and the product has already been photographed clearly. It can make an existing reference library useful in more contexts. It also introduces a new approval task: checking that the rendered garment still describes the item being sold. Pixelz's August 2026 [synthetic-model workflow](https://www.pixelz.com/blog/pixelz-creates-synthetic-models/) starts with real product imagery, prepares it before generation, reviews the generated result, and sends selected outputs through final post-production. Its description supports the practical boundary: generation is one stage of production, and convincing outputs can still contain product errors. This is a vendor-described process, not evidence of an approval rate. [Astria's ecommerce workflow](https://www.astria.ai/ecommerce) lets brands select creative templates, upload product photographs, and generate imagery for lookbooks, social, and campaigns. For a team moving beyond product-only presentation, that provides a route to test an on-model treatment using existing inputs. Our recommendation is to start with a garment whose identity you can verify from the references. Approve the neckline, sleeve length, print placement, seams, closures, and opacity before judging whether you like the model or background. If the generated image supplies a back view that you never photographed, it has not established what the back looks like. Keep a physical worn view when drape or fit is the selling point and the generated result cannot be checked adequately. AI on-model is a visual interpretation, not a measurement-based fit test. The [virtual try-on guide](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) covers that distinction in more detail. ## Decide what leaves the gallery, not just what enters it A practical replacement brief names a gallery slot and the information it must retain. “Replace ghost mannequin” is too broad. | Example brief | Sensible decision | Condition for making the switch | | --- | --- | --- | | Basic tee needs a clearer graphic view | Test flat lay as the product-only front | Full graphic and garment proportions remain readable | | Tailored blazer needs its structured shape shown without a person | Keep ghost mannequin; improve or outsource the composite | The existing format serves the requirement | | Dress needs evidence of worn length and fall | Add or prioritize a live-model view | Sample size, wearer, and styling are documented | | Existing ghost images are accurate, but the gallery needs outfit context | Add an approved AI on-model image | Styling does not hide or alter product-defining features | | New drop has no usable product photographs | Capture references before selecting an AI route | The actual colorway and construction can be verified | These examples are recommendations for a pilot, not claims that a garment category always succeeds in one format. An old ghost mannequin image does not become obsolete when an AI model arrives. If it still gives shoppers a clearer view of the product, keep it. The alternative may replace the next shoot's presentation work while leaving useful existing assets in service. ## Compare the cost of a complete approved gallery A price per composite and a price per generation buy different things. For this decision, use the **cost per SKU with its required gallery approved**. Count sample preparation, capture, outside fees, generation, retouching, operator time, reviewer time, and corrections. Include a physical shoot fallback for products the alternative cannot handle. For planning, allocate shared setup across the expected batch and value internal time at the same rate in each route. Then count coverage. A cheaper front image is not a saving if it requires a new side image to recover information the original showed. Conversely, keeping an approved ghost image and adding one useful on-model view may cost less than replacing the entire set. For a numeric costing framework, use [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/). For the broader allocation of studio and AI work across campaigns, see [AI product photography versus a packshot studio](https://www.astria.ai/articles/ai-product-photography-vs-studio/). ## Run a six-SKU replacement test Choose two routine items, two recurring production problems, and two difficult products from your own range. This is a suggested operational sample, not a statistically representative experiment. 1. **Name the bottleneck.** Record whether you want to reduce dressing time, composite revisions, shoot scheduling, or missing on-model coverage. 2. **Fix the gallery brief.** Require the same product information, output dimensions, and deadline for the current route and the candidate alternative. Preserve the approved physical references. 3. **Set acceptance rules before production.** Product-changing errors are failures. Name who checks them, and set a revision limit after which the SKU returns to physical capture or the existing workflow. 4. **Record the whole job.** Track elapsed time, human minutes, outside spend, rejected outputs, missing views, and whether each complete gallery passes. Count failed attempts and fallback work. 5. **Switch only the passing group.** If basics pass and patterned items do not, use separate routes. Keep the current gallery available until the replacement is approved. Check the destination channel's current image requirements before making a candidate your main image. For Amazon, start with the [apparel image guide](https://www.astria.ai/articles/amazon-product-images-apparel/) and the category-specific source it points to. Production approval and sales performance are separate questions. After the accuracy review, test gallery presentation with your own traffic. Do not attribute a sales change to on-model imagery if the same launch also changed pricing, promotions, or assortment. ## Frequently asked questions ### What is the best alternative to ghost mannequin photography? Choose flat lay for a simple product-only view, hanger photography for a suspended presentation, live-model photography to document the garment worn by a real person, or AI on-model imagery for styling variations from approved references. Keep ghost mannequin when isolated body volume is the requirement. The best choice depends on the job of the image. ### Can AI replace a ghost mannequin shoot? AI can replace some new presentation work when suitable product references already exist and the outputs pass review. It does not remove the need to document the garment. If a generated image changes seams, print placement, opacity, or proportions, use a corrected output or physical photography instead. ### Is outsourcing ghost mannequin retouching an alternative? It is an alternative to doing the composite in-house, but it keeps the ghost mannequin format and source-photography requirements. Consider it when editing capacity is the bottleneck and the existing product presentation already works. ### Is flat lay cheaper than ghost mannequin photography? Flat lay can avoid mannequin dressing and interior compositing, but styling, capture, retouching, and approval still take time. Compare the total cost of an approved product gallery with the same required views, including any extra images needed to explain volume or worn appearance. ### Should I remove existing ghost mannequin images when adding AI models? Keep accurate existing images while testing the new format. A product-only view and an approved on-model image can serve different gallery roles. Remove a ghost mannequin view only when its replacement covers the same information and the remaining gallery still represents the product accurately. ## Sources and methodology Research checked September 5, 2026: [Shopify's clothing photography guide](https://www.shopify.com/blog/clothing-photography), [Pixelz's ghost mannequin service](https://www.pixelz.com/invisible-ghost-mannequin-service/), [Pixelz's synthetic-model production account](https://www.pixelz.com/blog/pixelz-creates-synthetic-models/), and [Astria's ecommerce product page](https://www.astria.ai/ecommerce). These are first-party descriptions of formats and services. The decision tables and pilot are our editorial recommendations; no side-by-side production trial was conducted for this article. The cover is an AI-generated editorial illustration, not a tested product conversion. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Lumoo Alternatives (2026): A Shortlist for Fashion Catalogs Source: https://www.astria.ai/articles/lumoo-alternatives/ If Lumoo Air is on your shortlist, compare **WearView for focused on-model work, FASHN for try-on and developer integration, and Astria for template-led collection imagery**. Keep Lumoo in the evaluation: switching only pays if another workflow solves a specific problem with your products, team, or output requirements. This guide is for buyers considering several alternatives. The [Astria vs Lumoo comparison](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/) covers the two-product decision in more depth. **Disclosure:** this guide is published by Astria. Reviewed September 5, 2026 against public vendor material; no controlled image-quality or cost benchmark was run. Workflow recommendations are our interpretation, not measured vendor rankings. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## Establish what Lumoo already covers Lumoo Air's [product page](https://lumoo.com/products/air) presents a connected workflow from one product photo to model shots, detail views, and video, with editing and live collaboration. Check [Lumoo pricing](https://lumoo.com/products/air) for current rates, allowances, workflow templates, and integration access. Those capabilities matter: a search for an alternative should not start by assuming Lumoo lacks templates, collaboration, or a store connection. Its plan illustrations are estimates of output, not guarantees of accepted catalog images. Test the relevant workflow and confirm the integration's actual scope before buying. ## Three alternatives, three reasons to evaluate | The specific requirement | Put on the shortlist | What must improve in the trial | | --- | --- | --- | | On-model images with repeatable casting and pose control | WearView | Garment accuracy and casting continuity across the same product set | | Try-on inside an application or automated process | FASHN | Integration behavior, output quality, and operational cost | | One photographic direction across a collection and its campaign | Astria | Reuse of the approved treatment and quality across products | | The current workflow already meets quality, budget, and delivery needs | Lumoo Air | Another supplier must show a concrete benefit to justify migration | This is deliberately a short list. Adding ten nearly identical generators makes evaluation slower without making the purchase clearer. If you are still deciding which category of tool you need, use the broader [fashion photography platform guide](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/). ## WearView: compare the on-model workflow WearView's [plan page](https://www.wearview.co/pricing) lists product-to-model, consistent model generation, and pose control on Lite. Pro adds model swap, ghost mannequin, video, and five team members; Advanced lists fifteen team members. API access is presented under Enterprise. These make WearView a relevant candidate for a team concentrating on garment presentation. **The evaluation:** choose one recurring face and a fixed pose brief. Produce several garments, then add a new colorway. Compare face continuity, garment silhouette, and how much manual correction the operator needs. Test your own inputs; product-page examples do not establish performance on sheer fabric, unusual sleeves, or small prints. **The tradeoff:** check the feature tier you actually need before comparing price. Use [WearView pricing](https://www.wearview.co/pricing) for current credit requirements for your resolution and workflow; our [WearView comparison](https://www.astria.ai/articles/astria-vs-wearview-fashion-ai/) supplies the wider buying context. ## FASHN: evaluate try-on and integration separately FASHN offers a web app and a developer API. Its [API documentation](https://docs.fashn.ai/) covers try-on, product-to-model, model creation, editing, and image-to-video endpoints. This is useful when the job includes integrating generation into a service, rather than operating only a visual editor. **The evaluation:** give the developer and creative reviewer the same test set. Require a successful garment transfer, a useful response to an invalid input, retrieval of the output, and a repeat run without losing the product identifier. An attractive manual result alone does not prove the automated workflow is ready. **The tradeoff:** API integration requires someone to own input preparation, error handling, output storage, and review. If no one will maintain those parts, evaluate the app first. Its [pricing page](https://fashn.ai/pricing) distinguishes app and developer pricing, and notes that tool costs rise with settings. Daily credits expire within their daily window, so they should not be treated as a single allowance available on launch day. If “try-on” means a shopper-facing experience rather than images prepared by your team, read [virtual try-on for fashion brands](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) before comparing plans. The [FASHN comparison](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/) covers its overlap with Astria. ## Astria: test the collection's creative direction Astria's [fashion workflow](https://www.astria.ai/ecommerce) combines product references and templates for lookbook, campaign, and social imagery. It belongs on the shortlist when the buying question is whether one approved direction can produce a useful set across your collection. **The evaluation:** agree a treatment for casting, crop, lighting, and setting. Run five products, including the difficult ones. Then change the output format and add another SKU. Ask the reviewer whether the collection still reads as one production, and record the operator time needed to make that happen. **The tradeoff:** creative direction and product review still need an owner. A brand satisfied with its current catalog images may not benefit from adding a more elaborate campaign workflow. This guide does not establish that Astria is cheaper or more accurate than Lumoo. For automated work, review [Astria's API documentation](https://docs.astria.ai/docs/api/overview/) as well as the creative trial. Account balance, callbacks, error handling, and storage terms affect a production integration. Do not assume keeping a URL in a spreadsheet is the same as archiving an output. ## Price an accepted set, not a credit allowance Use one fixed brief across all shortlisted tools: ten products, two catalog views per product, and five campaign frames. That is **25 accepted images**. State the output resolution and which garment details must remain accurate before anyone starts generating. Track each tool's actual attempts, charged credits or balance, editing minutes, and accepted files. Do not mix euros and dollars without a stated exchange rate, and do not mix monthly subscriptions with an annual price advertised as a monthly equivalent. Divide the full production spend by the number of accepted images. If the approval count halves for the same spend, cost per accepted image doubles. Include labor, rejection, and rework. The [cost-per-SKU guide](https://www.astria.ai/articles/product-photography-cost-per-sku/) explains how to extend this to a collection budget. Check your launch calendar too. A plan sufficient for a steady trickle of images can behave differently when the whole collection is due on one day. Ask about additional usage and scheduling against that actual deadline. ## A migration test that catches product errors 1. **Use representative references.** Include a simple garment, a patterned item, a difficult material, and an unusual construction. Supply front, back, and detail photographs where required. 2. **Agree rejection criteria.** Wrong fastenings, invented seams, altered print placement, and changed product proportions should fail even if the picture looks attractive. 3. **Test revisions.** Add a colorway and request a new crop after the first set is accepted. Record what must be repeated. 4. **Test handoff.** Have another team member find the correct source product, approved result, and reusable setup without help from the original operator. 5. **Move a small batch first.** Retain source photography, SKU mapping, exports, and approval notes. Expand only when the new process produces a better outcome for the stated requirement. A generated back view deserves particular care. If the reference never shows the back closure or seam construction, the tool cannot establish those facts from appearance alone. Publish only after checking the view against the actual product. ## Frequently asked questions ### What is the best Lumoo alternative for a fashion brand? Evaluate WearView for a focused on-model workflow, FASHN for try-on and developer integration, and Astria for template-led collection imagery. Choose using representative products and the workflow you need to improve, rather than a universal ranking. ### Do I need to leave Lumoo to use templates or Shopify? Not necessarily. Lumoo Air's published Basic and Pro plans list workflow templates and Shopify integration. Confirm the exact integration steps you need before treating a move to another vendor as necessary. ### Which Lumoo alternative is cheapest? This review does not establish a cheapest option. Compare cost per accepted image at the required resolution, including failed attempts, editing, and plan costs. Credit counts and vendor output estimates are not directly comparable. ### Can an AI tool reliably invent a product's back view? A plausible generated view is not evidence of the product's real construction. Supply reference photography for unseen details and verify closures, seams, prints, and proportions before publishing a generated view. ## Sources and methodology Sources linked beside the claims are first-party product pages, published plan terms, and API documentation checked on September 5, 2026. Lumoo Air's rendered product page was also inspected directly because the page's content was not fully available in the text fetch. Use the linked pricing page for current Lumoo plans and confirm checkout terms before purchase. We did not run paid-account trials or independently verify vendor quality claims. The test brief and cost calculation are evaluation tools, not evidence of a vendor's results. Cover imagery is an editorial illustration, not a sample from any compared platform. For a design-stage decision, see [StyTrix alternatives](https://www.astria.ai/articles/stytrix-alternatives/). For a different starting supplier, see [Botika alternatives](https://www.astria.ai/articles/botika-alternatives/). To test the collection workflow, [explore Astria templates](https://www.astria.ai/gallery/workspaces). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # StyTrix Alternatives (2026): Design, Sampling, or Photoshoots? Source: https://www.astria.ai/articles/stytrix-alternatives/ The most useful StyTrix alternatives depend on what you need to approve next: a design idea, a garment that can be manufactured, or an image that can go on a product page. Those are different purchases. Start with **Fermat for design iteration, Browzwear for technical 3D development, and Astria for product imagery**. This is a shortlist for teams considering several suppliers. For the narrower two-product decision, read [Astria vs StyTrix](https://www.astria.ai/articles/astria-vs-stytrix-fashion-ai/). **Disclosure:** Astria publishes this guide and is one of the options. Reviewed September 5, 2026 against public vendor material; no controlled image-quality, speed, or cost benchmark was run. Recommendations below are editorial judgments about workflow fit. ## An Astria example: product photo to model image The sweatshirt on the left is one of the product photos used to create the styled look on the right. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos This shows an Astria result. The other platform was not tested with these same photos. [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## Choose the deliverable before the replacement | Your next approval is… | Start the evaluation with | The test that matters | | --- | --- | --- | | A concept, print, or material direction | Fermat alongside StyTrix | Change one design detail without losing the rest of the brief | | A pattern, fit decision, or digital sample | Browzwear | Revise the actual pattern and inspect the simulated garment | | A lookbook or campaign for products you already sell | Astria | Produce a coherent set, then add a product after approval | | A client presentation on a shared design canvas | Your current StyTrix workflow | Establish a specific failure before paying to migrate | Do not replace a design tool just because another platform makes attractive model photographs. Equally, do not buy a full technical development system to produce a few campaign frames. The handoff cost can outweigh the subscription difference. ## Keep StyTrix when the shared design canvas is working StyTrix's [homepage](https://www.stytrix.com/) describes collaborative visual design, including sketches, rendered garments, outfit compositions, and variations. Its [tool catalog](https://www.stytrix.com/tools) includes style training, model swapping, and virtual sampling. That is a reasonable starting point for a studio presenting options to a client. Use [StyTrix pricing](https://www.stytrix.com/pricing) for current plans, credit allowances, and automation access. Confirm checkout terms for your account rather than relying on a dated plan table in an article. Before switching, write down the failed task. “We cannot get a clean sleeve revision” is useful. “Another tool has more credits” is not yet a production requirement. ## Fermat: a close shortlist for visual design iteration Fermat is the first alternative to investigate when you want to stay in image-based fashion design. Its [product page](https://www.fermat.app/) shows sketch visualization, design changes, material and print work, and on-model imagery. It also describes photography workflows, so it should not be assessed only as a sketch renderer. **Why shortlist it:** your team needs to explore the same design through several material, construction, or styling proposals. **What to test:** submit one approved sketch and ask for a sleeve change, a new fabric, and a print-scale revision separately. Have the designer mark every unintended change. A compelling output that quietly changes the garment is a failed revision. Use [Fermat pricing](https://www.fermat.app/pricing) for current rates, credit usage, team access, and enterprise options. Budget for the same required deliverables and collaborators when comparing tools. **Stay with StyTrix if** your existing canvas already handles the brief and Fermat does not improve the revision result enough to justify moving projects. See the separate [Fermat comparison](https://www.astria.ai/articles/astria-vs-fermat-fashion-ai/) for its relationship to Astria. ## Browzwear: when the missing piece is technical 3D work Browzwear's [platform overview](https://browzwear.com/products) positions VStitcher around garment development and fit validation, Stylezone around review, and Lotta around development from validated pattern blocks. Its [main product description](https://browzwear.com/) specifies physics-based simulation using fabric data. **Why shortlist it:** the designer and technical team need to revise a garment's underlying construction, not just its appearance in an image. **What to test:** bring a real pattern, representative material data, and the person who will approve the result. Require a pattern change and review the resulting shape and tension before treating the visual as useful evidence. **The tradeoff:** this evaluation needs technical inputs and skills. Budget for preparing those inputs and training the team. Ask for pricing against the required products, seats, and onboarding scope; a headline subscription is not the entire adoption cost. If the deliverable is only a moodboard, that effort may not pay back. Our [Browzwear comparison](https://www.astria.ai/articles/astria-vs-browzwear-fashion-ai/) explains the distinction between digital garment development and fashion imagery. ## Astria: when the collection needs imagery Astria's [fashion offering](https://www.astria.ai/ecommerce) starts with product photos and templates, with outputs for lookbook, campaign, and social uses. That makes it a candidate when a designed collection is ready for visual production. **Why shortlist it:** you need to test a repeatable photographic treatment across existing products. **What to test:** approve one direction, run five representative items, then ask another teammate to add a sixth. Judge product accuracy, visual consistency, and the effort needed to recover an approved result. **The limit:** a photoshoot workflow is not a pattern editor. Keep the design tool if it is still doing valuable development work. Astria should earn the imagery brief through the [collection production test](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/), not through a claim to replace every design-room task. ## Compare costs using one completed brief Use a small brief you already understand: one jacket, three material proposals, two sleeve revisions, and one presentation export. Give each design tool the same inputs and stop when the designer accepts the result. Record the subscription or trial cost, credits consumed, operator minutes, and accepted deliverables. Keep a separate test for technical 3D work and a separate one for photography; pooling all three would create a misleading league table. Ask a second team member to reopen the work and make the final change. This reveals whether the output is easy to hand off or depends on the original operator remembering the process. Include that time in the decision. For migration, retain the original sketches, product references, exported files, and approval notes. Move one capsule first. A change of tool should preserve the brief, even when the tools store projects differently. ## Frequently asked questions ### What is the best StyTrix alternative? Shortlist Fermat for image-based design iteration, Browzwear for pattern-based 3D development, and Astria for imagery of existing products. The right choice depends on the deliverable; none is a universal replacement for every StyTrix workflow. ### Is Fermat cheaper than StyTrix? A lower subscription or larger credit number does not establish lower cost. Compare the same completed design brief, including revisions, required seats, and any enterprise features. Credits are different units across vendors. ### Can generated fashion images replace technical sampling? An attractive image does not establish garment measurements, construction, or physical fit. Use it for visual decisions; use pattern and material data, technical review, and appropriate sample checks for production approval. ### Should a design team replace StyTrix with Astria? Only if the work being moved is product imagery. Astria is worth evaluating for lookbook, campaign, and social assets; it is not a substitute for a pattern-development workflow. Keeping separate design and photography tools can be the better decision. ## Sources and methodology The linked first-party product, pricing, and documentation pages were checked on September 5, 2026. Use the linked pricing pages for current rates and billing terms. We did not test paid accounts, verify performance claims, or infer missing features from silence. The test briefs are proposed evaluation methods, not reported experiments. The cover is an editorial illustration, not a sample from a compared platform. For a broader photography shortlist, continue to [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/). If the starting point is an existing product photo, the [Lumoo alternatives guide](https://www.astria.ai/articles/lumoo-alternatives/) covers that decision. [Explore Astria templates](https://www.astria.ai/gallery/workspaces) when you are ready to test the imagery workflow. For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # AI vs Traditional Photoshoot: An Honest Cost Comparison Source: https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/ The comparison often sets one raw input cost against another approach's fully-loaded total, and brands that plan against it are surprised twice: first by how much human time AI production still needs, and second by how much they save on things they were not measuring. **The short version:** the honest saving is large, structural, and mostly not where the marketing says it is. ## Compare like with like Both approaches have fixed and marginal costs. Setting them out the same way is most of the work. | | Traditional shoot | AI production | | --- | --- | --- | | **Fixed / up-front** | Brief, casting, location scout, sample logistics, crew booking | Brief, casting, building and approving the treatment | | **Marginal per SKU** | A slice of the shoot day, plus per-image retouching | Generation, plus per-item review | | **Revision after approval** | Reshoot or a grading session | An edit applied across the set | | **Next season** | Re-book everything | Start from the approved treatment | | **Calendar** | Weeks, gated by availability | Days, gated by decision-making | Two structural differences drive everything: **Marginal cost per SKU.** A traditional shoot's marginal cost is bounded below by crew time — the day only holds so many setups. AI production's marginal cost is generation plus review, which is far lower and does not compete for the same scarce resource. **Revision cost.** Traditionally, changing your mind after delivery is expensive enough that most teams simply do not. With a stored treatment it is an edit. Teams consistently underestimate how much this changes what they are willing to attempt. ## What AI production actually costs Be suspicious of anyone quoting only the generation price. The real lines: - **Creative direction and setup.** A day or two of skilled time to reach an approved treatment. This is the job, not overhead — skipping it is how brands end up with 600 mediocre images. - **Review cycles.** With whoever owns the brand. Same as always. - **Rejected variants.** Everyone produces them; nobody publishes the ratio. Budget for it. - **Correction on hard garments.** Prints, sheers, hardware, structured pieces. Per item, not batched. - **Staff hours.** Someone runs this. On a small team it is the largest line by a distance. - **Generation.** Use [Astria pricing](https://www.astria.ai/pricing) for current rates and include every attempt, including rejected outputs. Add these up and AI production is not free. It is _differently shaped_: heavy at the front, light per unit, and cheap to revise. ## Where the break-even sits **Small volumes — a handful of hero images.** Traditional shoots are competitive and often better. The fixed cost of building a treatment is amortised across very little, and a good photographer delivers a distinctive result that is hard to specify in advance. **Medium volumes — a lookbook, a season's key styles.** Roughly comparable on cost, and the decision turns on other factors: speed to market, how many revisions you expect, whether samples exist yet. **Catalog scale — hundreds of variants.** AI production wins decisively, and not narrowly. This is the flat-cost-per-SKU problem from [cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/): traditional cost scales with crew days while AI cost scales with review time, and those diverge fast. **Repeat production — every season.** The gap widens further, because a stored treatment is reused while a shoot day is consumed. ## The savings nobody counts These rarely appear in a comparison and frequently matter more than the per-image price: **Speed to listing.** A product that goes live in three days instead of three weeks earns for the extra time. For seasonal goods this can dwarf the entire imagery budget. **Long-tail coverage.** Products that could never justify a shoot day get presented properly. The revenue effect is small per product and large across a catalog. **Revision willingness.** When changing your mind is cheap, teams try things. When it costs a reshoot, they ship the first acceptable version. This is a quality effect disguised as a cost effect. **Sample independence.** Producing imagery before samples arrive, or from a single sample, removes the most common cause of delay in the whole process. ## When a traditional shoot still wins Worth stating clearly, because a comparison that concludes "always use the new thing" is not a comparison. **Documentary truth.** If the image asserts that a specific person was in a specific place, it needs to be photographed. This is a straightforward honesty constraint, not a technical one. **Construction as the product.** Technical outerwear, structured tailoring, anything where a customer buys on how it is built. Output can look plausible and be wrong in exactly the details that matter. **A distinctive photographic eye.** For hero campaign imagery, a particular photographer's way of seeing is often the differentiator. It can inform an AI treatment, but the strongest version is usually still made by that person with a camera. **Very low volume, very high stakes.** One image that carries a brand's whole season deserves the full apparatus. Most brands need both, for different work — which is the actual answer, and the one that gets lost in a debate framed as a replacement. ## Building your own comparison 1. Take your **last production's loaded cost**, including your team's hours. Divide by SKUs that shipped. 2. Model the **AI equivalent**: setup days at your loaded rate, plus review minutes per SKU at that rate, plus generation. 3. Add the **revision line** to both. Assume one change of mind after approval — that assumption is where the two models genuinely diverge. 4. Extend to **next season**. Traditional starts from zero; AI starts from the approved treatment. 5. Add the **speed-to-listing value** if your category is seasonal. For step one, the [photoshoot calculator](https://www.astria.ai/photoshoot-calculator) will read your storefront and count the products needing imagery, which is usually the number people guess worst. Do this on real numbers rather than published averages. The result will not match anyone's marketing claim in either direction, and it will be the number your decision should actually rest on. ## Frequently asked questions ### Is AI photography really 90% cheaper? Figures in that range circulate widely but usually compare a raw generation cost against a fully-loaded shoot cost. Comparing like with like — including setup, review, rejects, and staff hours on both sides — the saving is still substantial but smaller than headline claims. ### Where is the break-even between AI and a traditional shoot? It depends on volume. AI production front-loads cost into creative direction and has a low marginal cost per SKU, so it wins decisively at catalog scale. For a handful of hero images, a traditional shoot is often competitive and sometimes better. ### What costs does AI production still have? Creative direction and setup, review cycles, rejected variants, correction on difficult garments, and the salaried hours of whoever runs it. Generation itself is a rounding error; the human time is not. ### When is a traditional photoshoot still the right choice? When the photograph must document something real — a specific person, place, or event — when the garment's construction is the product and must be shown exactly, and for hero campaign imagery where a distinctive photographic eye is itself the differentiator. ### What is the biggest saving from AI production? Usually not the per-image price. It is the removal of scheduling — no model booking, studio hire, or sample shipping — and the collapse in the cost of revision, since a change of mind after approval becomes an edit rather than a reshoot. [See current Astria pricing](https://www.astria.ai/pricing), or [explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Product Photography Cost Per SKU: Formula + Budget Example Source: https://www.astria.ai/articles/product-photography-cost-per-sku/ Ecommerce teams talk about photoshoot budgets. Catalog operations run on a different number: what it costs to get one product photographed, approved, and live. That number is the one that scales with the business, and most brands have never calculated it honestly. **The short version:** divide the full production cost by the products that actually received an approved image set. A cheap per-image quote can still produce an expensive catalog if it leaves out views, retouching, revisions, or your team's time. ## Calculate it properly Most teams compute cost per image, which flatters the number, or divide the photographer's invoice by the SKU count, which understates it badly. The honest formula: > **Total loaded cost ÷ SKUs that shipped usable imagery** Total loaded cost includes: - Crew — photographer, assistants, stylist, hair and makeup - Studio or location, plus permits - Sample logistics — pulling, shipping both ways, returns, insurance - Post-production, per image, at your actual approval standard - **Your team's hours** — briefing, attending, selecting, revising, uploading - Reshoots and rejects - A share of any usage licensing Two denominators matter. Use **SKUs that shipped**, not SKUs photographed — the ones that got dropped still consumed budget. For this calculation, use a style-colourway as the imagery unit. If your inventory assigns a SKU to every size, group sizes that share the same image set; count them separately only when they need different imagery. Use that same denominator in every quote. Run this once on your last production. Compare the invoice-only figure with the loaded figure to see which omitted costs matter in your workflow. ## Calculate cost per SKU with current quotes Assume 50 style-colourways need four final images each. That is 200 required images. Add photography, studio, styling, sample handling, retouching, internal review, and revisions using your own quotes. For AI generation rates, use [Astria pricing](https://www.astria.ai/pricing). Divide the total by the number of complete, approved SKU sets. If only 40 sets ship, divide by 40 SKUs or 160 approved images. Rejected frames still consume budget, but do not count as deliverables. For a per-image quote, multiply the quoted rate by the required views per SKU, then add any separately billed setup, logistics, licensing, and internal work. ## How to compare product photography quotes Send every supplier the same brief: number of style-colourways, required views, background, styling, output size, retouching standard, usage, and deadline. Ask whether the quote is per image, per product, per hour, or per day, and whether minimum charges apply. Then fill the gaps: who prepares samples, who handles difficult surfaces, how many revision rounds are included, and what happens to an unapproved image? For AI production, also count reference preparation, paid attempts, manual corrections, and human review. A credit buys an operation; your catalog needs a usable result. Use [fashion photoshoot cost](https://www.astria.ai/articles/fashion-photoshoot-cost/) for the shoot budget and [AI versus traditional photoshoot cost](https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/) for the production-method comparison. Keep this page's calculation at the product level. ## Why the long tail stays ugly Here is the structural problem, and it is not a failure of anyone's judgment: When products need similar handling and views, much of the per-item work repeats. Batching spreads setup costs, but it does not remove styling, checking, and retouching for each item. Expected sales need not follow that pattern. If a few styles carry most of your revenue, the same image spend takes a larger share of the margin on lower-volume products. So there is a crossover point where imagery cost exceeds any defensible share of a product's expected margin. Below it, the rational decision is the cheapest possible treatment — a flat lay, a ghost mannequin, or the supplier's own photo. That can leave the lower-volume products with less useful coverage. Before cutting it further, test whether the missing views are needed to explain the product; low sales alone do not tell you which images are expendable. ## What each SKU actually needs A typical apparel PDP set: | Image | Purpose | | --- | --- | | Front, on-model or ghost | The primary listing image | | Back | Fit and construction | | Detail ×1–2 | Fabric, hardware, trim | | Lifestyle or in-context | Scale, styling, desire | | Additional colorways | Accuracy per variant | For this planning example, allow four to eight images per style-colourway. Do not multiply by colourways again if your SKU count already includes them. A 200-style collection in three colorways is 600 variants and, at six images each, up to 3,600 individual assets per season. That multiplication is the whole reason per-SKU thinking matters. Budgets are approved per shoot; costs are incurred per variant. ## What actually moves the number **Batching.** More SKUs per setup amortises the fixed cost. Real, and limited by attention — quality degrades late in a long day. **Simplifying the standard.** Fewer images per SKU, less retouching. Cuts cost directly and visibly. **Offshoring post.** Reduces the per-image retouch line. Adds turnaround time and a review burden that partly offsets it. **Shooting less often.** Fewer, bigger productions. Amortises well and hurts speed to listing — new products wait for the next shoot. Compare these choices against turnaround time and the coverage customers need. The lowest production invoice is not necessarily the lowest cost of getting the catalog live. ## What changes the shape The reason AI production is interesting here is not that it is cheap per image — it is that it **decouples per-SKU cost from crew time**. The [AI product photography workflow](https://www.astria.ai/articles/ai-product-photography/) shows where reference preparation, review, rejects, and channel delivery enter that calculation. Once an approved treatment exists, the next SKU needs reference preparation, generation, and review. Some products will also need manual corrections or conventional photography. Measure those costs in a pilot rather than assuming they fall to the generation fee. If that combined cost is lower while the approved coverage stays useful, the budget can reach more products. Whether this improves sales is a separate test; cheaper production does not establish a revenue lift. Two honest caveats. First, the up-front direction work is real and should not be skipped; a bad treatment scaled across 600 variants is worse than no treatment. Second, review does not scale to zero — the hard garments still need looking at, per item. Budget for both, and see [flat lay to on-model](https://www.astria.ai/articles/flat-lay-to-on-model/) for which garments need the attention. ## A model you can fill in For your own catalog, work out: 1. **Loaded cost of last season's production** (all lines above, including your hours) 2. **SKUs that shipped usable imagery** 3. **Current cost per SKU** = 1 ÷ 2 4. **Expected gross margin per SKU over its life** — for a median SKU, not a hero 5. **Imagery as a share of margin** = 3 ÷ 4 If step five is uncomfortable for the median product, you have quantified why your long tail looks the way it does. Then model the alternative honestly: up-front direction cost, plus per-variant generation and review, divided across the same SKU count. Use your approved-image ledger for the denominator. The [photoshoot calculator](https://www.astria.ai/photoshoot-calculator) is another starting point for planning coverage; reconcile any estimate with the style-colourway counting rule above. The full side-by-side is in [AI versus traditional photoshoot cost](https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/), and the traditional line items are broken down in [what a fashion photoshoot costs](https://www.astria.ai/articles/fashion-photoshoot-cost/). ## Frequently asked questions ### How do you calculate product photography cost per SKU? Take the fully-loaded cost of a production — crew, studio, post-production, sample logistics, and your own team's hours — and divide by the number of SKUs that shipped usable imagery. Not by images produced, and not by SKUs photographed. ### How many images does each SKU need? A working apparel set might contain four to eight images: front, back, details, and on-model or lifestyle views. Set the count from the product and sales channel. Count each style-colourway once when its size variants share those images. ### Why is the long tail of a catalog photographed worse? When low-volume products require the same production work as bestsellers, imagery can consume a larger share of their expected margin. Check that relationship in your own catalog before reducing coverage. ### Do colorways need separate photography? Each colourway needs an accurate reference and approved coverage. A digital colour edit must be checked against the actual product, including trims, patterns, and fabric appearance. Budget separately when a colourway needs its own image set. ### What is a good cost per SKU target? There is no universal figure — it depends on price point, margin, and expected units. The useful test is relative: your imagery cost per SKU should be a defensible fraction of the gross margin that SKU is expected to generate over its life. [See current Astria pricing](https://www.astria.ai/pricing), or [explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # What a Fashion Photoshoot Actually Costs (2026 Breakdown) Source: https://www.astria.ai/articles/fashion-photoshoot-cost/ Ask what a fashion photoshoot costs and you will get answers ranging across two orders of magnitude, all of them true. The word covers a photographer with one light in a rented room and a full production unit on location with a crew of fifteen. **The short version:** the shoot fee is rarely the biggest line. Post-production, usage rights, and the cost of scheduling are what actually determine the invoice. ## The line items Every fashion shoot budget is assembled from the same components. Rates vary enormously by market — a day in London, New York, or Milan is not a day in a smaller city — so treat what follows as a structure to fill in rather than a price list. **Photographer.** Quoted as a day rate, sometimes with a separate creative fee for campaign work. The single most variable line: an experienced commercial photographer with a name commands a multiple of a competent working professional, and both may deliver what your brand needs. **Assistants.** Usually one or two, at a fraction of the photographer's rate. Non-optional on anything with real lighting. **Model.** Booked through an agency, at a day rate plus agency commission — commonly around 20% — plus usage. The usage line is the one that surprises people; see below. **Hair and makeup.** One artist for a single model; two if you are shooting several looks against the clock. **Stylist.** Day rate plus prep days, which are real and frequently forgotten. Pulling and returning samples takes as long as shooting them. **Studio or location.** A studio day, or a location fee plus permits, plus a scout day if the location is not already known. **Equipment.** Often included in the photographer's rate; specialist lighting, grip, or digital capture is not. **Production.** A producer, catering, transport, and shipping samples both ways. Small productions absorb this into someone's job. Large ones cannot. **Post-production.** Quoted per image, and this is where volume bites. Retouching 20 editorial images is a project; retouching 600 catalog variants is a different business entirely. **Usage rights.** Frequently the largest hidden line. Talent and location licensing is scoped by territory, media, and duration. Extending a campaign, or reusing an image in a market you did not license, means renegotiating — often at a worse rate, because you have already committed creatively. ## Three realistic shapes Rather than invent precise figures, here is how the _proportions_ typically fall — which is more useful for planning than any single number. **Small ecommerce day.** Photographer, one assistant, one model, one hair-and-makeup artist, studio, standard retouching. Crew is the majority of the cost; post scales with SKU count and often overtakes the shoot fee. Usage is usually limited and cheap. **Lookbook.** Adds a stylist with prep days, a location, and more considered post. Styling and location can rival the photographer's fee. Deliverable count is low, so cost per finished image is high — by design. **Campaign.** Adds a producer, a larger crew, casting with a recognisable face, and broad usage rights. Talent and usage frequently dominate everything else, and the shoot day becomes a minority of the budget. The consistent pattern: as ambition rises, the photography becomes a smaller share and the _rights and people_ become a larger one. ## The costs nobody quotes **Reshoots.** Something arrives late, a sample is wrong, the light was not there. Ask explicitly who pays. **Sample logistics.** Shipping a collection to a location, insured, and back again. **Your own time.** The hours your team spends briefing, attending, selecting, and revising are real cost. On small teams they are the largest uncounted line by a wide margin. **The calendar.** The most expensive item on this list and the one that never appears on an invoice: a product that cannot be listed for three weeks because the shoot is booked for the 14th. For a seasonal business that delay has a direct revenue number attached. **Revision.** Deciding after delivery that the grade should be warmer means a grading session, or a reshoot, or living with it. Most brands live with it, which is its own kind of cost. ## How the AI comparison actually works Use [Astria pricing](https://www.astria.ai/pricing) for current generation rates. The fully-loaded comparison has to include: - Setup time to reach an approved creative direction — real, and typically a day or two - Review cycles with whoever owns the brand - Rejected variants, which nobody publishes but everybody produces - Correction time on garments that do not convert cleanly - The salaried hours of whoever runs the process Counted properly, the per-image gap narrows considerably. It remains large — but the more defensible arguments are structural rather than arithmetic: - **Scheduling disappears.** No model availability, no studio booking, no sample shipping, no three-week wait. - **Revision becomes cheap.** A change of mind after approval is an edit, not a reshoot. For most teams this is the biggest practical difference. - **The long tail becomes affordable.** Products that could never justify a shoot day can be presented properly — which is where the revenue argument usually lives. - **Direction is retained.** The approved treatment is reusable next season rather than expiring with the shoot. The side-by-side model is worked through in [AI versus traditional photoshoot cost](https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/), and the per-variant view in [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/). For current Astria rates, use [Astria pricing](https://www.astria.ai/pricing) and apply them to the products and views your catalog requires. ## How to compare two quotes properly Ask every supplier the same six questions: 1. What is included — crew, studio, equipment, catering, transport? 2. How many finished, retouched images, and to what standard? 3. What usage rights, for which territories, media, and duration? 4. What happens if we need a reshoot, and who pays? 5. What is the revision policy after delivery? 6. What do we own at the end — final files only, or raws and layered files too? Question three is where most budget overruns originate. Question five is where most disappointment does. ## Frequently asked questions ### How much does a fashion photoshoot cost? A single-day commercial fashion shoot commonly lands somewhere between a few thousand and several tens of thousands, depending on crew size, location, talent, and usage rights. The range is wide because "photoshoot" describes anything from one photographer in a rented studio to a full production unit. ### What is the biggest hidden cost in a photoshoot? Usually post-production and usage rights. Retouching is quoted per image and scales with volume, and licensing for a model or location often costs more over time than the shoot day itself — particularly when a campaign is extended or reused. ### Why do photoshoot quotes vary so much? Because quotes bundle different things. One may include retouching, licensing, and a producer; another may cover the shoot day alone. Compare quotes line by line, and specifically check usage rights, retouching volume, and who pays for reshoots. ### How many images does a shoot day produce? Highly variable, but a well-run ecommerce day produces far more usable frames than an editorial day, because the setup is fixed. Editorial days may yield only a handful of finished images. Always agree deliverable counts rather than shooting hours. ### Is AI photography cheaper than a photoshoot? Per image, dramatically. Fully loaded, the gap is smaller than headline figures suggest once setup, review, rejected variants, and staff time are counted. The larger practical saving is usually the removal of scheduling and sample logistics rather than the per-image price. [See current Astria pricing](https://www.astria.ai/pricing), or [explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Fine-Tuning FLUX.1 on Astria: A Developer's Guide to the API Source: https://www.astria.ai/articles/fine-tuning-flux/ _Originally published September 2024 by Jayesh Gulani. Reviewed August 2026 — see [What changed since 2024](https://www.astria.ai/articles/fine-tuning-flux/#what-changed-since-2024) at the end for the current model line-up._ ## Introduction to FLUX.1 FLUX.1, launched in August 2024 by Black Forest Labs, represents a significant advancement in the field of generative deep learning. This innovative model is designed to push the boundaries of creativity, efficiency, and diversity in media generation, particularly focusing on images and videos. With a mission to develop state-of-the-art generative models, FLUX.1 leverages advanced architectures such as flow matching and Denoising Image Transformer (DIT), similar to the technologies employed in Stable Diffusion 3 (SD3). ### Key Features and Innovations FLUX.1 is built on a robust foundation of 12 billion parameters, allowing it to deliver exceptional image fidelity and controllability. This model excels in generating high-quality images that cater to a wide range of applications, from artistic creations to detailed photorealistic outputs. The architecture combines the strengths of transformer models and diffusion techniques, enabling FLUX.1 to outperform its predecessors, including SDXL and SD1.5, in terms of image quality and prompt adherence. ![alt_text](https://www.astria.ai/articles/img/fine-tuning-flux/image1.webp) ![alt_text](https://www.astria.ai/articles/img/fine-tuning-flux/image2.webp) ## Variants of FLUX.1 FLUX.1 comes in three distinct variants, each designed for different use cases: ### FLUX.1 \[Pro\] 1. Performance: This is the flagship model, offering state-of-the-art performance with exceptional image quality, detail, and diversity. 2. Access: Available exclusively via API, making it suitable for commercial applications. 3. System Requirements: High system requirements, not suitable for consumer hardware. ## FLUX.1 \[Dev\] 1. Purpose: A distilled version intended for non-commercial use, ideal for research and development. 2. Features: Provides similar quality and prompt adherence as the Pro version but is more efficient and can run on consumer hardware. 3. Availability: Open-weight model, downloadable for local use. ## FLUX.1 \[Schnell\] 1. Speed: Optimized for fast image generation, making it suitable for local development and personal projects. 2. Performance: While it sacrifices some image fidelity for speed, it is designed for quick outputs. 3. License: Released under the Apache 2.0 license, allowing for broader usage. Each variant caters to specific needs, from high-performance commercial applications to efficient local development and rapid prototyping. ## Fine-Tuning FLUX.1 on Astria.ai: Step-by-Step Guide Fine-tuning FLUX.1 on Astria is easy: you just need a handful of images to get started. ### Generate Your API Key Before you can start fine-tuning FLUX.1, you'll need to generate an API key on Astria.ai. Here's how: 1\. Login to Astria.ai: Visit Astria.ai and log in using your Gmail ID. 2\. Access the API section: Once logged in, navigate to the API section of your account. 3\. Generate the API Key: Click on the 'Generate API Key' button. This will create your unique API key, which you can use for all API requests. 4\. Check [current pricing](https://www.astria.ai/pricing) before creating fine-tunes and generating images using FLUX.1. ### Guide to Fine-Tuning Human Faces Here, we'll walk you through fine-tuning FLUX.1 using the API, specifically focusing on creating a model that excels at generating human face images. We'll use images of a [woman](https://www.pexels.com/photo/photo-of-woman-using-white-headphones-3756747/) from the free stock photo website, Pexels, to fine-tune our model. 1. Prepare your data: Gather around 8-16 high-resolution images. 2. Upload these images to PostImages to get the image URLs. 3. Ensure that the images are diverse in terms of lighting, angles, and expressions to capture the full range of features you want the model to learn. 4. Initiate fine-tuning: Replace YOUR\_API\_KEY and YOUR\_MODEL\_ID with your generated API key and Tune ID. 5. Use the following code to fine-tune FLUX.1 for human faces: ```python import requests from io import BytesIO # Replace with your actual API key API_KEY = 'YOUR_API_KEY' # Function to download images and convert them to binary def download_image(url): response = requests.get(url) if response.status_code == 200: return BytesIO(response.content) else: print(f'Error downloading image: {url}, status code: {response.status_code}') return None # Fine-tune a model using Flux.1 def fine_tune_flux_model(api_key): fine_tune_url = 'https://www.astria.ai/tunes' image_urls = [ 'https://i.postimg.cc/9fThRXgx/pexels-sound-on-3756747.jpg', 'https://i.postimg.cc/dttwHYD1/pexels-sound-on-3756750.jpg', 'https://i.postimg.cc/fysTTSwj/pexels-sound-on-3756752.jpg', 'https://i.postimg.cc/prPRtNZs/pexels-sound-on-3756917.jpg', 'https://i.postimg.cc/fRVZsfcQ/pexels-sound-on-3756944.jpg', 'https://i.postimg.cc/0yhvyKnM/pexels-sound-on-3756962.jpg', 'https://i.postimg.cc/LXKmNgZK/pexels-sound-on-3756993.jpg', 'https://i.postimg.cc/ZqyT8xnx/pexels-sound-on-3760859.jpg', 'https://i.postimg.cc/RF748Cc3/pexels-sound-on-3760918.jpg' ] images = [] for url in image_urls: image = download_image(url) if image: images.append(('tune[images][]', ('image.jpg', image, 'image/jpeg'))) fine_tune_data = { 'tune[class_name]': 'woman', 'tune[name]': 'woman', 'tune[title]': 'Flux Tune Model 1', 'tune[base_fine-tune]': 'Flux.1', 'tune[model_type]': 'lora', 'tune[branch]': 'flux1' } fine_tune_headers = { 'Authorization': f'Bearer {api_key}' } fine_tune_response = requests.post(fine_tune_url, headers=fine_tune_headers, data=fine_tune_data, files=images) if fine_tune_response.status_code == 200: tune_id = fine_tune_response.json()['id'] print(f'Fine-tuning started for tune ID: {tune_id}') return tune_id else: print(f'Error fine-tuning model: {fine_tune_response.status_code}, {fine_tune_response.text}') return None # Main execution flux_tune_id = fine_tune_flux_model(API_KEY) ``` 6\. Monitor the progress: Fine-tuning typically takes about 30-60 minutes. You can monitor this through the provided URL. 7\. Generate the images: Once fine-tuning is complete, use the following code to generate images: ```python import requests API_KEY = 'YOUR_API_KEY' MODEL_ID = 1504994 # The hardcoded ID for the Flux model # Generate images def generate_images(api_key, model_id): generate_url = f'https://www.astria.ai/tunes/{model_id}/prompts' generate_data = { 'text': ' sks woman In the style of TOK, a photo editorial avant-garde dramatic action pose of a person wearing 90s round wacky sunglasses pulling glasses down looking forward, in Tokyo with large marble structures and bonsai trees at sunset with a vibrant illustrated jacket surrounded by illustrations of flowers, smoke, flames, ice cream, sparkles, rock and roll', 'num_images': 4, 'model_type' :'lora', 'model': 'flux.1_dev' } generate_headers = { 'Authorization': f'Bearer {api_key}', 'Content-Type': 'application/json' } generate_response = requests.post(generate_url, headers=generate_headers, json=generate_data) if generate_response.status_code == 200: prompt_id = generate_response.json()['id'] image_url = generate_response.json()['image_url'] print(f'Images generated for prompt ID: {prompt_id}') print(f'Image URL: {image_url}') else: print(f'Error generating images: {generate_response.status_code}, {generate_response.text}') # Main execution generate_images(API_KEY, MODEL_ID) ``` \# Main execution generate\_images(API\_KEY, MODEL\_ID) ### Output ![alt_text](https://www.astria.ai/articles/img/fine-tuning-flux/image3.webp) **Prompt 2:** A woman dressed in ornate, golden armour, holding a sword, standing on the battlefield at sunrise, with a determined expression. ![alt_text](https://www.astria.ai/articles/img/fine-tuning-flux/image4.webp) **Prompt 3:** A woman driving a classic pink convertible with a shiny finish, reminiscent of a Barbie car, cruising down a candy-coloured boulevard lined with palm trees. She's wearing a stylish outfit in shades of pink and pastels, with oversized sunglasses and a bright smile. The scene is set in a whimsical, dreamlike world with cotton candy clouds, glittering starbursts in the sky, and playful details like oversized flowers and butterflies floating around ![alt_text](https://www.astria.ai/articles/img/fine-tuning-flux/image5.webp) ### Guide to Fine-Tuning Pet Photographs If you have a pet, here’s how you can fine-tune FLUX.1 to generate stunning new images. 1\. Collect your data: Use diverse images of a pet, capturing different poses, environments, and expressions. Ensure the images are consistent in quality for the best results. I have used images of a [golden retriever](https://www.pexels.com/photo/golden-retriever-lying-on-brown-sand-while-looking-afar-11920791/) from Pexels. 2\. The fine-tuning process: Follow the same steps as mentioned above for human faces, upload the images, and define your parameters. After completion, your model will be capable of generating new and fantastic pet images from your prompts. ### Example Prompts **Prompt 1**: A dog wearing cool sunglasses, lounging on a beach towel with the ocean in the background, under a colourful beach umbrella. ![alt_text](https://www.astria.ai/articles/img/fine-tuning-flux/image6.webp) **Prompt 2**: A dog dressed in a tuxedo, sitting at a beautifully set dinner table with a candlelit ambience, looking classy and elegant. ![alt_text](https://www.astria.ai/articles/img/fine-tuning-flux/image7.webp) ## How Flux.1 Compares to SD1.5 and SDXL While SDXL and SD1.5 have been industry standards for a while, FLUX.1's use of flow matching and DIT architecture gives it a distinct edge. Flux.1 has several key advantages over SD1.5 and SDXL: ### Image Quality and Prompt Adherence - Flux.1 achieves exceptional image fidelity, detail, and prompt adherence, setting a new standard for text-to-image generation. - It adapts to a fitting cartoon drawing style while still adhering to prompts almost every time. - Flux.1 successfully adhered to prompts in testing, outperforming the base SD3 model. ### Versatility - Flux.1 is more versatile than SD1.5, allowing the generation of many images that would be impossible with SD1.5 without specialized LoRAs or ControlNet. - It works well for a wide range of use cases, from realistic to cartoon styles. ## Summary and What’s Next FLUX.1, especially when fine-tuned, provides a powerful tool for creators and developers. Whether you're looking to generate human portraits, artistic renditions, or even specific pet images, fine-tuning FLUX.1 using Astria's API opens up new possibilities. It is important to remember that Flux image prompting tends to give better outputs with a more narrative-style prompt rather than the traditional comma-separated tags. Also, what’s truly unique about Flux is its ability to render text – not just single words, but entire sentences – with great clarity. This feature alone opens up a universe of possibilities for businesses looking to integrate text into their images. ## What changed since 2024 This guide still describes the fine-tuning flow accurately — you upload images, POST a tune, and generate against it — but the model landscape moved on considerably. Read it for the API mechanics, not the model recommendations. - **The FLUX line advanced.** FLUX.1 \[Pro\], \[Dev\], and \[Schnell\] have been joined by newer releases, including FLUX.2 Pro. The `branch` and `model` parameters shown below still work; check the current list before hardcoding one. - **Gemini-branch references removed the training wait.** For many use cases you no longer need to train a LoRA at all — a reference can be created instantly from a handful of images and used the same way in prompts. - **Editing models joined the line-up.** Nano Banana and Seedream handle instruction-based edits (background swap, object removal, colour change) that previously required inpainting or a second pass. - **Check current terms.** Use [current pricing](https://www.astria.ai/pricing) for API rates and any available offers. Always discover models programmatically rather than hardcoding a name — see the [API reference](https://docs.astria.ai/docs/api/tune/create) for the current endpoints and parameters. If you are producing fashion or ecommerce imagery rather than building on the API, the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/) covers the production side of the same platform. --- # Nano Banana in Photoshop: Generative Editing Without Leaving the Document Source: https://www.astria.ai/articles/nano-banana-photoshop-guide/ Generative editing has an annoying workflow problem: the models are in a browser and the work is in a document. Export, upload, prompt, download, re-import, mask, blend — repeat forty times and the tooling costs more time than it saves. **The short version:** the plugin puts the selection and the model in the same place, and returns results as layers you can mask and blend normally. ## What it does Make a selection, describe the edit, and the result comes back into the document. The plugin handles the export, the API call, and the re-import, and it returns generations as layers so the rest of your normal workflow — masks, blend modes, adjustment layers, non-destructive corrections — still applies. The models available include Nano Banana (Google's Gemini image line), Seedream, and Flux, with the current line-up shifting as new releases land. That is deliberate: the plugin's usefulness is partly that you do not have to migrate your workflow every time a better model appears. ## Why model choice matters The models are not interchangeable, and having several in one panel is most of the value. - **Instruction-following edits** — "remove the object", "change the wall to sage green", "make it evening" — are where the Gemini-line models are strongest. Natural-language editing against an existing image is what they are built for. - **Photographic generation** — new content that has to sit convincingly alongside a photograph — is where the Flux line has generally been strong. - **Speed and cost** — compare model turnaround and output quality, then check [Astria pricing](https://www.astria.ai/pricing) for current rates. For a hundred small corrections, the fast one is the right call. The practical habit is to try the cheap fast model first and escalate only when it fails. Most edits do not need the best available model, and running everything through the most expensive option is how a plugin subscription starts feeling expensive. ## Working with it properly **Select generously.** Give the model context around the edit — a tight selection produces edits that do not know what they are sitting next to, and the seam shows. **Keep generations on their own layers.** Then mask rather than erase. The best result is frequently a blend of two attempts, and that is only possible if you kept both. **Prompt the change, not the scene.** "Change the wall behind her to sage green" beats a full re-description of the image. Describing everything invites the model to reconsider everything. **Fix colour and grade in Photoshop.** Generated regions often come back very slightly off. A curves adjustment clipped to the layer is faster and more controllable than re-prompting for a colour shift. **Work at sensible resolution.** Generate at a working size, then upscale the finished composite. Generating everything at 4K is slow and expensive when most of it will be masked away. ## Where it fits in a production workflow For fashion and product work the division tends to fall out like this: - **Bulk production** — a whole collection against one approved treatment — belongs in a production workspace, not in Photoshop. That is a throughput problem, and doing it one document at a time is the wrong shape. See the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). - **Hero images** belong in Photoshop. The shot that carries a campaign gets individual attention, and this is where generative editing inside a real editing environment beats a web app. - **Corrections** — a distracting object, an awkward shadow, a background that needs extending to a different aspect ratio — belong in the plugin. These are the everyday tasks that were never worth a browser round trip. - **Client work** where layered files are a deliverable belongs in Photoshop, because the layers are the point. ## Credits and the direct mode Two billing paths, and the right one depends on who you are: **Astria credits.** Generations bill against an Astria balance, pay-as-you-go. Simpler, and it means the same balance covers plugin work and platform work. **Direct mode with your own Google API key.** Generations bill to your own Google account. This suits developers who already have Google billing set up and want the usage on that invoice. See [Astria pricing](https://www.astria.ai/pricing) for current plugin pricing, generation rates, and billing terms. ## Practical notes - **Photoshop version.** The maintained plugin is the UXP build, targeting recent releases including Photoshop 2026. An older JSX version existed for earlier Photoshop generations. - **Model availability changes.** Names in the panel will shift. Discover what is available in the plugin rather than assuming a specific model is present. - **Not every edit should be generative.** A clone stamp is still faster and more predictable for small repairs, and a real mask beats a generated one for anything with a hard edge. For the API-level equivalent of this work, see [fine-tuning FLUX.1 on Astria](https://www.astria.ai/articles/fine-tuning-flux/). ## Frequently asked questions ### What is the Nano Banana Photoshop plugin? An Astria plugin that connects a Photoshop selection to current generative image models — including Nano Banana, Seedream, and Flux — so edits happen against the live document and return as layers rather than requiring a round trip through a web app. ### How much does the plugin cost? See [Astria pricing](https://www.astria.ai/pricing) for current plugin pricing and generation rates. ### Can I use my own Google API key? Yes. The plugin offers a direct mode where you supply your own Google API key and generations bill to your Google account instead of Astria credits. This suits developers who already have a Google billing relationship. ### Which Photoshop versions are supported? The current UXP plugin targets recent Photoshop releases including Photoshop 2026. An older JSX-based version existed for earlier releases; the UXP plugin is the maintained one. ### How is this different from Photoshop's built-in generative fill? The main difference is model choice. Built-in generative features use Adobe's models; this plugin lets you route the same selection to different current models and pick per task, which matters because they have genuinely different strengths. [Get the Photoshop plugin](https://www.astria.ai/nano-banana-photoshop). --- # 3D Packshots for Ecommerce: What They Are and When to Use Them Source: https://www.astria.ai/articles/3d-packshots-ecommerce/ "3D packshot" is used for at least three different things: an interactive product model, a turntable video, and a polished still rendered from 3D. Those assets can share a production pipeline, but they are not interchangeable on a product detail page. **The short version:** use a true 3D model when letting the shopper control the view answers a buying question that still images do not. Keep photography as the source of truth, and do not sell a sequence of generated angles as dimensionally accurate 3D. ## From a dress photo to a packshot The generated image gives the dress volume without a visible mannequin. Compare the neckline, seams, and hem with the original photo. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot Details hidden in the original photo still need checking against additional product photos. [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) **Last fact check:** August 31, 2026. **Search-intent review:** September 7, 2026. Platform specifications change; verify the linked Shopify and Amazon requirements before delivery. ## First, name the asset correctly | Asset | What the shopper receives | Typical file or delivery | Best at | It is not | | --- | --- | --- | --- | --- | | **Interactive 3D model** | A product that can be rotated and zoomed freely | GLB/USDZ in a web viewer | Shape, hardware, construction, scale, AR | A video | | **360 spin** | A fixed sequence of views around the product | Image sequence, MP4, or WebM | Controlled all-around inspection | Free-camera 3D | | **Rendered still** | One camera angle exported from a 3D scene | JPEG, PNG, or WebP | A repeatable packshot or campaign angle | Interactive media | | **Generated multi-angle set** | Several plausible views created from references | Separate image files | Fast creative exploration and secondary assets | Evidence of hidden geometry | The distinction matters because the trust claim changes. A captured or carefully modeled object can preserve measured geometry. A generated rear view may look persuasive while inventing a seam, heel counter, pocket, or fastening the source image never showed. If the job is a clean primary image, start with a packshot. If it is apparel volume without a visible body, use [ghost mannequin photography](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/). If it is fit and styling, [flat lay to on-model](https://www.astria.ai/articles/flat-lay-to-on-model/) solves a different problem. Use 3D when the interaction itself is useful. ## Can AI generate multiple product angles from one photo? Yes, but it generates **plausible views**, not a measured reconstruction of surfaces it has never seen. That can be a useful AI product photography workflow when a team needs a three-quarter campaign still, a fast rotation, or secondary PDP imagery and can review every result. The evidence limit is simple: - A visible logo, seam, handle, or panel can be carried into another composition and checked against the source. - A hidden pocket, sole, back closure, interior, or underside must be inferred unless another reference shows it. - A smooth-looking sequence can still change product geometry between frames. - A generated side or back view should not be used to prove a feature that was absent from the input. Supply front, back, side, detail, material, and dimension references when those surfaces matter. Use generated angles as reviewed creative derivatives; use captured multi-angle photography, production CAD, photogrammetry, or a measured model when the output must establish literal shape and construction. This is also why a generated set is not automatically a 360 product spin. A spin needs consistent geometry through the full rotation. A true 3D packshot needs one coherent object that the shopper can inspect freely. The broader [AI product photography guide](https://www.astria.ai/articles/ai-product-photography/) explains how these derivatives fit into the complete production workflow. ## Where 3D earns its place 3D works best when the customer wants to inspect structure rather than just admire styling. **Footwear.** A shopper can check the sole, heel, toe shape, closure, and construction from angles that would otherwise require a long gallery. Material behavior still needs careful capture; a technically correct shape with plastic-looking leather is not a faithful model. The category-specific failure modes are covered in [AI footwear photography](https://www.astria.ai/articles/ai-footwear-photography/). **Bags and structured accessories.** Shape, handles, hardware, gussets, pockets, and scale are strong 3D jobs. An interior that matters needs to be modeled and exposed deliberately; rotating a closed bag does not answer an interior-capacity question. **Jewelry and watches.** 3D can reveal settings and mechanisms, but polished metal, stones, engraving, and real-world scale are difficult. Keep macro photography beside it. [Jewelry imagery](https://www.astria.ai/articles/ai-jewelry-photography/) has a higher accuracy bar than almost any other ecommerce category. **Furniture and home goods.** This is the clearest AR case because placement and physical scale are buying questions. It is also where dimensions must be trustworthy: an attractive model at the wrong scale defeats the purpose. **Soft apparel.** Possible, not easy. A handbag holds a stable form; a dress changes with body, size, pose, gravity, and fabric. One draped 3D garment is a visualization of one state, not proof of fit across customers. ## What a production-ready model needs A model is not finished because it rotates. ### Geometry The silhouette, dimensions, thicknesses, panel lines, and hardware placement must match the sold object. Small deviations become obvious once the shopper can choose the angle. For hard goods, record physical measurements. For soft goods, define the intended draped state and do not imply it represents every size. ### Materials Color, roughness, reflectivity, normal detail, and transparency are separate properties. A single beige texture does not describe suede, coated canvas, and pebble leather. Review the model under more than one neutral lighting environment so a material is not only convincing in the artist's preferred setup. ### Hidden views The bottom, back, interior, closure, and undercuts are exactly why someone opens a 3D viewer. If those surfaces were not in the reference set, capture them or mark the model incomplete. Never let a generative fill become product evidence by accident. ### Performance A beautiful model that delays the page is a poor commerce asset. Shopify accepts `GLB` and `USDZ` files up to 500 MB and automatically optimizes models over 15 MB, but an upload ceiling is not a performance target. Shopify's own 2026 guide calls roughly 4 MB an ideal GLB size; test the real page on a mid-range phone and a throttled connection. See Shopify's [product media type requirements](https://help.shopify.com/en/manual/products/product-media/product-media-types). ## Shopify and Amazon are different jobs ### Shopify implementation Shopify supports product images, hosted or embedded video, and interactive 3D models. A compatible theme is required for video and 3D, and Shopify notes that those two media types cannot be assigned as variant media. Its help center also says the first product-media item becomes the featured media used in places such as collections and cart, so the default order deserves deliberate testing. The operational details are in the [Shopify product media guide](https://www.astria.ai/articles/shopify-product-media-guide/). For 3D, Shopify accepts GLB and USDZ and converts files so both formats are available across Android and iOS devices. Uploading one correct model is the platform step; proving that materials, scale, load behavior, poster image, controls, and mobile interaction work is the production step. ### Amazon listing Treat the main Amazon image as a separate marketplace-safe asset. Amazon's public advertising guidance recommends a plain white background, at least 80% product fill, and images at least 1,000 pixels on a side to enable zoom. Seller Central's full rules and category guides govern acceptance and can be stricter. An interactive 3D experience is not a substitute for that image. A spin video or rendered detail may still be useful elsewhere in the Amazon asset system, subject to the account, category, placement, and current policy. Build outward from the approved main image; do not force a Shopify media stack into Amazon unchanged. See [Amazon product images for apparel](https://www.astria.ai/articles/amazon-product-images-apparel/). ## The evidence is promising, not universal Shopify's Rebecca Minkoff case study reports that shoppers who interacted with a 3D model were 44% more likely to add the item to cart and 27% more likely to order; it reports a 65% higher purchase likelihood among visitors who used AR. Those are **vendor-published results from one brand**, not a forecast for every catalog. The people who choose to interact may already have higher intent, and the page, products, traffic, and implementation all matter. The responsible use of that case study is to justify a test, not a business case with a guaranteed lift. ## A practical 3D packshot workflow 1. **Write the buying question.** “Show the clasp and interior depth” is useful. “We need 3D” is not a brief. 2. **Choose the source of truth.** Physical product, production CAD, or a measured scan. Record version and colorway. 3. **Capture the reference set.** Front, back, sides, top, bottom, interior, hardware, material close-ups, and measurements. 4. **Model and texture.** Separate geometry approval from material approval so a convincing render does not hide a wrong shape. 5. **Review against the object.** Use matched angles and neutral light. Check dimensions, construction, labels, closures, color, roughness, and hidden surfaces. 6. **Optimize a delivery copy.** Keep a high-resolution master; decimate geometry and resize textures for the web version without damaging the parts customers inspect. 7. **Test the live viewer.** Desktop, iOS, Android, keyboard, touch, zoom limits, AR scale, poster image, loading state, and fallback behavior. 8. **Measure by product cohort.** Start with categories where inspection should matter, then compare against similar products without 3D. The unit of work is not one hero model. It is the source package, approved master, channel exports, poster image, QA record, and measurement plan. ## The QC gate Before publication, check: - Overall dimensions and scale against the physical specification - Silhouette from front, side, back, top, and bottom - Hardware count, placement, color, and mechanical state - Seams, panels, edges, sole geometry, pockets, and closures - Material response under neutral and bright lighting - True product color against an approved photographed reference - Interior and underside completeness - File size, first load, interaction smoothness, and fallback image - AR scale and floor placement where AR is enabled - Correct product and colorway mapping in the catalog If any view is invented, label the asset as a generated derivative in the production record and keep it out of positions where the shopper expects literal product evidence. ## When not to use 3D Skip it when still images already answer the buying question, the product changes too frequently to recover the modeling cost, the only references are incomplete, or the team cannot maintain the model when construction changes. Also skip it when the purpose is only motion. A short rotating video can be lighter to produce, easier to art-direct, and more predictable across channels. The right format is the smallest one that explains the product honestly. ## Frequently asked questions ### What is a 3D packshot? A 3D packshot is a digital product model that a shopper can rotate and zoom. In ecommerce it is usually delivered as a GLB or USDZ file and rendered in an interactive viewer. A still render or a rotating video may be made from the same model, but neither is itself interactive 3D. ### Is a 360 product spin the same as a 3D model? No. A 360 spin is a sequence of photographed or rendered views, usually played as an animation. A true 3D model lets the shopper control the camera freely and can support augmented reality when the platform and model are configured for it. ### Which fashion products are best suited to 3D packshots? Footwear, bags, jewelry, watches, and structured accessories are usually stronger candidates than soft garments because their geometry is stable. Clothing can work, but believable drape, interiors, and size-dependent fit make it a materially harder modeling job. ### Can a 3D packshot replace product photography? Usually not. Keep an approved photograph as the source of truth for color, finish, construction, and marketplace compliance. Use 3D to add controlled inspection, configurable views, or augmented reality where those interactions answer a real buying question. ### How should a brand measure whether 3D product media works? Instrument model loads, interactions, add-to-cart, purchase, and returns, then compare equivalent products or run a controlled test. Interaction cohorts are useful diagnostics, but they do not prove that 3D caused the result because more interested shoppers are also more likely to interact. ### Can AI generate multiple product angles from one photo? AI can generate plausible front, side, back, or three-quarter product views from one photo, but any surface missing from the source is inferred rather than recovered. Use generated angles for creative and secondary assets after review; supply real reference views or measured 3D data when the output must prove construction. [Explore Astria's 3D packshot workspace](https://www.astria.ai/w/3d-packshots), or [see Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # AI Fashion Film: Turning a Campaign Into Motion Source: https://www.astria.ai/articles/ai-fashion-film-guide/ Motion is the part of AI fashion production that has moved fastest and is still the least reliable. It is also where the compounding argument is most obvious: if a campaign's casting, garments, and world already exist as approved references, the film is a derivation rather than a second production. **The short version:** short shots cut together work now. Long takes, complex fabric motion, and walking still fight you. ## From a fashion photo to a nine-second video The brief asks the model to walk in, pause with a hand on her hip, and walk past the camera. Press play to see the generated result. ![Starting image of a model in a navy pinstriped dress and sandals in a grey studio](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-first-frame.jpg) Starting image [Watch the generated video](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-720p-live-runway.mp4) AI video · 9 seconds · Seedance 2 Fast · no audio [View this generation](https://www.astria.ai/prompts/46608981)[Compare video models](https://www.astria.ai/articles/video-model-comparison-calibration) ## Start from stills, not from a prompt The productive workflow is not "describe a fashion film." It is: 1. Produce and approve the stills — casting, garment, world, grade. 2. Choose the frames that should move. 3. Animate from those frames, with the motion described separately from the content. This matters because the still is where all the expensive decisions already live. Animating from an approved frame means the film inherits the casting, the styling, the light, and the grade instead of re-rolling them. Prompting a film from scratch means re-deciding everything, badly, in a medium where errors are harder to spot. It also means the campaign and the film match — same person, same garment, same world — which is the thing that is genuinely expensive to guarantee conventionally. ## Structure it as shots, not as a film Fashion films are already cut fast. That convention happens to align with what the technology does well: short shots, assembled. A workable structure for a 30-second piece: | Beat | Shots | Purpose | | --- | --- | --- | | Open | 1–2 | Establish world and mood | | Introduce | 2–3 | The model, the first look | | Detail | 3–4 | Fabric, hardware, movement close-up | | Motion | 2–3 | Walking, turning, gesture | | Looks | 4–6 | The collection, one beat each | | Close | 1–2 | Hero frame, logo | Fifteen to twenty shots of one to three seconds. Every one of them derived from an approved still, driven by the same references. The detail beats are where AI video is strongest and most underused — a slow push on a cuff, fabric catching light, a chain settling. These are cheap to produce, read as expensive, and avoid every hard failure mode. ## What still breaks **Walking.** Gait is the single most reliable tell. Human walking has a specific weight transfer, and generated walks tend toward a glide, a hitch, or feet that do not quite carry the body. If the film needs a walk, keep it short, shoot it from an angle that hides the full stride, or cut around it. **Fabric under sustained motion.** A dress in a breeze looks convincing for a second and increasingly wrong thereafter, as the fabric behaves like something lighter or heavier than it is. Shorter shots are the practical mitigation. **Hands and garment interaction.** Adjusting a collar, putting hands in pockets, holding a bag. Same problem as stills, moving. **Continuity of small details.** Across cuts, a button, a lace, or a hair detail can change. Nobody notices in isolation; the edit exposes it. **Faces at length.** Sustained close-ups drift more than short ones. The general mitigation for all of these is the same: shorter shots, more cuts, motion that serves the garment rather than showing off the technology. ## Continuity across cuts The rule is that continuity is a setup property, not an edit property. Every shot should be driven by the same stored references — the model, the specific garment, the location — rather than re-described per shot. Describing "the same woman in the red coat" in fifteen prompts produces fifteen women in fifteen red coats. The mechanics are the same as for stills, covered in [consistent AI fashion models](https://www.astria.ai/articles/consistent-ai-fashion-models/). The stakes are higher in motion because a cut puts two frames side by side in a viewer's short-term memory, which is precisely the comparison that reveals drift. ## Sound is half of it Easy to forget when the work is visual, and it is where cheap films betray themselves fastest. Fashion films are carried by their sound design: room tone, footsteps, fabric movement, and music that sets the register. A well-cut sequence with generic library music reads as a demo; the same cut with considered sound reads as a campaign. Budget for it. It is comparatively cheap and it is the highest-leverage improvement available to a finished sequence. ## Where the value actually is **Social cutdowns.** Vertical, short, in volume. The format is forgiving and the demand is endless. **Product motion.** A garment turning, fabric moving, a shoe from multiple angles — the PDP video that most brands skip because it needs its own shoot. **Campaign extension.** The stills campaign, in motion, without a second production. **Concept testing.** Show a director or a client what a film could look like before committing budget to shooting it. This is possibly the strongest current use: not the final artefact, but the thing that gets the final artefact approved. For a SKU-specific demonstration, use the [apparel product video production guide](https://www.astria.ai/articles/apparel-product-video-guide/). For acquisition edits and controlled hook tests, use the [fashion video ads guide](https://www.astria.ai/articles/fashion-video-ads-guide/). For the wider production context, see the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/) and the [AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/). If you are choosing the production stack, the [Astria vs Higgsfield comparison](https://www.astria.ai/articles/astria-vs-higgsfield-fashion-ai/) separates a fashion-collection workflow from a broader cinematic toolset. ## Frequently asked questions ### Can AI produce a fashion film? Yes, in short form. Multi-shot sequences of a few seconds per shot, cut together, are achievable now. Long continuous takes with sustained character and garment continuity remain the hard limit. ### How do you keep the same model across video shots? By driving every shot from the same stored references — the model, the garment, the location — rather than describing them per shot. Continuity across cuts is a setup decision, not something fixed in the edit. ### How long can an AI-generated fashion video shot be? Individual shots are typically produced in short increments of a few seconds. Longer films are assembled from many such shots, which is close to how fashion films are conventionally edited anyway. ### What still fails in AI fashion video? Fabric physics under sustained motion, walking gait, hands interacting with garments, and continuity of small details across cuts. Fast movement and complex drape are where artefacts concentrate. ### Should video come from the same production as the stills? Ideally yes. When motion is derived from approved stills using the same references, the film and the campaign imagery feature the same person, garment, and world — which is otherwise expensive to guarantee. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # AI Fashion Models: A Casting Guide for Brands Source: https://www.astria.ai/articles/ai-fashion-models/ An AI fashion model can be an invented face, a digital version of a working model, or the person a shopper sees wearing an item in a try-on preview. Those images may look similar. The casting decision behind them is quite different. **The short version:** choose the relationship you need with the person in the image before choosing the face. A fictional cast can suit a repeatable brand lookbook. A licensed digital twin can extend a real talent relationship. Physical talent remains the stronger choice when the job depends on performance, personal testimony, or evidence of fit. **Reviewed September 5, 2026.** This guide is published by Astria. Product examples below come from primary sources; the casting and approval framework is our editorial recommendation, not a comparative product test. ## What is an AI fashion model? In fashion production, an **AI fashion model** is a digitally generated or AI-transformed representation of a person used to present clothing or accessories. Here, “model” means the cast member customers see. It does not mean the underlying image-generation software. The face can be invented or based on a real person. The clothing can come from actual product references or be imagined along with the scene. Only the former gives a brand a product reference against which to review the result; even then, accuracy needs checking. Creating the person and dressing the person are separate operations. For example, FASHN's documentation distinguishes model creation, product-to-model generation, model swapping, and virtual try-on. A tool that makes an excellent portrait has not thereby demonstrated that it can reproduce your jacket. [FASHN documentation](https://docs.fashn.ai/). A virtual influencer adds a different job: an ongoing public persona with a voice, audience, and content programme. A catalog cast member does not need a biography or social account. Decide whether you need someone to _show the product_ or a character through whom the brand will speak. ## Choose the casting route These are casting choices, not software tiers. A single platform may support more than one. | Route | Choose it when | What to establish before approval | | --- | --- | --- | | **Fictional model from a shared library** | You need a suitable face for a bounded product or campaign job | Whether other brands can use the same likeness, permitted channels, and reuse availability | | **Custom fictional brand cast** | You want a recurring visual identity without representing a named real person | How the reference is retained, whether exclusivity is actually offered, and what happens if you leave the service | | **Licensed digital twin of real talent** | The identity of a particular model is part of the campaign's value | The person's agreement to synthetic use, compensation, approval rights, and the scope of each usage | | **Physical model photography** | Actual wear, movement, expression, or a real person's experience is central | The shot list, product sizes, talent agreement, and evidence the shoot must capture | Shared casting is not automatically a weakness. A short-lived seasonal image may not need an exclusive face. For a recurring brand character, seeing the same likeness in another store could undermine the reason you chose it. Ask that question before paying for a custom look: _customised appearance_ and _exclusive availability_ are different promises. A digital twin is also not necessarily a measured, rigged 3D body. In campaign imagery the term can refer to a reusable visual likeness. If you need body measurements, garment simulation, or animation controls, ask for those deliverables explicitly. There is a documented real-brand example of the likeness route: H&M announced images featuring digital twins on July 2, 2025, describing a collaborative production and including comments from a photographer and model. That establishes use in a campaign; the announcement does not establish conversion gains, cost savings, or the terms of other talent agreements. [H&M's announcement](https://hmgroup.com/news/hm-continues-its-exploration-of-creativity-with-ai/). ## Match the model to the job For a **product page**, casting should help the customer read the garment. Start with body proportions, useful poses, and unobstructed views. A beautiful face is a poor reason to approve an image whose arm hides the waistband. For a **lookbook**, evaluate the cast as an ensemble. The relevant question is whether these people belong in the same collection story, not whether each would win a portrait contest. Use the [lookbook planning guide](https://www.astria.ai/articles/how-to-create-a-lookbook/) to define the sequence and the role of each look. For a **campaign**, decide whether the creative idea needs a real person's identity or performance. Fictional casting can support an invented scene. If the work depends on a known athlete's presence, a model's distinctive performance, or someone's account of wearing the product, that relationship is part of the brief. A generated likeness alone does not supply it. For **shopper-facing try-on**, the person is there to help an individual visualise a choice. That is a different purchasing decision from casting a brand catalog. The [virtual try-on guide](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) separates those workflows. ## Cast for the garment and customer Write a casting brief before browsing faces. Keep it concrete: the customer you serve, the garments the cast must carry, the desired age range and body proportions, styling, useful poses, and the channels where the images will appear. For example, a brand selling relaxed tailoring to mature customers might brief an adult cast with a range of builds, restrained grooming, standing and seated views, and hands that leave the jacket closure visible. That is an illustrative brief, not a claim about any platform's library. Review body variety in full-length images. Changing skin tone while keeping every body proportion identical does not answer a brief for varied casting. Nor does selecting a fuller-bodied model demonstrate that a generated garment is a correctly graded larger size. Representation and product sizing need separate review. Approve the cast wearing representative garments. Include something structured, something loose, and a piece whose details matter to the buying decision. A headshot establishes almost nothing about whether the model works for your collection. Once the casting choice is made, use the separate guide to [keeping the same AI model across a collection](https://www.astria.ai/articles/consistent-ai-fashion-models/) for reference handling and drift control. That is the production step after deciding whom to cast. ## A convincing model does not prove fit A generated image can contain a plausible face, natural hands, and attractive drape while still showing the wrong product. Check sleeve length, lapel shape, rise, hem position, fastenings, print scale, and material behaviour against approved references. If the input never shows the back, do not treat a generated back view as evidence of its construction. This distinction also applies to try-on. Google's own help page says its generated preview does not determine or guarantee actual garment fit and can contain errors in body shape and clothing details. That is a limitation Google documents for its product, not a benchmark of every system. [Google Shopping's try-on guidance](https://support.google.com/googleshopping/answer/16253678?hl=en-GB). Our recommendation is to retain measured garment information and physical fit photography wherever the page makes a fit claim. Do not attach a “model wears size M” statement to an invented body as though a physical fitting took place. Use verified sizing information separately from the generated illustration. If your immediate problem is supplying clothing references, start with [flat lay to on-model photography](https://www.astria.ai/articles/flat-lay-to-on-model/). If you need to decide what should still be photographed, use the [AI and studio workflow guide](https://www.astria.ai/articles/ai-product-photography-vs-studio/). ## Set the usage terms before making a recurring cast Treat the following as procurement questions to resolve in writing, rather than rights that come automatically with a generated file: - **Source permission:** can you use the reference photographs for this workflow, including the intended synthetic use of any real person's likeness? - **Publication scope:** which channels, territories, campaign types, and duration does the agreement cover, including paid media and video? - **Control:** who approves new scenes or changes to the likeness, and how is real talent compensated? - **Continuity:** can you retain and reuse the reference after a subscription or campaign ends, and can the provider offer it to another brand? For a digital twin, an ordinary photography booking should not be treated as proof that all those questions are settled. Have the relevant agreement reviewed for the intended use. For a fictional model, establish the service's commercial-use and exclusivity terms instead of assuming the absence of a named person settles everything. Disclosure also belongs in the publishing plan. Separate the customer-facing description from the file's provenance metadata. For images supplied to Google Merchant Center, Google's current guidance requires AI-source metadata and says not to strip embedded source tags. This is a specific channel requirement, not a universal rule for every image on the web. [Google Merchant Center's AI-generated content policy](https://support.google.com/merchants/answer/14743464?hl=en). As an editorial practice, describe generated modeling clearly where a customer could otherwise mistake it for physical wear evidence. Confirm any additional requirements for the actual channel and market before launch. ## Approve a casting proof, not just a face Before committing a whole collection, make a small proof that answers the casting brief. The example below is an evaluation exercise, not a measured benchmark or a required Astria input count. 1. **Choose three candidate likenesses and two actual garments.** Use the same direction so styling does not decide the winner accidentally. 2. **Request a useful view and a demanding view for each pairing.** A clear standing image plus a seated or side view exposes more than twelve similar portraits. 3. **Review product truth first, then casting.** Reject changed construction or misleading proportions before discussing expression and brand fit. 4. **Record the reason for each rejection.** Separate unsuitable casting, product distortion, pose problems, and missing usage permission. They need different fixes. 5. **Approve the cast with its permitted uses.** Keep the selected references, product evidence, usage scope, and acceptance examples together for the next person producing images. This exercise produces twelve images to assess. Count usable images and review time, including corrections. The cheapest face to generate may be expensive to put into production if every pose needs repair. For a fuller cost model, use [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/). If none of the candidates passes the garment check, pause the rollout. Improve the inputs, narrow the requested views, or use physical photography for that part of the job. Choosing a fourth face will not necessarily fix a clothing reconstruction problem. ## Where Astria fits Astria's public fashion workflow starts with a creative template and product photos, then generates imagery for uses including lookbooks, social, and campaigns. It is a relevant route when you have product references and need to turn an approved creative direction into a collection of assets. [Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). Our recommendation is to bring the casting brief and a small product set into that workflow first. Approve the likeness and the garment results together before extending the direction. A template offers a starting point; it does not replace your casting judgment, product checks, or talent permissions. When you are ready to choose software, the [AI fashion photography platform shortlist](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/) compares the buying categories. Use this page to decide what kind of cast you need; use that one to decide which production system should support it. ## Frequently asked questions ### What are AI fashion models? AI fashion models are digitally generated or AI-transformed representations of people used to present clothing and accessories. The likeness may be fictional or based on a real person. Choosing a likeness and accurately showing a garment are separate production decisions. ### What is the difference between an AI fashion model and a digital twin? A fictional AI fashion model is an invented cast member. A digital twin represents a particular real person. For brand work, a twin calls for an explicit agreement covering synthetic likeness use, approvals, compensation, and where and for how long the output can appear. ### Can AI models show how clothes will fit? A generated image can suggest how clothing might look, but it does not by itself establish physical fit or size. Use measured garment information, size guidance, and physical fit photography when making fit claims. A convincing body and drape are not measurements. ### Can a brand use AI fashion models commercially? Commercial use depends on the generation service's terms, the rights to the source images and any real person's likeness, and the requirements of the publishing channel. Confirm those separately. A downloadable image or a commercial software plan does not establish exclusive rights to a face. ### Can you put your own clothes on an AI model? Yes. Product-to-model and virtual try-on workflows use garment images to generate on-model visuals. Supply clear product references and check the result against the actual item, especially the cut, print, closures, transparency, and details hidden in the input. ## Sources and methodology Reviewed against first-party material on September 5, 2026: FASHN's documentation for the distinction between creation and garment-transfer operations; H&M's dated announcement for a digital-twin campaign example; Google's try-on help for its stated fit limitations; Google Merchant Center for its image-provenance requirement; and Astria's ecommerce page for its public workflow. Each source is linked at the relevant claim above. No cross-vendor image test, performance study, or rights audit was conducted for this article. The casting routes, example brief, and twelve-image proof are editorial decision aids. Product scope, service terms, and publishing requirements can change; recheck those against the specific project. The cover is an AI-generated editorial illustration of fictional casting cards, not a customer result or a sample model library. [Explore Astria's fashion templates](https://www.astria.ai/gallery/workspaces). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # AI Footwear Photography: Packshots, On-Foot, and the Shoes That Fight Back Source: https://www.astria.ai/articles/ai-footwear-photography/ Footwear has the most rigid conventions in product photography. Customers expect a specific angle set, in a specific order, and they read those images faster than they read any description. Which makes it both an easy category to serve and an unforgiving one to get wrong. **The short version:** on-foot and lifestyle imagery is where AI production earns the most here, because it is the expensive shot conventionally and the one that most affects whether someone can picture wearing the shoe. ## The standard angle set Deviating from this costs conversions, because shoppers scan for the views they expect: | Angle | What it communicates | | --- | --- | | Three-quarter | Silhouette and toe shape — almost always the hero | | Lateral profile | Proportion, heel height, upper design | | Top-down | Lacing, tongue, opening | | Rear | Heel counter, branding, back detail | | Sole | Tread, construction, often branded | | Detail | Material texture, stitching, hardware | | On-foot | Fit, scale, styling context | The first six are documentation. The seventh is the one that sells, and it is the one most brands under-produce because it requires a foot model, a location, and an afternoon. ## Where AI production is strongest **On-foot context.** The expensive shot becomes cheap. A customer can see the shoe on a leg, with a trouser break or a sock, at a plausible angle — which answers proportion questions no packshot can. **Lifestyle and environment.** Trail shoes on a trail, dress shoes on a floor that suggests somewhere worth going. Conventionally this means a location shoot; here it is a variation. **Colorway coverage.** Ten colorways of one silhouette, all with the same angle set and the same treatment, without ten times the production. **Seasonal and campaign work.** Same product, new context, without rebooking. **The long tail.** Older or slower styles that currently share one tired photo. ## Where it fights back **Lacing.** Complicated, load-bearing, and rendered wrong more often than anything else. Check the pattern, the crossing order, how the lace enters each eyelet, and whether the aglets exist. Get this wrong on a sneaker and enthusiasts will notice immediately. **Stitching.** Count and spacing carry construction quality. Decorative stitch lines that wander are an obvious tell. **Sole tread.** Geometric, repeating, and mathematically specific. Approximations look like a different shoe. On technical footwear the tread pattern is frequently a signature. **Branding.** Logo scale, placement, and orientation — on the tongue, the heel, the sole, the side wall. A logo that has drifted a few millimetres reads as counterfeit. **Ground contact.** Where the sole meets the surface, and the shadow it casts. Floating shoes are the most common giveaway in generated footwear imagery. **Ankles and feet.** In on-foot shots, the anatomy above the shoe needs the same scrutiny as hands do elsewhere. **Technical performance footwear.** The hardest category. Panel construction, support structures, and material transitions _are_ the product, and a customer buying running shoes cares about exactly those. ## Material behaviour is not one thing A silhouette in suede, smooth leather, patent, mesh, and canvas is five different lighting problems. Suede scatters light and has a nap direction; patent is nearly specular and mirrors its environment; mesh is semi-transparent with visible structure. This matters most for colorway extension. Producing one colorway's angle set and extending it across the range works cleanly when the material is constant — and produces confidently wrong images when colorway also means material change. Check every material variant individually; the tooling will happily render suede that behaves like leather. ## A practical split Most footwear brands land somewhere like this: - **Photograph:** the three-quarter hero, detail, and sole shots for each new silhouette. Material and construction are the claim. - **Generate:** on-foot, lifestyle, campaign, seasonal variation, and the long tail of colorways — with per-material review. - **Either:** alternate angles, depending on how much construction detail the silhouette carries. This keeps the material claim photographic and moves the volume — which is mostly context and variants — to production that scales. The reasoning behind that split is the per-variant arithmetic in [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/). ## Review checklist - Lacing pattern, eyelet count, lace entry, aglets - Stitch lines: count, spacing, straightness - Sole tread geometry and any branded elements - Logo scale, placement, orientation on every surface it appears - Ground contact and shadow — is it standing on the surface? - Material behaviour appropriate to the actual material - Left and right consistency — mirrored asymmetric detail is a common error - Heel height and toe shape against the real product - In on-foot shots: ankle anatomy, sock or trouser interaction, plausible weight distribution The left/right check deserves its own mention. Generated pairs frequently mirror detail that should not be mirrored — branding on the outer face of both shoes, or a lace pattern that reverses. For the general ecommerce process, see the [AI product photography guide](https://www.astria.ai/articles/ai-product-photography/); for collection production, continue to the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). For jewelry, which fails differently, see [AI jewelry photography](https://www.astria.ai/articles/ai-jewelry-photography/). ## Frequently asked questions ### Can AI generate on-foot shoe photography? Yes, and it is one of the strongest use cases, because on-foot context is expensive to shoot conventionally and communicates fit and proportion that a packshot cannot. Ankle and foot anatomy needs review, as does how the shoe meets the ground. ### What angles does a footwear listing need? Conventionally a three-quarter hero, a lateral profile, a top-down, a rear view, a sole shot, and at least one on-foot or lifestyle frame. The three-quarter is almost always the primary image because it shows silhouette and toe shape together. ### Which footwear details does AI get wrong most often? Lacing patterns, stitch lines and their count, sole tread geometry, branding placement and scale, and the contact point where the shoe meets the ground. Technical performance footwear is the hardest category because its construction is the product. ### Do you still need to photograph the actual shoe? For the primary listing images, yes in most cases — material, texture, and construction detail are what customers judge. Generated imagery is strongest for on-foot context, lifestyle, colorway coverage, and campaign work. ### How do you handle colorway variants efficiently? Produce the full angle set for one colorway, then extend to the others against the same treatment — but verify material behaviour on each, since a suede and a leather version of the same silhouette do not reflect light the same way. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # AI Headshots and Portraits Without Training a Model Source: https://www.astria.ai/articles/ai-headshots-without-model-training/ _Originally published April 2024. Reviewed August 2026 — see [Where this stands in 2026](https://www.astria.ai/articles/ai-headshots-without-model-training/#where-this-stands-in-2026) below._ You can now generate instant custom headshot photos for professional use in just a few clicks. Several industries could benefit from it. Here are some key ones: **1\. Online Platforms & Gig Economy:** - Freelancers and independent contractors on platforms like Upwork or Fiverr need professional headshots for their profiles to appear credible and attract clients. - People signing up for ride-sharing services like Uber or Lyft often require profile pictures that meet platform guidelines. **2\. Remote Work & Video Conferencing:** - With the rise of remote work, employees need professional headshots for video conferencing platforms like Zoom or Google Meet. - Many companies request profile pictures for internal directories. **3\. Events & Conferences:** - Attendees at conferences or trade shows might need quick headshots for badges or presentations. - Event organizers may require speaker headshots for promotional materials. **4\. Retail & Hospitality:** - Retailers or restaurants can use headshot generators for employee name tags or online staff directories. **5\. Education & Training:** - Online courses or educational platforms can benefit from student profile pictures. - Professional development programs often require headshots for certificates or online profiles. **6\. Media & Marketing:** - Content creators or bloggers frequently need quick headshots for social media profiles or website bios. - Marketing agencies can use headshot generators for clients who need profile pictures on short notice. So how do we at Astria.ai come in? Astria's composer can preserve a person's likeness from a reference photo without training a custom model. You can start with one clear photograph and generate a new portrait directly from the composer. ![alt_text](https://www.astria.ai/articles/assets/images/image4-50c404a130edf50dbda1782c6fa78127.webp "image_tooltip") This feature comes in very handy if you need to generate images quickly and efficiently – such as if you’re offering a free-tier service in a user app and need profile images to be generated in a jiffy. It can also be applied in real-time applications like live-streaming or virtual try-ons. In e-commerce applications, reference-based generation can let users visualize products with their own images. It can also support personalized avatars, profile photos, social content, and other experiences where the result should resemble the person in the supplied photo. Use a sharp, well-lit reference with one unobstructed face. A front-facing or three-quarter portrait is easier to preserve than a distant group photo, heavy filter, or image where hair, glasses, or hands cover important facial details. Astria offers full fine-tuning tools using the [Dreambooth](https://huggingface.co/docs/diffusers/en/training/dreambooth) API. This is a technique that updates the entire Stable Diffusion model by training on just a few images of a subject or style. This is a pretty efficient way of fine-tuning as it allows for the generation of realistic and diverse images of the specific subjects or concepts. Apart from this, Astria also has the option of LoRA fine-tuning. In this technique, instead of fine-tuning the entire model, a low-rank adapter layer is inserted into the model architecture. This reduces the computational time and storage requirements leading to a lower cost of fine-tuning. Both techniques above are well suited to high-fidelity identity preservation, but they require a training step. Reference-based generation is the faster option when you want to begin from an attached photo. Reference-based generation does not update the model's weights. The attached image conditions the generation at inference time, which is why there is no separate model-training wait. # How to Create No-Training AI Portraits in the Composer You can begin with one photo. For the examples below, we used three photos of a model from [Unsplash](https://unsplash.com/) so the face is visible from more than one angle. ![Three portrait references of the same model from different angles](https://www.astria.ai/articles/assets/images/image2-d208ef6f99cabce6a3cc4d2a4f23baea.webp) 1. Open the [Astria composer](https://www.astria.ai/prompts). 2. Drag a clear face photo into the composer to attach it as a reference. Add another angle only when it helps clarify the likeness. 3. Describe the portrait you want: framing, wardrobe, expression, lighting, background, and photographic style. 4. Generate, review the likeness, and adjust the reference or prompt if an important facial feature drifts. The reference remains attached in the composer, so the prompt only needs to describe the new portrait. Here are four starting points. ## Use-Case 1: Professional Networking ```text Prompt: A professional headshot of a female software engineer, wearing a blue blazer, with a friendly smile and confident gaze, studio lighting, high-resolution, 8k, sharp focus, Nikon D850, 85mm lens, f/1.8, 1/200s, ISO 100 Negative Prompt: unprofessional, casual, blurry, low-resolution, poor lighting, unflattering angles, awkward pose, unfriendly expression, distracting background, snapshot, amateur, overexposed, underexposed, harsh shadows, uneven skin tone ``` ![alt_text](https://www.astria.ai/articles/assets/images/image1-dcd017ac0b7d454bed5a596207dfcf47.webp "image_tooltip") ## Use-Case 2: Fitness & Wellness Coach ```text Prompt: A vibrant and inspiring headshot of a fitness coach, wearing a bright green athletic top, with an energetic smile and motivated expression, outdoor natural lighting, high-resolution, 8k, sharp focus, Nikon Z7 II, 85mm lens, f/2.8, 1/200s, ISO 200, vivid color palette, blurred park background, sun flare Negative: unhealthy, unmotivated, low-energy, poorly lit, low-quality, blurry, awkward pose, unflattering angles, harsh shadows, distracting background, snapshot, amateur, overexposed, underexposed, uneven skin tone, no retouching, no visible workout equipment ``` ![alt_text](https://www.astria.ai/articles/assets/images/image6-acc5f1dd16d893224422dc1a95874a0d.webp "image_tooltip") ## Use-Case 3: Social Media and Marketing Influencer ```text Prompt: A vibrant and engaging headshot of a female fashion influencer, wearing a stylish red dress, with a charming smile and confident pose, golden hour lighting, high-resolution, 8k, sharp focus, Canon EOS R5, 50mm lens, f/1.4, 1/160s, ISO 100, cinematic color grading, bokeh background Negative: unfashionable, poorly lit, low-quality, blurry, awkward pose, unflattering angles, dull colors, flat lighting, distracting background, snapshot, amateur, overexposed, underexposed, harsh shadows, uneven skin tone, no makeup, no retouching ``` ![alt_text](https://www.astria.ai/articles/assets/images/image5-00479591d639bc12c0a5093ed8e2b714.webp "image_tooltip") ## Use-Case 4: Educational Platform & Online Learning ```text Prompt: A friendly and approachable headshot of a female history professor, wearing a navy blue sweater, with a warm smile and inviting gaze, soft natural lighting, high-resolution, 8k, sharp focus, Sony A7R IV, 85mm lens, f/2.8, 1/125s, ISO 200, neutral color palette, clean background Negative: intimidating, unapproachable, unprofessional, poorly lit, low-quality, blurry, awkward pose, unflattering angles, harsh shadows, distracting background, snapshot, amateur, overexposed, underexposed, uneven skin tone, no retouching ``` ![alt_text](https://www.astria.ai/articles/assets/images/image8-313e6745fe67e60b3c24e439ff46eb43.webp "image_tooltip") # When a No-Training Portrait Workflow Fits The reference-first composer works well when you need a portrait quickly and do not want to create and manage a trained model. Common uses include: 1. Professional Networking 2. Social Media and Influencer Marketing 3. Educational Platforms 4. Fitness and Wellness Apps 5. Event Apps 6. E-commerce Apps 7. Free-Tier Services For an app integration rather than the visual composer, use Astria's current [API documentation](https://docs.astria.ai/docs/category/api) as the source of truth for reference-image inputs. ## Where this stands in 2026 The core promise — a consistent likeness without waiting for a model to train — still holds, and the instant-reference approach has since been extended across newer model branches. Two things changed worth knowing: - **Instant references are no longer only about faces.** The same idea now covers products, poses, backgrounds, and garments, which is what makes a whole photoshoot template reusable rather than just a person. - **A trained model still wins on hard likenesses.** For a face that has to hold up across dozens of images at close crop, training on eight to sixteen images remains more reliable than a single reference. Dragging a reference into the composer is the fast path, not the strictly better one. If you are producing portraits for a whole team rather than one person, the constraint is consistency across people — see [AI headshots for teams](https://www.astria.ai/articles/ai-headshots-for-teams/). For the fashion production equivalent, see the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # AI Jewelry Photography: Where Generic Product Tools Fall Apart Source: https://www.astria.ai/articles/ai-jewelry-photography/ Jewelry is where general-purpose AI product photography tools stop being convincing. Apparel is forgiving — a knit rendered slightly wrong still looks like a knit. A ring rendered slightly wrong looks like a different ring, and your customer is paying four figures for the specific one. **The short version:** use it for context, scene, and campaign work. Keep a macro photograph of the actual piece as your primary listing image. ## The original ring and its AI product image Compare the band, setting, and stones in the generated close-up with the original ring photo. ![Original ring reference showing its setting and band](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-reference.webp) Original ring photo ![AI-generated close-up of the referenced ring](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-result.webp) AI-generated ring close-up [View this generation](https://www.astria.ai/prompts/33498743?ws=297)[Try the ring template](https://www.astria.ai/p/jewelry-ring) ## Three reasons jewelry is genuinely hard **Metal has no colour of its own.** A polished gold band is almost entirely a picture of its surroundings, curved. Change the environment and the metal changes. This is why jewelry photographers build light tents and control every reflective surface in the room — they are not lighting the object, they are constructing what it reflects. Generated imagery that gets the reflections merely plausible produces metal that reads as _rendered_ even to viewers who cannot say why. **Stones are optical instruments.** A brilliant-cut diamond has a defined facet geometry that produces a specific pattern of return light. Approximate it and you get sparkle that looks like glitter rather than like a stone. Coloured stones add depth and saturation behaviour that varies with cut and inclusion. **Scale is invisible.** A ring photographed alone could be any size. Customers read scale from context — a hand, an ear, a neck. Without it, generated pieces routinely render at the wrong size, and a 2mm band that looks like a 6mm one is a return. ## What works well **On-model context.** A necklace on a neck, a ring on a hand, earrings in place. This is where generated imagery earns its place: it solves scale, it shows how the piece sits, and it is exactly the shot that is expensive to produce conventionally because it needs a hand model booked for an afternoon. **Scene and seasonal variation.** The same piece across campaign contexts — summer light, winter interior, editorial still life. Cheap to vary, and it is the work that most often gets skipped for budget reasons. **Campaign and social.** Where mood carries more than specification. **The long tail.** Variants — metal options, stone sizes, chain lengths — that would never justify individual production but currently ship with one shared photo. ## What needs care **The primary listing image.** For fine jewelry especially, this should be the actual piece. A customer spending significantly is examining the setting, the prong count, the finish, the inclusion pattern. Generated approximations of a specific item invite both returns and a legitimate complaint. **Material claims.** Metal colour and finish, stone cut and colour grade, carat presentation. If the image asserts a material fact, it should be true of the item shipped. **Hands.** Jewelry is worn on the most difficult thing to render. Rings especially: check finger count, joint anatomy, how the band sits against the skin, and whether the hand is doing something a hand does. ## A workable division of labour The approach that works for most jewelry brands is a split rather than a wholesale switch: | Shot | Source | | --- | --- | | Primary listing macro | Photograph the actual piece | | Alternate angles | Photograph, or generate from the macro with review | | On-model / scale | Generate | | Lifestyle and scene | Generate | | Campaign and social | Generate | | Variant coverage | Generate, with per-variant material check | This keeps the material claim anchored in photography and moves the expensive contextual work — which is most of the volume — to production that scales. ## A five-shot ring prompt ladder A ring set should move from proof to context without asking one image to do both jobs. Keep the verified macro of the actual piece as the factual anchor. Then use a stable preservation clause in each generated prompt: preserve the ring's stone count, cut, setting geometry, prong count, band profile, metal colour, finish, and realistic scale. The public [Jewelry Product Photography workspace](https://www.astria.ai/w/jewelry-product-photography) and [Ring template](https://www.astria.ai/p/jewelry-ring) provide a working starting point. Its finished examples cover [macro still life](https://www.astria.ai/prompts/33498743?ws=297), [lifestyle](https://www.astria.ai/prompts/33498723?ws=297), and [on-model context](https://www.astria.ai/prompts/33498478?ws=297). Direct prompt pages may require sign-in; pair them with the public workspace and template when sharing the workflow. **1\. Verified macro — the anchor** > Straight-on macro product photograph of the actual reference ring, centered and fully visible. Show the stone, setting, prongs, band width, metal finish and hallmark-facing profile with accurate colour and geometry. Neutral light-grey sweep, controlled reflective environment, crisp edge detail, even focus across the piece, square composition, no props. This is the image to photograph or verify most strictly. It carries the product claim. **2\. Editorial macro — add material context** > Macro product photograph of the reference ring resting in soft folds of light textured fabric. Use the fabric curves to frame the ring without obscuring its setting, band profile or stone. Preserve stone count, setting geometry, prongs, metal colour and finish. Warm diffused natural light, clean neutral background, crisp detail, shallow depth of field, restrained luxury mood, square composition. **3\. On-hand scale — answer size** > Close on-hand photograph of the reference ring worn naturally on the ring finger, with the hand resting lightly on pale stone. Preserve the ring's geometry, prong count, band width, metal finish and realistic scale against the finger. Soft side light, natural skin texture, anatomically correct hand, clean nails, shallow depth of field, vertical 4:5. **4\. Gesture — show wear without hiding the piece** > Wrist-to-fingertip lifestyle photograph of the same hand wearing the reference ring while gently holding the stem of a plain ceramic cup. Keep the ring unobscured and preserve its setting, band profile, finish and scale. Quiet morning interior, warm window light, neutral wardrobe, physically natural grip, vertical 4:5. **5\. Campaign close — create desire** > Tight luxury campaign photograph of the reference ring on the same hand near a softly shadowed cream silk sleeve. Preserve the stone, setting, prongs, band, metal colour and realistic scale. Directional late-afternoon light creates one controlled highlight across the metal; deep but legible shadow, refined grain, generous negative space, vertical 4:5. The ladder goes from specification to atmosphere. Review every generated rung against the anchor before it enters the product page or campaign. For copyable ring, necklace and earring recipes with a visual example, continue with [Jewelry Photography Prompts](https://www.astria.ai/articles/jewelry-photography-prompts/). For reusable product-page structures across categories, use [E-commerce Product Photography Prompts](https://www.astria.ai/articles/ecommerce-product-photography-prompts/). ## Reviewing jewelry output Slower and more specific than apparel review. Per image: - **Metal finish** — does polished read polished, brushed read brushed? Is the reflection environment coherent with the scene? - **Stone behaviour** — is the facet pattern consistent with the cut? Does the sparkle look optical or applied? - **Setting detail** — prong count and position, bezel continuity, pavé spacing and regularity. - **Scale against anatomy** — band width relative to the finger, pendant size relative to the collarbone. - **Chain and clasp** — link direction consistency, clasp type, how the chain falls under its own weight. - **Hands** — anatomy, and how the skin meets the metal. - **Colour accuracy** — yellow versus rose versus white gold are close enough that drift is easy and consequential. Review at full resolution. Jewelry errors hide at thumbnail size and are glaring on the product page, which is exactly the wrong way round. ## Casting and styling notes Jewelry casting is skin and hands more than faces. Metal reads differently against different skin tones — a genuine reason to produce variant imagery rather than a diversity gesture — and hands vary enormously in how well they present a ring. Because casting can be held fixed across a collection, a jewelry brand can maintain the same hands and the same neck through an entire season, which is a consistency most brands never achieve conventionally because hand models are booked by the day. The mechanics are in [consistent AI fashion models](https://www.astria.ai/articles/consistent-ai-fashion-models/). For the general production process, see the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/); for footwear, which has a different set of problems, see [AI footwear photography](https://www.astria.ai/articles/ai-footwear-photography/). ## Frequently asked questions ### Can AI produce usable jewelry photography? For lifestyle and on-model context, yes, and it saves considerable time. For the primary listing image where a customer judges the exact stone, setting, and metal finish, generated imagery needs careful review and often should not replace a macro photograph of the actual piece. ### Why is jewelry harder than apparel for AI? Three reasons: metal is defined by what it reflects rather than by its own colour, faceted stones behave optically in ways that are easy to render plausibly and wrongly, and scale is ambiguous — a ring photographed alone gives no size cue. ### How do you keep jewelry at the correct scale in generated images? Show it on a body — a hand, an ear, a neck — because human anatomy is the scale reference customers read instinctively. Generated pieces floating without context routinely render a size larger or smaller than the actual product. ### Is it legal to show a gemstone that differs from the one shipped? Misrepresenting a product's material characteristics risks both consumer-protection issues and returns. For fine jewelry in particular, the listing image should represent the actual piece; use generated imagery for context and lifestyle rather than for material claims. ### What is the best use of AI in jewelry ecommerce? On-model context, campaign and social imagery, seasonal scene variation, and the long tail of variants that could never justify a shoot — with the macro detail shot of the actual piece kept as the primary listing image. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # AI Lookbook Guide: From Product Photos to a Collection Source: https://www.astria.ai/articles/ai-lookbook-guide/ An AI lookbook uses generated fashion images to show a collection through a consistent cast, setting, and styling direction. Start with product references, approve one complete look, then produce the remaining images against that treatment. The generator supplies images; you still decide which ones belong in the collection. **The short version:** a useful AI lookbook workflow preserves both the clothes and the direction. This guide covers inputs, generation, and image review. For the brief, page layout, wholesale information, and delivery checklist, use [how to create a lookbook](https://www.astria.ai/articles/how-to-create-a-lookbook/). ## From a skirt photo to a styled look This look combines the skirt with separate photos of the sweatshirt, socks, and shoes. Compare the skirt’s pleats, length, and color with the original. ![Grey pleated skirt product reference](https://www.astria.ai/articles/img/prompt-recipes/fashion-skirt-reference.webp) Original skirt photo ![Generated model wearing the referenced skirt with a sweatshirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using the skirt and other product photos [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## What an AI lookbook generator should produce Worth stating plainly, because half of all lookbook problems are actually category confusion: | | Catalog | Lookbook | | --- | --- | --- | | **Job** | Let someone buy this item | Make someone want this collection | | **Coverage** | Every SKU, every colorway | A curated selection | | **Framing** | Consistent, neutral, complete | Varied, composed, editorial | | **Styling** | The product, isolated | The product, in a look | | **Success** | No returns from misunderstanding | Someone feels something | Both are necessary. They are produced differently, judged differently, and should not be attempted in the same pass. A brand that tries to make one set of images do both jobs ends up with a catalog that feels sterile and a lookbook that feels incomplete. ## What to upload for an AI fashion lookbook Prepare a folder for each look before opening the generator. Include clear product photos for every garment in the outfit, the intended colourway, and detail references for prints, fastenings, and fabric. Keep the style numbers attached to those references so review never becomes a guessing game. One front photo cannot establish what the back of a garment looks like. Supply the views you expect to show. If a generator invents a plausible back, that is a concept image until someone checks it against the product. The input pass is covered in [flat lay to on-model photography](https://www.astria.ai/articles/flat-lay-to-on-model/). Keep three kinds of reference distinct: **product** for what the clothes are, **casting** for who wears them, and **art direction** for light, pose, and setting. Changing a location reference should not quietly change the jacket. Astria's [fashion workflow](https://www.astria.ai/ecommerce) starts with a template and uploaded product photos. Its [lookbook gallery](https://www.astria.ai/gallery/workspaces) gives examples to inspect. Use a complete set as the reference for consistency, rather than judging a tool by one hero image. ## Decide the four things before generating anything The mistake is starting at the keyboard. A lookbook needs four decisions made first, and made by someone whose taste the brand trusts. **1\. Who is she, or he, or they.** Not "a female model, 25." A specific person with a specific presence — the way you would brief a casting director. The register of the face, the posture, the energy. If you cannot describe your casting in a sentence that excludes most people, you have not cast yet. **2\. What world this collection lives in.** A place, a season, a time of day, a quality of light. "Studio, white background" is a catalog answer. A lookbook needs somewhere that means something: the specific coastal town in October, the apartment at 4pm, the empty municipal pool. **3\. How it is styled.** Which pieces go together, what is layered, what is left undone. This is where a stylist earns their fee and where AI is least able to help you, because styling is knowledge about clothes rather than about images. **4\. The sequence.** A lookbook is read in order. Which look opens, which closes, where the rhythm changes from wide to close, where colour arrives. Sequencing is the part that turns twenty images into a story, and it is almost universally skipped. Only after these four does generation start. Everything before this point is the job; everything after is execution. ## Production, in order 1. **Fix the casting.** Establish the model as a stored reference so the same person appears in every look. Recognisable continuity is what makes a set read as one shoot — see [consistent AI fashion models](https://www.astria.ai/articles/consistent-ai-fashion-models/) for the mechanics. 2. **Build one hero look completely.** Get a single image exactly right — light, colour, framing, styling, mood. This is your reference point and it should take real iteration. 3. **Approve the hero.** With whoever owns the brand. Do not proceed on a maybe. 4. **Derive the treatment from it.** The hero look defines the light, grade, distance, and register that the rest of the set inherits. 5. **Produce the remaining looks** against it, varying pose, location beat, and framing while holding casting and grade fixed. 6. **Sequence and edit.** Lay them out in order. Cut the weakest three. A lookbook of eighteen strong looks beats one of twenty-four with six passengers. 7. **Derive the channel crops.** Vertical for social, wide for the site, square for the grid — from the same set rather than as new productions. ## A six-prompt lookbook sequence Write the sequence as one stable production block plus six controlled variations. The stable block should name the approved cast, reference outfit, styling, location family, light, colour treatment, and product details that must not change. The individual prompts then direct the rhythm of the edit. For this example, the invariant is: the same model, the same reference tailored outfit and shoes, a pale modernist courtyard, soft overcast light, a muted cream-and-mineral grade, realistic fabric behaviour, and vertical 4:5 output. Each frame must preserve the outfit's neckline, lapels, fastening, waist, trouser length, footwear, colour, and material texture. **1\. Opener — establish the world** > Wide environmental fashion photograph of the model wearing the reference tailored outfit at the entrance to a pale modernist courtyard. Show the complete silhouette and shoes at a smaller scale, with generous architecture around her. She stands still and looks beyond frame left. Soft overcast daylight, muted cream-and-mineral colour grade, restrained contrast, vertical 4:5. **2\. Hero — establish the look** > Full-body front three-quarter photograph of the same model and reference outfit in the same courtyard. She stands at a 45-degree angle with her weight on the back foot and looks toward the camera. Keep the complete outfit, fastening and shoes clearly visible. Eye-level camera, soft overcast light, quiet shadow, vertical 4:5. **3\. Movement — change the energy** > Full-body photograph of the same model and reference outfit walking parallel to the courtyard wall, captured mid-step. Preserve the jacket construction and trouser length; allow restrained movement at the jacket hem only. Eye-level camera, consistent soft overcast light and muted grade, natural anatomy, vertical 4:5. **4\. Detail — give the sequence a pause** > Shoulder-to-hip detail of the same model wearing the reference outfit, focused on the lapel, fastening, waist construction and fabric surface. One hand adjusts the cuff without covering the garment. Same pale-stone setting and soft overcast light, sharp product detail, shallow background, vertical 4:5. **5\. Profile — reveal different construction** > Full-body side-profile photograph of the same model wearing the reference outfit, paused beside a courtyard column. Keep the jacket length, side seam, trouser silhouette and shoes legible. Camera at waist height, consistent light and colour treatment, subtle asymmetrical framing, vertical 4:5. **6\. Closer — resolve rather than repeat** > Rear three-quarter photograph of the same model and reference outfit walking toward the courtyard exit, head turned slightly toward the light. Preserve the back construction, hem, trousers and footwear. Add a gentle late-afternoon warmth while staying within the established muted grade, with restrained fabric movement and vertical 4:5 framing. The order moves wide, full, active, close, graphic, and away. That is enough variation to feel edited while retaining one visual world. If a frame fails, revise its one variable rather than rewriting the shared treatment. For the visual, copyable eight-frame version of this sequence, use [AI Lookbook Prompts](https://www.astria.ai/articles/ai-lookbook-prompts/). For broader catalog, movement, detail and editorial structures, continue with [AI Fashion Photography Prompts](https://www.astria.ai/articles/ai-fashion-photography-prompts/). The public [Dark Swimwear workspace](https://www.astria.ai/w/dark-swimwear), [Swimsuit Set 1 template](https://www.astria.ai/p/swimsuit-set-1), [turning frame](https://www.astria.ai/prompts/40584408?ws=368), and [transitional frame](https://www.astria.ai/prompts/40584411?ws=368) show how neighboring poses can belong to one set. Direct prompt pages may ask anonymous visitors to sign in, so the public workspace and template are the reliable starting points. ## Why AI lookbooks look like AI lookbooks They share a recognisable aesthetic, and it is worth naming the tells because they are all avoidable: - **Perfect light everywhere.** Real editorial has shadow, blown highlights, and moments where the light is not flattering. Uniform beautiful lighting reads as synthetic faster than any anatomical error. - **Symmetrical, centred composition.** Real photographers frame off-centre, cut limbs at the edge, let things be occluded. - **Nobody is doing anything.** Standing and looking at the camera, over and over. Movement, weight on one hip, mid-gesture, looking away — this is most of what makes a person in a photograph look alive. - **The mid-Atlantic nowhere.** A vaguely European street, a vaguely Californian beach. Specific places have specific architecture, light, and weather. - **No texture.** No grain, no lens character, no imperfection. Clean is not the same as good. - **Every garment sitting perfectly.** Real clothes wrinkle, ride up, and catch. A specific brief helps with these problems, but it does not eliminate model limitations. Reject images that cannot preserve the garment or the intended pose. ## What to check before publishing Run this pass with fresh eyes, ideally someone who has not been staring at the set: - **Garment fidelity.** Is every print aligned, every logo correct in scale and placement, every closure and hardware right? Reject any detail that misrepresents the product. - **Casting continuity.** Is it the same person in look 3 and look 17? Look at the face at full size, not thumbnail. - **Hands and interaction.** Especially where a hand meets a bag, a pocket, a cuff. - **Grade drift.** Put all the looks on one screen. Warmth and contrast creep is invisible one image at a time. - **Styling logic.** Would a stylist have put these pieces together? Is the layering physically possible? - **Does it say anything?** The hardest question. If you removed the logo, would anyone know whose collection this is? ## Where the savings actually are Estimate the savings against your actual brief. The opportunities are: - **Scheduling.** Generating from suitable references can reduce live-shoot coordination. You still need accurate product inputs and time for review. - **Revision.** A warmer treatment can be tested on an approved frame before revising the set. Check the garment colours again after any grading change. - **Reuse.** Next season starts from approved casting, references, and treatment rather than from nothing. - **Extension.** Campaign frames, social crops, and motion come from the same direction — see [AI fashion film](https://www.astria.ai/articles/ai-fashion-film-guide/). The cost that does not go away is the creative direction. It should not; it is the only part a customer can tell apart from anyone else's. The full production picture, including catalog work, is in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/), and the process-level walkthrough is in [how to create a lookbook](https://www.astria.ai/articles/how-to-create-a-lookbook/). ## Coordinate a lookbook with an agent **Workflow update October 11, 2026.** Use an agent when reference intake, shot tracking, revisions, and file handoff become the coordination bottleneck. Keep the creative treatment and product review criteria in a written brief; rendering completion should enter review rather than count as approval. The [Codex lookbook guide](https://www.astria.ai/articles/create-lookbooks-with-codex/) covers local manifests and Astria tool calls. The [Claude Code guide](https://www.astria.ai/articles/create-lookbooks-with-claude-code/) uses the same collection contract with Claude-specific connection and project instructions. Both end with selected images, an ordered layout, and a delivery manifest. For a shared team, use [the collaborative studio workflow](https://www.astria.ai/articles/collaborative-ai-studio/). ## Frequently asked questions ### What is an AI lookbook generator? An AI lookbook generator creates fashion images from product and creative references. Producing a finished lookbook still requires selecting the images, checking the garments, and arranging the set into a deliberate sequence. ### What references do you need for an AI lookbook? Supply clear references for each garment and colourway, including the back and any details that will appear. Keep product, casting, and art-direction references separate so a scene change does not become a product change. ### Can one AI model appear across an entire lookbook? Yes, and it should. Casting continuity is what makes a set of images read as one shoot. It requires the likeness to be stored as a reusable reference rather than re-described in each generation. ### How long does an AI lookbook take to produce? Allow time for references, a pilot look, revisions, garment checks, and final selection. Generation time alone is not a delivery estimate. Time a small approved batch before committing to the full collection. ### Will an AI lookbook look generic? It will if the direction is generic. The tools default to a glossy, well-lit, mid-Atlantic aesthetic, and accepting that default is what makes AI lookbooks recognisable. Specific casting, specific location logic, and deliberate imperfection are what break it. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # AI Photoshoots for Fashion Brands: The Complete Guide (2026) Source: https://www.astria.ai/articles/ai-fashion-photoshoot-guide/ Nearly everything written about AI fashion photography is either a tool listicle or a demo reel. Neither tells you what actually happens when a brand tries to produce a season this way — where the time goes, which garments fight back, and what breaks at the two-hundredth image rather than the second. **The short version:** the hard part is not generating a good image. It is generating six hundred images that look like they came from the same brand, and being able to change your mind afterwards. ## From a product photo to an image on a model The sweatshirt photo supplies the garment details. Astria combines it with the other clothing references to create the styled look shown here. ![Brown graphic sweatshirt product reference on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Original sweatshirt photo ![Generated model wearing the referenced sweatshirt with a skirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using this sweatshirt and other product photos [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## What an AI fashion photoshoot actually is Strip away the marketing and it is a substitution. A traditional shoot expresses creative direction through logistics: you book a model who embodies the casting, a location that carries the mood, a stylist who assembles the look, and a photographer who frames it. The logistics are the _medium_ through which the direction reaches the image. An AI photoshoot expresses the same direction as a stored specification, and applies it to products. The casting, the scene, the lighting, the crop, the grade — all of it is written down and reused. The garment is the variable. This has one consequence that matters more than any other: **direction becomes reusable.** A shoot day produces images. A stored treatment produces images _and_ the ability to produce more of them, next month, in the same voice, without reconvening anyone. ## The seven stages of an actual production What follows is the shape of a real collection run, in order. Most tool demos cover stage four and skip the rest, which is why they feel misleading once you try it. **1\. Get the products in.** The SKU set arrives from the store or a shared drive — product shots, flat lays, packshots. This is unglamorous and it is where the first day usually goes. A collection with inconsistent source photography produces inconsistent output, and no amount of cleverness downstream fixes a source problem. **2\. Cast.** Decide who wears the collection. This is a brand decision, not a technical one: ages, sizes, bodies, the register of the faces. Getting it wrong here poisons everything downstream, and it is the decision most teams make too quickly because it feels like picking from a menu. **3\. Build the treatment.** Scene, lighting, crop, grade, format, styling conventions. This is the creative work, and it should take real time — a day or two of iteration is normal and healthy. You are not producing images yet. You are deciding what all your images will look like. **4\. Approve it.** Someone senior looks at five representative products rendered against the treatment and says yes, or says what is wrong. Do this before running the collection, not after. **5\. Run the collection.** Now it is throughput. Every product goes through the approved treatment. This stage is fast and boring, which is exactly what you want. **6\. Extend to channels.** The same direction becomes campaign frames, social crops, and motion. Because the treatment is stored, these are variations rather than new productions. **7\. Keep it.** Next season starts from the approved casting, references, and templates rather than from nothing. This is the compounding part, and it is invisible in any single-shoot comparison. Stages 3 and 4 are where quality is decided. Stage 5 is where cost is decided. Confusing the two is the most common planning error. ## What is genuinely hard Vendor demos are shot on garments that behave. Yours will not all behave. **Reliably good:** solid-colour knits, tees, simple dresses, tailored pieces with clean lines, structured outerwear. Standard studio and lifestyle scenes. **Check every time:** directional prints and stripes, which misalign across seams; logo scale and placement; sheer and semi-sheer fabric; complex drape and gathering; fine hardware — buckles, eyelets, chain straps, zip pulls. **Still hard:** garments whose _construction_ is the product. Technical outerwear, structured tailoring, performance wear — anything a customer buys on how it is built rather than how it looks. **Hard for a different reason:** hands, and the interaction between a hand and a product. If your category involves holding things — bags, accessories — budget review time for it specifically. The practical rule for any evaluation: build your test set from your ten hardest garments. A tool that handles those handles your catalog. A tool evaluated on your ten easiest tells you nothing. ## The consistency problem This is the whole game, and it is worth being precise about what "consistent" means, because it is four separate things: - **Casting consistency** — the same person, recognisably, across the collection - **Treatment consistency** — same light, same scene logic, same grade - **Framing consistency** — same crops, same proportions, same formats - **Product fidelity** — the garment is _your_ garment, in every frame Tools tend to be good at one or two of these. A catalog needs all four simultaneously, held across hundreds of images and several months, through staff changes and creative revisions. The failure is rarely dramatic. It is drift: image 340 is a little warmer than image 12, the crop has crept up, the model's face has subtly changed. Nobody notices any single image. Everybody notices the grid. Drift is a systems problem, not an image-quality problem. It is solved by storing the decision rather than repeating it — which is why the [consistent AI fashion models](https://www.astria.ai/articles/consistent-ai-fashion-models/) question is really a question about workflow, not about model quality. ## What it costs Use [Astria pricing](https://www.astria.ai/pricing) for current generation rates. The unit that matters is **cost per approved, published image**, which includes: - Setup and iteration time to reach an approved treatment - Rejected variants — a real number, and one nobody publishes - Review cycles with whoever owns the brand - Retouching or correction on the garments that fight back - The salaried hours of whoever runs the process A brand comparing a generation rate against a full shoot-day quote is comparing a raw input cost against a fully-loaded one, and will be surprised. The honest comparison is worked through in [fashion photoshoot cost](https://www.astria.ai/articles/fashion-photoshoot-cost/) and, per-SKU, in [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/). For the wider workflow across catalog, lifestyle, on-model, and campaign assets, use the [AI product photography guide](https://www.astria.ai/articles/ai-product-photography/). The genuine saving is usually not the per-image price. It is the disappearance of scheduling: no sample shipping, no model availability, no studio booking, no waiting three weeks because a product arrived late. ## Where the photographer goes The framing that AI "replaces the photographer" is a sales line, and it describes very little real practice. What actually happens is that the photographer's judgment moves upstream: instead of executing a look on the day, they define the look as a treatment that a brand's team can run against new products for a year. That is a better business for a photographer who sets it up, and a worse one for a photographer who only sells shoot days. It is also why studios and photographers are heavy users of these tools rather than victims of them — the craft was never the shutter, it was knowing what the picture should look like. ## Choosing a platform Four questions worth more than any feature list: 1. **Can an approved direction be stored and reused, or is each session configured from scratch?** This determines whether you have a system or a generator. 2. **What happens when you change your mind after approval?** Add a colorway, change the crop convention. Count the operations. This single test predicts your real cost better than anything else. 3. **Can more than one person work on it?** Fashion production involves a stylist, a founder, an ecommerce lead, and often an agency. A single-operator tool creates a bottleneck exactly where review needs to happen. 4. **What do you own at the end?** Images only, or the casting, references, and templates that produced them? Head-to-head assessments of the main platforms are collected under [comparisons](https://www.astria.ai/articles/tags/comparisons/), and the shortlist is summarised in [best AI fashion photography platforms](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/). ## Where to go next - **Generating lookbook images from products** — [the AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/) - **Planning the lookbook document** — [how to create a lookbook: shot list, page layout, and delivery](https://www.astria.ai/articles/how-to-create-a-lookbook/) - **Turning flat lays into on-model shots** — [flat lay to on-model](https://www.astria.ai/articles/flat-lay-to-on-model/) - **Keeping one model across a collection** — [consistent AI fashion models](https://www.astria.ai/articles/consistent-ai-fashion-models/) - **Garment-on-body specifically** — [virtual try-on for fashion brands](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) - **Jewelry** and **footwear**, which have their own conventions — [AI jewelry photography](https://www.astria.ai/articles/ai-jewelry-photography/), [AI footwear photography](https://www.astria.ai/articles/ai-footwear-photography/) - **Motion** — [AI fashion film](https://www.astria.ai/articles/ai-fashion-film-guide/) ## Carry an approved direction into a collection **Workflow update October 11, 2026.** Store the selected casting, light, setting, styling, and shot rules as a treatment. New products should bring their own references into that direction. A late-arriving SKU still needs a pilot and comparison against the selected collection images. Use [fashion collection production](https://www.astria.ai/articles/ai-fashion-collection-production/) to branch the treatment into lookbook, PDP, and campaign work. Use [campaign iteration with ChatGPT and Astria](https://www.astria.ai/articles/iterate-fashion-campaigns-with-chatgpt/) to develop and refine the direction, and [the collaborative AI studio guide](https://www.astria.ai/articles/collaborative-ai-studio/) to assign product review, creative approval, and delivery ownership. ## Frequently asked questions ### What is an AI fashion photoshoot? A production process that generates on-model and lifestyle imagery from your actual products, using a stored creative direction — casting, styling, scene, lighting, crop, and format — instead of booking a model, studio, and crew for each shoot. ### Does an AI photoshoot replace a photographer? It replaces the shoot day, not the judgment. Someone still has to decide the casting, styling, lighting, and framing. In practice photographers and studios are among the heaviest users, because the tools let them encode a look and hand it to a brand's team to run. ### How long does an AI fashion photoshoot take? The first approved look typically takes a day or two of iteration, because that is a creative decision, not a rendering one. Once approved, additional products run against it in minutes rather than requiring a new shoot. ### Which garments are hardest to produce with AI? Directional prints and stripes across seams, sheer fabrics, fine hardware, complex drape, and any garment whose construction is the selling point. Solid-colour knits and simple silhouettes are the easiest. Evaluate on your hardest pieces. ### What does an AI fashion photoshoot cost? See [Astria pricing](https://www.astria.ai/pricing) for current generation rates. The meaningful comparison is total cost per approved, published image across a collection, including setup time, review cycles, and rejected variants. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # AI Product Photography for Ecommerce: Workflow & QA Source: https://www.astria.ai/articles/ai-product-photography/ AI product photography is not one button that replaces a studio. It is a set of production methods: clean an existing photograph, put the product into a new scene, show it with a person, or turn one approved reference set into a family of catalog and campaign assets. **The short version:** start with evidence of the real product, generate only the variations the evidence can support, and approve product truth before art direction. The useful metric is not how many images the system makes. It is how many accurate images your team can publish. **Reviewed September 7, 2026.** This guide is published by Astria. Product and channel features can change; current first-party sources are linked where they support a claim. ## A product photo and an AI packshot for the listing This dress example shows how a product photo becomes a clean packshot. Check the garment details and your store’s image requirements before using the result. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) ## What is AI product photography? AI product photography uses generative or editing models to make commercial images from product references and creative direction. Some workflows preserve the photographed product and change only its surroundings. Others regenerate more of the frame to create a new angle, person, pose, or composition. Those operations carry different risk. Removing a background does not ask the system to reconstruct a shoe. Generating the back of that shoe from one front photograph does. | Workflow | What changes | Best use | Main review risk | | --- | --- | --- | --- | | **Cleanup and standardization** | Background, crop, exposure, shadow, or canvas | Marketplace and catalog consistency | Edge damage, false shadow, color shift | | **Scene generation** | Environment, surface, lighting, props | Lifestyle, campaign, social, and ads | Product shape, label, reflection, or scale drift | | **Product-to-model** | Person, pose, drape, and scene around a garment or accessory | On-model PDP images and fashion campaigns | Fit implication, print, construction, anatomy | | **Campaign variation** | Casting, crop, location, format, or message across a set | Collection launches and channel variants | The individual images pass but the set loses consistency | Start by naming the operation. “Make this look premium” is not a production brief. “Preserve the approved bottle, label, cap geometry, and color; replace only the surface and background” is reviewable. ## Begin with a product source of truth The first input should establish what the customer will receive. It does not need to be the final hero image, but it needs to show the product clearly enough to catch a wrong output. A useful reference set can include: - clean front and back photographs; - side, sole, interior, or closure views when construction matters; - a neutral-light color and material reference; - macro details for logos, labels, hardware, seams, stones, texture, and print; - dimensions or scale references for structured objects; - an approved on-model view when drape or placement must remain consistent. One source image can be enough for a candle, a simple bag, or a campaign experiment. It is rarely enough to prove hidden construction. If a photograph does not show the back pocket, the system can create a plausible pocket; it cannot know that this is the pocket on the sold item. That is the dividing line between **creative interpretation** and **product evidence**. Use AI freely for the former after approval. Preserve reliable physical capture for the latter. The [AI-versus-studio guide](https://www.astria.ai/articles/ai-product-photography-vs-studio/) explains how to split those jobs in a hybrid workflow. ## How to create AI product photography: a six-step workflow ### 1\. Define the asset's job Write the channel and buying question before choosing a tool or prompt. - A marketplace main image identifies the exact product quickly. - A PDP alternate view explains scale, use, fit, or construction. - A lifestyle image supplies context. - A campaign hero creates desire and leaves room for layout. - A social variation needs a native crop and an immediate visual idea. The same attractive image will not do all five jobs equally well. ### 2\. Build the minimum sufficient reference set Include every view needed to judge the requested output. A generated front-facing model image may need one clean garment reference. A side-and-back sequence needs evidence of the side and back if accuracy matters. Prepare the files consistently: neutral light, accurate white balance, enough resolution for texture, and no folds or props that hide the product. For clothing, the [flat-lay-to-on-model guide](https://www.astria.ai/articles/flat-lay-to-on-model/) shows which source photographs survive conversion and which details need individual review. ### 3\. Separate invariants from creative variables List what cannot change: - silhouette and proportions; - color and material; - logo, label, and printed copy; - hardware count, finish, and placement; - garment construction, pattern direction, and closures; - any feature tied to a performance or fit claim. Then list what may change: model, pose, location, surface, crop, lighting, props, and aspect ratio. This gives the operator and reviewer the same definition of success. ### 4\. Approve one hard product before running a batch Do not prove a workflow on the easiest white T-shirt in the catalog. Use a product with a directional print, reflective hardware, fine text, transparency, asymmetry, or difficult drape. Generate several outputs, record why each one passes or fails, and correct the workflow rather than rescuing every image manually. When one demanding SKU passes, test the same treatment on a small range before extending it to the full catalog. ### 5\. Review product truth before creative quality Use two gates, in order. **Product QA:** compare the result with the approved references. Check silhouette, color, scale, material, print, logo, label text, closures, hardware, transparency, reflections, and any visible claim-bearing feature. **Creative approval:** only after product QA passes, judge casting, styling, composition, lighting, crop, hierarchy, brand tone, and channel fit. This order prevents a beautiful image from being approved before anyone notices that it shows a different product. ### 6\. Export a channel-aware asset kit Keep explicit asset classes instead of one folder named `final`: - physical source-of-truth photographs; - marketplace main and alternate images; - approved generated on-model and lifestyle images; - campaign, social, email, and paid-media crops; - video or motion derivatives; - rejected outputs retained where they help audit the process. For Shopify, the first media item also appears in collection and cart contexts, and video or 3D behavior depends on the theme. Use the [Shopify product media guide](https://www.astria.ai/articles/shopify-product-media-guide/) to design the gallery order. For Amazon apparel, check the current marketplace and category rules against the [Amazon product image guide](https://www.astria.ai/articles/amazon-product-images-apparel/) rather than assuming a creative asset can serve as the main image. ## Which product categories need the most caution? | Product | Useful AI jobs | Keep as physical evidence | Watch closely | | --- | --- | --- | --- | | **Simple apparel** | On-model, styling, location, crop, colorway campaign | True color, construction, fit evidence | Hem length, closures, drape, print | | **Footwear** | On-foot context, campaign scenes, secondary angles | Sole, heel, interior, material, dimensions | Pair symmetry, laces, logo, hardware | | **Bags** | On-body scale, lifestyle, launch campaign | Interior, closures, pockets, exact hardware | Handle attachment, gusset, shape, scale | | **Jewelry and watches** | On-body context, mood, social crops | Macro geometry, stone count, engraving, finish | Reflections, tiny proportions, placement | | **Beauty and packaged goods** | Surfaces, sets, model context, campaign layouts | Shade, formula appearance, exact packaging and text | Label spelling, cap geometry, color, transparency | The stricter guide for reflective small objects is [AI jewelry photography](https://www.astria.ai/articles/ai-jewelry-photography/). Footwear has its own review conventions in [AI footwear photography](https://www.astria.ai/articles/ai-footwear-photography/). ## Can AI generate multiple product angles from one photo? It can generate plausible alternate views. That is useful for creative exploration, campaign motion, and secondary imagery after review. It is not the same as recovering measured geometry. If the input shows only the front, the back is an inference. The result may invent a seam, pocket, heel counter, clasp, or label. Supply real reference views for every surface that customers use to verify the product. Choose the format according to the promise: - generated angle stills for fast creative coverage; - a captured 360 spin for a fixed, evidence-based rotation; - a modeled GLB or USDZ for interactive viewing and AR; - physical detail photographs when the view must prove construction. The [3D packshot guide](https://www.astria.ai/articles/3d-packshots-ecommerce/) separates those deliverables and explains why a convincing rotation is not automatically true 3D. ## How to choose an AI product photography tool The useful buying questions are operational: 1. Can it preserve your hardest products, not just its demo objects? 2. Does it support the input types you actually have: cutout, flat lay, ghost mannequin, on-model, or several reference views? 3. Can an approved treatment be reused across products, colorways, people, and formats? 4. Can several people review, correct, and reproduce the work? 5. Does it expose a correction path when one detail fails? 6. Can it export the required resolution, aspect ratio, metadata, naming, and channel formats? 7. What is the cost per approved image after rejects and review? A one-off generator can be the right purchase for a seller making five listing images. A fashion production system is a different purchase: the treatment has to survive a collection, a team, and a change of brief. The [AI fashion photography platform shortlist](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/) compares those categories without treating them as interchangeable. ## Calculate cost per approved output The advertised generation price is only one line in the budget. Count: - reference capture and preparation; - workspace or template setup; - generation attempts and failed outputs; - product QA and creative review; - correction, regeneration, compositing, and retouching; - crops, exports, metadata, naming, and catalog integration; - reshoots or physical detail photography still required. Then divide the total by assets that pass review and reach a real channel. The [product photography cost-per-SKU formula](https://www.astria.ai/articles/product-photography-cost-per-sku/) provides the fuller worksheet. For a direct production comparison, use [AI versus traditional photoshoot cost](https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/). ## A reusable brief Use a brief that a second operator can reproduce: ```text Asset job: [marketplace alternate / PDP lifestyle / campaign hero / social] Product references: [front / back / side / detail / color / dimensions] Must preserve: [silhouette / material / color / logo / label / hardware] May change: [model / pose / background / lighting / crop / props] Required output: [count / aspect ratio / resolution / file type] Product rejection rules: [specific visible failures] Creative approval rules: [composition / brand tone / channel fit] Physical evidence retained: [main image / detail views / fit or scale proof] ``` For scene and lighting language, the [ecommerce product photography prompts](https://www.astria.ai/articles/ecommerce-product-photography-prompts/) guide provides production-ready prompt structures. The brief above controls the job around the prompt. ## Where Astria fits Astria's public ecommerce workflow starts with a creative template and product photos, then generates product and campaign imagery for lookbooks, social, ads, and other digital channels. The same public page positions the system around repeatable brand direction and large catalogs rather than isolated one-off images. [Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). The practical starting point is a small proof using your own products. Choose one easy SKU and one difficult SKU, define the invariants, run one approved treatment across both, and measure product accuracy, review time, rejects, and cost per approved output. Browse the public [brand lookbooks gallery](https://www.astria.ai/gallery/workspaces) for finished directions, then judge the workflow on your own reference set. ## Move from a pilot to a catalog batch **Workflow update October 11, 2026.** Define required slots by SKU, colorway, and shot before scaling. A batch is complete when approved files cover those slots, not when the generator returns a large image count. Keep factual product views tied to actual source evidence. Use [bulk PDP image production](https://www.astria.ai/articles/bulk-pdp-images-with-ai/) for category treatments, coverage, filenames, and gallery review. Use [PDP image agents](https://www.astria.ai/articles/automating-pdp-images-with-agents/) for input validation, status tracking, safe retries, and export records. Start with [reference preparation](https://www.astria.ai/articles/prepare-product-references-for-ai/) if intake quality is the limiting factor. For category-specific production, see [shoe photography](https://www.astria.ai/articles/ai-shoe-photography/), [beauty launch assets](https://www.astria.ai/articles/ai-beauty-campaign-production/), and [supplement packaging workflows](https://www.astria.ai/articles/ai-supplement-product-photography/). Evaluate the pilot with the [CMO scorecard](https://www.astria.ai/articles/ai-creative-production-pilot/) before expanding the assortment. ## Frequently asked questions ### What is AI product photography? AI product photography uses generative or editing models to create and modify commercial product images. It can clean a source photo, replace its background, place a product with a model, or extend an approved reference into catalog and campaign variations. Generated images still need to be checked against the real product. ### What photo do you need to create AI product images? Start with a sharp, evenly lit photo that shows the complete product at useful resolution. Add front, back, side, detail, material, and color references whenever those views matter. An AI system can infer a surface it has not seen, but it cannot establish that the invented detail matches the sold product. ### Can AI product photography replace a photoshoot? It can replace or extend some production, especially background, on-model, campaign, crop, and channel variations made from approved references. Keep physical photography for product-proof views, exact color and material evidence, construction details, measured fit claims, and any marketplace image that requires a faithful source photograph. ### How do you check an AI product photo for accuracy? Compare the output with the approved product references before judging its creative quality. Check silhouette, dimensions, color, texture, print, logo, label text, closures, hardware, transparency, reflections, and any detail that could change what a customer thinks they are buying. ### How should ecommerce teams calculate the cost of AI product photography? Use cost per approved published asset, not cost per generation. Include source preparation, setup, generations, rejected outputs, review, correction, retouching, export, and the physical reference photography the workflow still requires. ## Sources and methodology Astria workflow statements use its current ecommerce page and public workspaces gallery. Google Merchant Center's current [AI-generated content guidance](https://support.google.com/merchants/answer/14743464?hl=en) requires supported generative images to retain the applicable IPTC digital-source metadata; that is a specific channel requirement, not a universal publishing rule. Marketplace implementation details remain with the separately sourced Shopify and Amazon guides linked above. This is a production framework, not a cross-vendor image benchmark or a claim that every product can be reconstructed from one photograph. The cover is an AI-generated editorial illustration of a fictional, brand-neutral handbag workflow, not a customer result. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # AI Team Photos: Consistent Headshots for Remote Teams Source: https://www.astria.ai/articles/ai-headshots-for-teams/ AI team photos can mean a grid of individual headshots or one group image assembled from separate portraits. Decide which you need first. For a company team page, matching individual headshots are easier to review, replace, and extend when someone joins. A group composite needs a separate layout and a check that every person still looks like themselves. **The short version:** individual quality is table stakes now. Consistency across people is the part that is difficult, and it comes from fixing the treatment once rather than from everyone using the same tool. ## AI team photo generator or individual headshots? | What you need | Production approach | Approval check | | --- | --- | --- | | Company website or staff directory | Generate one portrait per employee with the same treatment | Compare the complete grid, then let each person approve their likeness | | Remote team group photo | Compose approved portraits into one designed group image | Check head count, relative scale, lighting, edges, and every face | | Photo of a real company event | Use a photograph from that event | Confirm it represents the people and occasion shown | For a group composite, approve people separately before arranging them together. Leave enough space around shoulders for the layout, match camera height, and use the same background and light direction. Keep the original portraits so a staffing change does not mean rebuilding every face. Describe the result as a composite where viewers might otherwise mistake it for a shared shoot or event. A matching portrait grid is usually the practical first deliverable. It works on a small screen and lets you update one employee at a time. ## Why team headshots break where individual ones don't Put forty independently-generated headshots on one page and the failures are immediate, even to people who cannot name them: - Light comes from the left on some faces and the right on others. - Backgrounds are eleven different greys. - Some portraits are cropped at the collarbone, others mid-chest. - Colour grade drifts — warm here, neutral there. - Depth of field varies, so some faces sit flat against the background and others float. Each image is fine. The grid is not. And a visitor reads that grid in about two seconds, well before they read anyone's job title. ![Portraits produced against a single approved treatment](https://www.astria.ai/articles/assets/images/image6-8f18af726d37554036e027843cc48b49.webp) This is precisely what a photographer's day gives you almost for free: one room, one light setup, one lens, one afternoon. Consistency is a _by-product_ of the constraint. Remove the constraint — everyone generating their own portraits, whenever, wherever — and consistency has to be engineered back in deliberately. ## The treatment is the deliverable The useful mental shift is that the reusable asset is not the images. It is the specification that produced them: - **Lighting** — key direction, softness, fill ratio, whether there is a rim - **Background** — an exact colour or an exact environment, not "a neutral studio" - **Crop and headroom** — where the frame cuts, consistently - **Grade** — warmth, contrast, saturation - **Wardrobe register** — how formal, and how tightly enforced - **Expression and posture** — the register of the whole set Approve that once, with whoever owns the brand. Then every person runs through it. The first portrait establishes the direction; the fortieth still needs a likeness and quality check. And when someone joins in March, they run through the same specification and match the set — which is the failure mode of the photographer's day, where the new hire is visibly the person photographed against a different wall. ![The same treatment applied to different people](https://www.astria.ai/articles/assets/images/image25-736d4c885fff3cc410ba305a686d7a39.webp) ## What each person needs to submit Set the photo count from your chosen workflow before sending the brief. For a multi-photo submission, ask for: - **Varied angles** — front, three-quarter both ways, slight up and down - **Varied lighting** — indoor, outdoor, window light; not all from one room - **Varied expressions** — neutral, slight smile, full smile - **Face unobstructed** — no sunglasses, no hands, no heavy shadow across the face - **Recent** — matching current hair and any glasses they actually wear What degrades results: sixteen frames from one burst, heavy filters, group photos cropped down to one face, low resolution, and photos that are all from the same three-month period five years ago. Astria's [headshot input guidance](https://docs.astria.ai/docs/use-cases/ai-photoshoot/#tips-for-training-images) also emphasises varied poses, backgrounds, and lighting, with no other people mixed into the reference set. Send the whole team this list once. The submission quality drives everything downstream, and it is the cheapest place to intervene. ## Where these images do and don't work Worth being straightforward, because overreach is what makes people distrust the whole category. **Works well:** team and about pages, LinkedIn and internal directories, conference speaker bios, sales collateral, email signatures, press kits where a consistent set is expected. **Works with care:** anything a journalist may republish as documentary. Some publications have policies about synthetic imagery. Ask before supplying. **Doesn't work:** identity documents, anything requiring a verified likeness, and any context where the image asserts that a specific moment happened. That last one is not a technical limit — it is a straightforward honesty limit. ![Consistent portraits across a distributed team](https://www.astria.ai/articles/assets/images/image22-cb2e6f5ab9820aec651cdedd8b153b92.webp) ## Handle consent properly People are giving you photographs of their faces. That deserves more care than a calendar invite: - Tell them what is being produced and where it will be published. - Say how the source photos are stored and when they are deleted. - Give a real opt-out that does not require an explanation, with a fallback that does not look like a punishment on the grid. - Let each person approve their own final portrait. A likeness someone dislikes is worse than no portrait at all, so make personal approval part of delivery. Distributed teams get the biggest practical win here — a photographer's day requires everyone in one city on one date, which for a remote company is either impossible or an expensive offsite. But the consent conversation gets _more_ important when there is no shoot day, because there is no moment where someone can simply decline to stand in front of the camera. ## A team photo brief you can reuse Use this as a starting specification, then test it on the pilot: - **Destination:** company team page, square display crop, with an uncropped master retained. - **Frame:** head and shoulders, eyes at the same height, space above the hair and beside both shoulders. - **Treatment:** plain light-grey background, soft light from camera left, neutral colour grade. - **Wardrobe and expression:** agreed level of formality, natural expression, current hairstyle and usual glasses. - **Approval:** one production owner checks the grid; each employee checks their own likeness before publication. - **Delivery record:** employee name, approved file, treatment version, approval status, and planned usage. Do not use the prompt as the identity reference. The employee's submitted photos supply the likeness; the brief supplies the photographic treatment. The distinction is explained in [AI headshots and portraits without training a model](https://www.astria.ai/articles/ai-headshots-without-model-training/). Keep the written treatment and approved examples for new hires, and check the provider's retention settings separately. Saving a brief does not mean an uploaded face model remains available indefinitely. Astria's [storage FAQ](https://docs.astria.ai/docs/use-cases/faq/#what-happens-to-my-model-training-pictures-and-image-generations) describes its default fine-tune retention and extension option. ## A sensible sequence 1. **Decide the treatment** with whoever owns the brand — lighting, background, crop, grade, wardrobe register. Approve it on two or three people first. 2. **Send one submission brief** to everyone, with the photo guidance above and the consent details. 3. **Run a pilot of five**, deliberately varied — different skin tones, hair, glasses, ages. Fix the treatment against the hardest cases, not the easiest. 4. **Run the team**, then let each person approve their own. 5. **Keep the treatment** for new hires. This is the part that pays back, and the part most companies forget to ask about. The same logic — approve a treatment once, run many subjects through it, keep it for later — is what the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/) applies to products instead of people. It is the same idea with a different variable. ## Frequently asked questions ### Can AI make a group team photo from separate portraits? A group composite can be assembled from separately approved portraits. Match camera height, lighting, crop, and relative scale, then check every person's likeness in the finished layout. Keep the individual files so the team image can be updated later. ### Can AI headshots look consistent across a whole team? Yes, but only if consistency is designed in. Consistency comes from fixing lighting, background, crop, and grade as a single approved treatment and running every person through it — not from everyone using the same app. ### How many photos does each person need to submit? Follow the input requirements of the workflow you choose. For a set of reference photos, use recent, unfiltered images with varied angles, lighting, and expressions. Each should show one person clearly; do not submit the whole team as one training subject. ### What does a team headshot session cost compared to a photographer? Traditional corporate headshot sessions are commonly quoted per person or per day plus retouching, and require scheduling everyone into one location. AI portraits can be produced from separate submissions, reducing the need to bring a distributed team together. Include coordination, revisions, and approval time in the comparison. ### How do you handle new hires after the initial batch? This is the main advantage of storing the treatment rather than booking a session. A new hire runs through the same approved setup months later and matches the existing set, instead of being visibly the person photographed on a different day. ### Should employees be told their headshots are AI-generated? Yes. People are submitting photos of their own faces, and they should know what is being produced, where it will appear, how the source images are handled, and that they can opt out. Treat participation as consented rather than assumed. [Explore Astria](https://www.astria.ai/). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Amazon Product Images for Apparel: Main-Image Rules, Detail Views, and Video Source: https://www.astria.ai/articles/amazon-product-images-apparel/ An Amazon main image is not a campaign image with the background removed. It is a marketplace compliance asset: product identification first, then persuasion in the alternate views. **The short version:** read the current category guide before shooting, keep the main image literal, and use the gallery to answer fit, construction, texture, scale, and use. Shopify assets can supply source material, but the Amazon set needs its own manifest and approval gate. ## A product photo and an AI packshot for the listing This dress example shows how a product photo becomes a clean packshot. Check the garment details and your store’s image requirements before using the result. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) **Last fact check:** August 31, 2026. Amazon requirements vary by marketplace, product type, account, and placement. The signed-in [Seller Central product image requirements](https://sellercentral.amazon.com/help/hub/reference/G1881) and category style guide control the listing; this article is a production framework, not a substitute for those rules. ## Main image and gallery have different jobs The main image appears in search results, recommendations, ads, and the detail page. It needs to tell the shopper what the product is before it tells a story. The alternate views have more room to explain. | Asset | Primary job | Production standard | | --- | --- | --- | | **Main image** | Identify the exact item and earn the click without risking suppression | Literal product evidence, current category presentation, clean required background, no promotional decoration | | **Alternate angle** | Reveal front, back, side, interior, or construction | Same SKU/colorway, matched color, scale, and grade | | **Detail** | Show fabric, seam, hardware, print, label, or feature | High enough resolution to inspect; no exaggerated texture or invented feature | | **On-model / use** | Communicate fit, proportion, styling, or operation | Sample size and product state should be clear; body and garment interaction reviewed | | **Infographic** | Explain verified dimensions, contents, or use | Claims and units checked; readable on mobile; never substitute for product evidence | | **Video** | Show movement or changing state | Product visible immediately, understandable without sound, claims reviewed | Amazon Ads' public PDP guidance recommends four or more images, a plain white background, product fill of at least 80%, and at least 1,000 pixels in height or width for zoom. That is a useful planning floor, not the full acceptance policy. Category rules in Seller Central can be more specific and take precedence. ## Read the category guide before capture “Apparel” is not one image rule. Adult clothing, childrenswear, shoes, handbags, accessories, multipacks, and jewelry can have different model and presentation requirements. Marketplaces can also differ. Before creating the shot list, record: - Marketplace and product category - Seller Central style-guide version or access date - Whether the main image must be on a human model, off-model, or follows another category-specific treatment - Required background value and product fill - What must and must not be visible on the model - Rules for props, packaging, multipacks, color charts, badges, and text - Accepted file format and technical dimensions - Available image, A+ Content, and video placements for the account If the category is uncertain, resolve the browse node and listing classification first. Shooting an off-model set and discovering that the main image requires an adult human model is not a retouching problem. ## Build the main image as product evidence Use an original professional photograph of the actual sold product for the main image unless the current category rule expressly allows another method. The image should show only what the customer receives. Remove dust and capture defects; do not redraw construction. ### Background Seller Central's general standard calls for pure white on main images; Amazon's public Ads guide describes a plain white background. Produce and verify the required value rather than judging “white” on an uncalibrated monitor. Near-white gray can look elegant and still fail an automated or human review. ### Product fill and crop Amazon Ads recommends at least 80% image-area fill. Seller Central or category guidance may specify a stricter target. Fill is not permission to crop the product: retain the entire required silhouette and enough margin to avoid clipping sleeves, hems, shoes, or accessories at responsive sizes. ### No campaign furniture Do not add text, borders, watermarks, decorative shadows, color blocks, unpurchased props, or accessories that imply a bundle. If a permanent logo is physically part of the garment, reproduce it accurately; do not add a brand mark as graphic decoration. ### Exact child variant The image attached to a color child should depict that color. Generated recoloring can change texture and perceived material, and parent/child mapping errors are especially damaging because the listing tells the shopper one color while the image shows another. ## A useful apparel gallery After the compliant main image, answer the questions that drive uncertainty. A strong starting set is: 1. **Main image:** exact product, exact colorway, compliant presentation. 2. **Back view:** length, yoke, vents, pockets, print continuation, and closure logic. 3. **Fit or scale:** on-model frame with useful body crop and sample-size context in the listing content where appropriate. 4. **Fabric detail:** weave, knit, finish, transparency, or lining at realistic scale. 5. **Construction detail:** zipper, buttons, pockets, seams, hem, sole, or hardware. 6. **Use or movement:** the feature changing state, not a generic lifestyle scene. 7. **Verified dimensions or contents:** only where the information prevents a likely misunderstanding. 8. **Additional color or styling context:** only if it still represents the selected product clearly. Do not spend three slots on near-identical poses while the back, interior, and fabric remain invisible. Every image should remove a different uncertainty. Ghost mannequin can be an excellent construction view where the category and placement permit it, but it is not automatically a valid main image. See the [ghost mannequin photography guide](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/) for the source inserts and composite checks. ## Apparel accuracy is a returns issue The easiest output to approve is a plausible outfit. The correct output is the sold garment. Review: - Color against an approved photographed sample, not another generated output - Garment length and proportion on the stated sample size - Print scale, direction, and alignment across seams - Logo size, position, orientation, and spelling - Button count and side, zipper length and pull, pocket count and placement - Fabric behavior: stretch, weight, transparency, drape, sheen - Collar, cuff, waistband, hem, lining, and closure construction - Included pieces versus styling-only pieces - Model anatomy and hand-to-garment interactions If an AI process creates a new pose or scene from product references, record it as a generated derivative and require visual approval. Missing product views are not creative whitespace. A model that invents the back of a jacket has made a different product image, even if the front is perfect. [Flat lay to on-model](https://www.astria.ai/articles/flat-lay-to-on-model/) explains which source garments convert well and where print, sheer fabric, gathering, and hardware need individual review. ## Video: distinguish listing media from Sponsored Brands Amazon has several video placements with different eligibility and specifications. Do not copy one ad specification into a PDP delivery sheet. For any commerce video, show the product immediately, make the first frame useful, and ensure the story survives muted playback. Demonstrate movement, closure, texture under changing light, pocket access, scale, or use. A rotating product clip is useful when it exposes new surfaces; it is decoration when every frame repeats the same front view. For **Sponsored Brands video specifically**, Amazon's current public specification says: - The unit autoplays when at least half of it is on screen - It starts muted - The product should appear within the first few seconds - Accepted duration is 6 to 45 seconds, with 20 seconds or less highly recommended - Accepted dimensions are 1280 × 720, 1920 × 1080, or 3840 × 2160 in 16:9 - Important text should avoid the lower-right control area Verify the current [Sponsored Brands video specification](https://advertising.amazon.com/resources/ad-specs/sponsored-brands-video) for codecs, frame rates, file size, language, safe area, and policy before export. Those details apply to that ad unit, not automatically to every Amazon video surface. ## Shopify source, Amazon derivative A shared master capture can reduce duplication, but “shoot once” does not mean “publish the same file everywhere.” | Master asset | Shopify use | Amazon use | | --- | --- | --- | | Clean high-resolution product photo | Featured image in the brand's chosen ratio | Category-compliant main-image derivative | | Front / back / details | Flexible gallery order | Alternate views after main-image approval | | On-model capture | Fit, styling, editorial sequence | Main or alternate only as category rules allow | | Ghost mannequin | Featured or construction view | Alternate or main only where current rules permit | | Short motion master | Hosted product video | Placement-specific Amazon export and policy review | | True 3D model | Interactive Shopify media where theme supports it | Not a substitute for Amazon's required main image | The [Shopify product media guide](https://www.astria.ai/articles/shopify-product-media-guide/) explains that platform's file limits, theme support, order, and variant constraints. The broader [AI product photography guide](https://www.astria.ai/articles/ai-product-photography/) separates approved physical evidence from generated derivatives. Build channel assets from the same product source, then attach separate roles, crops, and approvals. ## The Amazon asset manifest Track at least: | Field | Example | | --- | --- | | Marketplace / category | US / Clothing | | ASIN / parent / child | Exact listing relationship | | SKU / colorway | Sold product represented | | Asset role | MAIN, PT01, detail, A+ module, video | | Source | Original capture ID or approved master | | Method | Photography, composite, generated derivative | | Rules checked | Category guide date, background, fill, model rule | | Accuracy checked | Color, construction, logo, inclusions, claims | | Approver | Named reviewer and date | | Status | Ready, rejected, rework, replaced | This prevents a common failure: a compliant source is exported correctly, then attached to the wrong child ASIN or overwritten by an older derivative. ## Pre-upload checklist - Current Seller Central category guide has been reviewed for this marketplace - Main image uses the permitted model/off-model treatment - Required background and product fill are verified - Entire required product is visible and the crop has safe margin - No graphic text, border, watermark, badge, or confusing prop on main image - Product is the exact SKU and color child - File dimensions support zoom and meet the live technical rule - Gallery covers back, fit/scale, material, and decisive construction details - All measurements and claims match verified product data - Generated derivatives have passed garment-level review - Video matches its exact placement specification and works muted - Files are mapped to the correct ASIN roles in the manifest Amazon can suppress or remove noncompliant images. Make the policy check a production step, not a rescue after upload. ## Frequently asked questions ### What should an Amazon apparel main image show? It should identify the exact product being sold, use the background and presentation required by the current category style guide, show the product clearly at useful scale, and contain no unsupported props, promotional text, borders, or watermarks. Apparel model requirements vary by category and marketplace, so check Seller Central before production. ### How large should Amazon product images be? Amazon Ads publicly recommends images at least 1,000 pixels in height or width so zoom can be enabled. Produce larger clean masters for crop flexibility, but validate the current technical and category requirements in Seller Central before upload. ### How many images should an Amazon apparel listing have? Amazon Ads recommends four or more high-quality images. Apparel usually needs enough views to cover the compliant main image, front and back, fit or scale, fabric and construction detail, and any feature that would otherwise surprise the buyer. Use each slot to answer a different question. ### Can AI-generated apparel images be used on Amazon? Use an original product photograph for the main image unless the current Amazon category policy expressly permits another method. Generated derivatives can invent color, fit, seams, logos, or hardware, so review every supporting image against the sold garment and keep claims grounded in verified product information. ### What should an Amazon apparel video show first? Show the product immediately and make the opening understandable without sound. For Sponsored Brands video specifically, Amazon says the unit autoplays muted and recommends showing the product within the first few seconds; its current accepted duration is 6 to 45 seconds. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Apparel Product Video: A Production Guide for Ecommerce PDPs Source: https://www.astria.ai/articles/apparel-product-video-guide/ An apparel product video earns its place when it answers something a shopper cannot confidently resolve from the photographs. Where does the jacket end when someone turns? How does the closure work? What is inside the bag? Each question calls for a different shot—and a different standard of evidence. **Start with one product, one buying question, and one short edit.** Film the physical behavior you need to prove. Use generated motion selectively for presentation, with the actual SKU as the review reference. ## From a fashion photo to a nine-second video The brief asks the model to walk in, pause with a hand on her hip, and walk past the camera. Press play to see the generated result. ![Starting image of a model in a navy pinstriped dress and sandals in a grey studio](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-first-frame.jpg) Starting image [Watch the generated video](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-720p-live-runway.mp4) AI video · 9 seconds · Seedance 2 Fast · no audio [View this generation](https://www.astria.ai/prompts/46608981)[Compare video models](https://www.astria.ai/articles/video-model-comparison-calibration) **Reviewed September 5, 2026.** Platform capabilities below were checked against first-party documentation. Shot lengths, budgets, and pilot design are production recommendations, not measured conversion benchmarks. ## Write the buying question before the shot list A product detail page (PDP) video is a small demonstration attached to a specific item for sale. Its brief should name the SKU, colorway, sample size, unanswered question, and action that will answer it. “Make the collection feel cinematic” belongs in the [AI fashion film guide](https://www.astria.ai/articles/ai-fashion-film-guide/). “Show how this jacket opens” belongs here. Read customer questions, return reasons, and support notes for the selected product family. Pick a question motion can actually resolve. A shopper asking about sleeve length may need measurements and a fit photograph before they need a video. | Buying question | Useful shot | Evidence needed | | --- | --- | --- | | Where does this jacket fall from the side? | A slow, clearly framed turn on a model | Actual sample, recorded sample size, model measurements | | How does this fastening work? | One uninterrupted opening and closing | Physical garment and functioning closure | | How does the fabric move? | A restrained step or arm movement | Footage of the actual material under stated conditions | | What fits in the bag? | Insert named everyday objects, then close it | Actual bag and objects of known dimensions | | What does the back look like? | A stable rear view held long enough to inspect | Accurate rear reference; a still may be sufficient | Pick one or two questions per clip. Combining a walking sequence, a fabric close-up, a packing demonstration, and a campaign montage makes approval harder and weakens the reason for watching. ## Choose filmed, generated, or hybrid production **Film the sample when behavior is the claim.** Stretch, transparency under movement, compression, waterproofing, pocket capacity, and the mechanics of a closure need direct evidence. A generated sequence can depict plausible behavior without establishing that the sold product behaves that way. Do not use simulated drape as a fit test. **Use AI motion for a reviewed presentation treatment.** A subtle camera move around an approved composition can extend the visual language of the catalog. Review every newly revealed surface against source photographs. If the source only shows the front, an invented back is a failed product asset even if the animation looks convincing. **Use a hybrid edit when different shots have different jobs.** For example, open on a reviewed editorial frame, cut to filmed footage of a closure, and end on an accurate product still. Keep the demonstration itself intact: changing a background must not change an edge, a fabric property, or the point where a hand touches the garment. For the source stills, Astria's [ecommerce workflow](https://www.astria.ai/ecommerce) describes product-photo inputs and reusable creative templates. Approve the garment in those outputs before considering animation. Product acceptance remains a production responsibility; access to a generation tool is not proof of accuracy. ## A practical 12-second jacket brief This is an example production target, not a platform requirement. The question is: “How does this jacket sit from the side, and how does it fasten?” Use the same real jacket and model throughout the demonstration. | Time | Shot | Approval criterion | | --- | --- | --- | | 0–3 seconds | Front view, neutral stance, jacket fully visible | Correct color and sample; hem and sleeves stay inside frame | | 3–7 seconds | Model turns slowly to a three-quarter side view | Shoulder, sleeve, and side silhouette remain inspectable | | 7–10 seconds | Close-up of fastening being closed | Real mechanism visible; hand does not obscure the decisive action | | 10–12 seconds | Hold the finished front view | Closure state and garment shape match the preceding shot | Record longer takes than the finished windows require, then select stable sections. Keep light, camera height, focal length, and white balance consistent. Lock exposure where an automatic adjustment would change the apparent color between views. Capture a clean still for the poster image during the same setup. If you need to explain the sample size, place it in readable edited text or the adjacent product information. Keep essential detail away from player controls. Music is optional; the demonstration should make sense muted. Caption any spoken explanation and retain the caption text in the delivery package. ## Review motion against the SKU, frame by frame An accurate first frame does not approve the rest of a generated clip. Play the entire export at normal speed, then scrub the parts where the model turns, the camera reveals a new surface, or hands overlap the product. Compare those frames with front, back, and detail photographs. Check four things separately: - **Identity:** correct SKU, colorway, print placement, and included accessories. - **Construction:** stable seams, button count, pocket position, hem, fastenings, and hardware. - **Movement:** no shifting logos, melting edges, sudden length changes, or implausible contact between hand and cloth. - **Meaning:** the sequence does not imply an unverified material property, fit result, or capacity. The merchandising owner approves product truth; the creative owner approves presentation. A color error is not a styling preference. If a generated shot repeatedly changes a defining feature, replace that shot with filmed footage or an accurate still. Shortening it until the error is hard to notice does not solve the product problem. The [consistent AI fashion models guide](https://www.astria.ai/articles/consistent-ai-fashion-models/) addresses recurring casting. It does not replace the garment checks needed for each SKU. ## Deliver a product asset, not just an MP4 Hand off the final clip with its poster, caption text or track when needed, SKU and colorway, intended gallery position, sample information, approval owner, and source references. Give the files explicit versions, such as `JK104_charcoal_closure_v03.mp4` and `JK104_charcoal_closure_poster_v03.jpg`. Choose the aspect ratio by testing the actual product viewer. A portrait garment does not automatically require a full-screen social crop. Keep the hem, sleeves, and demonstrated detail visible in both the main player and its thumbnail. Retain a high-quality master and create a delivery export that balances readable texture with mobile load time. For Shopify, directly uploaded product videos currently accept MP4, MOV, or WebM, up to 10 minutes, 1 GB, and 4K. Those are upload ceilings. Shopify also requires a theme that supports video; satisfying the file limits alone does not validate the storefront experience. Verify the current [Shopify product media requirements](https://help.shopify.com/en/manual/products/product-media/product-media-types) before delivery. Gallery ordering, variant behavior, and theme integration belong in the [Shopify product media guide](https://www.astria.ai/articles/shopify-product-media-guide/). For this production handoff, require a mobile preview showing the correct product, a useful poster, accessible playback controls, and no unexpected crop before signing off. ## Budget by approved clip and demonstrated action A quote for “20 videos” hides most of the work. State how many SKUs, colorways, actions, ratios, and revisions are included. Separate sample preparation, capture or generation, edit, product review, captions, export, and storefront QA. Record rejected generations and retakes as production costs. A useful calculation is: **Cost per approved SKU clip = total pilot production cost ÷ approved SKU clips.** Divide your pilot spend by the number of approved SKU clips. Three aspect-ratio exports of each clip remain the same underlying product demonstration; count the exports separately when estimating delivery work. Use [Astria pricing](https://www.astria.ai/pricing) for current generation rates. Record the reason for each rejection. If most expense comes from a complex fastening, film that action once per relevant sample setup. If it comes from mismatched colorways, improve the reference and approval process before increasing generation volume. The [cost per SKU guide](https://www.astria.ai/articles/product-photography-cost-per-sku/) covers the wider catalog budget. ## Run a pilot that can justify the next batch Choose a small product family with a repeatable question, adequate traffic, and stable stock. Keep photography, copy, price, promotions, and gallery order consistent except for the video being tested. Where tooling permits, randomly assign eligible PDP visitors to a gallery with or without that video. Name one primary outcome before the test, such as purchase rate per eligible visitor. Track playback and completion as diagnostics, alongside add-to-cart, mobile loading, and later returns or support questions. Count the full assigned group, including visitors who never press play. People who choose to watch a product video may already be more interested in buying. Set the required sample and decision window from the store's baseline traffic and the effect worth detecting. Do not prescribe a universal seven-day or hundred-view winner. If the pilot is inconclusive, record that result and the production cost; it does not justify rolling motion out to the whole catalog. An approved PDP demonstration can also supply evidence for an ad. The [fashion video ads guide](https://www.astria.ai/articles/fashion-video-ads-guide/) explains how to build and test acquisition edits without turning the product page into an advertising montage. ## Frequently asked questions ### How long should an apparel product video be? Start with the time needed to answer one buying question clearly. A 12-second jacket demonstration is a useful brief to test, not a universal optimum. Remove introductions and repeated angles before shortening the action the shopper needs to inspect. ### Can AI-generated video replace filmed garment demonstrations? Use filmed evidence when the claim concerns actual stretch, drape, transparency, fit, capacity, or a working closure. Generated motion can support reviewed presentation shots, but plausible movement does not establish the physical behavior of the sold product. ### What should a clothing product-video handoff include? Deliver the approved clip, a poster image, caption material where needed, SKU and colorway, sample information, source references, version, and approval owner. Validate the crop and playback in the actual mobile product viewer. ### How do you measure whether PDP video helps sales? Compare eligible visitors assigned to a gallery with or without the video while holding other changes steady. Use a preselected commerce outcome and track loading and playback as diagnostics. Comparing watchers with non-watchers alone does not establish a causal effect. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Fashion Video Ads: Build and Test Creative from One Collection Source: https://www.astria.ai/articles/fashion-video-ads-guide/ A fashion video ad has to earn attention, identify the product, and give the viewer a reason to visit a matching page. A beautiful campaign cut can do the first job while leaving the other two unfinished. **Start with one product proposition and two opening hooks.** Keep the evidence, offer, destination, and closing action the same. This gives the production team a small deliverable and the marketing team a question it can actually test. ## From a fashion photo to a nine-second video The brief asks the model to walk in, pause with a hand on her hip, and walk past the camera. Press play to see the generated result. ![Starting image of a model in a navy pinstriped dress and sandals in a grey studio](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-first-frame.jpg) Starting image [Watch the generated video](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-720p-live-runway.mp4) AI video · 9 seconds · Seedance 2 Fast · no audio [View this generation](https://www.astria.ai/prompts/46608981)[Compare video models](https://www.astria.ai/articles/video-model-comparison-calibration) **Reviewed September 5, 2026.** TikTok specifications and testing features were checked against first-party documentation. Timings and creative structures below are suggested briefs, not platform rules or promised performance results. ## Give acquisition creative its own brief A campaign film builds a world. A product-page demonstration helps someone inspect an item. An acquisition ad has to connect an interruption in a feed with a useful next step. Write that connection in one sentence before choosing footage: “Show the jacket's removable collar, then take interested shoppers to that jacket.” The [AI fashion film guide](https://www.astria.ai/articles/ai-fashion-film-guide/) covers the broader motion production. Here the unit of work is an ad variant with a defined hypothesis. If your main need is a faithful demonstration for an existing shopper, start with [apparel product-video production](https://www.astria.ai/articles/apparel-product-video-guide/). A complete brief names the audience, product or collection, proposition, evidence, destination, placement, offer, and primary success metric. An ad for a single jacket should land where that jacket is immediately identifiable. If the selected color is unavailable, fix the brief or destination before producing more variants. ## Build a small source kit Collect an approved hero frame, an accurate detail view, any real demonstration footage, product names, current offer copy, and the destination URL. Store the SKU and colorway with each visual. Add the approved logo, type treatment, audio permissions, and final copy as separate editing assets. Astria's [ecommerce workflow](https://www.astria.ai/ecommerce) describes creating imagery from product inputs and creative templates. Those reviewed stills can form the visual source kit for an ad. They still need product approval before they become motion, and the final cut needs editing, audio, and placement review. Use generated scenes to explore framing or extend an approved visual treatment. Use real product footage when the ad claims something demonstrable, such as a detachable part or pocket capacity. Keep text, prices, and end cards in the edit so a commercial revision does not require regenerating the garment. Record what is permitted for the assets you use, including paid advertising and the planned channels. For a creator-led ad, obtain the creator's agreed usage scope; do not turn a synthetic presenter into an invented customer testimonial. These are production checks, not a substitute for reviewing the applicable platform and campaign requirements. ## A 15-second edit with a clear job for each beat Consider a jacket with a genuinely removable collar. The following brief is illustrative: confirm the product feature before using this copy. The timing is an editing target, not a claim about the ideal length of every fashion ad. | Time | Visual | Job | | --- | --- | --- | | 0–3 seconds | Hook A: full jacket with collar; Hook B: close-up of collar being removed | Test product-first versus detail-first opening | | 3–8 seconds | The same filmed removal and reattachment demonstration in both versions | Establish that the feature is real | | 8–12 seconds | Two approved styling views of the exact jacket | Show how the feature changes the look | | 12–15 seconds | Stable jacket hero and edited “Shop the jacket” end card | Give a clear next step matching the destination | Keep the audio, text treatment, total duration, and all footage after the opening the same across A and B. If you change the first image and the opening copy together, call the test an opening-package test. It cannot isolate which of those two changes drove the result. Design speech and music deliberately, but make product identity and the action understandable with sound off. Caption speech that carries the proposition. Watch the actual export on a phone: a neat desktop layout can still make a product name or fastening too small to see. ## Two useful variants beat an unplanned matrix Generating three hooks, four offers, three avatars, and two soundtracks creates 72 combinations before any evidence tells you which variation matters. It also spreads review effort and media spend across many different questions. For the first test, produce two exports of the same proposition: | Element | Version A | Version B | | --- | --- | --- | | Opening | Full product in the first frame | Feature detail in the first frame | | Demonstration | Same approved footage | Same approved footage | | Offer and CTA | Same wording | Same wording | | Landing page | Same product destination | Same product destination | | Audience and delivery settings | Held steady within the test | Held steady within the test | TikTok's [split-testing documentation](https://ads.tiktok.com/help/article/split-testing?lang=en) describes separating the audience into exclusive groups for a controlled comparison. Its [testing-variable guide](https://ads.tiktok.com/help/article/split-testing-variables?lang=en) lists creative assets, including opening hooks, and permits one selected variable per split test. Check compatibility with your objective and campaign setup before promising a particular experiment. Use the platform's experiment tool when available. Two ads placed in an ordinary optimized ad group are not automatically a balanced experiment: unequal delivery can reflect the delivery system's choices as well as the creative. Treat that result as directional unless the setup supports the comparison you intend to make. ## Export for the placement, then inspect its overlays Make a 9:16 master for a vertical-feed brief, with enough room to reposition copy for the destination's actual interface. Do not use one fixed “safe zone” for every platform, language, and ad format. As checked on September 5, 2026, TikTok recommends 9:16 for Non-Spark in-feed ads, with a minimum of 540 × 960 pixels. Its safe-zone guidance varies with dimensions, caption length, and additional formats, and it warns that preview and live display can differ. Use the current downloadable templates and verify your specific placement in the [TikTok in-feed ad specifications](https://ads.tiktok.com/help/article/tiktok-auction-in-feed-ads?lang=en). An editorial delivery target of 1080 × 1920 gives this brief a practical full-HD master; it is not the quoted platform minimum. Confirm accepted encoding, duration, file weight, and account-specific options at upload. For Reels or another placement, use its own current Ads Manager specification and preview rather than assuming TikTok's rules apply. Keep hems, footwear, product names, and the call to action clear of visible controls. Reframe a wide campaign shot if the vertical crop removes the product feature. Make a separate end card for another ratio when necessary; squeezing the whole composition into a smaller box usually weakens legibility. ## Review every claim through the landing page Approve the ad and destination together. The product, color, feature, price, availability, and promotion should agree. If the ad opens on one jacket and lands on an unrelated collection, a low purchase rate may be a destination problem rather than an opening-hook problem. Inspect generated motion for changing prints, logos, fastenings, silhouettes, and accessories. Review the transitions as carefully as the hero frames. A collar cannot disappear before the demonstration removes it, and a bag cannot gain capacity during a packing shot. Have the product owner sign off the merchandise, the creative lead sign off the cut, and the marketer sign off the destination and test setup. Preview the final uploaded file rather than approving only the editing timeline. Compression, player overlays, or a misplaced caption can change what the viewer understands. ## Measure the commercial result, then diagnose the edit Select the primary metric from the campaign objective before launch. For an acquisition test, that might be cost per purchase or purchase conversion under a consistent attribution setting. Set the test budget, duration, and sample requirements from baseline volume and the difference worth detecting. A handful of orders is not a universal stopping rule. Use attention and click measures to investigate the result: | Observation | Next thing to inspect | | --- | --- | | Weak early retention | Whether the opening identifies a relevant product quickly enough | | Good retention, few destination visits | Proposition clarity and the reason to take the next step | | Many clicks, few purchases | Destination match, load time, stock, price, and product information | | Strong commerce result, high production cost | Whether the concept can be repeated economically across products | These are diagnostic hypotheses. They do not prove that the hook, page, or price caused the result. Compare metrics using the same definitions and attribution window; a “view” on one platform is not necessarily the same event elsewhere. Keep the full test record, including inconclusive results. Avoid declaring a winner from a single early spike or silently changing the offer midway. If both edits fail, revisit the proposition and destination before commissioning ten more openings. ## Price a repeatable concept, not a folder of exports Track concept development, source production, all generation attempts, product review, editing, audio, captions, and delivery. Keep media spend separate so you can distinguish an expensive production process from an expensive acquisition channel. Divide your production spend by the number of approved creative variants. Cropping two variants into three ratios each gives six files, not six independent creative ideas. Budget for both the underlying concepts and the additional finishing work. Use [Astria pricing](https://www.astria.ai/pricing) for current generation rates. A promising concept should survive a second SKU without losing its logic. Reuse the shot structure and editing treatment, but replace the evidence with footage of the actual new product. A removable-collar ad is not a template for a jacket whose collar is fixed. For planning across a collection, connect this record to the [AI lookbook workflow](https://www.astria.ai/articles/ai-lookbook-guide/). If the open question is which production suite fits the team, the [Astria vs Higgsfield comparison](https://www.astria.ai/articles/astria-vs-higgsfield-fashion-ai/) covers that buying decision. The ad test should decide the creative question, not serve as an unmeasured comparison of tools. ## Frequently asked questions ### What should a fashion video ad include? Include a clear product opening, evidence for the proposition, a legible next step, and a matching destination. A short sequence can do all four jobs. Choose its duration around the message and placement rather than assuming one length is always best. ### How many fashion ad variants should a first test use? Start with two versions that differ in one defined element, such as the opening hook. Keep the product evidence, offer, destination, and other delivery conditions steady so the result answers a useful question. ### Can AI create fashion video ads from product photos? Reviewed product photos can supply the visual basis for generated motion and an edited ad. Check every shot for garment changes, keep commercial text editable, and use filmed evidence when demonstrating the actual behavior or function of the product. ### Can the same fashion video run on every social placement? Reuse the approved source assets, then adapt framing, text, audio, and end cards to each placement. Check the current upload specification and preview overlays. A common vertical ratio does not guarantee that the same layout is safe everywhere. ### Which metric should decide a fashion video-ad test? Choose a primary metric that matches the campaign objective, with consistent attribution and a planned sample. For acquisition, commerce outcomes matter more than views alone. Use retention and clicks to diagnose the edit, and retain an inconclusive result when the evidence is insufficient. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Flat Lay to On-Model: Put Clothes on an AI Model Source: https://www.astria.ai/articles/flat-lay-to-on-model/ An AI clothing-on-model generator can turn a flat lay, ghost mannequin, or garment photo into an image of a person wearing the product. That makes on-model coverage possible for catalog items that would not justify another model-and-studio day. **The short version:** use a complete, evenly lit garment reference; tell the workflow what must not change; and check product truth before judging the model or scene. A convincing person does not prove that the clothing is accurate or that it will physically fit a customer. **Reviewed September 7, 2026.** Search language and workflow boundaries were refreshed; this is a production guide, not a cross-tool accuracy benchmark. ## Flat lay to model, product-to-model, or virtual try-on? Several search terms describe overlapping jobs: | Term | Usually means | What to verify | | --- | --- | --- | | **Flat lay to model** | Turn a garment photographed from above into a person wearing it | The full garment is visible and unfolded | | **Product-to-model** | Combine a product reference with a selected or generated model | Which parts of the product and person the workflow preserves | | **Clothing-on-model generator** | A tool-led name for either of the above | Whether it accepts your real clothing image rather than generating a similar outfit from text | | **Model swap** | Replace the person in an existing fashion image | Garment, pose, crop, and lighting remain unchanged | | **Brand-side virtual try-on** | Generate merchandising imagery of a product on selected models | This is not the same as a shopper fitting-room widget or measured fit prediction | If the immediate goal is to put your clothes on an AI model, the operation is product-to-model. If the goal is to let each shopper upload a selfie, that is a different product and privacy workflow. The [virtual try-on guide](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) separates the two. ## Why the long tail stays flat Still choosing a format? The [ghost mannequin alternatives comparison](https://www.astria.ai/articles/ghost-mannequin-alternative/) covers when to use flat lay, a hanger, live-model photography, or AI on-model. This guide covers the conversion after you have chosen on-model output. The economics are brutal and familiar. Your top sellers get the full treatment — model, studio, stylist, retouch. Everything below the top 20% gets whatever is cheapest, which means a flat lay or a ghost mannequin, because the marginal revenue does not justify a shoot day. So the long tail is presented worse than the head, which suppresses its performance, which confirms the decision not to invest in it. The loop is self-sealing, and it is the single clearest place where cheap on-model conversion changes something material about a business. ## Your source image decides everything This is the part that gets skipped, and it causes most disappointing results. **Good source material:** - The entire garment visible, unfolded, nothing cropped off - Even, diffuse lighting with no hard shadow - Accurate colour — if your source is warm, every output inherits that warmth - Resolution high enough to resolve weave, texture, and stitching - Ghost mannequin shots, which are frequently the best input available because the garment is already holding a human shape **Problematic source material:** - Styled flat lays with sleeves folded or the hem tucked — folded areas have to be invented - Partial crops - Hard directional shadow, which gets baked in as if it were fabric colour - Garment on a hanger, distorting the shoulders - Screenshots, or images compressed so far that texture is gone A brand with clean, consistent product photography gets clean, consistent on-model output. A brand whose product shots were taken by six different people over three years gets exactly that variance back, amplified. If your outputs are inconsistent, audit your inputs before blaming the tool. ## What survives conversion, and what needs watching **Converts reliably:** solid-colour knits and jersey, tees, simple dresses, structured outerwear, tailored pieces with clean lines. Anything where drape is predictable and the surface is uniform. **Needs per-item review:** directional prints and stripes, which can mirror or misalign across a seam; logo scale and placement; sheer and semi-sheer fabric; complex gathering, pleating, and ruching; fine hardware — buckles, eyelets, zip pulls, chain straps. **Expect to intervene:** garments whose construction is the product. Technical outerwear with visible seam-sealing, structured tailoring, performance wear with panel logic. The output will look plausible and be wrong in the ways your customer specifically cares about. The pragmatic approach is triage rather than uniform treatment: batch the easy categories with spot checks, and route the hard ones to individual review. Treating all 600 variants identically means either over-reviewing the simple ones or under-reviewing the difficult ones — usually both. ## Where drift creeps in Running a large batch introduces a second-order problem: the individual images are fine and the _set_ is not. Same causes as everywhere else in this work — light and grade creeping, crops shifting, casting wandering across a long run. For a catalog this matters more than for a lookbook, because the grid view puts thirty products side by side and every inconsistency is on display simultaneously. Two habits prevent nearly all of it: - Fix the treatment — model, light, background, crop, grade — before running volume, and store it rather than re-specifying it. - Review in grid view at the size the customer sees, not one image at a time at full resolution. The mechanics of holding casting fixed are covered in [consistent AI fashion models](https://www.astria.ai/articles/consistent-ai-fashion-models/). ## The pre-publish checklist Per product, and quickly — this should take seconds once you know what you are looking for: - **Colour matches the actual garment**, not the source photo's white balance. This is the most common cause of returns from imagery. - **Print alignment** across seams, and print scale relative to the body. - **Logo** correct in size, position, and orientation. - **Closures** — buttons on the correct side, zip direction, buckle threading. - **Fabric behaviour** — does it drape like the material it is? Silk that hangs like denim is instantly wrong. - **Length and proportion** — a cropped jacket that renders hip-length is a returns problem, not an aesthetic one. - **Anatomy**, especially hands and any hand-to-garment interaction. ## What this does and does not solve It solves presentation for the long tail, colorway coverage without reshooting, consistent framing across a catalog, and the ability to change your mind about the treatment without re-booking anything. It does not solve fit information — no image tells a customer whether a size 12 will fit them — and it does not replace the photography of your hero products, where the real thing photographed properly still carries weight that a conversion does not. For the wider production picture, see the [AI product photography guide](https://www.astria.ai/articles/ai-product-photography/) and [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). For the garment-on-body question specifically, see [virtual try-on for fashion brands](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/). For what the whole exercise costs per variant, see [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/). ## Frequently asked questions ### Can you turn a flat lay into an on-model photo? Yes. The garment is analysed from the product image and rendered on a model with plausible drape, fit, and lighting. Quality depends heavily on the source image: an evenly-lit, complete, unfolded garment shot converts far better than a styled or partial one. ### What makes a good source image for on-model conversion? The whole garment visible and unfolded, even diffuse lighting, no heavy shadow, accurate colour, and enough resolution to resolve texture and detail. Ghost mannequin shots are usually the best starting point because the garment already holds its shape. ### Do on-model images convert better than flat lays? On-model imagery is widely regarded as helping shoppers judge fit and scale, which flat lays cannot convey. The size of the effect varies by category and price point, so it is worth measuring on your own catalog rather than assuming a published figure applies. ### Which garments do not convert well? Directional prints across seams, sheer and semi-sheer fabrics, complex drape and gathering, fine hardware, and garments whose construction is the selling point. These need per-item review rather than batch acceptance. ### Is this the same as a ghost mannequin shot? No. A ghost mannequin shows the garment holding a human shape with the mannequin removed — no person. On-model conversion puts the garment on a rendered person, which communicates fit, scale, and styling that a hollow shape cannot. ### Can AI put my clothes on a model? Yes. A product-to-model or brand-side virtual try-on workflow uses your garment photograph as a reference and generates a person wearing it. The result must still be checked against the real garment for color, cut, print, closures, texture, and any detail hidden in the source image. ### Can you change the model without changing the clothes? A model-swap workflow is designed to change the person while preserving the photographed garment, pose, and composition. Treat preservation as a testable requirement rather than an automatic guarantee, and compare every result with the approved clothing reference before publishing it. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Ghost Mannequin Photography for Ecommerce: The Complete Apparel Guide Source: https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/ Ghost mannequin photography is not a one-click cutout. It is a small composite: the garment is photographed while holding body volume, the support is removed, and separately captured hidden areas are joined back in. **The short version:** the result lives or dies on the source views. A clean neck join comes from photographing the inside neck, not asking a retoucher or model to guess it. ## From a dress photo to a packshot The generated image gives the dress volume without a visible mannequin. Compare the neckline, seams, and hem with the original photo. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot Details hidden in the original photo still need checking against additional product photos. [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) **Last fact check:** August 31, 2026. Marketplace main-image rules vary by category and region; verify the current Seller Central guide before delivery. ## What the technique actually does If you are deciding whether to keep this format, start with [ghost mannequin alternatives](https://www.astria.ai/articles/ghost-mannequin-alternative/). That comparison weighs replacement workflows; this guide covers capturing and finishing the hollow garment image. A flat lay documents a garment on a surface. A hanger shot suspends it from the shoulders. A ghost mannequin gives it the chest, waist, sleeve, and hip volume of a worn object, then removes the visible support. It is sometimes called invisible mannequin or hollow-man photography. That makes it useful for ecommerce because the product remains the only visible subject while construction is easier to read than in a flat lay. It does **not** prove fit. The volume comes from one mannequin form, pinning choices, steaming, camera position, and retouching. The usual production is: 1. Style and photograph the garment on a mannequin. 2. Photograph the back and any hidden interiors separately. 3. Mask out the mannequin. 4. Composite the inside collar, neck, hem, or other hidden areas into place. 5. Rebuild only real edges, then normalize background, crop, and shadow. 6. Review the composite against the garment before export. The neck is where weak work reveals itself. A collar that is too deep, an impossible lining, or a rear label floating at the wrong angle means the hidden view was missing or the join was rushed. ## The capture list ### Every garment - Front on mannequin, full garment visible - Back on mannequin, matched camera height and distance - Inside neck and collar with the garment reversed or supported separately - Front and back detail references for closures, labels, trims, and texture - Color target or approved reference under the same light ### Add views when the construction needs them - **Hood:** inside hood, hood attachment, and back fall - **Open jacket or shirt:** inside front edges, facing, lining, and under-collar - **Dress or top with deep neckline:** inside front and back necklines - **Trousers or skirt:** inside waistband and any area revealed by the intended hollow opening - **Rolled or turned cuff:** inside cuff and sleeve opening - **Asymmetric garment:** both sides and every fastening state used in the final The rule is simple: if the mannequin currently hides an area that will appear in the finished image, photograph that area. “Repair in post” is not a source plan. ## Setup before the shutter Use a mannequin close to the sample size. A garment stretched across a form does not become truthful when the form disappears. If the sample is larger, pin from positions that will remain hidden and keep tension symmetrical. If it is smaller, use the correct form rather than forcing closures or changing silhouette. Steam or press deliberately. The objective is not to erase every wrinkle; it is to show the material as it should arrive while retaining believable fabric behavior. Over-clipping and heavy retouching produce a hard paper silhouette that no longer describes the garment. Fix the camera, focal length, height, lighting, and garment centerline across the set. Front, back, and insert views need to agree geometrically. A collar insert shot from above cannot be dropped convincingly into a body frame photographed straight on. Use soft, even light that resolves texture without a hard shadow crossing the garment. Keep exposure and white balance fixed across colorways. Shopify recommends consistent aspect ratios for featured images because collection grids display them side by side; that consistency begins in capture, not in the final crop. See Shopify's [product media guidance](https://help.shopify.com/en/manual/products/product-media/product-media-types). ## Which garments work best **Strong candidates:** shirts, simple tops, tailored jackets, coats, knitwear with stable edges, dresses with clear construction, trousers, and skirts. These benefit from visible volume and have interiors that can be captured predictably. **More demanding:** sheer fabric, lace, fringe, open knits, reflective materials, complex layering, very loose drape, deep gathers, and garments with many ties or straps. Removing the support through transparency can become a pixel-level job, and reconstructing hidden layers can turn into invention. **Often better on-model:** garments whose central question is fit, movement, stretch, or proportion on a body. Ghost imagery can show the product cleanly, but a shopper may still need a real or reviewed on-model view. [Flat lay to on-model](https://www.astria.ai/articles/flat-lay-to-on-model/) covers the source and review requirements for that derivative. ## Retouching without breaking the garment ### Build from evidence The base silhouette comes from the dressed frame. The hidden interior comes from the insert frame. Use cloning or generation only for small continuity repairs where the underlying construction is known. A plausible lapel or seam is not necessarily the sold lapel or seam. ### Preserve asymmetry that is real Do not mirror one sleeve, pocket, or lapel over the other unless the garment is actually symmetrical and the duplicated details remain correct. Logos, embroidered marks, button sides, zipper pulls, and directional prints expose mirroring immediately. ### Keep fabric physics believable The garment should hold volume where the form supported it, then fall according to its material. Removing every tension cue leaves an object floating in a vacuum. Retain controlled depth and a restrained grounding shadow if the channel permits it. ### Match the set, not just the frame Review twenty outputs together. Shoulder width, hem height, scale, crop, background, and shadow should stay fixed across the catalog. A beautiful individual composite can still damage the collection grid if it is 8% larger than every neighboring product. ## Where AI belongs AI is useful for segmentation, background cleanup, small edge reconstruction, shadow normalization, and applying a reviewed treatment across many products. It can also create alternate packshot or contextual assets from an approved reference set. The risky part is hidden geometry. If only a front view exists, a model may invent the inside collar. If a sleeve is folded behind the torso, it may generate a plausible cuff position. Those are generated derivatives, not product evidence. Use a three-level review queue: - **Low risk:** solid, simple, opaque garments with complete source views; batch and spot-check. - **Medium risk:** prints, hardware, layered collars, open fronts; review every output. - **High risk:** sheer fabric, intricate construction, asymmetric details, or missing inserts; capture again or send to manual retouching. The point of automation is to spend human attention where construction makes it valuable, not to remove the approval gate. ## Shopify and Amazon implementation ### Shopify Shopify accepts product images alongside video and 3D media, and its image service creates sizes for different storefront contexts. A ghost mannequin frame can be the featured image when it is the clearest representation of the product, followed by back, detail, on-model, scale, and motion views in the order that answers buying questions. The [Shopify product media guide](https://www.astria.ai/articles/shopify-product-media-guide/) covers ordering and technical limits. Keep the ghost master uncropped, then make the storefront crop from it. Do not permanently destroy sleeve, hem, or whitespace just to fix one theme breakpoint. ### Amazon Do not transfer the Shopify choice automatically. Amazon's public advertising guidance recommends a plain white background, at least 80% product fill, four or more views, and a minimum 1,000-pixel dimension for zoom. But Seller Central category rules decide whether a particular apparel main image must be on a human model or off-model, and those rules can differ by garment type and marketplace. Check the live category style guide first. Use the ghost image as a supporting view when it clarifies construction and the category permits it. The main-image workflow is detailed in [Amazon product images for apparel](https://www.astria.ai/articles/amazon-product-images-apparel/). ## The pre-publish QC gate Check the final image against the physical sample, not against the retoucher's previous export: - Neck and collar depth are anatomically and structurally plausible - Inside label, facing, lining, and seam positions are correct - Shoulder width and slope match the garment - Sleeves are equal only where the garment is equal - Buttons, buttonholes, zipper, pockets, and hardware are complete - Directional prints cross seams correctly - Fabric transparency and edge softness are believable - Hem is level where intended and retains real drape - Color matches the approved photographed reference - Shadow is allowed by the destination and consistent across the set - Scale, crop, and whitespace match the catalog standard - Front, back, and detail files map to the correct SKU and colorway If the reviewer cannot tell whether an odd join is a real feature or a composite error, return to the sample. Do not resolve ambiguity by making the image prettier. ## A reusable file manifest At collection scale, name and track the views. A minimal row needs: | Field | Example | | --- | --- | | SKU / variant | `JK-104-NVY-M` | | Source view | `front_dressed`, `inside_neck`, `back_dressed` | | Product source of truth | Sample version or approved photo set | | Output role | Shopify featured, Amazon alternate, wholesale line sheet | | Retouch method | Manual composite, AI-assisted cleanup | | Accuracy checks | Color, collar, hardware, print, hem | | Approver / status | Named reviewer, approved or reshoot | That manifest is what turns a retouching trick into a production system. It also makes cost per SKU honest: capture, insert views, retouch, review, and rework all belong in the model. See [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/). ## Frequently asked questions ### What is ghost mannequin photography? Ghost mannequin photography shows a garment holding three-dimensional body volume without a visible person or mannequin. It is usually made by photographing the garment on a mannequin, capturing hidden areas such as the inside neck separately, and compositing the views into one clean product image. ### What photos are needed for a ghost mannequin image? At minimum, capture the dressed front and back plus separate insert views for every area the mannequin hides: commonly the inside neck and collar, and sometimes the inside hem, hood, sleeves, or waistband. Complex garments need more inserts, not more invention in retouching. ### Is a ghost mannequin image the same as an on-model photo? No. Ghost mannequin photography isolates garment shape and construction without a visible person. On-model photography shows styling, scale, and one fit state on a particular body. Most apparel catalogs benefit from using each for the question it answers. ### Can AI create ghost mannequin images? AI can help remove a mannequin, reconstruct simple hidden regions, normalize backgrounds, and create controlled derivatives. It can also invent a plausible but wrong neck join, seam, lining, or shape. Use supplied insert views and require visual approval against the actual garment. ### Can ghost mannequin photography be used as an Amazon main image? Do not assume so. Amazon main-image rules vary by category and marketplace, and some apparel rules require adult clothing to be shown on a human model while other product types must be off-model. Check the current category style guide in Seller Central before producing the main-image set. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # How to Create a Lookbook: 8 Steps + Layout Checklist Source: https://www.astria.ai/articles/how-to-create-a-lookbook/ To create a fashion lookbook, choose the audience, write a brief, select and photograph the looks, then sequence the images into a PDF or web edition. Add the buying information your audience needs and check every page before sending. The eight stages below take a small brand or studio from the first brief to delivery. **The short version:** plan the finished document before producing its images. Page order, product information, and delivery format determine which shots you actually need. ## From a skirt photo to a styled look This look combines the skirt with separate photos of the sweatshirt, socks, and shoes. Compare the skirt’s pleats, length, and color with the original. ![Grey pleated skirt product reference](https://www.astria.ai/articles/img/prompt-recipes/fashion-skirt-reference.webp) Original skirt photo ![Generated model wearing the referenced skirt with a sweatshirt, socks, and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp) AI image using the skirt and other product photos [View this generation](https://www.astria.ai/prompts/35818293?ws=54)[Try the shirt template](https://www.astria.ai/p/basic-shirt) ## Stage 1 — Decide who it is for Before anything visual. A lookbook made for wholesale buyers and one made for customers are different documents wearing similar clothes. **Wholesale buyers** need to place an order. They want clear garment views, style numbers, colorway ranges, fabric composition, price points, and delivery windows. Mood matters, but a beautiful lookbook that omits the style numbers will be sent back. **Press** need one striking image and a clear story, delivered in a format they can drop into a layout. **Customers** need desire and context — the world, the styling, the sense that this collection is for someone like them. Trying to serve all three in one document produces something that serves none of them. Decide, then build a variant if you must. ## Stage 2 — Write the brief One page. If it runs longer, it is not a brief. - **The collection in a sentence.** What is this season about? - **The reference points.** Three to five images that are not fashion photographs — a film still, a building, a colour, a painting. Fashion references produce imitation; non-fashion references produce direction. - **The casting.** Described as a person, not a demographic. - **The world.** Place, season, time of day, quality of light. - **What this collection is not.** The most useful line in any brief and the one most often missing. ## Stage 3 — Line sheet and shot list The line sheet is every piece with style number, colorways, fabric, and price. Boring and non-negotiable. The shot list is what you will actually produce, and it must be shorter than the line sheet. A working ratio for a season: - 12–24 full looks - 4–8 detail shots — fabric, hardware, trim, the things a buyer wants to see up close - 2–3 hero images for the cover and press - Optionally 1–2 group shots, which are disproportionately expensive to produce well Include every SKU and you have made a catalog. That is a different, also-necessary document. ### A shot list you can copy Give each planned image a job before booking production. Here is a sample for three looks; expand it to fit the collection. | Image | Look and products | View | Where it goes | | --- | --- | --- | --- | | 01 | Look A: jacket J01, trouser T01 | Hero with space for the collection title | Cover and opening web section | | 02 | Look A: J01, T01 | Full length, clear silhouette | First look page | | 03 | Look B: knit K02, skirt S02 | Three-quarter, showing the layering | Second look page | | 04 | Look B: K02 | Close detail of the knit | Detail page beside the full look | | 05 | Look C: dress D03 | Full length, a different pose | Closing look page | Add colourway, sample readiness, orientation, and approval owner as columns in your working sheet. A missing back view is easy to request here and expensive to discover during layout. ## Stage 4 — Casting, location, styling The three decisions that determine whether the result looks like anything. **Casting** — a specific presence, not a checkbox. Whether this is a booked model or a stored reference does not change how carefully it should be chosen. **Location** — specific beats generic. "A coastal town in October" gives a photographer something to work with; "outdoors" does not. **Styling** — pull the looks and photograph them on a hanger before shoot day. Every stylist knows some combinations that read beautifully on paper fall apart on a body. ## Stage 5 — Produce **Traditionally:** book the crew, ship the samples, shoot for one or two days, select, retouch. The dominant cost is scheduling — everyone and everything in one place on one date — and the dominant risk is that samples arrive late. **With AI production:** build one hero look completely and get it approved, then derive the rest against that approved treatment. The calendar changes shape rather than simply shrinking: creative direction still takes days, but sample logistics and scheduling largely disappear, and revisions stop being reshoots. Either way, the discipline is the same — approve one look completely before producing twenty. The mechanics of the AI route are in the [AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/), and casting continuity specifically in [consistent AI fashion models](https://www.astria.ai/articles/consistent-ai-fashion-models/). ## Stage 6 — Sequence The stage almost everyone skips, and the one that separates a lookbook from a folder. A lookbook is read in order. Which look opens? Which closes? Where does the rhythm shift from wide to close? Where does colour arrive, and where does it rest? Lay every image out small, all at once. Then: - Cut the weakest three. Eighteen strong looks beat twenty-four with six passengers. - Check the rhythm — three wide shots in a row is monotonous; alternating every frame is agitated. - Group by story, not by product category. "Knitwear" is a merchandising unit, not a narrative one. - Read it back to front. Weak endings are more common than weak openings and less often noticed. ### A simple lookbook page layout For a first edition, use this page order as a starting point: 1. **Cover:** brand, collection or season, and one strong image. 2. **Opening:** a short collection note and a look that establishes the direction. 3. **Look pages:** give the clothes enough space; keep captions and look numbers in consistent positions. 4. **Detail pages:** place fabric or construction close-ups beside the looks they explain. 5. **Closing page:** a final image, contact or order link, and the next action for the reader. In the wholesale edition, connect each look to its style numbers and line sheet. In the consumer edition, link to the relevant products. Keep buying information as editable text so a price or delivery change does not require altering the photography. You can assemble this in a slide editor, page-layout tool, or your site's content system. Choose the tool by what must remain editable and who will update it next season. ## Stage 7 — Format and deliver From one production, derive: - **PDF** for buyers and press. Aim for a compact download, and send a link if the file is too large for the recipient's attachment limit. Style numbers and delivery windows on the wholesale version. - **Web** for the site, in the correct aspect ratios, optimised, with real alt text. - **Vertical crops** for social, planned from the start rather than salvaged afterwards. - **Print**, if you are producing one, at the right colour profile and resolution. Planning these formats before production is the difference between four deliverables and four productions. ### Before you export the lookbook Read the document at the size the recipient will use. Check that captions remain legible on a phone, image crops preserve the garments, and each product link opens the correct style or colourway. Match wholesale codes and dates against the current line sheet. Open the exported PDF itself: a correct working file can still produce clipped text or broken links. Use descriptive image filenames and keep a master with editable text, full-size images, and a version date. Have someone who did not build the layout check it before delivery. ## Stage 8 — Distribute - Send to buyers with a clear subject line, order deadline, and delivery window. - Send to press with a short note and a link, not a 40 MB attachment. - Roll out on-site and on social over weeks rather than in one dump. - Archive everything — the brief, the approved treatment, the selects — so next season starts from something. ## An example production calendar These are planning allowances for a small seasonal brief, not measured industry averages. Replace them with your team's estimates and supplier commitments. | Stage | Traditional | With AI production | | --- | --- | --- | | Brief and references | 3–5 days | 3–5 days | | Line sheet and shot list | 2–3 days | 2–3 days | | Casting, location, styling | 1–2 weeks | 2–4 days | | Production | 1–2 shoot days + 1–2 weeks post | 2–4 days including revisions | | Sequencing and layout | 3–5 days | 3–5 days | | Formats and delivery | 3–5 days | 1–2 days | The pattern is worth reading carefully: the creative stages barely move, because they are judgment. The logistical and revision stages compress hard. Any vendor claiming a lookbook in an afternoon is quoting the render time and ignoring the job. ## Hand off generated imagery to lookbook design **Workflow update October 11, 2026.** A generated collection still needs selection, sequencing, and layout. Before design begins, verify that each selected file maps to the correct SKU, colorway, and approved revision. Supply product names and wholesale information as approved data rather than deriving them from the picture. - Deliver accepted images separately from candidates and rejected versions. - Include the shot coverage matrix, casting and treatment references, and named exceptions. - Mark missing product views explicitly; do not let layout hide a coverage gap. - Review final crops, product text, reading order, margins, and export quality at delivery size. Use the [studio handoff template](https://www.astria.ai/articles/downloads/ai-production-workflows/studio-handoff.md) and [output manifest](https://www.astria.ai/articles/downloads/ai-production-workflows/output-manifest.csv). The [Codex workflow](https://www.astria.ai/articles/create-lookbooks-with-codex/) and [Claude Code workflow](https://www.astria.ai/articles/create-lookbooks-with-claude-code/) show how to coordinate the inputs before the design step. ## Frequently asked questions ### What should a lookbook include? A curated selection of styled looks in a deliberate order, usually twelve to twenty-four for a season, with enough detail shots to convey fabric and construction. Wholesale lookbooks additionally carry style numbers, colorways, fabric composition, and delivery windows. ### How long does it take to make a lookbook? For the small seasonal production outlined here, allow roughly four to eight weeks as a planning example, not a guaranteed turnaround. Sample availability, approvals, revisions, and layout can take longer than the shoot itself. ### What is the difference between a wholesale and a consumer lookbook? A wholesale lookbook is a buying tool: it needs style numbers, colorways, composition, price points, and delivery dates alongside clear garment views. A consumer lookbook is a mood piece and carries almost none of that. ### What format should a lookbook be delivered in? Usually several: a PDF for buyers and press, a web version for the site, and vertical crops for social. Deriving all of them from one production is far cheaper than treating each as a separate deliverable. ### Do you need a stylist for a lookbook? For anything beyond single-product shots, yes. Styling is knowledge about clothes — proportion, layering, what goes with what — and it is the part that most visibly separates a considered lookbook from a grid of garments. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # How to Keep the Same AI Model Across a Whole Collection Source: https://www.astria.ai/articles/consistent-ai-fashion-models/ Look at an AI-produced catalog that has gone wrong and you will usually find the same failure: it is not one model wearing forty garments, it is forty slightly different people. The face changes. The age wanders. The build shifts between the knitwear and the outerwear. **The short version:** description is not identity. If your model is specified in words, you are recasting on every generation. ## Why description fails "A woman in her late twenties, shoulder-length brown hair, olive skin, natural makeup" feels specific. It is not. It describes a category that contains an enormous number of distinct faces, and each generation is free to choose a different one. Add more adjectives and you narrow the category slightly; you never reduce it to a person. This is why prompt-only workflows produce a _type_ rather than a _model_. The images are consistent in the way that stock photography is consistent — same genre, different humans. The fix is categorical rather than incremental: the likeness has to exist as a stored thing that gets pointed at, not a description that gets re-interpreted. Once a face is a reference the production can reuse, "the same model" becomes a fact about the setup rather than a hope about the output. ## Four kinds of consistency, often confused Teams say "consistency" and mean different things. All four are needed, and they fail independently. **Identity consistency** — the same recognisable person. This is what references solve. **Styling consistency** — hair and makeup that stay the same. A stored likeness will happily give you the same face with a different fringe. Hair, in particular, drifts more than faces do and is worth calling out explicitly in the direction. **Treatment consistency** — same light, same grade, same lens character, same background logic. Independent of who the person is. **Framing consistency** — crops and proportions that match across the set. A catalog can fail on any one of these while passing the other three, and the symptom looks similar to a non-specialist: "it looks off." Diagnosing which of the four broke is the first step in fixing it. ## What actually works **Store the likeness, don't describe it.** Whether via a trained model from eight to sixteen varied photos or an instant reference from fewer, the point is the same: the identity becomes an asset in the production rather than a sentence in a prompt. **Vary the source material.** Reference images should span angles, lighting conditions, and expressions. Sixteen frames from one burst teach the system one lighting condition and one angle, and the likeness collapses the moment you ask for anything else. **Separate the person from everything else.** Casting, garment, pose, and scene should be independently specifiable. When they are entangled — one long prompt describing all four — changing the pose perturbs the face. This separation is the single biggest practical difference between tools that hold consistency and tools that do not. **Lock the treatment before scaling.** Approve light, grade, and framing on a handful of products first. Drift compounds; a slightly warm bias in the setup becomes visible warmth by image 200. **Review as a set, never as singles.** This is a process fix, not a technical one, and it catches more than any technical measure. Contact-sheet the whole run at a size where faces are legible. **Re-anchor periodically.** On long runs, check late images against the earliest approved ones rather than against the images immediately before them. Drift is a slow gradient; comparing neighbours will never reveal it. ## What does not work **Seeds.** Reusing a seed reproduces a similar result for a near-identical prompt. Change the pose, the garment, or the scene — which is the entire job — and the likeness moves. Useful for reproducibility, not for identity. **Longer descriptions.** Adding adjectives narrows the category but never collapses it to one person. Twenty-word casting descriptions are a symptom of the wrong approach, not a solution to it. **Picking the best of many.** Generating forty options and choosing the most similar is expensive, slow, and still drifts, because you are selecting from a distribution rather than fixing it. **Face-swapping at the end.** Pasting a consistent face onto inconsistent bodies produces a characteristic uncanny result: the head belongs to someone whose build, skin tone, and lighting do not match the body beneath it. ## The extended-casting problem One consequence worth stating: consistency work is what makes broad casting practical. If every image is a fresh roll of the dice, representing a range of ages, sizes, and bodies across a collection is unmanageable — you cannot hold six different people consistent when you cannot hold one. Once casting is a stored reference, a brand can run a genuinely varied cast — including children's ranges and extended sizing — with the same reliability as a single model. Whether a brand does that is a values question. Consistency is what turns it from an aspiration into an operation. For portraits of real employees, the challenge is different: preserve each person while matching the photographic treatment across people. Use the [AI team photos and headshots workflow](https://www.astria.ai/articles/ai-headshots-for-teams/) for the submission brief and approval process. ## A test that predicts real-world behaviour Before committing to a platform, run this: 1. Establish one model. 2. Produce ten images: five garments, two poses each, one indoor scene and one outdoor. 3. Contact-sheet them at a size where faces are clearly legible. 4. Ask someone who has not seen them: "Is this one person or more than one?" 5. Then change the scene to something quite different — night, harsh light, a different location — and repeat. Step five is where most tools fail. Consistency under similar conditions is common; consistency across genuinely different lighting is what a real collection demands. For the full production context, see the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/). For lookbooks specifically, where casting continuity carries the narrative, see the [AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/). ## Frequently asked questions ### Why does the same AI model look different in every image? Because a text description is not an identity. Words like "woman, 28, brown hair" describe a category containing millions of faces, and each generation picks a different one. Consistency requires the likeness to be stored as a reusable reference, not re-described each time. ### Do seeds keep a character consistent? Only weakly. Reusing a seed reproduces similar results for a near-identical prompt, but the likeness drifts as soon as the pose, scene, or framing changes — which is exactly what a collection requires. Seeds are a reproducibility tool, not an identity tool. ### How many reference photos are needed for a consistent model? For a trained likeness, typically eight to sixteen varied images. Instant reference approaches can work from far fewer. Variety across angle, lighting, and expression matters more than volume in either case. ### What is casting drift? The gradual change in a model's appearance across a long production run — subtly different face, age, or build between early and late images. It is rarely visible image-by-image and obvious when the set is viewed together. ### Can you keep the same model across images and video? Yes, when the same stored reference drives both. This is what allows a still campaign and its motion cutdowns to feature recognisably the same person rather than two similar-looking people. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). --- # Shopify Product Media: Images, Video, 3D Models, and the Right Order Source: https://www.astria.ai/articles/shopify-product-media-guide/ Shopify product media is one ordered set, not three separate feature checklists. The first item may represent the product in collections and cart; the last item may never be reached. Images, video, and 3D only work when the sequence answers the shopper's questions in order. **The short version:** lead with a product-identifying still, earn each richer format with a distinct job, and test the live theme on mobile. Shopify's upload maximum is not your performance budget. ## A product photo and an AI packshot for the listing This dress example shows how a product photo becomes a clean packshot. Check the garment details and your store’s image requirements before using the result. ![Original dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Original dress photo ![Generated dress packshot with volume and no visible mannequin](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp) AI-generated dress packshot [View this generation](https://www.astria.ai/prompts/40128059?ws=268)[Try the dress template](https://www.astria.ai/p/3d-packshots-dress) **Last tested against Shopify documentation:** August 31, 2026. File and plan limits are volatile; verify Shopify's [product media types](https://help.shopify.com/en/manual/products/product-media/product-media-types) and [media management](https://help.shopify.com/en/manual/products/product-media/add-media) before a bulk delivery. ## The current Shopify media rules | Media | Current Shopify requirement | Operational consequence | | --- | --- | --- | | **Images** | Under 20 MB; up to 5000 × 5000 pixels or 25 megapixels; common image formats accepted | Use a consistent featured-image ratio and preserve a high-resolution master outside Shopify | | **3D models** | GLB or USDZ; up to 500 MB; files over 15 MB are automatically optimized | A 500 MB upload can still be unusably heavy; optimize and test the delivered model | | **Uploaded video** | MP4, MOV, or WebM; up to 10 minutes, 1 GB, and 4K | Export a short commerce edit; do not treat ten minutes or 1 GB as a target | | **Embedded video** | YouTube or Vimeo URL | Check privacy, branding, related-video behavior, consent, and performance in the theme | Shopify allows up to 250 media items on a product. It also applies store-level video and 3D upload counts and video-storage limits by plan. Those plan numbers can change, so model the catalog against the live help table rather than copying a limit into a permanent production brief. Two constraints are easy to miss: - The first item is the product's featured media and appears beyond the PDP, including collection, cart, checkout, and home contexts. - Videos and 3D models cannot currently be used as product variant media. That second point changes colorway production. A shared black-jacket video may still appear while a shopper selects cream, unless the theme or implementation deliberately manages the experience. Do not promise variant-specific motion without testing the actual store. ## Give each format one job ### Images: identify and prove Images load quickly, scan well, zoom, and map to variants. They are still the source of truth for exact color, construction, texture, and what is included in the sale. A useful apparel image set normally covers: - Clear front or three-quarter product identification - Back construction - One or two material, hardware, seam, or trim details - On-model fit and scale - A second pose or use state where movement matters - Colorway-specific views Ghost mannequin can isolate construction without a visible body; [the complete ghost mannequin guide](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/) covers the insert views and joins it needs. If approved physical references will also produce on-model, lifestyle, or campaign derivatives, use the [AI product photography workflow](https://www.astria.ai/articles/ai-product-photography/) to separate product evidence from generated variation. For a catalog where every extra angle multiplies across variants, calculate the real [product photography cost per SKU](https://www.astria.ai/articles/product-photography-cost-per-sku/) before setting the standard. ### Video: show change over time Use video for movement, a closure opening, a pocket in use, fabric behavior, scale in motion, assembly, or a controlled rotation. If nothing changes, a still usually communicates the same information faster. Start the video on the product, not a logo sting. Design the first frame as a useful poster even before playback. Keep critical information legible without audio, provide captions when speech carries meaning, and avoid fast edits that make inspection harder. Shopify accepts long and large video files, but the browser experience depends on connection speed and theme behavior. Test load, poster, play controls, looping, fullscreen, captions, and the return to the gallery after playback. ### 3D: let the shopper choose the view Interactive 3D is for controlled inspection, configuration, and—in supported experiences—augmented reality. It is not another word for video. A GLB or USDZ model carries geometry and materials; the shopper can move the camera instead of watching a fixed turn. Use it where underside, hardware, shape, or spatial scale is a real buying question. Footwear, bags, accessories, and furniture are stronger candidates than soft garments with size-dependent drape. [3D packshots for ecommerce](https://www.astria.ai/articles/3d-packshots-ecommerce/) covers the format distinction, modeling inputs, performance gate, and AR review. ## The right order is a question order There is no universal seven-slot formula, but this is a defensible starting sequence for apparel: 1. **Featured still:** identify the exact product and selected color at thumbnail size. 2. **Alternate product view:** show the back or the construction hidden in the hero. 3. **Fit or scale:** on-model view with an explicit sample size and useful body framing. 4. **Critical detail:** texture, closure, pocket, lining, sole, hardware, or finish. 5. **Motion:** show fabric behavior, use, opening, or a short rotation. 6. **Additional proof:** interior, care-relevant detail, second scale view, or colorway evidence. 7. **Interactive 3D:** place it earlier only when free inspection is central to the purchase. This is a hypothesis, not a rule. A technical bag may need the opening demonstration in slot three. A sculptural shoe may benefit from 3D before a lifestyle frame. A simple tee may need neither. Write the unanswered question under every slot. If two neighboring assets answer the same question, remove one or replace it with evidence the gallery lacks. ## The featured image has a wider job Shopify calls the first item the main or featured media. Because it appears away from the product page, judge it in four states: - A small collection-card thumbnail - A search result or recommendation rail - The cart with the selected variant - The large product viewer A dramatic close crop that works in the viewer may be illegible in a collection grid. A video or 3D poster may not clearly identify the chosen color. A lifestyle image may make the product too small. The safe default is a clear, colorway-specific still with a consistent aspect ratio and product scale across the catalog. Shopify says square product images usually display best at 2048 × 2048 pixels, but theme layout and brand art direction still decide the ratio. The important part is consistency: collection neighbors should not jump in apparent size because their whitespace was handled differently. ## Theme support is part of production All Shopify themes support product images. Video and 3D need a theme that supports them or custom theme work. Shopify's help center says its Online Store 2.0 themes and Horizon family support those media, while third-party themes depend on their current implementation. Do not sign off media in the admin preview alone. Test the storefront: - Desktop mouse and keyboard interaction - iOS and Android touch behavior - Swipe conflict between gallery and 3D rotation - Poster image before video play or model load - Loading, failure, and reduced-data states - Zoom and fullscreen behavior - Captions and controls - Selected variant after navigating media - Collection and cart thumbnails - Core Web Vitals and real mobile load time The theme may crop a thumbnail differently from the main viewer. It may lazy-load media well or badly. It may hide controls at one breakpoint. Those are shipping defects even when every source file meets Shopify's upload specification. ## Variant and colorway strategy Because images can map to variants while video and 3D cannot, choose one of three honest patterns: **Shared rich media.** Use one video or model only when the demonstrated behavior and visible color are genuinely common. A black product in a shared clip beside a selected cream variant is not neutral. **Color-neutral demonstration.** Frame the media around function or construction where color does not mislead, then keep color proof in variant images. **Custom implementation.** If variant-specific rich media is commercially necessary, scope theme or app work and test it. Do not encode a workaround in filenames and assume Shopify will understand it. Keep one approved photographed image per colorway as the truth reference. Generated recolors and derived motion still need comparison against the sold variant before publication. ## A collection-scale media map One row per SKU/variant prevents the PDP order from becoming a manual drag-and-drop exercise: | Field | What to record | | --- | --- | | Product / variant | Handle, SKU, color, size group | | Media role | Featured, back, fit, detail, video, 3D, poster | | Buying question | What this asset answers | | Source of truth | Approved photo, physical sample, measured model | | Shopify position | Intended order and variant mapping | | Accuracy checks | Color, construction, logo, hardware, scale | | Accessibility | Alt text, captions, audio dependence | | Performance | File size, duration, model weight | | Status | Review owner, approval, replacement date | Astria's useful role here is not replacing every source image. It is storing product references and an approved visual treatment so reviewed derivatives—on-model frames, details, social crops, and motion—can be produced consistently across the collection. The base evidence stays attached to the SKU. ## Measure the gallery, not the novelty Instrument media selection, video starts and completions, 3D loads and interactions, zoom, add-to-cart, purchase, and returns. Segment by device and product category. Do not read “people who used 3D converted more” as proof that 3D caused the conversion. High-intent shoppers are more likely to inspect everything. Prefer a controlled test or a phased rollout across comparable products, and watch performance cost alongside commerce behavior. The decision is not whether video or 3D is impressive. It is whether a specific asset makes the product easier to buy from. ## Frequently asked questions ### What product media does Shopify support? Shopify product media supports images, hosted or embedded video, and interactive 3D models. Images work in all themes; video and 3D require a compatible theme or theme customization. ### What should the first Shopify product media item be? Usually a clear still image that identifies the exact product and colorway at thumbnail size. Shopify uses the first item as featured media in other storefront contexts, including collection and cart surfaces, so an autoplay clip or ambiguous detail is rarely the safest default. ### Can Shopify video and 3D models be assigned to variants? No. Shopify's current help documentation says videos and 3D models cannot be used as product variant media. Images can represent variants, so colorway and variant logic needs to be designed around that constraint. ### What are Shopify's current video limits? For directly uploaded product video, Shopify currently allows MP4, MOV, or WebM files up to 10 minutes, 1 GB, and 4K resolution. It also supports YouTube and Vimeo embeds. Treat these as upload ceilings, not recommended production targets. ### Should every Shopify product have video and 3D? No. Add a format only when it answers a buying question better than the existing gallery. Video is useful for movement, use, and changing detail; 3D is useful for shopper-controlled inspection and scale. Extra media that repeats the same information adds load and review work without improving the decision. [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Virtual Try-On for Fashion Brands (2026): What It Actually Means Source: https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/ Two genuinely different technologies are sold under the name "virtual try-on," and the confusion costs brands real money. One produces the imagery you publish. The other is a widget your customer interacts with. They have different buyers, different economics, and different failure modes. **The short version:** if your problem is that producing on-model imagery for every SKU is slow and expensive, you want brand-side try-on — which is really a production question, not a fitting-room one. ## The two things called virtual try-on **Brand-side try-on** takes a garment — a flat lay, a packshot, a sample on a hanger — and produces a photograph of a model wearing it. The output is an image file. It goes on the product page, in the lookbook, into the ad, onto Instagram. The buyer is whoever owns imagery: an ecommerce manager, a brand marketer, a studio producing for the brand. **Shopper-side try-on** is an interface element. A customer uploads a photo or uses their camera, and sees the garment on their own body. The output is an experience, not an asset. The buyer is whoever owns conversion rate and returns. Vendors blur this because the underlying image work rhymes. The purchasing decision does not rhyme at all. ![A garment placed on a model without a physical shoot](https://www.astria.ai/articles/assets/images/image14-32f9e1bedb3aed3a55d93c09e13463b9.webp "image_tooltip") ## Why brand-side is where the money usually is The arithmetic is unsentimental. A brand with 200 SKUs across three colorways needs on-model imagery for 600 variants, refreshed each season. Traditional production means model booking, studio time, a stylist, sample logistics, a photographer, and retouching — and the whole apparatus has to be re-run when a product drops late or a colorway is added. That cost is certain, recurring, and proportional to catalog size. It is the kind of cost a tool can attack directly. Shopper-side try-on attacks a different number: conversion rate and return rate. The upside is real — vendors in this space commonly cite meaningful reductions in fit-related returns — but it is a percentage improvement on traffic you already have. If you have a lot of traffic, that percentage is worth a great deal. If you do not, you have installed a widget. Most brands have the imagery problem long before they have the traffic to justify the widget. ## Where the technology is reliable, and where it is not Being specific about this matters more than any vendor demo, because demos are shot on the garments that work. **Reliable:** solid-colour knits, tees, simple dresses, tailored pieces with clean lines, outerwear with defined structure. Standard studio and lifestyle scenes. Standard casting. **Needs checking every time:** directional prints and stripes, which can mirror or misalign across a seam; logo placement and scale; sheer and semi-sheer fabrics; complex drape and gathering; fine hardware — buckles, zips, eyelets, chain straps; anything where the _construction_ is the selling point. **Still genuinely hard:** garments whose fit is the product — technical outerwear, structured tailoring, compression and performance wear, anything where a customer buys on how it sits rather than how it looks. ![Garment detail reproduced on a generated model](https://www.astria.ai/articles/assets/images/image11-98336e60e17ce91ddc533e9376866047.webp "image_tooltip") The practical rule: build your evaluation set out of your hardest ten garments, not a representative sample. A tool that handles those handles your catalog. A tool evaluated on your easiest ten tells you nothing you can plan around. ## Try-on is one operation inside a much bigger job Here is the trap. Try-on answers "put this garment on a body." It does not answer any of the questions that actually make a catalog look like one brand: - Which body? Whose casting, at what ages and sizes, and is it the same person across the collection? - Lit how, and in what scene? - Framed how — full length, three-quarter, detail crop — and in which aspect ratios for which channels? - What happens in six weeks when the creative director wants a warmer grade across everything? A tool that only does try-on leaves all four to be re-decided every session, by whoever happens to be at the keyboard. That is how catalogs drift: not through bad images, but through 600 individually-fine images that do not look like they came from the same brand. The production framing treats the approved treatment — casting, styling, scene, crop, lighting, format — as the thing you store and reuse. The garment is the variable. Everything else is a decision you make once, approve once, and apply across the collection. When the creative director wants that warmer grade, it is one edit, not 600. That distinction is covered in more depth in the [AI fashion photoshoot guide](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/) and, for the garment-specific workflow, in [flat lay to on-model](https://www.astria.ai/articles/flat-lay-to-on-model/). ![One creative direction applied consistently across products](https://www.astria.ai/articles/assets/images/image8-2aef9e6c5085afbaed3b92635fa28039.webp "image_tooltip") ## A workable evaluation, in five steps 1. **Pick ten hard garments.** Prints, sheers, hardware, structured pieces. Not your bestsellers — your most awkward. 2. **Run them through every tool on your shortlist.** Same inputs, same day. 3. **Review blind, with a senior creative.** Garment fidelity first: is the print aligned, is the hardware right, does the drape read as this fabric? Then anatomy, then styling, then brand fit. 4. **Change something after approval.** Add a colorway. Change the crop convention. Count the operations each tool requires. This is the test most evaluations skip and the one that predicts your actual cost. 5. **Check what you own at the end.** Images only, or the casting, references, and approved treatment that produced them? Step four is the one that separates a tool from a system. Producing the first image is easy everywhere. Producing the six-hundredth consistently, and changing your mind afterwards, is where the difference lives. ## Disclosure and casting Two things worth deciding deliberately rather than discovering later. **Disclosure.** Requirements for labelling synthetic imagery in advertising vary by market and are actively changing. Confirm current obligations for every market you sell into. Beyond compliance, decide what your brand wants to say about it — some brands are explicit and it costs them nothing; others treat it as a production detail like retouching. **Casting.** Generated casting can represent ages, sizes, and bodies that would be expensive to book, which is a genuine argument for broader representation. It can equally become a way to depict diversity without paying anyone for it. That is a brand decision, and it is better made on purpose. ## Frequently asked questions ### What is virtual try-on? The term covers two different things. Brand-side try-on places a garment on a model to produce imagery the brand publishes. Shopper-side try-on lets a customer see a garment on themselves in the storefront. They solve different problems and are bought by different teams. ### Which type of virtual try-on do most brands need? Brand-side, in most cases. It addresses the immediate and recurring cost of producing on-model imagery for every SKU and colorway. Shopper-side try-on is a conversion experiment that depends on traffic volume to pay back. ### Does virtual try-on reproduce garments accurately? Accuracy varies by garment. Simple silhouettes in solid colours reproduce reliably. Complex construction, directional prints, sheer fabrics, and fine hardware are where errors appear. Test your hardest garments before committing, not your easiest. ### Do I need to disclose AI-generated model imagery? Requirements vary by market and are changing. Several jurisdictions have introduced or proposed disclosure rules for synthetic imagery in advertising. Check current obligations for the markets you sell in, and treat disclosure as a brand decision as much as a legal one. ### What is the difference between virtual try-on and an AI photoshoot? Try-on is one operation: put this garment on a body. An AI photoshoot is the whole production — casting, styling, scene, lighting, crop, and format — applied consistently across a collection. Try-on is a feature inside that larger job. For platform-level decisions, see how Astria compares with [Botika](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/), [FASHN](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/), and [DRESSX](https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/). [Explore Astria for fashion and ecommerce](https://www.astria.ai/ecommerce). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Best AI Image Models for Fashion and Product Photography (2026 Benchmark) Source: https://www.astria.ai/articles/best-ai-image-models-fashion/ **Nano Banana 2 is the best general choice for fashion.** GPT Image 2 is our first specialist choice for beauty and text. Seedream 5 is the practical choice for swimwear, rich color, difficult texture, and strong identity preservation. Muse is the reference-first model for jewelry and briefs with several simultaneous constraints. That is the result of 11 matched briefs, four core models, and 131 returned images—not a generic ranking copied from a public leaderboard. A later MAI-Image-2.6 qualification adds reference-led direct-edit evidence without pretending it was part of the original matched grid. GPT Image 2.5 is covered in a current matched follow-up: the same Sloane dress-and-bag reference brief rendered in **Sunburst**, **Flare**, **Nano Banana 2**, **MAI-Image-2.6-Flash**, and **Muse**. It is an additional comparison, not a retroactive change to the original benchmark scorecard. ## The result in one table | Production job | Our first choice | What decided it | | --- | --- | --- | | General fashion | **Nano Banana 2** | Best balance of direction, realism, garment use, identity, and ready-to-refine composition | | Beauty and prominent text | **GPT Image 2** | Most convincing combined beauty-ad finish, exact headline, product placement, and layout | | Swimwear and sensitivity fallback | **Seedream 5 Pro** | Reliable completion of tasteful adult briefs, plus excellent color, skin, texture, and identity | | Difficult color and texture | **Seedream 5 Pro** | Strongest surface and saturated-color rendering in the jacket comparison | | Jewelry and multiple references | **Muse** | Most dependable reference interpretation when product geometry or simultaneous constraints dominate | | One-image commercial edits | **Qualify MAI-Image-2.6 / Flash** | Strong supplied-jacket preservation; current reusable-reference and swimwear caveats prevent a core recommendation | No row means “always use this model.” It means this is where we would start before spending the next generation or retouching dollar. ## MAI-Image-2.6: a challenger, tested separately MAI-Image-2.6 entered Astria's public partner gallery after the four-model benchmark was designed. We attempted to add it to the same reusable-reference matrix on September 8, 2026. Those requests failed with an unexpected endpoint error. Direct single-image editing worked, so we ran a separate reference-led qualification rather than silently changing the benchmark controls. ![Supplied burnt-orange crane jacket reference](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-jacket.webp) **Source**Four cranes, four buttons, jacquard, and scalloped hem ![MAI Image 2.6 direct jacket edit qualification](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-jacket-edit.webp) **MAI-Image-2.6**Excellent first-output jacket preservation ![MAI Image 2.6 Flash direct jacket edit qualification](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-flash-jacket-edit.webp) **Flash**Strong campaign frame with simplified construction detail The jacket and jewelry selections cleared both publication gates: the result is attractive enough to show and the supplied product remains inspectable against its source. A benign adult swimwear edit was blocked. Flash finished the matched jacket cell faster, while the flagship preserved more construction detail. See [Astria's pricing page](https://www.astria.ai/pricing) for current model pricing. That evidence earns MAI a place in the model cluster, but not a fifth column in an incompatible reference benchmark. Read the complete [MAI-Image-2.6 and Flash review](https://www.astria.ai/articles/mai-image-2-6-review/). ## GPT Image 2.5: a matched Sloane dress-hero comparison GPT Image 2.5 launched after the original benchmark capture. To make the new models directly comparable, this addendum reruns source prompt `46557199`: the same Sloane woman, dress, and bag references with the same off-kilter flash-lit bar direction. The source assets were copied from Maison Urban into workspace `896` (Articles), then every model received the same semantic prompt, references, 16:9 request, and one-output count. Browse the live [GPT Image 2.5 Sunburst gallery](https://www.astria.ai/gallery/tunes/5634510/prompts) and [GPT Image 2.5 Flare gallery](https://www.astria.ai/gallery/tunes/5634511/prompts). ![Sloane identity reference for the GPT Image 2.5 comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-source-sloane.webp) **Sloane**Fixed identity reference ![Yellow belted dress reference for the GPT Image 2.5 comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-source-dress.webp) **Dress**Fixed garment reference ![Gold circular-detail bag reference for the GPT Image 2.5 comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-source-bag.webp) **Bag**Fixed accessory reference [![GPT Image 2.5 Sunburst Sloane dress hero with reference dress and bag](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-sunburst-sloane.webp)](https://www.astria.ai/gallery/tunes/5634510/prompts) **Sunburst**Prompt 46636365 · 4K request · stronger diagonal tension [![GPT Image 2.5 Flare Sloane dress hero with reference dress and bag](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-flare-sloane.webp)](https://www.astria.ai/gallery/tunes/5634511/prompts) **Flare**Prompt 46636364 · 4K request · complete bag visibility ![Nano Banana 2 Sloane dress hero with reference dress and bag](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-nano-banana-2-sloane.webp) **Nano Banana 2**Prompt 46636366 · matched 4K request ![MAI Image 2.6 Flash Sloane dress hero with reference dress and bag](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-mai-flash-sloane.webp) **MAI-Image-2.6-Flash**Prompt 46636367 · no shared 4K selector ![Muse Sloane dress hero with reference dress and bag](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-muse-sloane.webp) **Muse**Prompt 46636363 · returned 3:2 from the 16:9 request These are reference-led examples, not a new universal ranking. Sunburst and Flare both returned sharp, commercially credible frames; Sunburst pushed the editorial crop harder, while Flare kept the complete bag more legible. Read the dedicated [GPT Image 2.5 Sunburst vs Flare review](https://www.astria.ai/articles/gpt-image-2-5-review/) for the full controls, source limitation, and production guidance. ## How to read the grids The order is fixed throughout: **Nano Banana 2, GPT Image 2, Seedream 5 Pro, Muse**. Every card shows output `00`, selected before viewing the results. Three images were requested per model and brief, and the other outputs were retained rather than used to pick a prettier winner. All references are synthetic and brand-neutral. Portrait comparisons used 3:4 because it was supported by all four endpoints; jewelry used 1:1. Read the complete [benchmark methodology and disclosure](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/). ## 1\. General fashion: Nano Banana 2 is the default **Brief:** one fixed adult identity, one referenced white tank, ivory wide-leg trousers, black slingbacks, a walking pose, pale-stone gallery, late-afternoon side light, and no text or extra people. This general-fashion strip is omitted from public display because its supplied person-and-garment source set is not published with the benchmark. All four models produced usable fashion frames. Nano Banana 2 earns the default recommendation from its balance across this and the other fashion tasks, not from a runaway win in one image. This is the most important result because it reflects ordinary production. A default should be broadly competent before it is exceptional. Nano Banana 2 followed the art direction, preserved the cast and shirt, gave the fabric believable movement, and produced a composition that did not demand a new workflow decision. ## 2\. Difficult texture and color: Seedream 5 stands out **Brief:** preserve the synthetic jacket's burnt-orange jacquard, teal crane motifs, brass buttons, collar, sleeves, and scalloped hem over a black dress against deep teal. ![Nano Banana 2 orange jacquard jacket benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/texture-nano-banana-2.webp) **Nano Banana 2**Good balance of product detail and portrait ![GPT Image 2 orange jacquard jacket benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/texture-gpt-image-2.webp) **GPT Image 2**Strong finish with small detail variation ![Seedream 5 orange jacquard jacket benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/texture-seedream-5.webp) **Seedream 5 Pro**Best color and surface rendering here ![Muse orange jacquard jacket benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/texture-muse.webp) **Muse**Dramatic result; some details shift or crop The source jacket is synthetic. Ground truth follows the rendered source: four crane motifs and four brass buttons. Seedream 5's advantage is easiest to see rather than reduce to a single score: saturated color remains rich without becoming flat, and the fabric reads as a woven, light-reactive surface instead of an orange print pasted onto clothing. It also keeps the cast identity convincing under harder studio lighting. ## 3\. Swimwear: all four completed; Seedream is the practical fallback Two tasteful briefs tested the same adult identity and cobalt swimsuit: straight-on ecommerce and a calm poolside editorial. Both specified ordinary commercial framing, exact strap and ring details, and no provocative pose. ### Ecommerce ![Nano Banana 2 tasteful swimwear ecommerce benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-ecom-nano-banana-2.webp) **Nano Banana 2**Full-length and commercially clean ![GPT Image 2 tasteful swimwear ecommerce benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-ecom-gpt-image-2.webp) **GPT Image 2**Clean full-length presentation ![Seedream 5 tasteful swimwear ecommerce benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-ecom-seedream-5.webp) **Seedream 5 Pro**Strong garment and identity; tighter crop ![Muse tasteful swimwear ecommerce benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-ecom-muse.webp) **Muse**Dependable reference-grounded frame Seedream preserved the product well but its predetermined first ecommerce output did not satisfy the requested full-length framing. ### Poolside editorial ![Nano Banana 2 poolside adult swimwear editorial benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-editorial-nano-banana-2.webp) **Nano Banana 2**Tasteful output; two of three images returned ![GPT Image 2 poolside adult swimwear editorial benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-editorial-gpt-image-2.webp) **GPT Image 2**Polished resort composition ![Seedream 5 poolside adult swimwear editorial benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-editorial-seedream-5.webp) **Seedream 5 Pro**Reliable completion and strong cobalt tones ![Muse poolside adult swimwear editorial benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-editorial-muse.webp) **Muse**Reference-faithful editorial framing Every endpoint produced safe, tasteful outputs. The Nano Banana 2 request returned two images instead of three, with no reported error or moderation reason; we do not claim to know why. The measured run does not establish a moderation-rate ranking. It does support Seedream 5 as a strong production choice for the category: it completed both briefs, kept the unusual cobalt/coral relationship and asymmetric straps recognizable, and preserved the cast well. In broader use, that makes it the route to try when an otherwise benign adult swimwear job encounters stricter sensitivity handling. ## 4\. Beauty and exact text: GPT Image 2 is the specialist **Brief:** close beauty portrait, a referenced rose serum bottle held beside the face, exact bottle label, and the headline **COLOR, KEPT TRUE** once above the left shoulder. ![Nano Banana 2 beauty campaign with serum and headline](https://www.astria.ai/articles/img/model-benchmarks/2026-09/beauty-nano-banana-2.webp) **Nano Banana 2**Exact large headline; solid beauty result ![GPT Image 2 beauty campaign with serum and headline](https://www.astria.ai/articles/img/model-benchmarks/2026-09/beauty-gpt-image-2.webp) **GPT Image 2**Best combined beauty-ad composition ![Seedream 5 beauty campaign with serum and headline](https://www.astria.ai/articles/img/model-benchmarks/2026-09/beauty-seedream-5.webp) **Seedream 5 Pro**Strong skin and color; small label less stable ![Muse beauty campaign with serum and headline](https://www.astria.ai/articles/img/model-benchmarks/2026-09/beauty-muse.webp) **Muse**Exact headline and dependable product placement The large headline is readable in all four selected outputs. The specialist verdict combines typography with portrait finish, product placement, and advertising composition. GPT Image 2 is not the only model that can render text—the grid makes that obvious. It is our first choice here because the entire frame behaves like finished beauty advertising. The portrait, bottle, headline, negative space, and restrained palette work together. Small packaging copy remains a high-risk detail in every generated image. Use the generated frame as campaign creative, but verify the pack against the real product and replace critical legal or product copy in post when exactness is required. ## 5\. Jewelry: Muse is the reference-first choice **Brief:** place the same nine-emerald, rose-gold necklace on the cast; preserve the settings, small white diamonds, chain, spacing, and proportions; frame both face and necklace clearly. ![Nano Banana 2 on-model emerald necklace benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/jewelry-nano-banana-2.webp) **Nano Banana 2**Strong portrait; inspect fine geometry ![GPT Image 2 on-model emerald necklace benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/jewelry-gpt-image-2.webp) **GPT Image 2**Polished metal and gemstone presentation ![Seedream 5 on-model emerald necklace benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/jewelry-seedream-5.webp) **Seedream 5 Pro**Rich color and consistent identity ![Muse on-model emerald necklace benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/jewelry-muse.webp) **Muse**Most dependable reference interpretation here A convincing necklace is not necessarily the same necklace. Compare stone count, prongs, spacing, chain, clasp, scale, and placement before commerce use. Muse earns the reference-first recommendation because it treats the jewelry as a product constraint rather than a general aesthetic suggestion. That is also why it is the model we would add when other outputs look excellent but repeatedly simplify the referenced object. For primary listing imagery, keep a macro photograph of the actual piece. The wider decision framework is in [AI jewelry photography](https://www.astria.ai/articles/ai-jewelry-photography/). ## 6\. Identity across a four-shot campaign The next matrix changes crop and pose while keeping the person, jacket, pale-stone setting, and restrained light fixed. Read each row horizontally. The identity strips are omitted from public display because the supplied cast reference is not part of the published source set. All four rows keep a recognizable cast. Seedream 5 and Muse are particularly strong reference options; Nano Banana 2 remains the balanced fashion default across the complete set. Identity is not just a face crop. A production model has to survive changing camera distance, body visibility, pose, and fabric movement. This test is deliberately small, so the conclusion is qualitative: all four are viable; Seedream 5's skin and facial continuity reinforce its specialist role, while Muse's consistent interpretation reinforces its reference-first role. ## 7\. The multi-reference stress test: why Muse matters The final brief combines the cast, orange jacket, emerald necklace, serum bottle, exact right-hand placement, deep teal backdrop, coral floor strip, product attributes, and a no-extras list. The multi-reference output strip is omitted because the complete person, jacket, jewelry, and beauty source set is not published with the benchmark. The first outputs are unusually strong across the board. Muse's recommendation is about dependable reference-led behavior across the jewelry and complex tests, not a claim that the other three failed this frame. This is what “thinking model” means in our Muse recommendation: the output behaves as if the brief's references and constraints were considered together before composition. We are describing observable behavior, not asserting undocumented internal architecture. ## Completion and the honest caveat | Metric | Result | | --- | --- | | Briefs | 11 | | Models | 4 | | Outputs requested | 132 | | Outputs returned | 131 | | Complete three-output request cells | 43 of 44 | The later MAI qualification is reported separately because it used direct supplied-image editing rather than the core benchmark's reusable reference tokens. For current rates and billing terms, use [Astria pricing](https://www.astria.ai/pricing). This was an Astria-run editorial benchmark of endpoints available through Astria on September 6, 2026. Astria sells access to the tested models. The review was not blinded or independently funded, and three outputs per cell are not enough for statistical ranking. Model behavior, names, pricing, and availability will change. The value of the comparison is that the prompts, display rule, output counts, prompt IDs, and visual evidence are inspectable. The verdict is intentionally practical: **Use Nano Banana 2 as the general fashion default.** Route beauty and copy-led work to GPT Image 2; route swimwear, saturated color, difficult texture, and identity-sensitive work to Seedream 5; add Muse when reference fidelity and simultaneous constraints are the hard part. Qualify MAI flagship or Flash when one supplied product image and direct-edit speed are the opportunity. For a shorter operational version, use the [Astria image-model guide](https://www.astria.ai/articles/astria-image-models/). For exact test design and limitations, read [how we benchmark image models](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/). --- # FLUX 3 Image vs Nano Banana 2, GPT Image 2.5, and Seedream 5 Source: https://www.astria.ai/articles/flux-3-image-review/ **FLUX 3 Image is the strongest new option when composition control and targeted editing matter as much as the first render.** Black Forest Labs gives it up to ten references, native 4K output, web grounding, and bounding-box controls that place or edit individual elements without rebuilding the whole frame. It does not automatically replace Nano Banana 2 as our general fashion default. In our first matched Astria test, FLUX 3 produced the most naturally photographic frame, but Nano Banana 2 followed the requested off-balance diagonal pose more aggressively and completed faster. GPT Image 2.5 Sunburst was the fastest tested endpoint, while Seedream 5 Pro remains the specialist we would start with for difficult color and fabric texture. This is a one-brief qualification of the current endpoints, not a universal ranking. _Reviewed October 4, 2026._ ## The short comparison | Model | Best reason to choose it | References and editing | Maximum native output in the reviewed API | Observed server time\* | | --- | --- | --- | --- | --- | | **FLUX 3 Image** | Box-directed layouts, precise local changes, natural editorial realism | Up to 10 references; generate and edit through one endpoint; bounding-box placement and element-level edits | 4K, about 16 megapixels | 87.3 s | | **Nano Banana 2** | Best all-around balance for high-volume fashion and product work | Multi-turn generation and editing; up to 14 references across supported object and character roles | 4K | 32.3 s | | **GPT Image 2.5 Sunburst** | Precise editing, beauty, text, designed layouts, and flexible output controls | Text and multiple image inputs; generation and editing; masks, transparency, and low-to-max quality controls | 4K, within the documented custom-dimension limits | 24.6 s | | **Seedream 5 Pro** | Difficult fabric, saturated color, realistic material response, and design workflows | Multi-image fusion; point, lasso, box, and sketch-guided edits; layer separation | 2K in the current official API | 55.4 s | \*One simultaneous reference-led request per model through Astria, measured from the recorded server start to completion. These are small-sample observations, not latency guarantees. Queueing, quality settings, resolution, region, and provider load can change the result. The capability rows come from the current [Black Forest Labs FLUX 3 documentation](https://docs.bfl.ai/flux_3/flux3_image_overview), [Google Gemini image guide](https://ai.google.dev/gemini-api/docs/image-generation), [OpenAI image-generation guide](https://developers.openai.com/api/docs/guides/image-generation), and [ByteDance Seedream 5 Pro announcement](https://seed.bytedance.com/en/blog/beyond-generation-it-understands-design-introducing-seedream-5-0-pro). A native model capability is not a promise that every partner interface exposes every control. ## What FLUX 3 Image actually is FLUX 3 is Black Forest Labs' multimodal foundation-model family. **FLUX 3 Image** is the image-generation and editing part of that family, released on October 1, 2026. It is separate from FLUX 3 Video, even though both share the FLUX 3 name and underlying multimodal direction. The image endpoint has five useful production ideas: - one prompt can generate a new image or describe an edit; - one request can combine up to ten image references; - an element table with coordinates can place subjects, products, panels, or type inside explicit bounding boxes; - the same boxes can target several local edits while leaving unmentioned elements locked; - output runs from a quick square preview through 1K, 1.5K, 2K, and 4K, with web and image grounding available. Black Forest Labs also offers commercial-weight licensing for companies that want to fine-tune and deploy FLUX 3 Image on their own infrastructure. That makes the family relevant beyond a hosted creative tool, although licensing, serving, and workflow costs need a separate procurement review. In Astria, FLUX 3 appeared as public partner model `5811585` on October 2. It was available in the partner gallery at review time, but not yet in the curated popular-model selector returned by the live catalog. Browse the current [FLUX 3 gallery](https://www.astria.ai/gallery/tunes/5811585/prompts). ## The matched reference test We reused the reference set and semantic prompt from the published GPT Image 2.5 qualification. All four models received the same Sloane portrait, pale-yellow sleeveless belted dress, circular-detail gold bag, 16:9 aspect ratio, 2K request, and one-output count in Astria workspace `896` (`Articles`). ![Sloane identity reference used in the FLUX 3 image comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-source-sloane.webp) **Sloane**Fixed identity reference ![Pale-yellow sleeveless belted dress reference used in the FLUX 3 comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-source-dress.webp) **Dress**Fixed color, two buttons, waist ring, and silhouette ![Gold chain bag with repeated circular details used in the FLUX 3 comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-source-bag.webp) **Bag**Fixed chain and circular metal construction The dress and bag sources exceed 2K on the long edge. The supplied Sloane portrait is 679×722, below our preferred 1600-pixel source gate, and was not upscaled. Identity observations are therefore directional. ### Prompt > Dress hero shot. An off-kilter, flash-lit photograph of the referenced woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag. The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe. Reference tokens are omitted above for readability. The generation records contain the exact Articles-workspace tune IDs. ## The four outputs [![FLUX 3 Image output of Sloane wearing the yellow reference dress and holding the gold reference bag in a dark bar](https://www.astria.ai/articles/img/model-benchmarks/2026-10/flux-3-sloane-dress.webp)](https://www.astria.ai/gallery/tunes/5811585/prompts) **FLUX 3 Image**Prompt 47065867 · 2K · strongest natural editorial realism [![Nano Banana 2 output of Sloane wearing the yellow reference dress and holding the gold reference bag in a dark bar](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nano-banana-2-sloane-dress.webp)](https://www.astria.ai/gallery/tunes/4180298/prompts) **Nano Banana 2**Prompt 47065878 · 2K · strongest diagonal pose adherence [![GPT Image 2.5 Sunburst output of Sloane wearing the yellow reference dress and holding the gold reference bag in a dark bar](https://www.astria.ai/articles/img/model-benchmarks/2026-10/gpt-image-2-5-sunburst-sloane-dress.webp)](https://www.astria.ai/gallery/tunes/5634510/prompts) **GPT Image 2.5 Sunburst**Prompt 47065876 · 2K · fastest completion in this run [![Seedream 5 Pro output of Sloane wearing the yellow reference dress and holding the gold reference bag in a dark bar](https://www.astria.ai/articles/img/model-benchmarks/2026-10/seedream-5-pro-sloane-dress.webp)](https://www.astria.ai/gallery/tunes/5236038/prompts) **Seedream 5 Pro**Prompt 47065877 · 2K · coherent look with weaker bag-handle interaction All four outputs pass the article-size visual gate. Every model preserved the pale-yellow sleeveless dress, two-button front, circular waist hardware, recognizable Sloane identity, gold chain bag, and dark bar setting. None should be treated as an exact SKU reconstruction: folds, belt wrap, bag-disc count, bag scale, and hand contact vary. ### Image quality **FLUX 3 looks the least staged.** The worn wood, background clutter, restrained skin texture, direct flash, and slight lens imperfection create a convincing location photograph rather than a perfectly cleaned campaign render. The face remains recognizable and the dress construction stays unusually legible. This is the frame we would choose when the art direction calls for candid editorial realism. Nano Banana 2 produces the most forceful fashion composition. Its long diagonal body line, stretched arms, shadow, and bag placement make the “almost off-balance” instruction visible immediately. GPT Image 2.5 delivers a polished, product-legible frame but settles the pose against the pillar rather than pushing the requested instability. Seedream keeps the face, color, and overall campaign mood coherent, but the raised strap and the way it crosses the hand need retouching. ### Prompt adherence and reference fidelity There is no runaway winner in one image. Nano Banana 2 follows the pose and diagonal request best. FLUX 3 balances the scene instructions with the strongest naturalism and retains both dress buttons, the waist ring, the bag's circular surface, and the flash-lit bar. GPT Image 2.5 gives the bag and garment a clear commercial read, but interprets the pose more conservatively. Seedream preserves the key references while introducing the most distracting contact error. For production, judge two questions separately: 1. Does the image express the brief? 2. Does the product remain accurate enough to approve? The most attractive answer is not always the most faithful one. ## Editing and reference-image support FLUX 3's meaningful advantage is not that other frontier models lack image inputs. All four can generate from references and edit images. The difference is the control surface. ### FLUX 3 Image Use FLUX 3 when a brief contains explicit spatial relationships: a product in the lower-right, a headline in a reserved area, several people in fixed positions, or a multi-panel editorial. Its native layout prompt carries a global caption plus named elements and bounding boxes on a 0–1000 coordinate grid. Those same elements can be recolored, moved, replaced, resized, or removed while unmentioned elements remain locked. That is more deterministic than asking a general conversational editor to “move the bag slightly left” and hoping the face, garment, and background do not drift. The matched Astria run above tests reference composition, not the separate bounding-box workflow; box precision remains a provider capability to qualify on the exact interface you plan to use. ### Nano Banana 2 Nano Banana 2 is the most flexible generalist here. Google's current API supports multi-turn generation and editing, web and image-search grounding, and up to 14 references across documented object and character roles. It remains the model we would start with when a team needs one endpoint for ordinary campaign, catalog, product, and iterative editing work without designing a coordinate layout first. ### GPT Image 2.5 Sunburst GPT Image 2.5 is the strongest route when the work behaves like design: edit precision, readable type, flexible custom dimensions, transparent output, or an image built through several explicit changes. Sunburst is the higher-capability variant; Flare is the smaller option. OpenAI exposes low through max quality levels, so a fair speed or pricing comparison must hold quality and dimensions fixed. ### Seedream 5 Pro Seedream 5 Pro combines multi-image fusion with point, lasso, box, sketch, color, and material controls. It can also separate a finished design into independent transparent layers. That makes it especially relevant for posters and dense design work, while our wider Astria benchmark still gives it a specialist role for saturated garments, woven texture, skin, and repeated identity. ## Resolution and speed FLUX 3, Nano Banana 2, and GPT Image 2.5 all offer native 4K paths. Their limits are not identical: FLUX 3 describes 4K as roughly 16 megapixels, Google's dimensions vary with aspect ratio, and OpenAI constrains custom dimensions by edge length, aspect ratio, and total pixels. Seedream 5 Pro currently tops out at the 2K tier in its official image API. Do not buy 4K as a label. Test whether the extra pixels preserve product geometry, typography, skin, and small accessories after export. A larger hallucination is still a hallucination. In this single simultaneous Astria run, GPT Image 2.5 completed first, followed by Nano Banana 2, Seedream 5 Pro, and FLUX 3. FLUX 3's result justified the wait for this editorial brief, but the gap matters in live exploration. Use lower-resolution drafts when the endpoint offers them, approve composition early, and reserve the largest render for finalists. ## Pricing: compare approved outputs, not sticker rates The vendors use different billing units and quality controls, so a raw “price per image” comparison is easy to misread. - Black Forest Labs prices FLUX 3 Image by output-resolution tier, with a materially larger step for 4K. See [BFL pricing](https://docs.bfl.ai/quick_start/pricing). - Google meters Nano Banana 2 image generation through model input and image-output tokens, with output usage tied to resolution. See [Gemini API pricing](https://ai.google.dev/gemini-api/docs/pricing). - OpenAI meters text, image input, and image output tokens; output dimensions and quality affect the practical total. See [OpenAI API pricing](https://developers.openai.com/api/docs/pricing). - Volcengine prices Seedream 5 Pro outputs by resolution band and separately accounts for additional input images after the first. See [Volcengine model pricing](https://docs.volcengine.com/docs/ark/model-pricing?lang=en). For the Astria route used in this comparison, use [current Astria pricing](https://www.astria.ai/pricing). The useful budgeting formula is: > approved-asset cost = total generation and editing spend ÷ number of outputs that pass reference, legal, and creative review A slower or nominally higher-priced endpoint can still be cheaper if it removes retries and retouching. A fast draft model can still be the right first step when the team is exploring composition rather than approving a SKU. ## Which model should you use? ### Choose FLUX 3 Image when - the composition contains several elements with strict spatial relationships; - local edits must leave the rest of the frame stable; - you want candid, natural editorial realism rather than a highly cleaned render; - up to ten references need to become one deliberate composition; - web-grounded real-world context or a self-hosted commercial-weights path matters. ### Choose Nano Banana 2 when - you need the safest general starting point for fashion and product work; - speed, volume, references, editing, and 4K output all matter at once; - the workflow is conversational and iterative rather than box-directed; - the brief mixes creative art direction with product preservation. ### Choose GPT Image 2.5 Sunburst when - precise editing, beauty finish, typography, or a designed layout is central; - transparent output or custom pixel dimensions matter; - quality can be tuned explicitly against latency and budget; - text and image inputs need to become a polished campaign asset. ### Choose Seedream 5 Pro when - saturated color, embroidery, weave, sheen, or skin rendering is the risk; - point-, region-, or sketch-guided edits fit the design workflow; - multi-image fusion or layer separation is more valuable than 4K delivery; - a fashion brief has already shown that Seedream handles its material or safety constraints reliably. ## Final verdict FLUX 3 Image earns a place in the production shortlist immediately. Its combination of references, spatial layout, targeted editing, grounding, natural photographic character, and native 4K is genuinely differentiated. For ordinary reference-led fashion work, start with Nano Banana 2 and add FLUX 3 when composition control or a less polished, more believable editorial surface is the opportunity. Add GPT Image 2.5 for design precision and Seedream 5 for difficult color and material. Then approve the model on your hardest real SKU rather than on a general leaderboard. The exact prompt IDs, model IDs, timings, source provenance, delivered dimensions, and selection notes for this qualification are recorded in the repository benchmark manifest. Read the broader [fashion image-model comparison](https://www.astria.ai/articles/best-ai-image-models-fashion/), [GPT Image 2.5 review](https://www.astria.ai/articles/gpt-image-2-5-review/), [Seedream 5 guide](https://www.astria.ai/articles/seedream-5-for-fashion/), and [benchmark methodology](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/). ## Sources and review boundary This article compares public product information and live Astria endpoint behavior available on October 4, 2026. Primary sources are Black Forest Labs' [FLUX 3 Image overview](https://docs.bfl.ai/flux_3/flux3_image_overview), [October 1 release notes](https://docs.bfl.ai/release-notes), and [pricing page](https://docs.bfl.ai/quick_start/pricing); Google's [Nano Banana image guide](https://ai.google.dev/gemini-api/docs/image-generation) and [pricing](https://ai.google.dev/gemini-api/docs/pricing); OpenAI's [GPT Image 2.5 model page](https://developers.openai.com/api/docs/models/gpt-image-2.5-sunburst), [image-generation guide](https://developers.openai.com/api/docs/guides/image-generation), and [pricing](https://developers.openai.com/api/docs/pricing); ByteDance's [Seedream 5 Pro announcement](https://seed.bytedance.com/en/blog/beyond-generation-it-understands-design-introducing-seedream-5-0-pro); and Volcengine's [image API](https://docs.volcengine.com/docs/ark/image-generation-api?lang=en) and [model pricing](https://docs.volcengine.com/docs/ark/model-pricing?lang=en). Provider capability claims are labeled as such; the visual verdicts and timings come from Astria's disclosed one-output matched run. --- # GPT Image 2 for Product Photography, Beauty, and Text: Tested Guide Source: https://www.astria.ai/articles/gpt-image-2-for-product-photography/ **Choose GPT Image 2 when an image must behave like an advertisement, not only a photograph.** In Astria's controlled fashion and product benchmark, it was our first choice for beauty finish, designed copy space, a correct large headline, and polished commercial composition. ## GPT Image 2 at a glance | Best fit | Why | | --- | --- | | Beauty and cosmetics | Polished skin, lighting, product placement, and advertising finish | | Prominent text | Correct large headline and strong designed composition in our test | | Packaging-led concepts | Understands the relationship between portrait, pack, copy, and negative space | | General fashion | Highly capable, although Nano Banana 2 remains our more balanced default | The benchmark tested 11 matched briefs and requested three GPT Image 2 outputs per brief. All 33 returned. We display output `00` from every cell, fixed before review. ## Why beauty and text are one problem Beauty advertising asks several systems to work together: believable skin, controlled reflections, recognizable packaging, exact copy, and a composition with visual hierarchy. A model can succeed at four and still make the asset unusable. ![GPT Image 2 beauty campaign benchmark with serum bottle and headline](https://www.astria.ai/articles/img/model-benchmarks/2026-09/beauty-gpt-image-2.webp) **Beauty and text**Our strongest combined beauty-ad composition with the exact large headline. GPT Image 2 handled the requested **COLOR, KEPT TRUE** headline, portrait finish, held product, and open-space layout especially well. That does not eliminate packaging QA. Small text can look plausible while changing a character, quantity, or hierarchy. Use it first for: - cosmetic campaign concepts; - product-and-person advertising; - packaging, signage, and large headline work; - frames where the copy must feel integrated rather than overlaid afterward; - premium commercial compositions that need a designed finish. ## Fashion performance GPT Image 2 also produced one of the strongest general-fashion frames. The general-fashion output is omitted from this public page because its supplied person-and-garment source set is not published with the article. Why is it not the default? A default is judged across the complete workflow. Nano Banana 2 offered the more balanced route through ordinary garment, identity, movement, and reference tasks. Choose GPT Image 2 when the desired finish is closer to an advertisement or when text is a primary approval criterion. ## Product detail and jewelry The jewelry output combined controlled metal highlights, gemstones, and a strong portrait. It also reinforces the central QA rule: resemblance is not exactness. ![GPT Image 2 jewelry portrait benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/jewelry-gpt-image-2.webp) **Jewelry**Polished presentation; verify stones, prongs, chain, and scale against the source. For a campaign crop, GPT Image 2 may be the aesthetic winner. For strict reference interpretation across several assets, [Muse](https://www.astria.ai/articles/muse-image-model-review/) remains our first route. For one direct product edit, [MAI-Image-2.6](https://www.astria.ai/articles/mai-image-2-6-review/) is now worth qualifying. ## Identity across a series GPT Image 2 kept the benchmark cast recognizable across close-up, full-length, seated, and moving shots. The series sometimes editorialized the person slightly, so approval should happen across the complete campaign rather than from one hero frame. The identity strip is omitted because the supplied cast reference is not part of the published source set. ## When another model is better - Choose [Nano Banana 2](https://www.astria.ai/articles/nano-banana-2-for-fashion/) for the safest general fashion starting point. - Choose [Seedream 5](https://www.astria.ai/articles/seedream-5-for-fashion/) when saturated color, fabric texture, or swimwear is the risk. - Choose [Muse](https://www.astria.ai/articles/muse-image-model-review/) when several references must remain distinct. - Qualify [MAI-Image-2.6](https://www.astria.ai/articles/mai-image-2-6-review/) for direct single-image commercial edits. ## Prompting GPT Image 2 for commercial work Structure the brief around the deliverable: > Create a \[campaign/PDP/editorial\] image for \[purpose\]. Use `` and `` in these roles. Preserve \[pack geometry, label, color, and identity\]. Add the exact headline “\[copy\]” once in \[location\]. Reserve \[negative space\]. Use \[lighting and palette\]. No other text or products. Then review four layers independently: 1. Is the required copy exact? 2. Is the real product still the same product? 3. Is the person or cast consistent? 4. Does the overall image meet the creative brief? ## Bottom line GPT Image 2 is the model we would open first for beauty, packaging-led advertising, or prominent copy. It is also a strong fashion model, but its specialist advantage is the ability to make the frame feel designed. Never let polished typography distract from a changed label or product. See the [full visual comparison](https://www.astria.ai/articles/best-ai-image-models-fashion/) and [methodology](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) for the exact prompts and limitations. For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # GPT Image 2.5 Review: Sunburst vs Flare for Reference-Led Fashion Source: https://www.astria.ai/articles/gpt-image-2-5-review/ **GPT Image 2.5 is now available in Astria in two variants: Sunburst and Flare.** Both accept image references and expose 1K, 2K, and 4K output settings plus low-to-max quality controls. In our first matched fashion test, both produced polished, reference-led campaign frames. Sunburst pushed the diagonal pose and direct-flash tension harder; Flare kept the complete bag more legible. That is a first production read, not a universal ranking. We ran one controlled brief with one output per model so you can inspect the actual difference without a hand-picked winner. ## GPT Image 2.5 at a glance | Variant | Start here when… | Tested output | | --- | --- | --- | | **Sunburst** | The art direction depends on energetic posing, hard flash, and a stronger editorial crop | 4K request; 3792×2160 returned | | **Flare** | The product and accessory need a calmer, more complete campaign frame | 4K request; 3792×2160 returned | Both variants support references, 1K/2K/4K selection, and low, medium, high, xhigh, and max quality settings in the current Astria catalog. Browse the live [Sunburst gallery](https://www.astria.ai/gallery/tunes/5634510/prompts) and [Flare gallery](https://www.astria.ai/gallery/tunes/5634511/prompts). ## The fixed reference set This is a rerun of the semantic brief from Maison Urban prompt `46557199`. For publication, the same Sloane portrait, yellow sleeveless belted dress, and circular-detail gold bag were copied into Astria workspace `896` (Articles). The source prompt wording was preserved apart from the new Articles-workspace reference IDs. ![Sloane identity reference used for the GPT Image 2.5 comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-source-sloane.webp) **Sloane**Fixed identity reference ![Yellow sleeveless belted dress reference used for the GPT Image 2.5 comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-source-dress.webp) **Dress**Fixed color, buttons, waist, and silhouette reference ![Gold chain bag with circular metallic details used for the GPT Image 2.5 comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-source-bag.webp) **Bag**Fixed gold chain and circular-detail reference The dress and bag references exceed 2K on the long edge. The supplied Sloane portrait is only 679×722, below our preferred source size, and we did not upscale it. Treat the identity result as a practical reference test, not a high-resolution identity benchmark. ### Prompt > Dress hero shot. An off-kilter, flash-lit photograph of the referenced woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag. The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe. The model-specific reference tokens are omitted above for readability. The generation records use the exact Articles-workspace tune IDs. ## Sunburst vs Flare [![GPT Image 2.5 Sunburst output of Sloane in a yellow dress holding a gold bag in a dark bar](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-sunburst-sloane.webp)](https://www.astria.ai/gallery/tunes/5634510/prompts) **GPT Image 2.5 Sunburst**Prompt 46636365 · 4K request · stronger diagonal tension and hard-flash crop [![GPT Image 2.5 Flare output of Sloane in a yellow dress holding a gold bag in a dark bar](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-flare-sloane.webp)](https://www.astria.ai/gallery/tunes/5634511/prompts) **GPT Image 2.5 Flare**Prompt 46636364 · 4K request · complete bag and steadier campaign framing Both outputs pass the article-size visual gate and preserve the central reference relationships: Sloane remains recognizable, the dress stays pale yellow with the sleeveless belted construction and two gold buttons, and the bag retains its gold chain and repeated circular metal texture. Neither result is a literal product reconstruction. Pleat placement, the belt wrap, individual bag discs, and crop differ from the supplied images. For campaign work, those shifts may be acceptable. For a PDP or catalog asset, compare every construction detail against the product before approval. ## What the variants do differently **Sunburst** is the more aggressive editorial interpretation in this pair. The body line is strongly diagonal, the flash contrast is harder, and the crop feels closer to a magazine campaign. The tradeoff is that the bag reaches the frame edge. **Flare** is more product-complete. It keeps the bag fully visible, gives the dress more breathing room, and still holds the direct-flash nightlife brief. Its pose is less precarious and therefore slightly less surprising. Our routing rule from this one brief is simple: - start with **Sunburst** when shot energy and art direction are the approval risk; - start with **Flare** when garment and accessory visibility are the approval risk; - run both when one extra comparison is cheaper than guessing which interpretation the client meant. ## The same brief in three other models We also ran the identical Articles-workspace references, semantic prompt, 16:9 request, and one-output count through Nano Banana 2, MAI-Image-2.6-Flash, and Muse. The resolution switch stayed at 4K where the endpoint supports it. MAI and Muse do not expose the same 4K selector; that incompatibility is recorded instead of silently changing the GPT Image 2.5 settings. ![Nano Banana 2 matched Sloane dress and bag comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-nano-banana-2-sloane.webp) **Nano Banana 2**Prompt 46636366 · 4K request · strongest accessory scale and a clean diagonal pose ![MAI Image 2.6 Flash matched Sloane dress and bag comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-mai-flash-sloane.webp) **MAI-Image-2.6-Flash**Prompt 46636367 · 1365×768 · graphic tilt with simplified surface detail ![Muse matched Sloane dress and bag comparison](https://www.astria.ai/articles/img/model-benchmarks/2026-09/gpt-image-2-5-muse-sloane.webp) **Muse**Prompt 46636363 · 1920×1280 · complete styling, but returned 3:2 from the 16:9 request The wider context does not produce a new universal winner. Nano Banana 2 remains a strong general fashion default. Muse keeps the product relationships coherent but misses the requested canvas ratio. MAI Flash gives a usable campaign frame at lower output resolution. The new GPT Image 2.5 variants are immediately competitive when a polished, flash-led fashion treatment is the goal. ## Production checklist 1. Use the same references in both GPT Image 2.5 variants. 2. Keep aspect ratio, resolution, quality, prompt, and output count fixed. 3. Review identity, garment construction, and accessory geometry separately. 4. Choose Sunburst or Flare from the job's approval risk, not from the model name. 5. Recheck the live [Astria pricing page](https://www.astria.ai/pricing) and model selector before building a production budget. ## Bottom line GPT Image 2.5 arrives as two useful reference-led routes rather than one vague upgrade. **Sunburst is our first test for energetic campaign direction; Flare is our first test for a steadier frame with clearer product visibility.** Both produced sharp, commercially credible 4K fashion outputs from the same modest identity source and the same dress-and-bag references. See the [full fashion model comparison](https://www.astria.ai/articles/best-ai-image-models-fashion/), or start directly in the [Sunburst](https://www.astria.ai/gallery/tunes/5634510/prompts) and [Flare](https://www.astria.ai/gallery/tunes/5634511/prompts) galleries. --- # How We Benchmark AI Image Models for Fashion and Product Work Source: https://www.astria.ai/articles/how-we-benchmark-ai-image-models/ We benchmark image models on the work a fashion or product team actually has to approve: garments, color, texture, identity, jewelry, copy, benign swimwear, and briefs containing several references at once. The September 2026 pilot compares **Nano Banana 2, GPT Image 2, Seedream 5 Pro, and Muse** across 11 controlled briefs. Three outputs were requested for every model-and-brief pair: 132 requested, 131 returned. On September 8 we added a separate **MAI-Image-2.6 and Flash qualification** using supplied product images. It is not folded into the core matrix because the working MAI path accepted one direct input image, while the reusable Astria reference-token requests used by the core benchmark failed. ## Public leaderboards are context, not a production verdict Public image leaderboards answer useful questions. They do not all answer the same question. [Artificial Analysis](https://artificialanalysis.ai/image/methodology) separates text-to-image from image editing, uses human preference, and reports endpoint quality and generation time. Its methodology also distinguishes localized editing from reference-to-image generation. [GenAI-Bench](https://github.com/TIGER-AI-Lab/GenAI-Bench) studies how model judgments align with human preferences collected in a generative arena. [ImgEdit-Bench](https://github.com/pku-yuangroup/ImgEdit) evaluates instruction adherence, editing quality, detail preservation, and single- versus multi-turn editing. Those are valuable priors. A fashion production decision adds different questions: - Did the number and placement of buttons survive? - Is a woven pattern merely plausible, or is it the same pattern? - Does cobalt remain cobalt under a new lighting treatment? - Does the same person remain recognizable from close-up to full length? - Is the packaging copy exact? - Did the model keep four references separate rather than blend them? We therefore use public benchmark design as context, then test the current endpoints inside the workflow being recommended. ## What the pilot tested The pilot has 11 briefs: | Track | Briefs | Primary risk | | --- | --- | --- | | General fashion | 1 | Overall art direction, garment use, realism, and composition | | Color and texture | 1 | Jacquard, embroidery, buttons, saturated color, and sheen | | Swimwear | 2 | Tasteful completion, garment preservation, framing, and unwanted alteration | | Beauty with text | 1 | Skin, bottle geometry, exact headline, and small packaging copy | | Jewelry | 1 | Stone count, settings, spacing, chain, scale, and reflections | | Identity series | 4 | One person and one garment across close-up, full-length, seated, and movement shots | | Complex references | 1 | Person, garment, necklace, bottle, placement, background, and exclusions in one frame | All people are adults. The swimwear prompts use ordinary ecommerce and editorial framing and do not attempt to evade safety systems. ## The source assets The four product references are synthetic and brand-neutral. We created them for this test so no customer product, artwork, or campaign would become public benchmark material. One dedicated, approved synthetic cast identity was reused across all on-model briefs. ![Synthetic orange jacquard jacket benchmark reference](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-jacket.webp) **Textured jacket**Color, weave, cranes, buttons, and silhouette ![Synthetic cobalt and coral swimsuit benchmark reference](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-swimsuit.webp) **Swimsuit**Color, asymmetric straps, piping, and rings ![Synthetic rose serum bottle and carton benchmark reference](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-beauty.webp) **Beauty pack**Geometry, material, label, and carton ![Synthetic emerald and rose gold necklace benchmark reference](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-necklace.webp) **Jewelry**Stones, settings, spacing, chain, and clasp Ground truth is the rendered source, not the text prompt that produced it. That distinction prevents an evaluator from penalizing a comparison model for an attribute absent from the actual reference. The visual ground truth is recorded before judging. For example, the rendered jacket contains four crane motifs and four buttons, while the rendered necklace contains nine emeralds. The comparison prompts use those observed facts. ## Controls and selection rule Every model received the same semantic brief and the same Astria reference tokens. Portrait and editorial work used a shared **3:4** aspect ratio; the jewelry macro used **1:1**. The initial calibration used 4:5, but Muse did not support that ratio, so 3:4 became the fair shared portrait format. Nano Banana 2, GPT Image 2, and Seedream 5 used 2K where exposed; Muse used its native endpoint settings. Three outputs were requested per cell. The article shows **output 00**, the first predetermined result, for every completed cell. We did not inspect all three and choose the prettiest. Alternate results remain in the run archive, including failures and unwanted variations. That rule removes one common source of misleading model comparisons: showing the best lucky draw from one model beside an ordinary draw from another. ## What we record For each request, the benchmark stores: - model and endpoint identifier; - exact brief and reference tokens; - aspect ratio and resolution policy; - prompt ID; - output count requested and received; - public display asset; - completion, instruction, reference, identity/text, and editorial notes. The raw run manifest is machine-readable JSON and the first-pass review is a CSV. The public article does not turn those notes into a fabricated universal score. ## How results are judged We keep accuracy separate from preference. **Instruction following** asks whether the requested view, pose, framing, objects, and exclusions appeared. **Reference fidelity** checks product color, silhouette, construction, texture, hardware, and separation of roles. **Identity** asks whether the cast remains recognizably the same. **Text** checks wording and layout, with small package copy treated more strictly than text that only looks typographic. **Editorial usability** asks whether a creative team could reasonably take the result into approval or light retouching. The publication review for this pilot is a disclosed editorial assessment by Astria, not a blinded multi-reviewer scientific study. The output order was fixed before review, but model names are visible in the published comparison. The qualitative verdicts should therefore be read as production guidance supported by inspectable examples, not as statistically powered proof. A larger follow-up should add at least three blinded reviewers, randomize the presentation order, record pairwise preferences, and run OCR for exact text. ## Completion The core run requested **132 images and returned 131**. Forty-three of 44 model-and-brief requests returned all three images. The Nano Banana 2 swimwear-editorial request returned two of three; the endpoint reported no `user_error` and no reason for the missing image. Every model produced safe, tasteful imagery for both swimwear briefs. For current model rates and billing terms, use [Astria's pricing page](https://www.astria.ai/pricing). ## How the MAI challenger round differs MAI-Image-2.6 was visible in Astria's public partner gallery but was not part of the original four-model run. We first submitted the general-fashion prompt with its existing cast and garment tokens. It failed with `Unexpected error` and returned no images. Direct single-image editing succeeded, so the publishable qualification is limited to that reference-led path. The round contained: | Track | Flagship cells | Flash cells | Outcome | | --- | --- | --- | --- | | Direct jacket edit | 1 | 1 | 6 of 6 returned | | Direct jewelry edit | 1 | 0 | 3 of 3 returned | | Direct adult-swimwear edit | 1 | 0 | Blocked; 0 of 3 returned | In the matched jacket cell, the recorded server processing intervals were 24.2 seconds for the flagship and 19.0 seconds for Flash. These small samples describe the tested endpoint, not guaranteed latency. Consult [current Astria pricing](https://www.astria.ai/pricing). As with the core benchmark, candidate selection was fixed before review. Publication then applied a second gate: an output had to be both attractive and faithful enough to its supplied reference to teach a useful production decision. The jacket and jewelry results met that bar. ## Known limitations - Eleven briefs are enough to show useful differences, not to rank the entire model market. - One synthetic identity and four synthetic products do not represent every skin tone, textile, body, material, or brand system. - Three outputs per cell reveal variation but do not provide a large statistical sample. - Endpoint behavior, safety handling, and catalog availability can change after September 6, 2026. - The MAI results use a different input path and must not be treated as a fifth matched core-model score. - Muse used native endpoint settings while the other core models used exposed 2K settings. - Text and jewelry geometry were reviewed visually; no OCR or automated geometric score is presented in this pilot. - Astria publishes this comparison and sells access to the tested endpoints. The recommendation is about the tested Astria endpoints on the capture date. It is not a claim about every deployment of the underlying models. ## Reproducing the comparison The exact prompts, source descriptions, reference IDs, prompt IDs, and judging notes live in the repository benchmark package. To reproduce the logic in another workspace: 1. Create rights-cleared source products and one approved adult identity. 2. Record the visual ground truth of each source. 3. Convert each source to a stable reference. 4. Send the same semantic brief to every compatible model. 5. Request the same number of outputs and preserve all of them. 6. Choose the public display rule before opening the results. 7. Review aesthetic quality and product accuracy as separate decisions. Read the [full side-by-side results](https://www.astria.ai/articles/best-ai-image-models-fashion/) or use the shorter [Astria model-selection guide](https://www.astria.ai/articles/astria-image-models/). --- # MAI-Image-2.6 Review: Reference-Led Fashion and Product Editing Source: https://www.astria.ai/articles/mai-image-2-6-review/ **The short answer:** MAI-Image-2.6 is a credible reference-led commercial-image challenger. In our Astria qualification it preserved a difficult supplied jacket unusually well and placed a supplied necklace convincingly. **MAI-Image-2.6-Flash finished the matched jacket test faster**, but the flagship retained more garment detail. It is not yet our default model in Astria. Direct single-image edits completed, but our existing Astria reusable-reference prompts failed, and a benign adult swimwear edit was blocked. Use MAI for a qualified direct-edit job, not as an automatic replacement for the core routing table. ## Our MAI verdict in one table | Production question | What our test found | | --- | --- | | Product-to-model editing | Strong on the difficult orange jacket; the first output kept four cranes, four buttons, jacquard texture, collar, sleeves, and scalloped hem | | Jewelry placement | Convincing on-body result with strong overall design preservation; stone count and setting geometry still require commerce QA | | Swimwear | The tasteful adult ecommerce cell was blocked by the endpoint's sexual-content classifier | | Astria reusable references | Direct input-image editing worked; prompts using existing Astria reference-tune tokens returned an unexpected error during this test | | Flagship versus Flash | Flagship preserved more jacket detail; Flash completed the matched jacket cell faster | This is a **qualification round**, not a universal model ranking. The public evidence is limited to attractive reference-led outputs whose source products can be inspected beside the result. Consult [Astria's pricing page](https://www.astria.ai/pricing) for current pricing. ## What MAI-Image-2.6 is MAI-Image-2.6 is Microsoft's diffusion-based image model for generation and controllable image editing. Microsoft's September 2026 [model card](https://microsoft.ai/pdf/MAI-Image-2.6-Model-Card.pdf) describes a 20-billion-parameter model with a 32K context and up to 1.5K-square output. Microsoft highlights surgical editing, product and commercial design, portraits, photorealism, and consistency across iterative edits. The family has two current variants: - **MAI-Image-2.6** prioritizes precision and output quality. - **MAI-Image-2.6-Flash** prioritizes throughput. Microsoft launched both for developers in Foundry on September 4, 2026. Its [launch announcement](https://microsoft.ai/news/pushing-the-quality-cost-frontier-with-mai-image-2-6/) reported strong public-arena positions, multi-image reference editing, web grounding, dynamic aspect ratios, and up to 1.5K resolution. Those are provider claims and capabilities; this article separates them from what we observed through Astria. ## Product editing: the strongest result in the round The hardest successful test started from the benchmark's synthetic jacket: burnt-orange jacquard, four embroidered cranes, four brass buttons, a pointed collar, long sleeves, and a scalloped hem. We asked MAI to place that garment on an adult model over a black column dress. ![Synthetic orange crane jacket source reference](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-jacket.webp) **Source**Four cranes and four buttons ![MAI Image 2.6 edit placing the orange jacket on a model](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-jacket-edit.webp) **MAI-Image-2.6**Best detail preservation ![MAI Image 2.6 Flash edit placing the orange jacket on a model](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-flash-jacket-edit.webp) **MAI-Image-2.6-Flash**Faster; some construction details simplified The flagship's first predetermined output retained the four cranes, four buttons, woven surface, collar, sleeve shape, and scalloped edge. Flash preserved the product's identity and campaign treatment but reduced the visible button count and simplified some detail. On a SKU-sensitive job, that is enough reason to start with the flagship. ## MAI vs Nano Banana 2 vs Seedream 5 vs Muse The most useful comparison is not a beauty contest; it is **which model removes the most expensive failure from your workflow**. Use the side-by-side view for composition and the scan line to inspect the same region across any two outputs. ### Four treatments of the orange jacket Choose two models in scan mode, then drag across the garment to compare crane count, buttons, woven texture, collar, hem, and color. ![Product-only source image of the burnt-orange jacquard jacket with four embroidered cranes and four brass buttons](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-jacket.webp) Source product**Burnt-orange crane jacket** Ground truth: four cranes, four brass buttons, pointed collar, long sleeves, woven leaf texture, and a scalloped hem. ![MAI Image 2.6 model wearing the burnt-orange crane jacket](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-jacket-edit.webp) **MAI-Image-2.6**Best jacket-detail retention in its direct-edit qualifier ![Nano Banana 2 model wearing the burnt-orange crane jacket](https://www.astria.ai/articles/img/model-benchmarks/2026-09/texture-nano-banana-2.webp) **Nano Banana 2**Best-balanced default across the complete fashion benchmark ![Seedream 5 Pro model wearing the burnt-orange crane jacket](https://www.astria.ai/articles/img/model-benchmarks/2026-09/texture-seedream-5.webp) **Seedream 5 Pro**Strongest saturated color and woven-material rendering ![Muse model wearing the burnt-orange crane jacket](https://www.astria.ai/articles/img/model-benchmarks/2026-09/texture-muse.webp) **Muse**Strong reference-led choice when constraints accumulate **Comparison boundary:** all four outputs use the same synthetic jacket and closely aligned product requirements. Nano Banana 2, Seedream 5, and Muse used Astria reference tokens plus a fixed cast identity. MAI used the jacket as a direct input-image edit without that identity reference because its reference-token requests failed. Treat the visual result as directional evidence, not a controlled four-model score. | Decision | MAI-Image-2.6 | Nano Banana 2 | Seedream 5 Pro | Muse | | --- | --- | --- | --- | --- | | Best role from our tests | Direct commercial edit with strong local product detail | General fashion default | Difficult color, texture, identity, and swimwear | Jewelry and multi-reference constraint work | | Jacket result | Retained four cranes, four buttons, collar, sleeves, jacquard, and scalloped hem | Strong overall garment and portrait balance | Richest burnt-orange color and convincing woven surface | Solid product interpretation with dependable reference behavior | | Reference workflow tested | Direct input image worked; reusable reference tokens failed | Reusable identity and product references completed | Reusable identity and product references completed | Reusable multi-reference prompts completed | | Main caution | Not yet compatible with the full reusable-reference workflow; swimwear cell blocked | Specialists can beat it on a specific hard constraint | Use when saturated color and material rendering dominate | Use when reference accuracy justifies specialist routing | **Choose MAI** when one supplied image must become a polished commercial edit and garment details dominate approval. **Choose Nano Banana 2** when you need the safest general starting point across ordinary fashion work. **Choose Seedream 5** when saturated color, material texture, identity, or swimwear completion is the risk. **Choose Muse** when jewelry geometry or several references must remain distinct in one frame. ## Jewelry: convincing at first glance, inspect at macro scale The necklace edit produced a strong beauty portrait and a recognizable nine-stone design. ![Supplied rose-gold emerald necklace source reference](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-necklace.webp) **Source**Nine emeralds with rose-gold settings ![MAI Image 2.6 edit placing the supplied rose-gold emerald necklace on a model](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-jewelry-edit.webp) **MAI-Image-2.6**Strong overall preservation; inspect prongs, diamonds, spacing, and stone count before commerce use. Jewelry exposes the difference between resemblance and fidelity. A result can look expensive while changing a clasp, prong, spacer, or stone. MAI is promising here, but the [Muse Image review](https://www.astria.ai/articles/muse-image-model-review/) remains the stronger starting point when several references and simultaneous constraints are the job. ## Beauty text: a reference-led headline edit We also qualified MAI on a supplied skincare source from Sivan Tests. The edit keeps the original UV-protector cream tube, gold cap, orange sun mark, and printed product copy while adding one short campaign headline in the open space. ![Supplied Sivan Tests UV-Protector Cream SPF 60 source reference](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-sivan-uv-protector-creme.jpg) **Source**4,000 × 6,000 Sivan Tests skincare reference ![MAI Image 2.6 direct edit of the UV-Protector Cream with the exact DAILY DEFENSE headline](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-beauty-headline.jpg) **MAI-Image-2.6**2K vertical direct edit; the headline is clear and the supplied pack remains recognizable. The selected output passed both gates for this article: it reads as a finished commercial frame at article size, and the tube's geometry, gold cap, orange mark, and label remain visibly tied to the supplied source. The source tune and generation record are documented in the benchmark audit. ## The two production caveats ### Reusable Astria references did not complete We attempted the existing fashion brief with an Astria cast reference and garment reference. The MAI endpoint returned `Unexpected error`. Repeating without workspace scoping produced the same result. Direct single-image editing worked. This describes the tested Astria integration on September 8, 2026—not Microsoft's underlying model capability. Microsoft documents multi-reference editing. Until the Astria path is requalified, use direct input-image edits and test the exact reference workflow required by your production. ### The benign swimwear cell was blocked The swimsuit test used an adult subject, a product-only one-piece source, straight-on ecommerce framing, relaxed arms, commercial lighting, and an explicit non-provocative instruction. The complete three-image cell was blocked by the endpoint classifier and returned no output. That makes MAI a poor current default for swimwear through this endpoint. Start with [Seedream 5](https://www.astria.ai/articles/seedream-5-for-fashion/) for that workload. ## MAI-Image-2.6 versus Flash | Matched reference-led cell | MAI-Image-2.6 | MAI-Image-2.6-Flash | | --- | --- | --- | | Jacket-edit processing time | 24.2 sec | 19.0 sec | | Best use from this test | Final candidate generation and fidelity-sensitive edits | Exploration, layout options, and high-volume qualification | The timings are server processing intervals for this matched cell, not a latency guarantee. Queueing, resolution, load, and integration behavior can change. Use [current Astria pricing](https://www.astria.ai/pricing) for planning. Our practical routing rule is simple: **use Flash to explore; rerun the selected direction in the flagship when product detail matters.** For low-stakes social variations where the product passed review, Flash may be sufficient by itself. ## A reusable MAI prompt structure MAI responded well to a concrete commercial brief: > Using the supplied \[product image\], create a \[campaign or catalog asset\]. Preserve \[source attributes that must not change\]. Place it in \[scene\]. Use \[composition\], \[lighting\], and \[material behavior\]. Exclude \[unwanted elements\]. Put preservation requirements before mood adjectives. List countable details—buttons, stones, straps, packages—rather than relying on “exact.” Then evaluate those details independently from whether the image looks good. ## Final recommendation Try MAI-Image-2.6 when the job is a commercial edit based on one supplied product image. Try Flash first when iteration speed dominates. Keep Nano Banana 2 as the general Astria fashion default, GPT Image 2 as the established beauty specialist, Seedream 5 for swimwear and difficult texture, and Muse for multi-reference constraint work. The benchmark inputs, prompt IDs, failures, and selection rule are recorded in the [benchmark methodology](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/). Model availability and integration behavior change; this verdict describes the Astria endpoints tested on September 8, 2026. --- # Muse Image Model Review: Multi-Reference Fashion and Product Tests Source: https://www.astria.ai/articles/muse-image-model-review/ **Muse creates images from several reference photos.** Use it when you need a specific person, garment, or product to appear together in the result. The examples below show what it preserved and what still needs checking. ## A necklace reference and Muse’s result We gave Muse the necklace image on the left and asked it to show the necklace on a person. Compare the stones, chain, and setting with the generated image on the right. ![Synthetic emerald necklace supplied as the source reference](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-necklace.webp) **Input: necklace reference**Synthetic product image used in this test. ![Muse-generated portrait of a person wearing the emerald necklace](https://www.astria.ai/articles/img/model-benchmarks/2026-09/jewelry-muse.webp) **Output: Muse image**The necklace shown on a generated person. ## Muse at a glance | Best fit | Why we route it there | | --- | --- | | Several references in one frame | Dependable interpretation of separate asset roles | | Jewelry and small products | Strong reference-led placement and overall design preservation | | Explicit inclusion/exclusion rules | Treats the brief as a set of simultaneous constraints | | Identity plus product continuity | Strong cast and garment reference behavior across the tested series | We use “thinking model” as an editorial description of observable behavior. Muse's outputs act as though the references and constraints were considered together before composition. We are not making an undocumented claim about internal architecture. The controlled benchmark requested 33 Muse outputs across 11 briefs; all returned. Output `00` from every cell was selected before review. ## The multi-reference stress test The final benchmark brief combined: - one adult cast identity; - an orange crane jacket; - an emerald necklace; - a serum bottle held upright in the right hand; - a deep teal background and coral floor strip; - product-specific counts and colors; - an explicit list banning extra garments, jewelry, bottles, text, objects, and people. The complex-reference output is omitted because the complete person, jacket, jewelry, and beauty source set is not published with the article. Nano Banana 2, GPT Image 2, and Seedream 5 also produced unusually strong first outputs for this brief. Muse's recommendation comes from its dependable behavior across this case and the product-reference tests, not from a claim that every competitor failed. ## Jewelry: why reference-first behavior matters A necklace is not a green-and-gold aesthetic. The source has a countable number of emeralds, prongs, diamond spacers, a chain, clasp, spacing, scale, and a relationship to the wearer's body. The result still requires macro inspection. Generated jewelry can look convincing while changing construction. For a primary listing image, retain a photograph of the real piece; use generated on-body and campaign imagery only after the product passes a detail checklist. ## Identity and garment continuity Muse kept both face and jacket recognizable across four crops and poses. The identity strip is omitted because the supplied cast reference is not part of the published source set. This is useful for production because references accumulate. A campaign rarely asks only for a face; it asks for the same person, garment, accessory, location language, and exclusions across a shot list. ## When Muse is unnecessary Do not add a specialist when the default already works. - Use [Nano Banana 2](https://www.astria.ai/articles/nano-banana-2-for-fashion/) for the broadest everyday fashion workflow. - Use [GPT Image 2](https://www.astria.ai/articles/gpt-image-2-for-product-photography/) for beauty, advertising layout, and prominent text. - Use [Seedream 5](https://www.astria.ai/articles/seedream-5-for-fashion/) for saturated color, material rendering, identity, and swimwear. - Qualify [MAI-Image-2.6](https://www.astria.ai/articles/mai-image-2-6-review/) for direct one-image edits or fast commercial exploration. Muse is worth routing to when a failed reference creates more rework: the wrong necklace, omitted bottle, changed garment, or blended product attributes. ## Prompting Muse as a constraint system Assign every reference one role: > `` is the only person. They wear `` and ``. They hold exactly one `` upright in the right hand. Preserve \[countable attributes for each asset\]. Place them against \[scene\]. Do not merge product attributes. No \[explicit exclusions\]. Then score the output with a checklist rather than one overall impression: 1. Was every reference included? 2. Did each reference stay in its assigned role? 3. Were countable product details preserved? 4. Were placement instructions followed? 5. Did any banned object appear? 6. Is the image aesthetically usable after it passes accuracy? ## Bottom line Muse is not the model we would use for every fashion frame. It is the model we add when references and constraints are the risk. That makes it especially valuable for jewelry, accessories, product-and-person compositions, and complicated campaign briefs. See the [full four-model comparison](https://www.astria.ai/articles/best-ai-image-models-fashion/) and [benchmark methodology](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Nano Banana 2 for Fashion and Product Photography: Tested Guide Source: https://www.astria.ai/articles/nano-banana-2-for-fashion/ **Nano Banana 2 is our default AI image model for fashion work in Astria.** It did not win every specialist test. It earned the recommendation by producing the best overall balance across everyday fashion direction, garment use, identity, movement, and complex references. ## The verdict | Question | Answer from our test | | --- | --- | | Best for | General fashion editorials, lookbooks, garment-to-model work, movement, and mixed briefs | | Main advantage | No single weakness dominates the ordinary fashion workflow | | Watch for | Specialist models can beat it on exact text, difficult texture, swimwear completion, or reference-first work | | Switch to | GPT Image 2 for beauty/text; Seedream 5 for texture or swimwear; Muse for strict multi-reference work; MAI for qualifying direct commercial edits | The controlled benchmark ran 11 briefs with three requested outputs per model. Nano Banana 2 returned 32 of 33 requested images; one poolside swimwear cell returned two images without a reported reason. The article displays the first predetermined output from each completed cell, not a hand-picked favorite. ## Why it is the fashion default The general-fashion output is omitted from this public page because its supplied person-and-garment source set is not published with the article. A useful default has to reduce routing decisions. Nano Banana 2 followed the walking fashion brief, preserved the cast and referenced tank, rendered believable trouser movement, and produced a composition ready for refinement. GPT Image 2 gave a polished alternative; Seedream 5 handled color beautifully; Muse read references carefully. Nano Banana 2 was the best-balanced starting point across the complete set. Use it first for: - fashion campaign and lookbook concepts; - placing a referenced garment on a model; - changing crop, pose, or scene while keeping the overall treatment; - identity-led shot lists; - briefs mixing creative direction with several preservation requirements. ## Product and texture fidelity Nano Banana 2 kept the orange jacket recognizable while giving it a credible campaign treatment. ![Nano Banana 2 orange jacquard jacket benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/texture-nano-banana-2.webp) **Product-grounded fashion**Strong overall fidelity; Seedream 5 rendered the saturated color and woven surface better in this case. That distinction is worth preserving. Nano Banana 2 is good at the whole frame. Seedream 5 is the model we would add when fabric sheen, saturated color, embroidery, or weave is the approval risk. Product fidelity should be scored separately from beauty: a more attractive picture can still be the wrong SKU. ## Identity and movement The four-shot series changed camera distance and pose while keeping one cast identity, jacket, setting, and light. Nano Banana 2 maintained a recognizable person from close-up to full length, seated, and walking. The movement frame was especially useful because the body, garment, and face all had to remain plausible at once. The identity strip is omitted because the supplied cast reference is not part of the published source set. For a real collection, four images are only a qualification. Approve the cast across the actual shot list before generating hundreds of assets. ## Complex references The stress test combined a person, jacket, necklace, serum bottle, hand placement, background colors, and a no-extras list. The complex-reference output is omitted because the complete person, jewelry, and beauty source set is not published with the article. Nano Banana 2 is often enough for multi-reference work. Add Muse when repeated generations keep omitting, blending, or simplifying assets. The decision is not “creative versus accurate”; it is which failure creates more rework on this job. ## Where Nano Banana 2 is not first choice - **Beauty advertising and prominent copy:** start with [GPT Image 2](https://www.astria.ai/articles/gpt-image-2-for-product-photography/). - **Swimwear, saturated color, and difficult fabric:** start with [Seedream 5](https://www.astria.ai/articles/seedream-5-for-fashion/). - **Jewelry or several strict references:** start with [Muse Image](https://www.astria.ai/articles/muse-image-model-review/). - **Fast text-led exploration or one-image product edits:** qualify [MAI-Image-2.6 or Flash](https://www.astria.ai/articles/mai-image-2-6-review/). ## A prompt structure that works > Create a \[shot type\] of `` wearing ``. Specify pose, crop, scene, and light. List the product attributes and identity details that must remain unchanged. End with exclusions: no text, logos, extra people, or unrequested accessories. Avoid burying the product requirements under camera jargon. Start with the role of each reference, then state the creative direction. A model can only be evaluated fairly when the brief defines what counts as success. ## Bottom line Start with Nano Banana 2 when you do not yet know which specialist you need. Keep it when the result preserves the product, identity, and direction. Switch only when a specific failure—text, texture, swimwear completion, or reference fidelity—points to a better route. See the [full 131-image comparison](https://www.astria.ai/articles/best-ai-image-models-fashion/) and [benchmark methodology](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) for prompts, source assets, failures, and limitations. For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Nano Banana 2.1 for Fashion, Ecommerce, and Marketing: Comparison Source: https://www.astria.ai/articles/nano-banana-2-1-fashion-ecommerce-marketing/ **Nano Banana 2.1 is a strong candidate for commercial reference-based image work: it ranks third in Arena's multi-image-edit Product, Branding & Commercial Design category as checked on October 7, 2026.** In our 31 reference-led samples, it preserves the jacket's main features and produces convincing on-model portraits, but does not win every identity, label, or campaign constraint. It belongs on a production shortlist alongside Sunburst and Flare, with product-specific approval before use. For a fashion brand, a prettier picture with the wrong embroidery is a failed asset. For an ecommerce team, readable copy cannot compensate for a changed product shape. For a marketing team, composition, product fidelity, and copy accuracy need separate approvals. ![Reference-driven Nano Banana 2.1 portrait of the synthetic cast wearing the orange crane-embroidered jacket](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/hero-portrait-nano-banana-2-1.webp) Nano Banana 2.1 · separate native 4K cover render · supplied cast and garment references. This cover is outside the matched 2K comparison. **Tested October 7, 2026.** Five original briefs across five models, plus a clean-packshot-to-real-scene brief across six models, one output per cell. We show reviewed selections and report the rejected cells in text; there are no replacement runs in the comparison. ## What Nano Banana 2.1 changes Google's released model ID is `gemini-nano-banana-2.1`. Its predecessor, called Nano Banana 2 or 2.0 in this comparison, is `gemini-3.1-flash-image`. Nano Banana Pro is a separate model. Google describes improvements to realism, instruction following, text rendering, and character consistency through successive edits. Outputs support 1K, 2K, and 4K; wide-image tiling fixes target panoramic ratios at higher resolutions. It supports up to 14 reference images, with documented consistency roles for up to four characters and ten objects. These are provider capabilities to qualify on a real brief, rather than proof of exact garment reconstruction. See [Google's model documentation](https://ai.google.dev/gemini-api/docs/models/gemini-nano-banana-2.1). Google Cloud dates the generally available release to October 6, 2026. Its [technical specifications](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/gemini/nano-banana-2-1) also rule out provider-level seed and sampling controls. An identical seed is therefore not a fair-control promise for this comparison. ## Arena's current commercial multi-image-edit ranking **Checked October 7, 2026; leaderboard displayed October 6, 2026 as its update date.** The table below transcribes the relevant rows from [Arena's multi-image-edit commercial-design leaderboard](https://arena.ai/leaderboard/image-edit/multi-image-edit/commercial-design), with the Multi Image Edit category selected and license filter set to All. | Displayed rank | Model in Arena | Score and displayed uncertainty | Rank spread | Votes | Status | | --- | --- | --- | --- | --- | --- | | 1 | GPT Image 2.5 Sunburst | 1557 ±10 | 1–1 | 6,958 | Preliminary | | 2 | GPT Image 2.5 Flare | 1503 ±10 | 2–3 | 6,998 | Preliminary | | **3** | **Gemini Nano Banana 2.1** | **1490 ±13** | **2–4** | **2,389** | **Preliminary** | | 4 | GPT Image 2 (medium) | 1484 ±7 | 3–4 | 25,342 | — | | 5 | Seedream 5.0 Pro | 1431 ±8 | 5–6 | 14,067 | — | | 6 | Muse Image | 1422 ±7 | 5–6 | 20,732 | — | | 7 | Gemini 3.1 Flash Image / Nano Banana 2 **\[web-search\]** | 1403 ±7 | 7–9 | 16,876 | — | | 8 | Ideogram 4.5 | 1391 ±16 | 7–11 | 1,327 | Preliminary | | 9 | Gemini 3 Pro Image 2K / Nano Banana Pro | 1384 ±6 | 8–11 | 24,605 | — | | 10 | Reve 2.0 | 1383 ±14 | 7–11 | 1,795 | — | Nano Banana 2.1 sits behind Sunburst and Flare by displayed score and ahead of GPT Image 2 (medium). Its displayed score intervals overlap those of Flare and GPT Image 2, and Arena gives it a rank spread of 2–4. A rigid claim that it is conclusively the third-best commercial model would overstate this snapshot. These scores concern human preference in **commercial multi-image editing**. They are not overall image-generation ranks, overall editing ranks, garment-accuracy percentages, or our test scores. The predecessor's entry explicitly includes web search; our Astria briefs do not request search grounding. We do not treat those configurations as identical. ## Our comparison design We selected Sunburst and Flare because they lead this specific Arena category, and Seedream 5 Pro because it ranks fifth and offers a relevant material-and-fashion alternative. The direct predecessor is required to isolate the practical upgrade question. Muse remains a useful multi-reference challenger; Ideogram 4.5 and Reve 2.0 are not interchangeable with the different versions currently exposed in Astria's catalog. The five original cases use the same supplied images, exact semantic prompt, 4:3 requested aspect ratio, 2K requested resolution, and one requested output per model in Astria workspace **896, Articles**. Face inpainting and film grain are disabled. The only returned image is the predetermined comparison candidate; there are no selective reruns. We use 2K as the common resolution supported by Seedream Pro and the other selected endpoints. Actual dimensions differ between providers. The cover is a separate native 4K render with the supplied portrait and jacket. No source or output was enlarged to meet the resolution gate. The synthetic jacket and serum references come from our earlier benchmark assets. The jacket source has four teal cranes, four brass buttons, two lower floral motifs, orange leaf-pattern jacquard, a pointed collar, and a scalloped hem. The serum source supplies the bottle, pump, carton, palette, and label design. These are fictional products made for testing, not customer SKUs. We checked the deployed integration before submitting 2.1: production release v4821 contains the routing update. Tune `4180298` now targets `gemini-nano-banana-2.1`; separate tune `5850983` targets `gemini-3.1-flash-image`. The two product-only 2.0 baselines were generated before the upgrade, with the verified former mapping; all later 2.0 cases use the separate legacy tune. Spicy Mayo is deprecated and excluded. The curated MCP catalog still carries an older display label, which is why we verified release history and committed provider mappings instead of relying on names. Attempt-level transport telemetry is not exposed by the tool. ## Garment detail: strong main features, imperfect labels The brief asks for a complete front view on an invisible mannequin, with the referenced jacket's construction, motif counts, color, and weave preserved. This tests a catalog transformation before adding the complexity of a person or location. The exact brief supplied to all five models was: ```text Create a product-only ecommerce photograph of jacket on an invisible mannequin, front view with sleeves hanging naturally, against a warm ivory seamless studio background. Preserve the exact burnt-orange silk jacquard leaf weave, all four teal crane motifs and lower floral details, four brass dome buttons, pointed collar, cropped length and scalloped hem. Show the complete garment without clipping. Soft even catalog lighting, realistic material detail. No person, no accessories, no added text, no new logos. ``` ### Jacket — four reviewed comparison selections The same supplied jacket and brief. The 2.1 output is withheld because it changes the collar label; its result is reported below. ![Orange leaf-pattern jacket with four teal cranes, two floral motifs, four brass buttons and scalloped hem](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/source-jacket.webp) Source product**Supplied synthetic jacket** ![Nano Banana 2.0 jacket catalog baseline](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/jacket-nano-banana-2-0.webp) **Nano Banana 2.0 — pre-upgrade**Retains four cranes and four buttons; recognizably follows the source collar and hem. Cloth relief and feather details are redrawn. ![GPT Image 2.5 Sunburst jacket catalog baseline](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/jacket-sunburst.webp) **GPT Image 2.5 Sunburst**Clear hanging-garment presentation and strong motif counts. Jacquard relief is more pronounced; the source collar label is omitted. ![GPT Image 2.5 Flare jacket catalog baseline](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/jacket-flare.webp) **GPT Image 2.5 Flare**Preserves the recognizable color, cranes, buttons and scalloped outline. Texture and embroidery are reinterpreted; the collar label is omitted. ![Seedream 5 Pro jacket catalog baseline](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/jacket-seedream5pro.webp) **Seedream 5 Pro**Strong warm color and recognizable weave, four cranes and four buttons. The collar label is omitted and the lower flowers are simplified. These images preserve the main garment features well enough for this editorial comparison. They are not pixel-identical product reproductions. We would check embroidery close-ups and trim placement against an actual SKU before approving ecommerce use. The source includes an invented collar label; its removal by Sunburst and Flare is another reason to keep packaging or brand-label fidelity separate from general visual quality. Seedream also completed and retained the main garment structure, with simplified floral details and no collar label. The 2.1 sample also retains four cranes, four brass buttons, lower flowers, leaf-pattern cloth and a scalloped hem. However, it replaces the invented source collar label with different lettering. We keep that image internal rather than presenting it as an approved branded product asset. This is a specific miss on this run, not evidence that every 2.1 product edit fails. The five single-output cells do not establish a general winner for fabric fidelity. No missing or incompatible output is replaced with a text-only example. | Tested endpoint | Prompt ID | Actual pixels | | --- | --- | --- | | Nano Banana 2.1, label change; image withheld | 47133603 | 2400 × 1792 | | Nano Banana 2.0, before routing upgrade | 47132456 | 2400 × 1792 | | Sunburst | 47132457 | 2304 × 1792 | | Flare | 47132458 | 2304 × 1792 | | Seedream 5 Pro | 47132459 | 2368 × 1776 | These are Astria endpoint tests, not reproductions of Arena's evaluation prompts or serving configuration. The successful transport provider of each attempt is not exposed by the generation tool. ## Marketing copy: readable headlines are only half the job The second brief turns a supplied serum bottle and carton into a rose-and-ivory campaign layout. It requests three exact copy lines: “LUMEN SERUM”, “A daily ritual.”, and “DISCOVER THE COLLECTION”, with the product on the right and copy on the left. All five models render those three lines legibly in the completed baseline outputs. Sunburst adds an outlined call-to-action treatment; Flare uses an italic middle line; 2.0 uses a simpler sans-serif layout. Nano Banana 2.1 uses a restrained sans-serif headline and serif middle line. The copy pass therefore does not isolate an upgrade advantage on this brief. Seedream renders the three marketing lines too, but duplicates “30 mL” near the bottom of the carton. That is an unrequested packaging change, even though the large ad copy reads correctly. Product fidelity still needs a stricter judgment. Bottle and pump proportions differ, and the upright carton creates a new label presentation. The source bottle omits the “04” printed on the carton, while our prompt explicitly requests “No. 04 ROSE” on both. This tests controlled text normalization as well as preservation. Nano Banana 2.1 retains the source bottle's “No. ROSE” instead of applying the requested “No. 04 ROSE” normalization. We keep all five images internal because exact packaging and geometry did not pass. The typography findings remain useful, but none is an approved SKU advertisement. | Model | Three requested ad lines | Specific packaging or layout observation | | --- | --- | --- | | Nano Banana 2.1 | Correct and legible | Bottle omits requested “04”; carton includes it | | Nano Banana 2.0 | Correct and legible | Carton text remains sideways despite upright presentation | | Sunburst | Correct and legible | Adds an outlined CTA treatment; product proportions differ | | Flare | Correct and legible | Italic middle line; pump and bottle proportions differ | | Seedream 5 Pro | Correct and legible | Adds an extra “30 mL” near the carton bottom | For marketers, approve the product first, then check spelling, hierarchy, contrast, and room for channel-specific crops. For ecommerce teams, retain approved product photography when an AI transformation changes the SKU's defining shape or hardware. ## Clean packshot to real scene: five models preserve every text line **Nano Banana 2.1, Pro, Sunburst, Flare and Seedream tie at 11 / 11 preserved text lines. Nano Banana 2.0 scores 10 / 11.** With a clearly readable source, the leading group reproduces even the small blue French copy. There is no single text-fidelity winner in this round. We start with **one real product in a clean studio packshot**: a white La Roche-Posay Effaclar Mat tube on a plain background, photographed by [Cosmin Ursea on Unsplash](https://unsplash.com/photos/a-tube-of-la-roche-chesteal-effaclar-mat-on-a-2wM1AFH4At0), used under the [Unsplash License](https://unsplash.com/license). Every front-label line is readable in the original, including the small blue wording above the cap. Each model gets that unchanged packshot and the same brief: put the product in an adult hand beside a bathroom window, keep the label uncovered, and reproduce all packaging text. **The prompt does not supply the wording**; models must read it from the image. One output per model, no reruns. Nano Banana Pro is included in this round only. ### Single-product packshot → real scene — all six models The clean studio reference is the text ground truth. Compare every real-scene result below, or use Scan line to inspect a pair. ![Cosmin Ursea’s single Effaclar Mat skincare tube studio packshot with readable French, English and small blue label copy](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/packshot-scene-source.webp) Source product**Source: one product, clean studio packshot** ![Nano Banana 2.1 Effaclar Mat packshot-to-real-scene text reproduction test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/packshot-scene-nano-banana-2-1.webp) **Nano Banana 2.1**11 / 11 text lines preserved, including French accents and the small blue copy. Natural hand-held finish; type weight and cap details are redrawn. ![Nano Banana Pro Effaclar Mat packshot-to-real-scene text reproduction test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/packshot-scene-nano-banana-pro.webp) **Nano Banana Pro**11 / 11 text lines preserved. The small blue copy stays readable; the product sits smaller in the frame than requested. ![Nano Banana 2.0 Effaclar Mat packshot-to-real-scene text reproduction test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/packshot-scene-nano-banana-2-0.webp) **Nano Banana 2.0**10 / 11 text lines preserved. The small blue AVEC becomes AVBE; the remaining wording stays readable. ![GPT Image 2.5 Sunburst Effaclar Mat packshot-to-real-scene text reproduction test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/packshot-scene-sunburst.webp) **GPT Image 2.5 Sunburst**11 / 11 text lines preserved, including the small blue copy. Bright, clear product presentation; typography and cap detail are redrawn. ![GPT Image 2.5 Flare Effaclar Mat packshot-to-real-scene text reproduction test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/packshot-scene-flare.webp) **GPT Image 2.5 Flare**11 / 11 text lines preserved, including the small blue copy. Bright real-scene finish with a simplified opaque cap. ![Seedream 5 Pro Effaclar Mat packshot-to-real-scene text reproduction test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/packshot-scene-seedream5pro.webp) **Seedream 5 Pro**11 / 11 text lines preserved, including French accents and the small blue copy. Soft daylight and a believable low grip. | Model | Exact text lines preserved | Result | | --- | --- | --- | | **Nano Banana 2.1** | **11 / 11** | Keeps all measured wording, accents and small blue copy. | | **Nano Banana Pro** | **11 / 11** | Keeps all measured wording, including the small blue copy. | | Nano Banana 2.0 | 10 / 11 | Changes the small blue “AVEC” to “AVBE”. | | **Sunburst** | **11 / 11** | Keeps all measured wording and French accents. | | **Flare** | **11 / 11** | Keeps all measured wording and the small blue copy. | | **Seedream 5 Pro** | **11 / 11** | Keeps all measured wording and French accents. | We manually checked the 11 source lines against each full-resolution output. A line passes only when its complete wording, accents and punctuation remain correct and legible; letter case, spacing and apostrophe styling do not affect the score. This measures copy reproduction in these six images, not exact typography or product geometry. The models still redraw type weight and cap details. For this brief, 2.1 joins the leading group and avoids the predecessor’s small-copy error. A clear packshot makes this a useful test of text preservation when moving a product into a real scene; one sample per model cannot establish a general accuracy ranking. ## Identity across two poses For identity ground truth we adopted a detailed, synthetic portrait from our earlier GPT Image 2 benchmark: 1792 × 2304 pixels, not an enlarged casting thumbnail. Its wavy dark bob, green eyes, facial proportions and skin marks are the visual anchors. It already includes the jacket, so the separate garment reference reinforces that cue. This tests one supplied identity across views, not independent casting or every possible garment assignment. A source created by GPT Image 2 may also favor related models; a wider production study needs more casts and source styles. ![Detailed synthetic adult portrait adopted as the fixed cast identity](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/source-cast.webp) **Cast**Fixed synthetic portrait, adopted as this test's identity ground truth ![Fixed orange jacquard jacket with four teal cranes and four brass buttons](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/source-jacket.webp) **Jacket**Fixed construction, color, weave, motifs and trim ![Fixed synthetic rose-gold necklace with nine green stones and white stone separators](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/source-necklace.webp) **Campaign accessory**Nine green stones, white separators, rose-gold settings and cable chain The first brief asks for a front-view waist-up portrait including the complete jacket and hem. The second changes only the pose: a 30-degree turn to camera-left, with the face looking back at camera. Both request the same facial and garment features, ivory background, soft light, no jewelry and no added text. ### Front view — identity versus pose compliance Same cast, jacket, semantic prompt and requested settings. Use the source references above to assess identity and garment detail. ![Fixed detailed synthetic portrait used as identity ground truth](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/source-cast.webp) Source product**Supplied synthetic cast; jacket reference above** Use this face and the separate jacket reference above to check each output. ![Nano Banana 2.1 supplied-cast identity front jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-front-nano-banana-2-1.webp) **Nano Banana 2.1**Upright front pose, recognizable bob and face, four cranes and four buttons. Skin is smoother than the supplied portrait; a matching orange lower garment is invented. ![Nano Banana 2.0 supplied-cast identity front jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-front-nano-banana-2-0.webp) **Nano Banana 2.0**Upright front pose with recognizable facial anchors and main garment features. Face appears narrower; a matching orange lower garment is invented. ![GPT Image 2.5 Sunburst supplied-cast identity front jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-front-sunburst.webp) **GPT Image 2.5 Sunburst**Stays close to the source skin detail, face and head tilt; four cranes and buttons. Hands-in-pockets styling and ivory trousers are added. ![GPT Image 2.5 Flare supplied-cast identity front jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-front-flare.webp) **GPT Image 2.5 Flare**Close facial resemblance and strong garment detail. Retains the source head tilt rather than a strictly upright front pose; adds dark trousers. ![Seedream 5 Pro supplied-cast identity front jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-front-seedream5pro.webp) **Seedream 5 Pro**Recognizable cast and upright pose with the main garment attributes intact. Skin, embroidery and surface texture are redrawn. Nano Banana 2.1 and 2.0 follow the upright front-pose direction more clearly. Sunburst and Flare retain more of the supplied portrait's skin texture and head tilt, which helps likeness but weakens strict pose compliance. All five preserve the recognizable bob, eyes and face alongside the main jacket features. These are human visual judgments, not biometric similarity measurements. ### Three-quarter view — the same identity and garment Same cast, jacket, semantic prompt and requested settings. Use the source references above to assess identity and garment detail. ![Fixed detailed synthetic portrait used as identity ground truth](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/source-cast.webp) Source product**Supplied synthetic cast; jacket reference above** Use this face and the separate jacket reference above to check each output. ![Nano Banana 2.1 supplied-cast identity three quarter jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-three-quarter-nano-banana-2-1.webp) **Nano Banana 2.1**Clear body turn and direct gaze; recognizable cast, four cranes and four buttons. Skin and embroidery remain somewhat simplified. ![Nano Banana 2.0 supplied-cast identity three quarter jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-three-quarter-nano-banana-2-0.webp) **Nano Banana 2.0**Clear body turn and gaze, retaining the cast and garment anchors. Smooths skin and adds a matching orange lower garment. ![GPT Image 2.5 Sunburst supplied-cast identity three quarter jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-three-quarter-sunburst.webp) **GPT Image 2.5 Sunburst**Strong source resemblance and material detail with the requested body turn. Adds hands-in-pockets styling and ivory trousers. ![GPT Image 2.5 Flare supplied-cast identity three quarter jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-three-quarter-flare.webp) **GPT Image 2.5 Flare**Recognizable face, detailed cloth and a clear turn. Retains a stronger head tilt and adds pocket styling. ![Seedream 5 Pro supplied-cast identity three quarter jacket test](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/identity-three-quarter-seedream5pro.webp) **Seedream 5 Pro**Recognizable cast, clear body turn and garment structure. Facial finish and fabric relief differ from the source. The pair does not reveal a decisive 2.1-over-2.0 identity win. Both Google versions create a coherent cast across these two views, while the supplied facial texture stays closer in the GPT variants. Skin smoothing, tiny marks, weave and crane feathers still change. A campaign can tolerate more variation than a casting approval or a product detail page; decide that tolerance before reviewing outputs. ## Three-reference outfit and brand-direction test The fifth brief combines the same cast, jacket and nine-stone necklace in a pale limestone gallery, with ivory trousers, warm side light and a relaxed walking pose. The references have explicit roles; the necklace should stay separate from the teal embroidery. We supplied this identical brief to every model: ```text Create a full-length fashion campaign photograph of woman wearing jacket with plain ivory trousers and necklace, in a pale limestone gallery. Brand direction: restrained ivory, burnt orange and teal, warm late-afternoon side light, relaxed walking pose, premium editorial realism. Preserve cast identity, jacket construction, four teal crane motifs, four brass buttons, leaf weave and scalloped hem. Preserve the necklace's nine green stones, white stone separators, rose-gold settings and cable chain; wear it visibly at the neckline without merging it with embroidery. Plausible anatomy and garment contact, no extra people, no additional jewelry, no invented product, no added text. ``` All five combine the three reference roles into a coherent fashion scene. None passes every strict campaign constraint. We keep these images internal and report the observations rather than featuring a compromised product example. | Model | What works | Why this cell needs revision | | --- | --- | --- | | Nano Banana 2.1 | Complete walking figure, plausible hands, warm limestone setting, recognizable cast and four cranes | Open neckline leaves only three buttons clearly visible; tiny necklace settings and stone geometry are not verified as exact | | Nano Banana 2.0 | Complete walking pose and strong warm directional light | Introduces a cream collar that changes the jacket; necklace geometry is reinterpreted | | Sunburst | Strong likeness, rich cloth detail and convincing warm architecture | Clips the top of the head, opens the neckline and shows three buttons; necklace settings change | | Flare | Close likeness, four visible buttons and polished garment-and-jewelry separation | Crops below the knees instead of a full-length frame; necklace stone geometry changes | | Seedream 5 Pro | Recognizable cast, four visible buttons, coherent architecture and lighting | Crops the lower legs; tiny necklace settings cannot be approved as an exact accessory reproduction | The useful distinction is between **assembling the references** and **preserving every product constraint**. The models manage the first more consistently than the second. For a brand campaign, review the neckline, closures, jewelry settings, hands and crop before judging the atmosphere. For a full catalog, qualify additional identities and garments, then inspect repeated edits for drift. ## Which model would we start with? For this cast and garment, **Nano Banana 2.1 is a credible starting point for pose-directed, reference-led fashion portraits**. It maintains the main garment anchors and produces a coherent identity pair. The product-only label miss and uncertain campaign accessory detail mean we would not approve every output automatically. **Sunburst and Flare deserve the same brief** when close source likeness and editorial material detail matter. In these samples they retain more facial texture, while occasionally retaining the source pose too strongly or choosing a crop the brief did not request. Their Arena lead is relevant shortlist evidence, not a substitute for product review. **Nano Banana 2.1, Pro, Sunburst, Flare and Seedream all preserve the 11 measured lines in the clean-packshot-to-real-scene round.** Nano Banana 2.0 misses one small-copy line. **Seedream 5 Pro remains a useful fabric-and-color alternative at 2K.** It keeps the principal jacket features and coherent casting here, but the packaging duplication and campaign crop need correction. **Nano Banana 2.0 remains competitive on these small samples**; we did not measure a consistent upgrade advantage in identity or three-line ad copy. These are 31 qualification samples, including six packshot-to-real-scene results, not a statistically reliable model ranking. They use provider defaults rather than matched internal inference budgets: Google's 2.1 default thinking is medium, while its predecessor defaults to minimal. Actual delivered ratios also vary slightly. We did not test long editing chains, wider real-product catalogs, wider cast diversity or a matched latency distribution. Keep aesthetic, identity, product and copy approvals separate. ## What the community is saying Grokbot was unavailable in this session. We used web search and checked the following short quotations against the original posts. Both pages displayed “2h ago” when checked on October 7, 2026; the October 7 posting date is inferred from that relative timestamp. These are community anecdotes, not Astria measurements. - **u/TimeCounty7878**, posting in reAPI's official subreddit, wrote: “2.1 kept the exact wording, and the layout is cleaner.” Their comparison used one run per prompt and different models' default thinking settings. The vendor-affiliated venue and small sample matter when assessing the claim. [Original post, checked October 7](https://www.reddit.com/r/reAPIOfficial/comments/1wzlz32/i_ran_nano_banana_21_vs_nano_banana_2_on_the_same/). - **u/mementomori2344323**, describing a recursive FLUX 3 versus 2.1 edit test, qualified its metric as measuring “only preservation of untouched regions.” The test used different model-specific interfaces and three source images. It raises a useful concern about drift through repeated edits, but does not measure overall commercial-image quality. [Original post, checked October 7](https://www.reddit.com/r/FluxAI/comments/1wz97mh/flux_3_crushes_nano_banana_21_in_repeated_edit/). Those reports suggest useful qualification tests: exact-copy layouts, repeated local edits, and settings-aware latency checks. They do not replace our controlled reference comparison. ## Standard versus Flex, resolution, and budgeting **Standard is available; Nano Banana 2.1 does not support Flex.** Google's Cloud pricing page explicitly says so in the Nano Banana 2.1 footnote, even though a combined table heading mentions Flex/Batch/Off-peak. Batch support is separate and should not be mistaken for Flex availability. See [Google Cloud pricing](https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing) and [Gemini API pricing](https://ai.google.dev/gemini-api/docs/pricing), checked October 7, 2026. For an interactive creative review, plan around Standard and measure the complete round trip. For an asynchronous approved catalog batch, check whether your chosen interface exposes Google's supported Batch path. The Astria generation tool used here does not expose a service-tier selector; we make no claim that these runs used Batch or Flex. Priority availability differs across Google's documented surfaces, so confirm the specific API before designing a deadline-critical workflow around it. Budget with current rates for input, reasoning or text output, image output at the chosen resolution, and any search grounding. Divide total generation and review effort by the number of approved assets. A lower output component does not guarantee a cheaper completed campaign when references, reruns, and manual correction change. Get current Astria rates from [Astria pricing](https://www.astria.ai/pricing). For reproducibility, archive the source images and provider model ID, not just the interface's model name. Use approved reference packs for production, and requalify them after a provider upgrade. Our [benchmark methodology](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) explains how we separate material fidelity, identity, copy, and aesthetic judgments. --- # One Live Workspace Brief Across Seven AI Video Models Source: https://www.astria.ai/articles/video-model-comparison-calibration/ Five endpoints received the exact nine-second prompt from Astria's **Live** workspace. Veo received the required eight-second timing variant. Kling received a 480-character version after an Astria blank-line parser incorrectly classified the formatted prompt as a multi-shot prompt. All seven requests retained the same four references and action order. This is a transparent calibration set, not a ranking. The results have not yet received blinded picture review. ## From a fashion photo to a nine-second video The brief asks the model to walk in, pause with a hand on her hip, and walk past the camera. Press play to see the generated result. ![Starting image of a model in a navy pinstriped dress and sandals in a grey studio](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-first-frame.jpg) Starting image [Watch the generated video](https://www.astria.ai/articles/video/model-benchmarks/2026-09/live-workspace/seedance-2-fast-720p-live-runway.mp4) AI video · 9 seconds · Seedance 2 Fast · no audio [View this generation](https://www.astria.ai/prompts/46608981)[Compare video models](https://www.astria.ai/articles/video-model-comparison-calibration) Matched-input benchmark ## The Live runway brief across seven video models Five exact-prompt outputs plus clearly labeled Veo duration and Kling parser variants. Each tile uses the familiar native player for play, timeline, volume, and fullscreen. **Seedance 2 Fast**Astria default · 1280×720 · 24fps · prompt 46608981[Open source prompt](https://www.astria.ai/prompts/46608981) **Seedance 2.5**1280×720 · 24fps · prompt 46608977[Open source prompt](https://www.astria.ai/prompts/46608977) **Wan 3.0**1280×720 · 30fps · prompt 46608979[Open source prompt](https://www.astria.ai/prompts/46608979) **MiniMax H3 Max**1344×768 · 24fps · prompt 46608978[Open source prompt](https://www.astria.ai/prompts/46608978) **Kling 3.0 Standard**480-character prompt variant · 1284×716 · 24fps · prompt 46609258[Open source prompt](https://www.astria.ai/prompts/46609258) **FLUX 3 Video**1280×704 · 24fps · prompt 46608980[Open source prompt](https://www.astria.ai/prompts/46608980) **Veo 3.1 Lite**8-second timing variant · 1280×720 · 24fps · prompt 46609256[Open source prompt](https://www.astria.ai/prompts/46609256) ## The exact reference-aware prompt The earlier draft was wrong because it began `video_prompt` with a reference-stripped description. Working video prompts in the Live workspace put the complete still-generation text first. The five exact-prompt jobs preserve the following structure, with real line breaks and the reference names immediately after their tokens; the two disclosed variants are documented below. ```text woman elegant posture, high-fashion runway silhouette woman wearing dress sandals, full body view with much headroom background background, concrete grey floor 0s-2s: Fast walking into the frame from the left with a continuous, fluid, and confident stride. Full body view with plenty of headroom. 2s-4s: She smoothly comes to a complete stop while facing forward. She naturally places one hand on her hip and holds the pose briefly with confident posture. 4s-9s: She lowers her hand, resumes walking with the same fluid, confident stride, walks directly toward and past the camera, and completely exits the frame. Static camera, continuous motion, no cuts, seamless transitions. ``` The four Live-workspace references are Elara (`woman`), the navy pinstriped sleeveless midi dress (`dress`), the gold rhinestone flat sandals (`sandals`), and the concrete-gray studio (`background`). The source pattern comes from workspace 602 and prompt [46434600](https://www.astria.ai/prompts/46434600). ## What the endpoints returned | Model | Requested | Delivered | Submit-to-result | | --- | --- | --- | --- | | Seedance 2 Fast | 9s, 16:9, 720p | 9.04s, 1280×720, 24fps, silent | 254.7s | | Seedance 2.5 | 9s, 16:9, 720p | 9.04s, 1280×720, 24fps, silent | 474.7s | | Wan 3.0 | 9s, 16:9, 720p | 9.00s, 1280×720, 30fps, silent | 327.1s | | MiniMax H3 Max | 9s, 16:9, 768p | 9.44s, 1344×768, 24fps, AAC track present | 44.0s | | Kling 3.0 Standard | 9s, 16:9, standard; 480-character variant | 9.04s, 1284×716, 24fps, silent | 221.7s | | FLUX 3 Video | 9s, 16:9, 720p | 9.04s, 1280×704, 24fps, silent | 209.4s | | Veo 3.1 Lite | 8s, 16:9, 720p; timing variant | 8.00s, 1280×720, 24fps, silent | 92.9s | MiniMax returned an AAC stream even though this comparison did not ask for generated audio. For current video generation rates, see [Astria pricing](https://www.astria.ai/pricing). The players do not crop, stretch, upscale, or re-encode any output. ## Disclosed Kling and Veo variants Kling 3.0 Standard initially rejected the formatted Live prompt with `each multiprompt text must be 512 characters or less`. Inspection of Astria's server code showed why: for Kling 3, any blank paragraph activates multi-shot parsing. The ordinary paragraph breaks before the background and motion blocks were therefore interpreted as shot boundaries, and the long motion section exceeded the per-shot validator. The accepted comparison prompt removes blank-line separators and compresses the wording to 480 characters while retaining all four references, the three timed actions, the static camera, and the no-cuts instruction. ```text woman elegant posture, high-fashion runway silhouette woman wearing dress sandals, full body view with much headroom background background, concrete grey floor 0-2s: Walk quickly in from left, fluid and confident, full body with headroom. 2-4s: Stop facing front; place one hand on hip and hold. 4-9s: Lower hand, walk toward and past camera, then fully exit. Static camera; one continuous shot; no cuts. ``` Veo 3.1 Lite accepts fixed 4, 6, or 8-second durations. Its comparison prompt is otherwise complete and changes only `4s-9s` to `4s-8s`. These two outputs belong in the visual grid but must remain labeled as compatibility variants, not exact-input matches. The older six-second synthetic-still calibration remains archived separately. ## What comes before a winner The full benchmark still needs three outputs per finalist, randomized labels, and at least three reviewers. Review should score action order, reference assignment, identity, garment silhouette, shoes and gait, camera behavior, temporal defects, usable-output rate, latency, and cost per approved second. Until that review is complete, treat this page as a published calibration rather than a final ranking. --- # Seedream 5 for Fashion Photography: Texture, Identity, and Swimwear Test Source: https://www.astria.ai/articles/seedream-5-for-fashion/ **Seedream 5 Pro is our fashion specialist for difficult color, fabric texture, stable identity, and swimwear.** Nano Banana 2 remains the broader default, but Seedream produced the best surface rendering in the jacket test and completed both tasteful adult swimwear briefs. ## Seedream 5 at a glance | Best fit | Observed advantage | | --- | --- | | Saturated garments | Rich color without flattening the fabric | | Jacquard, embroidery, and sheen | Best surface rendering in our matched jacket comparison | | Repeated casting | Strong face, skin, and identity continuity across four shots | | Adult commercial swimwear | Both tested briefs completed with tasteful outputs | The controlled run used 11 matched briefs, three requested outputs per cell, and 33 returned Seedream 5 Pro images. The conclusions are qualitative and tied to the Astria endpoint tested in September 2026. ## Where Seedream visibly wins The orange jacket is deliberately difficult: saturated burnt orange, teal embroidery, woven jacquard, brass buttons, and a scalloped hem under hard studio light. ![Synthetic orange jacquard jacket source](https://www.astria.ai/articles/img/model-benchmarks/2026-09/source-jacket.webp) **Source**Color, weave, embroidery, and hardware to preserve ![Seedream 5 Pro orange jacket fashion benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/texture-seedream-5.webp) **Seedream 5 Pro**Best color and surface rendering in the four-model comparison Seedream's useful distinction was material behavior. The orange remained rich, the teal motifs stayed distinct, and the garment read as a light-reactive woven surface rather than a flat print. Small product details still need a checklist; material realism does not guarantee exact construction. Use Seedream first when approval depends on: - embroidery, knit, lace, jacquard, or metallic thread; - saturated colors that tend to drift or clip; - silk, coated fabric, or another sheen-sensitive surface; - repeated skin and facial rendering across a campaign. ## Swimwear: the practical production route The benchmark used one adult identity and one product-only one-piece source in two ordinary briefs: straight-on ecommerce and a calm poolside editorial. Seedream completed all six requested images. ![Seedream 5 Pro tasteful adult swimwear ecommerce output](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-ecom-seedream-5.webp) **Ecommerce**Strong garment and identity; the first output cropped tighter than the requested full length ![Seedream 5 Pro tasteful adult swimwear editorial output](https://www.astria.ai/articles/img/model-benchmarks/2026-09/swim-editorial-seedream-5.webp) **Editorial**Reliable completion with strong cobalt and skin tones This is about completion of a benign commercial brief, not evading safeguards. Keep subjects adult and the framing unambiguous. Nano Banana 2, GPT Image 2, and Muse also produced tasteful swimwear outputs in the core test; one Nano Banana request returned two rather than three images. In the later MAI qualification, the entire swimsuit cell was blocked. Seedream is therefore the route we would qualify first for a real swimwear collection. ## Identity across camera distance and pose The identity strip is omitted from this public page because the supplied cast reference is not part of the published source set. The jacket and swimsuit examples above remain reference-auditable. The face, skin tone, and overall cast remained especially stable as the framing changed. A four-shot strip is not proof of catalog-scale consistency, but it is enough to justify a production qualification. ## Where another model starts first - For an ordinary fashion brief without a known specialist risk, use [Nano Banana 2](https://www.astria.ai/articles/nano-banana-2-for-fashion/). - For beauty advertising and prominent copy, use [GPT Image 2](https://www.astria.ai/articles/gpt-image-2-for-product-photography/). - For jewelry and strict multi-reference constraints, use [Muse](https://www.astria.ai/articles/muse-image-model-review/). - For direct product edits, qualify [MAI-Image-2.6](https://www.astria.ai/articles/mai-image-2-6-review/)—but avoid it as the current swimwear default. ## Prompting Seedream for fashion > Create a \[crop\] fashion photograph using `` and ``. Preserve the exact garment color, material, weave, construction, hardware, print, and silhouette. Specify one lighting setup that reveals the material. Define pose and background. Exclude text, logos, extra people, and unrequested accessories. Name the difficult material explicitly and describe how light should behave on it. “Orange jacket” tests color; “burnt-orange silk jacquard with woven leaf texture under controlled hard side light” tests the thing the art director actually needs to approve. ## Bottom line Seedream 5 Pro is not our universal winner. It is the model to choose when color, material, identity, or swimwear completion is the hard part. That is a more useful role than an abstract leaderboard position. See the [complete side-by-side benchmark](https://www.astria.ai/articles/best-ai-image-models-fashion/) and [methodology](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/). For current Astria rates and billing terms, see [Astria pricing](https://www.astria.ai/pricing). --- # Which AI Image Model Should You Use in Astria? (2026 Guide) Source: https://www.astria.ai/articles/astria-image-models/ **The short answer:** start with **Nano Banana 2** for fashion. Choose **GPT Image 2** when beauty art direction or readable text is central, **Seedream 5** for swimwear and demanding color, texture, or identity work, and **Muse** when several references must survive one complicated brief. That is the useful model hierarchy. It is not a claim that one model wins every image prompt. ## The model-selection table | If the job is… | Start with… | Why | | --- | --- | --- | | General fashion editorial, lookbook, or campaign work | **Nano Banana 2** | Best overall balance of direction, realism, garment handling, identity, and usable composition in our test | | Beauty, cosmetics, packaging, or prominent copy | **GPT Image 2** | Particularly good beauty-ad finish, layout, and readable headline work | | Swimwear or another benign brief affected by stricter sensitivity handling | **Seedream 5** | Practical fallback, with strong color, texture, skin, and identity rendering | | Saturated color, woven texture, sheen, or consistent casting | **Seedream 5** | Excellent surface rendering and identity preservation in the tested campaign | | Jewelry or a brief combining several references and constraints | **Muse** | Deliberate, reference-first behavior and dependable interpretation of each asset's role | | One direct supplied-product edit | **MAI-Image-2.6 Flash or flagship** | Flash completed the matched jacket qualifier faster; the flagship preserved more construction detail | The first five rows come from a controlled Astria comparison captured on September 6, 2026: 11 briefs, four models, three requested outputs per brief, and 131 returned images. MAI was tested separately on September 8 because the working Astria path accepted direct input images rather than the reference-token setup used by the core benchmark. See the [full visual benchmark](https://www.astria.ai/articles/best-ai-image-models-fashion/), [MAI review](https://www.astria.ai/articles/mai-image-2-6-review/), and [methodology](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/). ## [Nano Banana 2](https://www.astria.ai/articles/nano-banana-2-for-fashion/): the fashion default Nano Banana 2 is the answer when a creative team should not have to become a model-routing team before it can make a lookbook. Across the general fashion, movement, garment-reference, and multi-reference briefs, it most consistently landed in the middle of the useful production triangle: the image looked directed, the references remained recognizable, and the composition was close to something an art director could continue with. Some specialists matched or exceeded it in individual tests. None displaced it as the safest starting point across ordinary fashion work. The general-fashion output strip is omitted because its supplied person-and-garment source set is not published with this article. See the linked specialist reviews for source-auditable product examples. Matched 3:4 brief and references. Each card is the first predetermined output, not a hand-picked favorite. Use Nano Banana 2 first for: - fashion editorials and campaign scenes; - moving from a garment reference to an on-model image; - shot-list variety with one cast identity; - mixed creative and preservation briefs where no single constraint dominates. ## [GPT Image 2](https://www.astria.ai/articles/gpt-image-2-for-product-photography/): beauty and text GPT Image 2 is the specialist to try when the frame has to behave like an advertisement, not only a photograph. In our beauty test it combined a polished portrait, a held cosmetic product, open copy space, and the exact headline **COLOR, KEPT TRUE** especially well. The distinction matters. Exact wording, hierarchy, product geometry, and beauty finish are separate failure points. A model can produce beautiful skin and still miss the headline; it can spell the headline correctly and quietly redesign the bottle. GPT Image 2 gave the most convincing overall beauty-ad composition in this set, although Nano Banana 2 and Muse were competitive and also rendered the large headline correctly. Choose GPT Image 2 first for: - cosmetic campaigns and close beauty portraits; - packaging-led advertising; - headlines, signs, labels, and designed copy space; - layouts where typography is part of the image rather than added later. For a product listing, still compare every generated label against the real pack. Readable-looking small type is not proof of exact type. ## [Seedream 5](https://www.astria.ai/articles/seedream-5-for-fashion/): swimwear, color, texture, and identity Seedream 5 is the practical specialist for two different reasons. First, it is a useful alternate route for ordinary adult swimwear and other briefs that can encounter stricter sensitivity handling elsewhere. This is not advice to bypass safeguards. Keep the subject adult, the framing commercial, and the pose unambiguous. If a benign job still does not complete as expected, Seedream can keep the production moving. Second, its visual strengths are real. The test showed excellent cobalt and coral separation, rich teal and burnt-orange color, convincing jacquard and fabric sheen, and stable identity across close-up, seated, full-length, and movement shots. In this particular benchmark every model produced tasteful swimwear images. One Nano Banana 2 editorial request returned two images rather than three, without a reported error or moderation reason. That isolated omission is recorded; it is not presented as proof of why the third image was absent. Choose Seedream 5 first for: - swimwear and skin-exposure briefs with ordinary commercial intent; - saturated color and exact color relationships; - embroidery, woven surfaces, sheen, and other difficult textures; - repeated casting where face and skin continuity matter. ## [Muse](https://www.astria.ai/articles/muse-image-model-review/): reference-first, constraint-heavy work Muse is the option for the brief that reads like a small logic problem: use this person, that jacket, this necklace, one bottle in the right hand, a specified background, and no extra objects. We call it a **thinking model** in a practical editorial sense: its useful distinction is the behavior visible in the output—careful interpretation of multiple references and their separate roles. This guide does not make an undocumented claim about its internal architecture. Muse was particularly dependable for jewelry and the complex multi-reference frame. It is worth trying when reference reliability matters more than discovering the fastest general-purpose look. Choose Muse first for: - several references with explicit roles; - jewelry and accessory placement; - strict inclusion and exclusion lists; - a reference-led brief that other models keep simplifying or blending. ## [MAI-Image-2.6](https://www.astria.ai/articles/mai-image-2-6-review/): the commercial challenger MAI joined the cluster through a separate reference-led qualification round. Direct jacket and jewelry placement completed; a tasteful adult swimsuit edit was blocked. Existing Astria reusable-reference prompts failed during the test, while direct supplied-image editing worked. The flagship preserved the orange jacket's four cranes, four buttons, woven surface, collar, sleeves, and scalloped hem especially well. Flash completed the matched jacket cell faster, but simplified more product detail. Check [Astria's pricing page](https://www.astria.ai/pricing) for current model pricing. Use MAI first for: - turning one direct product image into a campaign candidate; - a Flash-first, flagship-final workflow when the product passes review. Do not make it the current Astria default for reusable multi-reference production or swimwear without requalifying those exact paths. ## What else is in Astria's model GUI? Astria's popular model selector changes as endpoints improve. In the September 6 catalog snapshot, it included current variants from the Nano Banana and Seedream families alongside Flux 2 Pro, GPT Image 2, Recraft V4.1, Ideogram V4, and WAN 2.7/Pro. The public partner-model gallery was broader: Reve, Riverflow, HyperRealism, Flux Turbo and Klein, P-Image, Recraft variants, Grok Imagine, Krea, Qwen Image, MAI Image, and Muse were among the visible families. MAI and Muse now have dedicated tested guides; the other challengers remain catalog options rather than editorial recommendations. Two cautions make that list easier to use: 1. **Public in the GUI does not mean open weights.** It means the model is visible as a public Astria partner model. 2. **A long catalog is not a reason to test everything on every job.** Start with the core routing table, then qualify a challenger only when it offers a relevant capability, speed, or aesthetic. Model names and availability are volatile. Treat this as a dated snapshot and check the live selector before committing a production pipeline. ## A simple routing workflow 1. Run the approved brief in Nano Banana 2. 2. If the job is beauty- or text-led, run GPT Image 2 alongside it. 3. If color, texture, identity, or swimwear completion is the risk, add Seedream 5. 4. If several references must be interpreted separately, add Muse. 5. If one supplied product image and direct-edit speed are the opportunity, qualify MAI flagship and Flash. 6. Judge product accuracy separately from aesthetic preference. 7. Approve a treatment before multiplying it across a collection. For current rates and billing terms, use [Astria pricing](https://www.astria.ai/pricing). Evaluate retries and retouching against your own approval rate. Continue to the [side-by-side results](https://www.astria.ai/articles/best-ai-image-models-fashion/), open the [MAI-Image-2.6 qualification](https://www.astria.ai/articles/mai-image-2-6-review/), or inspect [how the tests were designed](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/). --- # AI Fashion Photography Prompts: A Production-Ready Guide Source: https://www.astria.ai/articles/ai-fashion-photography-prompts/ AI fashion photography prompts work best when they read less like mood-board captions and more like compact call sheets. A useful prompt tells the system what the image must accomplish, what cannot change, what the camera sees, what the subject is doing, and what kind of world surrounds the product. **The short version:** lock the product and art direction first. Then change one shot variable at a time. This guide gives you a repeatable prompt structure, 15 copyable starting points, and public [fashion workspaces](https://www.astria.ai/gallery/workspaces), [templates](https://www.astria.ai/p/basic-shirt), and [finished prompt examples](https://www.astria.ai/prompts/35818275?ws=54) to study. ## Start with the image's job Before describing style, decide what the image is for. A front-facing product page image, a fabric-detail crop, a wholesale line-sheet frame, and a campaign opener have different jobs. If the prompt begins with “cinematic fashion photo” but never states the deliverable, the result may be attractive and operationally useless. A practical brief can be written in one sentence: > Create a full-body front three-quarter catalog image that shows the complete reference outfit clearly, preserves its construction and color, and leaves clean negative space for a product page crop. That sentence establishes purpose before decoration. It also gives reviewers a concrete standard: complete outfit, recognizable construction, faithful color, usable crop. ## The seven-part fashion prompt Use these seven fields in roughly this order: 1. **Job:** catalog front, campaign hero, lookbook transition, material detail, or social crop. 2. **Subject:** model, garment, styling combination, and visible accessories. 3. **Preservation:** silhouette, color, print placement, closures, seams, hardware, length, and fabric behavior that must remain accurate. 4. **Frame:** full body, three-quarter, waist-up, macro detail, camera height, angle, and aspect ratio. 5. **Action:** stance, step, turn, hand position, gaze, or fabric movement. 6. **World:** background, surface, location, props, and color relationships. 7. **Image character:** light direction, contrast, depth of field, texture, and editorial restraint. Not every prompt needs seven long clauses. The structure is a diagnostic tool: if an output fails, you can identify which field was vague. ## Catalog prompt family Catalog images need repeatability more than surprise. Keep the background, camera height, light, crop, and product-preservation language fixed across the family. Change only the angle or shot size. ### 1\. Front three-quarter outfit > Full-body front three-quarter view of the model wearing the reference shirt, skirt, socks and shoes. She stands at a 45-degree angle and looks toward the camera, with the complete outfit visible and natural fabric folds. Preserve each garment's silhouette, length, color, seams and styling. Seamless off-white studio background #F5F5F5, soft even studio light, realistic proportions, clean 2:3 catalog composition. Compare this structure with the public [America Basics workspace](https://www.astria.ai/w/america-basics), its [Basic Shirt template](https://www.astria.ai/p/basic-shirt), and this [full-look prompt](https://www.astria.ai/prompts/35818275?ws=54). Fashion catalog ### Build one look from three product references A PDP-ready full-look frame that keeps the separate shirt, skirt, and shoes readable. - Catalog - 2:3 - Studio Reference inputs ![Brown graphic sweatshirt photographed on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Reference 1 · shirt ![Grey pleated skirt photographed on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-skirt-reference.webp) Reference 2 · skirt ![Silver sneakers photographed on white](https://www.astria.ai/articles/img/prompt-recipes/fashion-shoes-reference.webp) Reference 3 · shoes Generated result [![Model wearing the referenced sweatshirt, pleated skirt, socks, and sneakers](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp)](https://www.astria.ai/prompts/35818293?ws=54) Public result · click to inspect the original generation **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` Full-body front three-quarter view of the model wearing the reference shirt, skirt, socks and shoes. She stands at a 45-degree angle and looks toward the camera, with the complete outfit visible and natural fabric folds. Preserve each garment's silhouette, length, color, seams and styling. Seamless off-white studio background #F5F5F5, soft even studio light, realistic proportions, clean 2:3 catalog composition. ``` **The challenge** Multiple product references compete for attention. The prompt has to preserve each item's construction while still producing one physically coherent outfit. **Things to notice** - Compare the shirt graphic, collar, cuffs, and hem with the source. - Check the skirt pleats, length, and waistband instead of judging only the silhouette. - Keep the shoes and socks separated and verify both feet at full size. - Reject poses that hide the waist or prevent a clean product-page crop. Reference and result images come from the linked public Astria example. ### 2\. Straight-on front > Full-body straight-on catalog photograph of the model wearing the reference outfit, standing neutrally with feet comfortably apart and arms relaxed away from the torso so the side seams remain visible. Preserve neckline, sleeve length, waist, hem, print placement and garment proportions. Seamless warm-white background, soft symmetrical light, minimal shadow, eye-level camera, centered 2:3 frame. ### 3\. Back view > Full-body back view of the model wearing the reference outfit, head turned slightly away and arms relaxed. Show the complete rear construction, including collar, center-back seams, closures, pockets and hem. Preserve the exact silhouette and color. Seamless warm-white studio, soft even light, eye-level camera, centered 2:3 catalog frame. ### 4\. Side profile > Full-body left-side profile of the model wearing the reference dress. Keep the torso upright and arms slightly separated from the body so the sleeve, waist and side-seam shape are legible. Preserve the dress length, neckline, closures and fabric weight. Neutral light-grey sweep, soft diffused light, restrained contact shadow, clean 2:3 frame. ### 5\. Fabric and construction detail > Close detail crop of the model wearing the reference shirt, framed from shoulders to waist with the upper face outside the frame. Focus on weave, seams, collar construction and hem; preserve the product's exact color and proportions. Seamless off-white background #F5F5F5, soft even studio light, sharp material detail, 2:3. The America Basics workspace also exposes a focused [fabric-detail prompt](https://www.astria.ai/prompts/35819692?ws=54). Pair close crops with a full product view; detail without context cannot establish the whole silhouette. ## Editorial prompt family Editorial images can carry more gesture, atmosphere, asymmetry, and cropping. The garment clause should remain literal even when the setting becomes expressive. ### 6\. Quiet campaign hero > Full-body fashion campaign portrait of the model wearing the reference tailored look, standing near a pale stone wall with one shoulder angled toward the camera. Preserve the jacket's lapels, fastening, waist shape, trouser length and fabric color. Cool morning light enters from frame left, long soft shadow, muted cream and mineral-grey palette, generous negative space, restrained luxury editorial mood, vertical 4:5. ### 7\. Controlled walking frame > Full-body street-level photograph of the model wearing the reference coat and boots, captured mid-step with the coat opening naturally but the front construction still readable. Preserve the coat length, collar, buttons, pockets and exact color. Quiet concrete arcade, low winter light, subtle motion in the hem only, realistic anatomy, refined 4:5 fashion editorial. ### 8\. Studio turn > Full-body shot of the model wearing the reference swimsuit and bracelet, stepping backward while turning her upper body toward the camera. One heel is lifted, the torso is gently twisted and the arms trail with restrained motion. Preserve the swimsuit cut, straps, coverage and color. Dark teal-grey studio background #1F2A2A with a soft center-to-edge gradient; diffused studio light and cinematic shadow falloff; refined 3:4 editorial frame. Study the public [Dark Swimwear workspace](https://www.astria.ai/w/dark-swimwear), the [Swimsuit Set 1 template](https://www.astria.ai/p/swimsuit-set-1), and its [turning](https://www.astria.ai/prompts/40584408?ws=368) and [transitional](https://www.astria.ai/prompts/40584411?ws=368) frames. The useful idea is not one pose; it is a sequence with controlled differences. ### 9\. Seated portrait > Three-quarter seated fashion portrait of the model wearing the reference knit set, perched on the front edge of a low charcoal plinth with knees angled away and torso turned gently toward camera. Preserve the knit texture, neckline, sleeve length and waistband. Dusty-lilac studio, large soft source from camera right, quiet shadow, tactile realism, vertical 4:5. ### 10\. Cropped gesture > Waist-to-knee editorial crop of the model wearing the reference skirt and shirt, one hand entering a pocket while the other adjusts the cuff. Keep both garments' waistlines, closures, hem shapes and material textures accurate. Warm neutral wall, directional window light, slight grain, natural hand anatomy, intimate vertical 4:5 composition. Wedding dresses ### Add movement without losing beadwork and volume A bridal campaign frame that introduces air and location while keeping the dress commercially recognizable. - Campaign - 3:4 - Soft movement Reference inputs ![Off-white beaded tulle wedding dress reference on a model](https://www.astria.ai/articles/img/prompt-recipes/bridal-dress-reference.webp) Reference · dress construction Generated result [![Bride wearing the referenced dress beside an arched terrace door](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.webp)](https://www.astria.ai/prompts/37146748?ws=126) Public result · click to inspect the original generation **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` Medium shot of a bride wearing the reference off-white beaded tulle dress, standing beside an open arched terrace door. A light breeze moves the sheer curtains and only the outer layers of the dress. Preserve the neckline, beading pattern, bodice construction, waist, skirt volume and color. Hair slightly windswept, soft-focus garden background, elegant cinematic storytelling, natural anatomy, vertical 3:4. ``` **The challenge** Bridal imagery combines fine construction, translucent layers, bead placement, large skirt volume, and movement. A generic wind instruction can redesign the dress. **Things to notice** - Trace the neckline, sleeve shape, waist, and bead density back to the reference. - Let the outer tulle move while the fitted bodice stays structurally stable. - Check that the arch, curtain, and model occupy believable depth planes. - Capture a separate full-length frame; this medium shot cannot prove the complete hem. Reference and result images come from the linked public Astria example. ## Lookbook sequence prompts A lookbook is not 12 unrelated “good images.” It is an edited rhythm. Repeat the cast, styling, location family, grade, and preservation clause; vary distance, angle, pose, and energy. The following five prompts can follow one approved hero. ### 11\. Establishing frame > Wide environmental fashion photograph of the model wearing the reference look at the entrance to a quiet modernist building. Show the complete silhouette at a smaller scale while keeping the garment color and length legible. Overcast daylight, pale stone and muted green surroundings, low contrast, generous architectural negative space, vertical 4:5. ### 12\. Medium connective frame > Three-quarter-length photograph of the same model and reference look in the same modernist setting, walking slowly parallel to the wall. Preserve the garment construction and styling. Eye-level camera, soft overcast light, subtle fabric movement, consistent muted color grade, vertical 4:5. ### 13\. Low-angle silhouette > Full-body low-angle photograph of the same model wearing the reference look, paused on a shallow exterior step. Keep the complete silhouette, footwear and hem visible without exaggerating limb proportions. Pale stone facade, diffuse sky light, controlled geometry, restrained editorial tension, vertical 4:5. ### 14\. Material punctuation > Tight shoulder-to-hip detail of the same model wearing the reference look, focusing on the garment's closure, seam architecture and fabric surface. Preserve the exact material color and construction. Same pale-stone location and overcast light, shallow depth of field, crisp product detail, vertical 4:5. ### 15\. Closing movement frame > Full-body rear three-quarter photograph of the same model wearing the reference look, walking away and turning her head slightly toward the building entrance. Preserve the back construction, hem, footwear and styling. Same modernist location, soft late-afternoon light within the established muted grade, gentle motion at the hem, vertical 4:5. For a broader production sequence, continue with the [AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/). The public [Language of Form workspace](https://www.astria.ai/w/angela-fraser) and [Chapter 2 lookbook template](https://www.astria.ai/p/chapter-2-the-lookbook) are useful references for thinking in chapters instead of isolated frames. ## What to repeat and what to change Make a small invariant block before generating: - the approved cast and grooming; - the exact product references and styling combination; - the location family and background palette; - the light family and contrast level; - the image ratio and color treatment; - the construction details reviewers must check. Then build a shot list whose variable is explicit: front, back, close, walking, seated, wide, or transition. If you change the set, light, pose, crop, and styling together, a failed image teaches you very little. ## Prompting for motion without losing the garment Motion should be physically small and visually clear. “Dynamic” can produce airborne fabric, hidden closures, tangled limbs, or a garment that no longer hangs like the product. Prefer observable actions: one step, a quarter turn, a hand entering a pocket, a lifted heel, a coat opening in a light breeze. State what must stay readable during the movement. A tennis-inspired frame in the [America Basics prompt set](https://www.astria.ai/prompts/35823359?ws=54) is useful because it connects action with wardrobe rather than treating movement as an abstract mood. ## Review the output like production, not inspiration Approve images at two levels. **Product review:** compare neckline, straps, sleeve and hem length, closures, pockets, seams, print placement, material texture, color, footwear, jewelry, and rear construction with the source images. **Image review:** check hands, feet, limb count, joint direction, contact with surfaces, shadows, reflections, background continuity, crop safety, and whether the pose is physically coherent. Beautiful is not the same as usable. A frame with the wrong button count is a different product image. A cropped campaign image may be strong but cannot substitute for the complete catalog view. ## A reusable prompt worksheet Write one line for each field before combining them: ```text JOB: [where the image will be used] SUBJECT: [model, garment, styling] PRESERVE: [construction, color, material, proportions] FRAME: [shot size, angle, camera height, aspect ratio] ACTION: [one observable pose or movement] WORLD: [background, location, props, palette] CHARACTER: [light, contrast, texture, editorial mood] ``` The worksheet is intentionally plain. Specific nouns and physical relationships do more work than a pile of style adjectives. ## Vertical prompt libraries Use the broad recipes above when planning a mixed campaign. Use the dedicated libraries when the product class changes what must be preserved and reviewed: - [AI Lookbook Prompts](https://www.astria.ai/articles/ai-lookbook-prompts/) — eight frames organized as an opener-to-closer sequence. - [Beauty Product Photography Prompts](https://www.astria.ai/articles/beauty-product-photography-prompts/) — packaging, formula texture, hands, routine and campaign coverage. - [Jewelry Photography Prompts](https://www.astria.ai/articles/jewelry-photography-prompts/) — rings, necklaces and earrings with geometry and scale checks. - [Wedding Dress Photography Prompts](https://www.astria.ai/articles/wedding-dress-photography-prompts/) — front, back, train, construction detail and restrained movement. - [Vitamin and Supplement Photography Prompts](https://www.astria.ai/articles/vitamin-supplement-photography-prompts/) — labels, closures, product form, ingredients and bundles with an explicit compliance review gate. ## Frequently asked questions ### How do you write a good AI fashion photography prompt? Write it like a compact call sheet: state the image's job, the garment that must remain accurate, framing, pose or action, setting, light, and visual character. Keep the product facts fixed while changing one creative variable at a time. ### What should stay consistent across AI lookbook prompts? Keep the cast, garment references, styling rules, location logic, light family, color treatment, and output ratio consistent. Vary the shot size, camera angle, pose, and moment so the sequence feels related without becoming repetitive. ### Should a fashion prompt include camera and lens names? Only when the camera language changes the result you need. Framing, camera height, distance, depth of field, and light direction are usually more useful than a long equipment list. ### How can prompts preserve garment accuracy? Name the visible construction details that matter, such as neckline, sleeve length, closures, seams, hem, print placement, and fabric behavior. Use clear front, back, and detail references, then review each output against them before publishing. ### Can the same prompt work for catalog and campaign images? Not well. Catalog prompts prioritize coverage, clean backgrounds, legible construction, and repeatable framing. Campaign prompts can prioritize gesture, atmosphere, cropping, and narrative, but they still need an explicit product-preservation clause. --- # AI Lookbook Prompts: Build a Cohesive Fashion Sequence Source: https://www.astria.ai/articles/ai-lookbook-prompts/ A useful AI lookbook prompt does two jobs at once: it protects continuity and gives one frame a reason to exist. If every prompt restates a different mood, the results feel like unrelated campaigns. If every prompt repeats the same composition, the sequence feels like a catalog contact sheet. **The short version:** write one invariant production block, then build a sequence by changing distance, angle, pose and action. The public [Language of Form workspace](https://www.astria.ai/w/angela-fraser) and [Chapter 2: The Lookbook template](https://www.astria.ai/p/chapter-2-the-lookbook) show the reusable-system approach; [America Basics](https://www.astria.ai/w/america-basics) and [Dark Swimwear](https://www.astria.ai/w/dark-swimwear) provide linkable finished examples. ## The invariant block Write this once before the individual shots: > The same approved model wears the same reference outfit and shoes throughout. Preserve the garment neckline, closures, waist, seam placement, length, material texture, color and styling. Use one pale modernist courtyard, soft overcast daylight, a muted cream-and-mineral grade, realistic anatomy and vertical 4:5 framing. The nouns should come from the actual references. The point is not to hide missing information inside “same.” It is to identify the facts that must survive the sequence. ## A visual reference-to-result pattern Lookbook ### Anchor the full look before changing the sequence One clear full-look frame becomes the product and styling reference for the surrounding editorial shots. - Hero - 2:3 - Studio anchor Reference inputs ![Brown graphic sweatshirt reference](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.webp) Reference 1 · top ![Grey pleated skirt reference](https://www.astria.ai/articles/img/prompt-recipes/fashion-skirt-reference.webp) Reference 2 · skirt ![Silver sneakers reference](https://www.astria.ai/articles/img/prompt-recipes/fashion-shoes-reference.webp) Reference 3 · shoes Generated result [![Model wearing the referenced sweatshirt, skirt and shoes](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.webp)](https://www.astria.ai/prompts/35818293?ws=54) Public result · full-look anchor **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` Full-body front three-quarter lookbook hero of the model wearing the reference shirt, skirt, socks and shoes. She stands at a 45-degree angle and looks toward the camera, with every item visible and natural fabric folds. Preserve each garment's silhouette, length, color, seams and styling. Seamless off-white studio background #F5F5F5, soft even studio light, realistic proportions, clean 2:3 composition. ``` **The challenge** A lookbook can be atmospheric later; the anchor must first prove which products and styling belong to the look. **Things to notice** - Check every garment separately before judging the overall styling. - Keep the waist and shoe contact visible so the outfit reads as one physical look. - Use this frame as the comparison reference for later crops and poses. - Reject later frames that quietly add, remove or restyle an item. Reference and result images come from the linked public Astria example. ## Eight copy-ready lookbook prompts Use the invariant block above with every prompt below. ### 1\. Environmental opener Establish the visual world before the product fills the frame. ``` Wide environmental fashion photograph of the model wearing the reference look at the entrance to the pale modernist courtyard. Show the complete silhouette and shoes at a smaller scale with generous architecture around the model. She stands still and looks beyond frame left. Eye-level camera, soft overcast daylight, restrained contrast, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 2\. Full-look hero Give the reader one unambiguous view of the complete styling. ``` Full-body front three-quarter photograph of the model wearing the reference look. She stands at a 45-degree angle with weight on the back foot and looks toward the camera. Keep the complete outfit, fastening, hem and shoes clearly visible. Eye-level camera, quiet shadow, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 3\. Controlled walking frame Movement changes energy without abandoning product coverage. ``` Full-body photograph of the same model and reference look walking parallel to the courtyard wall, captured mid-step. Preserve the garment construction and length; allow restrained movement at the outer hem only. Keep both shoes visible, arms clear of the garment and anatomy natural. Eye-level camera, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 4\. Construction detail Pause the sequence and prove material or workmanship. ``` Shoulder-to-hip detail of the same model wearing the reference look, focused on the neckline, fastening, waist construction and fabric surface. One hand adjusts the cuff without covering the garment. Sharp product detail, shallow background, the same soft overcast light and color grade, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 5\. Graphic profile Reveal side construction and change the silhouette's shape on the page. ``` Full-body side-profile photograph of the same model wearing the reference look, paused beside a courtyard column. Keep the garment length, side seams, silhouette and shoes legible. Camera at waist height, subtle asymmetrical framing, consistent soft light and muted grade, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 6\. Seated pause A quieter pose creates rhythm between standing and movement frames. ``` Three-quarter seated fashion photograph of the same model wearing the reference look on the front edge of a low pale-stone bench. Knees angle away while the torso turns gently toward camera. Keep the waist, neckline and footwear unobscured; preserve natural fabric compression and anatomy. Vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 7\. Back construction Show information the opener and hero cannot provide. ``` Full-body rear three-quarter photograph of the same model wearing the reference look, standing near the courtyard exit. Preserve the back neckline, seams, closures, hem, trousers and footwear. Head turned slightly toward the light, arms relaxed away from the body, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 8\. Closing frame Resolve the sequence with departure rather than another hero pose. ``` Rear three-quarter photograph of the same model and reference look walking toward the courtyard exit, head turned slightly toward the light. Preserve the complete back construction, hem and footwear. Add a gentle late-afternoon warmth while staying within the established muted grade, restrained fabric movement, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ## Things to notice in the edit - **Repetition versus continuity:** consistent does not mean identical. Change shot scale or energy in every neighboring frame. - **Hidden product:** hands, hair, bags and crossed limbs often cover the exact construction a frame should prove. - **Sequence rhythm:** alternate wide, full, active, close and quiet frames instead of grouping all similar crops. - **Cast drift:** compare face, hair, body proportions and age across the contact sheet, not one image at a time. - **Grade drift:** a slightly warmer frame can close a story; a completely different palette starts a new campaign. - **Product drift:** reject any frame that changes the neckline, closures, hem, print, material or accessories. ## How to run the sequence in Astria 1. Open [Chapter 2: The Lookbook](https://www.astria.ai/p/chapter-2-the-lookbook). 2. Add the approved model, garments, footwear and background references in their separate slots. 3. Generate the hero first and approve its product fidelity. 4. Reuse the invariant block across the remaining prompts. 5. Lay the results out together and cut repeated or inaccurate frames. For production strategy, approvals and sequencing beyond the prompt text, use the [AI Lookbook Guide](https://www.astria.ai/articles/ai-lookbook-guide/). For broader catalog and campaign recipes, continue with [AI Fashion Photography Prompts](https://www.astria.ai/articles/ai-fashion-photography-prompts/). ## Frequently asked questions ### What makes a good AI lookbook prompt? A good prompt gives the frame one job while preserving the shared production decisions: cast, products, styling, world, light and grade. ### How many prompts should a lookbook contain? Start with six to eight purposeful frames. More output is not automatically a better edit. ### How do you keep an AI lookbook consistent? Reuse one invariant block and change one compositional variable at a time. ### Should every lookbook image be full body? No. Combine product-proof frames with movement, details, environment and a closing image. --- # Beauty Product Photography Prompts: 8 Copy-Ready Setups Source: https://www.astria.ai/articles/beauty-product-photography-prompts/ Beauty prompts fail in small, expensive ways: a pump gains a seam, a cap changes finish, a label becomes plausible nonsense, or a translucent formula turns opaque. The fix is to treat packaging as a measured object and the setting as the creative variable. The public [Beauty workspace](https://www.astria.ai/w/beauty) and [Beauty template](https://www.astria.ai/p/beauty) let you inspect that workflow. Below, every prompt is copyable and selected prompts link to a public result. ## Reference → prompt → result Beauty ### Preserve the bottle before styling the campaign A direct reference and a restrained scene make packaging drift easier to see and reject. - Packshot - 4:5 - Reflective set Reference inputs ![Cosmetic bottle reference](https://www.astria.ai/articles/img/prompt-recipes/beauty-bottle-reference.webp) Reference · approved bottle Generated result [![Beauty bottle in a reflective campaign scene](https://www.astria.ai/articles/img/prompt-recipes/beauty-still-life-result.webp)](https://www.astria.ai/prompts/45986211?ws=761) Public result · styled packshot **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` Studio product photograph of the exact reference cosmetic bottle standing upright on a warm ivory reflective surface. Preserve the bottle proportions, shoulder curve, pump, cap, material, color and label placement. Add one soft oval shadow and a pale lilac background gradient. Front three-quarter camera, crisp product edges, controlled highlights, premium restrained beauty campaign, vertical 4:5. ``` **The challenge** Reflective packaging needs intentional highlight control while tiny label and closure errors remain visible. **Things to notice** - Compare the silhouette and closure before judging the styling. - Read every visible word at 100% zoom. - Reject doubled pumps, warped caps and melted edges. - Use approved artwork or a final compositing pass when exact text is mandatory. Reference and result images come from the linked public Astria example. ## Eight copy-ready beauty prompts ### 1\. Clean front packshot Establish the approved geometry and label before adding art direction. ``` Straight-on studio packshot of the exact reference beauty product, upright and centered on seamless soft white. Preserve the vessel proportions, closure, label position, colors, materials and finish. Even diffused light, short natural grounding shadow, sharp edges, no extra props, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 2\. Reflective campaign hero Turn the proof frame into a premium campaign image without redesigning it. ``` Front three-quarter campaign photograph of the exact reference beauty product on a pale lilac reflective plinth. Preserve packaging geometry, closure, label layout, colors and materials. One large softbox reflection, controlled highlight rolloff, clean oval shadow, quiet gradient background, premium editorial restraint, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 3\. Formula texture macro Show viscosity or finish while keeping the package legible. ``` Macro beauty still life of the exact reference product beside one deliberate swipe of its reference formula. Keep the package upright, undistorted and fully recognizable; preserve the formula color, opacity and viscosity. Raking side light reveals texture without blown highlights, shallow depth of field, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 4\. Hand-and-product scale A hand supplies scale and human context, but should not hide the label. ``` Close beauty photograph of a natural hand holding the exact reference product at chest height. Fingers rest behind the vessel and do not cover the front label, pump or cap. Preserve packaging geometry and materials; realistic skin texture and anatomy, soft window light, neutral background, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 5\. Bathroom routine Give the product a credible use context without visual clutter. ``` The exact reference beauty product beside a folded white towel and clear water glass on a pale stone bathroom shelf. Preserve the package exactly and keep the front label unobstructed. Early morning window light, fine condensation in the background only, calm editorial composition, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 6\. Ingredient overhead Build a visual ingredient story without making unsupported claims. ``` Overhead still life of the exact reference beauty product centered among a small number of supplied ingredient references. Preserve packaging geometry and label placement. Arrange ingredients with deliberate spacing on warm ivory paper, soft directional daylight, realistic shadows, no medical symbols, no added text, 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 7\. Collection trio Prove relative scale and keep a product family coherent. ``` Studio group photograph of the three exact reference beauty products arranged from tallest to shortest on stepped ivory plinths. Preserve each vessel, closure, label position, color and relative size. Soft frontal light with a narrow rim highlight, no overlap across labels, clean pale background, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 8\. Wide campaign banner Leave deliberate copy space for a landing page or paid-social crop. ``` Wide beauty campaign still life with the exact reference product in the right third on a low mineral plinth. Preserve the packaging exactly. Soft lilac-to-ivory gradient, long controlled shadow, generous clean negative space on the left, no generated text, crisp premium light, horizontal 16:9. ``` Replace “reference” with the product, model, or scene you upload. ## What changes by challenge | Challenge | Prompt for | Check before approval | | --- | --- | --- | | Reflective glass | One controlled softbox reflection | No doubled edges or clipped highlights | | Pumps and droppers | Exact closure geometry and orientation | Stem, collar and cap remain physically plausible | | Transparent formula | Color, opacity and viscosity | Formula does not become milk, gel or water accidentally | | Labels | Position and layout, not invented wording | Compare every visible character with approved artwork | | Hands and skin | Natural anatomy and unobstructed package | Five fingers, believable grip, readable product | ## How to use these prompts 1. Photograph or upload the front, side, closure and important material details. 2. Generate the clean packshot and approve geometry first. 3. Copy one prompt, replace generic nouns with facts from the references, and change one scene variable at a time. 4. Compare results as a set at full size and thumbnail size. 5. Composite approved label artwork when exact legal or retail copy must be guaranteed. For the wider e-commerce system, including coverage planning and evaluation, continue with [E-commerce Product Photography Prompts](https://www.astria.ai/articles/ecommerce-product-photography-prompts/). ## Frequently asked questions ### What should a beauty product prompt include? Specify the product construction, view, surface, light behavior, composition and format separately. ### How do I keep cosmetic packaging accurate? Use several clean reference views, prove the product in a plain packshot, then verify the final result against approved packaging artwork. ### Which beauty images belong on a product page? Start with a front, alternate angle, closure or applicator, formula texture, scale cue, routine context and one campaign frame. --- # Ecommerce Product Photography Prompts for PDP, Packshots and Campaigns Source: https://www.astria.ai/articles/ecommerce-product-photography-prompts/ An ecommerce prompt should describe a media slot, not just a mood. “Minimal luxury product photo” does not tell the system whether you need the primary product-page image, a rear construction view, a size reference, a texture crop, or a campaign banner. The useful unit of work is the complete image family for one SKU. **The short version:** define the channel, lock the product facts, and prompt each required view separately. Use this guide to build front, side, back, detail, lifestyle, and campaign images from a shared production brief. Public [product workspaces](https://www.astria.ai/gallery/workspaces), [packshot templates](https://www.astria.ai/p/3d-packshots-dress), and [finished prompt examples](https://www.astria.ai/prompts/40128059?ws=268) make the pattern easier to see. ## Write for the media slot Start with a table of required outputs. A useful apparel product page might need front, back, side, fabric detail, and on-model scale. A handbag may need front, side, open interior, hardware detail, and carry context. A bottle may need a primary packshot, side label, closure detail, texture, and a lifestyle image. Give every slot a single job: - **Primary:** identify the product immediately. - **Alternate angle:** explain depth and geometry. - **Back or underside:** disclose construction the hero cannot show. - **Detail:** prove texture, closure, finish, or workmanship. - **Scale or use:** show size and physical relationship. - **Campaign:** attract attention without replacing factual coverage. This prevents the common mistake of asking one beautiful image to do six incompatible things. ## The production prompt structure A dependable prompt contains eight decisions: 1. **Channel and slot:** PDP hero, marketplace primary, collection tile, email banner, or social crop. 2. **Product:** the reference item, colorway, variant, and configuration. 3. **Preservation:** silhouette, dimensions, materials, seams, labels, hardware, print, closure, and quantity. 4. **Orientation:** front, rear, side, three-quarter, top-down, open, closed, folded, or worn. 5. **Frame:** camera height, crop, negative space, and aspect ratio. 6. **Surface and background:** exact color, sweep, tabletop, plinth, or location. 7. **Light and shadow:** direction, softness, reflections, highlight control, and contact shadow. 8. **Exclusions:** props, hands, duplicate objects, glare, horizon lines, or decorative ingredients that do not belong. Write the preservation clause from the product itself. A handbag needs handle count, gusset, closure and hardware. A bottle needs cap shape, fill level, glass color and label position. A dress needs neckline, waist, sleeve, fastening, hem and drape. ## Compare the verticals before you prompt The same photographic language does not create the same risk in every category. Use this as the first review pass before choosing an example below. | Vertical | Hardest problem | References to supply | Notice first | | --- | --- | --- | --- | | Fashion catalog | Several garments must remain distinct while fitting one body | Clean product views for every garment and any visible back construction | Necklines, hems, print placement, layering and shoe contact | | Lookbooks | Continuity across a sequence without repeating one pose | Approved cast, each look, hero treatment and location family | Cast drift, grade drift, styling changes and sequence rhythm | | Jewelry | Tiny geometry changes can describe a different item | Verified macro, side profile and on-body scale reference | Stone count, prongs, band width, finish, reflection and scale | | Beauty | Reflective packaging competes with exact labels and product color | Straight packshot, cap or pump detail, verified label artwork | Vessel shape, label placement, legibility, condensation and reflections | | Wedding dresses | Beading, transparency, fitted construction and large moving volume interact | Front, back, bodice detail, hem and fabric close-up | Neckline, bead density, sleeve transparency, waist and train length | | Vitamins and supplements | Packaging contains regulated copy and claims that must not be invented | Verified packshot, every label panel, closure and product-form reference | Dosage, quantity, seals, claims, ingredient copy and capsule count | For vitamins and supplements, generate the scene around a verified package rather than treating generated label text as evidence. No dedicated public supplement workspace is claimed in this guide; the beauty workflow below is a visual starting point for bottles, not proof of label accuracy. ## Apparel and ghost packshot prompts ### 1\. Dress front view > Front view of the reference dress as a clean volumetric packshot. Preserve its silhouette, waist, seams, closures, pleats, fabric flow and hem. Center the full garment with clear negative space on a solid #F2F2F2 studio background; soft diffused light, restrained contact shadow, sharp focus, square composition. The public [3D Ghost Packshots workspace](https://www.astria.ai/w/3d-packshots) shows this as a repeatable system. Compare its [dress template](https://www.astria.ai/p/3d-packshots-dress), [front-view prompt](https://www.astria.ai/prompts/40128059?ws=268), and [back-view prompt](https://www.astria.ai/prompts/40128064?ws=268). Ecommerce packshots ### Turn a flat product view into a clean PDP asset A square front view that explains the whole dress before editorial styling is introduced. - PDP hero - 1:1 - #F2F2F2 Reference inputs ![White sleeveless dress with a small red floral print on a white background](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.webp) Reference · product and print Generated result [![Volumetric front packshot of the referenced floral dress](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.webp)](https://www.astria.ai/prompts/40128059?ws=268) Public result · click to inspect the original generation **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` Front view of the reference dress as a clean volumetric packshot. Preserve its silhouette, straps, waist, seams, closures, floral print placement, fabric flow and hem. Center the full garment with clear negative space on a solid #F2F2F2 studio background. Soft diffused light, restrained contact shadow, sharp focus, square composition, no hanger, no mannequin and no visible label. ``` **The challenge** A volumetric packshot has to add believable body and drape without inventing a different neckline, waist, print, or hem. **Things to notice** - Compare the strap width, neckline depth, waist seam, and flare with the source. - Check that the floral motif stays small and distributed rather than becoming a new print. - Inspect both side seams and the hem for accidental asymmetry. - Measure the exported background and margins after generation; prompting a hex value is not a pixel-level check. Reference and result images come from the linked public Astria example. ### 2\. Dress back view > Back view of the same reference dress as a clean volumetric packshot. Preserve the rear neckline, closure, center-back seam, waist construction, pleats and hem length. Center the full garment at the same scale as the front view on a solid #F2F2F2 studio background; soft diffused light, restrained contact shadow, sharp square composition. ### 3\. Knitwear folded view > Top-down ecommerce photograph of the reference knit folded into a neat rectangle with the collar, cuff and hem visible. Preserve the yarn color, knit pattern, ribbing, label position and material thickness. Warm-white seamless surface, large soft overhead source, gentle natural shadow, crisp texture, centered square composition, no props. ### 4\. Apparel construction detail > Close product detail of the reference jacket's lapel, top button, chest seam and fabric weave. Preserve the exact lapel shape, button count, thread color and surface texture. Neutral light-grey background, soft raking light that reveals construction without changing the color, sharp focus across the product, square crop. For apparel coverage beyond object-only imagery, use the [AI fashion photography prompt guide](https://www.astria.ai/articles/ai-fashion-photography-prompts/) and the [AI lookbook guide](https://www.astria.ai/articles/ai-lookbook-guide/). ## Shoes, bags and accessories Rigid products expose geometry errors quickly. Prompt the exact orientation and describe parts that must connect correctly. ### 5\. Shoe three-quarter hero > Front three-quarter ecommerce packshot of one reference shoe, toe pointing slightly toward frame left. Preserve the toe shape, sole thickness, heel height, panel construction, stitching, laces and material color. Pale warm-grey sweep, camera at product height, soft key light from upper left, controlled highlights, subtle contact shadow, centered square image, no props. The public [Fashion 3D Packshots workspace](https://www.astria.ai/w/fashion-3d-packshots) includes dedicated [shoe](https://www.astria.ai/p/3d-packshots-for-shoes), [handbag](https://www.astria.ai/p/handbag), and [backpack](https://www.astria.ai/p/backpack) templates. ### 6\. Shoe side profile > Exact outer side profile of one reference shoe, placed flat with the sole parallel to the bottom edge. Preserve the heel, outsole, toe, upper panels, fastenings and stitching. Pure-white seamless background, neutral color balance, soft even light, clean edge separation, restrained contact shadow, square marketplace composition, no props. ### 7\. Handbag front view > Straight-on front ecommerce photograph of the reference handbag standing upright with handles in their natural position. Preserve the bag's width-to-height ratio, gusset, seam lines, handle count, closure, pockets and hardware finish. Seamless warm-white background, camera centered at mid-product height, soft symmetrical studio light, clean contact shadow, square crop. ### 8\. Handbag interior > Elevated three-quarter product photograph of the reference handbag opened naturally to show the interior organization. Preserve the exterior silhouette, handle attachments, zipper, lining color, pockets and hardware. Light neutral tabletop and background, soft overhead light with controlled interior fill, clear construction detail, square ecommerce composition, no unrelated props. ## Beauty bottles and packaged products Generated packshots can support concepting, campaigns, and variant production, but packaging requires strict review. Do not invent or silently alter label copy, quantities, seals, dosage information, claims, or regulated marks. When exact text matters, composite the verified artwork or use approved product photography. ### 9\. Bottle primary packshot > Straight-on product-page packshot of one reference cosmetic bottle, upright and centered. Preserve the bottle proportions, glass color, fill level, shoulder shape, cap, pump, label size and label position. Seamless pure-white background, neutral color balance, soft vertical highlights that describe the form, clean edges, subtle contact shadow, square composition, no props. ### 10\. Bottle material detail > Tight three-quarter detail of the reference cosmetic bottle focusing on the cap, shoulder, glass thickness and product texture. Preserve all proportions, material colors and hardware finishes. Pale stone surface, soft side light with controlled reflections, sharp product detail, shallow background only, vertical 4:5 crop. The public [Beauty workspace](https://www.astria.ai/w/beauty) and [Beauty template](https://www.astria.ai/p/beauty) provide a starting point for campaign-oriented beauty imagery. Some finished prompt-detail pages may require sign-in, so use the public workspace or template as the durable entry point. Beauty ### Keep the bottle factual while the scene becomes expressive A campaign still life built around a single verified product reference. - Campaign - 3:4 - Reflective set Reference inputs ![Pearlescent cosmetic bottle reference on white](https://www.astria.ai/articles/img/prompt-recipes/beauty-bottle-reference.webp) Reference · bottle and label position Generated result [![Cosmetic bottle standing on a reflective wet surface against a purple background](https://www.astria.ai/articles/img/prompt-recipes/beauty-still-life-result.webp)](https://www.astria.ai/prompts/45986211?ws=761) Public result · click to inspect the original generation **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` Still-life photograph with no person in frame. Place the reference cosmetic bottle upright on a shallow film of water over a dark reflective surface, with a soft mirrored reflection directly beneath it. Preserve the bottle proportions, pearlescent finish, cap, label size and label position. Add a cold glass pane with fine condensation immediately behind it and a seamless dusk-purple background. Low straight-on camera, one large diffused source from camera left, open shadows, faint warm haze, fine 35mm grain, gentle highlight halation, vertical 3:4. ``` **The challenge** Reflective vessels, condensation, mirrored surfaces, and label fidelity all compete. The set can change; the package geometry and approved copy cannot. **Things to notice** - Follow the cap-to-body proportion and the bottle silhouette before judging mood. - Keep droplets and glare away from the label-reading area. - Check that the reflection mirrors the actual bottle rather than a different container. - Use verified label artwork in the final production file whenever exact copy matters. Reference and result images come from the linked public Astria example. ## Jewelry: treat context and proof differently Jewelry needs its own review standard because tiny changes can describe a different product. Preserve stone count, setting, prongs, band profile, metal color, finish, and scale. Keep a verified macro photograph when those facts drive purchase decisions. ### 11\. Ring macro context > Macro product photograph of the reference ring resting in soft folds of light textured fabric. Use the fabric curves to frame the ring without obscuring its setting, band profile or stone. Preserve stone count, setting geometry, prongs, metal color and finish. Warm diffused natural light, clean neutral background, crisp metal and gemstone detail, shallow depth of field and a restrained luxury editorial mood; square composition. See the public [Jewelry Product Photography workspace](https://www.astria.ai/w/jewelry-product-photography), [Ring template](https://www.astria.ai/p/jewelry-ring), and its [macro](https://www.astria.ai/prompts/33498743?ws=297), [lifestyle](https://www.astria.ai/prompts/33498723?ws=297), and [on-model](https://www.astria.ai/prompts/33498478?ws=297) examples. For category-specific caveats, read the [AI jewelry photography guide](https://www.astria.ai/articles/ai-jewelry-photography/). Jewelry ### Move from a verified ring to an editorial macro A contextual detail image that adds material atmosphere without replacing the factual product macro. - Detail - 1:1 - Macro Reference inputs ![Gold sculptural ring shown on a hand as a reference](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-reference.webp) Reference · geometry and worn scale Generated result [![Gold sculptural ring resting in folds of cream fabric](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-result.webp)](https://www.astria.ai/prompts/33498743?ws=297) Public result · click to inspect the original generation **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` Macro product photograph of the reference ring resting in soft folds of light textured fabric. Use the fabric curves to frame the ring without obscuring its setting, band profile or stone. Preserve the stone count, setting geometry, prongs, sculptural shape, metal color and finish. Warm diffused natural light, clean neutral background, crisp metal detail, shallow depth of field, restrained luxury editorial mood, square composition. ``` **The challenge** Metal is defined by reflections, and a tiny geometry change can turn one ring into another. A plausible result is not automatically an accurate result. **Things to notice** - Trace the sculptural outline, openings, band width, and stone positions against the source. - Ask whether the warm environment changed the apparent metal color. - Check that fabric does not conceal the product feature the image is meant to sell. - Keep the verified macro or on-hand reference beside this image on the PDP. Reference and result images come from the linked public Astria example. ## Lifestyle and campaign prompts Lifestyle images answer “how does this fit into a life?” Campaign images answer “why should I look?” Neither should be the only evidence of what a product is. ### 12\. Handbag carry context > Waist-to-knee lifestyle photograph of a model carrying the reference handbag at her side while walking through a quiet pale-stone arcade. Preserve the bag's dimensions, handles, closure, hardware and color; keep the full bag unobscured and at realistic scale. Soft morning light, neutral wardrobe, muted architectural palette, natural hand anatomy, vertical 4:5. ### 13\. Skincare bathroom context > Vertical lifestyle photograph of the reference skincare bottle standing beside a folded cream towel on a pale limestone vanity. Preserve the bottle shape, cap, glass color and label placement. Soft indirect morning light, controlled reflection, minimal condensation away from the label, refined natural texture, vertical 4:5, no additional products. ### 14\. Collection tile > Clean collection image containing exactly three reference products arranged at different heights on low dusty-lilac plinths. Preserve the size relationship, shape, materials, hardware and color of each item; do not duplicate or merge products. Warm-white sweep, soft diffused studio light, balanced contact shadows, generous margins, square composition. ### 15\. Wide campaign banner > Wide ecommerce campaign still of the reference handbag on a pale stone bench near a muted sage wall. Preserve the bag's exact geometry, handles, closure, hardware and color. Place the product in the right third with clean negative space on the left for page layout, low warm side light, refined long shadow, tactile realism, 16:9 composition, no text. ## Exact backgrounds need a separate check If a marketplace requires white, write “seamless pure-white background, no horizon line” and request clean edge separation. But prompting is not color measurement. Inspect the delivered pixel values, clipping path, transparency, margins, and file profile after generation. A visually white image may still contain a warm cast or grey corners. The same rule applies to aspect ratios. State square, 4:5, or 16:9 in the prompt, then export and validate the required pixel dimensions separately. ## Build one shot matrix for every product class Do not rewrite the entire brief per SKU. Create a matrix with rows for media slots and columns for product classes. Keep the studio block stable, then swap only the class-specific preservation clause. | Product class | Primary facts to preserve | Essential views | | --- | --- | --- | | Apparel | silhouette, neckline, sleeves, seams, closures, length, drape | front, back, side, detail, worn scale | | Footwear | toe, sole, heel, panels, stitching, fastening | three-quarter, outer side, rear, sole, on-foot | | Bags | proportions, gusset, handles, closure, pockets, hardware | front, side, back, open interior, carried | | Bottles | vessel proportions, fill, cap, pump, label position | front, side, closure detail, texture, context | | Jewelry | stone count, setting, prongs, band, finish, scale | verified macro, side profile, detail, on-body, lifestyle | This turns prompting into a production system rather than a collection of isolated sentences. ## Vertical prompt libraries - [AI Lookbook Prompts](https://www.astria.ai/articles/ai-lookbook-prompts/) turns one approved outfit and treatment into an edited fashion sequence. - [Beauty Product Photography Prompts](https://www.astria.ai/articles/beauty-product-photography-prompts/) covers reflective vessels, formula texture, hands and campaigns. - [Jewelry Photography Prompts](https://www.astria.ai/articles/jewelry-photography-prompts/) separates ring, necklace and earring geometry from on-body context. - [Wedding Dress Photography Prompts](https://www.astria.ai/articles/wedding-dress-photography-prompts/) combines gown construction proof with bridal art direction. - [Vitamin and Supplement Photography Prompts](https://www.astria.ai/articles/vitamin-supplement-photography-prompts/) adds label, product-form and claims safeguards for supplement imagery. ## Review before the images enter the catalog Use the source product beside the generated output. Check: - silhouette, dimensions and part count; - front, side and rear geometry; - seams, stitching, closures and pockets; - labels, logos, print and packaging copy; - material texture, transparency and reflectivity; - hardware color and attachment points; - shadows, reflections and contact with the surface; - scale relative to hands, bodies, furniture or props; - correct variant, quantity and product configuration; - channel crop, safe margins, background and file dimensions. If a discrepancy could change what a buyer believes they will receive, revise or reject the image. The prompt is the specification; review is the release gate. ## Frequently asked questions ### What should an ecommerce product photography prompt include? Include the image's channel and slot, the referenced product, the geometry and materials that must remain accurate, camera angle, product orientation, background, light, shadow behavior, crop, and aspect ratio. ### How are PDP prompts different from campaign prompts? PDP prompts prioritize truthful coverage, repeatable angles, clean edges, readable construction, and consistent scale. Campaign prompts can use props, atmosphere, unusual crops, and stronger shadows, but should still state the product features that cannot change. ### Can AI product images replace every ecommerce photograph? No. Keep verified photography where exact label text, regulated information, fine material claims, gemstone details, safety-critical features, or precise color are essential. Generated images are strongest when the product reference and review standard are both clear. ### How do you prompt for a pure white background? Specify a seamless pure-white background, clean product edges, neutral color balance, a restrained contact shadow, no horizon line, no props, and the required crop. Verify the exported pixels and marketplace requirements separately. ### How many prompts does one ecommerce product need? Build prompts around the media slots the page needs: usually a hero, alternate angle, back or side view, construction detail, scale or use context, and one or more campaign crops. The exact count depends on product complexity and channel requirements. --- # Jewelry Photography Prompts: Rings, Necklaces and Earrings Source: https://www.astria.ai/articles/jewelry-photography-prompts/ Jewelry is a geometry problem disguised as a lighting problem. A beautiful result is unusable if it changes the prongs, stone count, chain pattern or scale. Strong prompts therefore lock the piece first, then specify how metal, stones and skin should respond to light. Inspect the public [Jewelry Product Photography workspace](https://www.astria.ai/w/jewelry-product-photography), then choose the [ring](https://www.astria.ai/p/jewelry-ring), [necklace](https://www.astria.ai/p/jewelry-necklace) or [earrings](https://www.astria.ai/p/earrings) template. ## Reference → prompt → result Jewelry ### Verify the ring in an isolated macro The first image should make stone count, setting, profile and metal finish easy to compare with the reference. - Macro - 4:5 - Ring proof Reference inputs ![Ring product reference](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-reference.webp) Reference · approved ring Generated result [![Macro result of the referenced ring](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-result.webp)](https://www.astria.ai/prompts/33498743?ws=297) Public result · ring macro **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` High-resolution macro product photograph of the exact reference ring standing at a front three-quarter angle on smooth warm ivory. Preserve the band width, setting height, stone count, stone shapes, prongs, metal color and polish. Broad rectangular highlight across the metal, small crisp gemstone reflections, clean grounding shadow, no extra stones, vertical 4:5. ``` **The challenge** Tiny changes in prongs, chain links or stone layout create a different product even when the image looks luxurious. **Things to notice** - Count stones and prongs before reviewing mood. - Compare the profile height and band thickness. - Check reflections for false seams or duplicated metal. - Use an on-body frame only after the isolated piece passes. Reference and result images come from the linked public Astria example. ## Eight copy-ready jewelry prompts ### 1\. Ring verification macro Make the design easy to audit. ``` High-resolution macro packshot of the exact reference ring at a front three-quarter angle on warm ivory. Preserve the band profile, setting height, stone count, cuts, prongs, metal color and finish. Controlled broad highlight, crisp gemstone facets, clean grounding shadow, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 2\. Ring side profile Reveal construction hidden by the hero angle. ``` Exact side-profile macro of the reference ring, camera level with the band. Show the setting height, gallery, prongs and band taper without distortion. Neutral pale background, raking light along the metal, restrained stone sparkle, focus stacked product detail, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 3\. Ring on hand Communicate real scale after verifying the product. ``` Close photograph of the exact reference ring worn on the correct finger of a relaxed natural hand. Preserve the ring design, orientation and realistic scale. Five anatomically correct fingers, clean natural nails, soft daylight, no other jewelry, ring and nearest skin texture sharp, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 4\. Necklace front crop Show chain drape, pendant scale and placement. ``` Collarbone-to-chest beauty photograph of the exact reference necklace worn centered on bare skin. Preserve chain pattern, length, clasp logic, pendant proportions, stone layout and metal finish. Hair and clothing remain outside the necklace, soft directional daylight, natural skin texture, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 5\. Necklace tabletop curve Prove the full chain without body or clothing occlusion. ``` Overhead product photograph of the exact reference necklace arranged in one relaxed continuous curve on pale limestone. Preserve every link pattern, pendant, clasp, proportions and metal color. No tangles or broken chain, delicate side light, sharp product detail, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 6\. Earring ear crop Show scale and fastening position in use. ``` Close side portrait crop showing one exact reference earring worn correctly on the visible ear. Preserve its dimensions, stone layout, metal finish, post or hook and orientation. Hair swept behind the ear, realistic anatomy and skin, quiet neutral background, soft side light, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 7\. Pair still life Check that a pair remains matched and correctly mirrored. ``` Macro still life of the exact pair of reference earrings arranged side by side on warm grey silk. Preserve both designs, dimensions, stones and fastenings; mirror their intended orientation without duplicating or adding parts. Controlled highlights, subtle folds, both pieces sharp, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 8\. Collection banner Create campaign atmosphere while retaining a clean audit path. ``` Wide campaign still life of the exact reference ring, necklace and earring set on low ivory forms. Preserve every product and its relative scale. Use controlled pools of light, soft mineral shadows and generous negative space on the left. No generated text, horizontal 16:9. ``` Replace “reference” with the product, model, or scene you upload. ## Different products, different failure modes | Product | Lock in the prompt | Inspect closely | | --- | --- | --- | | Ring | Stone count, cuts, prongs, setting height, band profile | Added stones, merged prongs, incorrect scale | | Necklace | Chain pattern, length, pendant, clasp, drape | Broken links, impossible curves, shifted pendant | | Earrings | Pair symmetry, orientation, fastening, dimensions | Unmatched pair, wrong ear placement, extra parts | | On-body | Product scale, contact point, visible construction | Fingers, ears, skin contact and occlusion | ## How to build a jewelry set 1. Upload sharp front, three-quarter and profile views; include the clasp or fastening. 2. Start with the isolated verification macro. 3. Count structural details against the reference at 100% zoom. 4. Move to on-body scale and lifestyle only after the product passes. 5. Review the complete set for one metal color, one scale system and coherent highlights. For production planning, reference standards and retouching decisions, use the [AI Jewelry Photography Guide](https://www.astria.ai/articles/ai-jewelry-photography/). For a broader catalog framework, see [E-commerce Product Photography Prompts](https://www.astria.ai/articles/ecommerce-product-photography-prompts/). ## Frequently asked questions ### How do you prompt jewelry without changing its design? Name every product-defining feature as fixed, and vary the scene around it. ### What lighting works for AI jewelry photography? Use broad highlights for metal, crisp accents for facets and enough fill to retain the silhouette. ### Why include on-body jewelry images? They prove scale and wearability, provided anatomy and product placement pass review. --- # Vitamin and Supplement Photography Prompts for E-commerce Source: https://www.astria.ai/articles/vitamin-supplement-photography-prompts/ Supplement photography combines ordinary packshot problems with regulated copy, dosage details and claims that should never be improvised. Use generation to create composition, surface, lighting and a visual system—not to invent certifications, medical symbols, ingredients or label wording. **Evidence note:** Astria’s public gallery does not currently provide a supplement-specific prompt/result pair suitable for citation. The visual recipe below is the closest public [Beauty workspace](https://www.astria.ai/w/beauty) packaging example, clearly labeled as adjacent evidence. These supplement prompts are conservative adaptations to test with your own approved references. ## Adjacent reference → adapted prompt Supplements ### Adapt a proven bottle-lighting pattern carefully Use the public bottle example to understand composition and reflections, then test supplement geometry and artwork with your own references. - Adjacent evidence - 4:5 - Bottle system Reference inputs ![Adjacent beauty bottle reference](https://www.astria.ai/articles/img/prompt-recipes/beauty-bottle-reference.webp) Adjacent reference · beauty bottle Generated result [![Adjacent beauty bottle campaign result](https://www.astria.ai/articles/img/prompt-recipes/beauty-still-life-result.webp)](https://www.astria.ai/prompts/45986211?ws=761) Adjacent public result · not supplement proof **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` Studio product photograph of the exact reference supplement bottle, upright and centered on warm white. Preserve the bottle proportions, cap, safety seal, label dimensions, colors and material finish. Keep all label panels flat and undistorted. Soft frontal light, narrow rim highlight, short grounding shadow, no ingredients, medical symbols, certifications or added claims, vertical 4:5. ``` **The challenge** The public example validates a general bottle-and-lighting pattern only. Supplement packaging needs its own product tests plus artwork and compliance review. **Things to notice** - Treat generated label text as a placeholder until verified. - Do not add claims, seals, ingredients or certification marks. - Check bottle-to-cap ratio and tamper-evident details. - Review the final artwork for every required market and sales channel. This is adjacent public beauty-packaging evidence, not a supplement-specific proof. The prompt is a conservative adaptation for a category that requires additional artwork and compliance checks. ## Eight copy-ready supplement prompts ### 1\. Primary bottle packshot Create the clean geometry proof used to judge every later scene. ``` Straight-on studio packshot of the exact reference supplement bottle, upright and centered on seamless warm white. Preserve bottle proportions, cap, seal, label dimensions, colors and material finish. Keep the front label plane flat, even diffused light, short natural grounding shadow, no props, added claims or symbols, vertical 4:5. ``` Adjacent template: test with your own approved supplement references and artwork. ### 2\. Side and back panel Document the label surfaces the hero cannot show. ``` Rear three-quarter studio packshot of the exact reference supplement bottle showing the supplied side and back label panels. Preserve bottle and cap geometry, label proportions and panel order. Even shadowless light, sharp printable area, no invented words, icons, certifications or dosage details, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 3\. Cap and seal detail Make closure construction and tamper evidence reviewable. ``` Macro product detail of the exact reference supplement bottle closure, focused on the supplied cap, neck, liner and tamper-evident seal. Preserve dimensions, materials, ridges and assembly. Clean warm-white background, raking side light, sharp construction detail, no added markings, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 4\. Capsule or gummy scale Show product form only when a verified reference is supplied. ``` Studio still life of the exact reference supplement bottle beside three pieces of the supplied product-form reference. Preserve the capsule or gummy shape, dimensions, color, coating and opacity; preserve the bottle and closure. Neutral stone surface, soft daylight, no loose powder, ingredient claims or added text, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 5\. Controlled ingredient story Create context without implying unsupported contents or benefits. ``` Overhead still life of the exact reference supplement bottle with only the supplied approved ingredient references, each physically separate and clearly recognizable. Preserve packaging. Spacious warm-white layout, soft directional shadows, no laboratory props, medical symbols, certifications, claims or generated text, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 6\. Sachet or stick pack Lock count, seals and print area for flexible packaging. ``` Straight-on packshot of the exact reference supplement sachet standing beside one flat sachet, both showing the approved front panel. Preserve dimensions, crimped seals, material sheen, colors and artwork boundaries. Soft frontal light, no warped edges or invented copy, pale background, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 7\. Subscription bundle Show the exact delivered quantity and relative scale. ``` Studio e-commerce photograph of the exact supplied monthly supplement bundle: the reference bottles and packages only, arranged in one clean row with labels unobstructed. Preserve product count, relative scale, closures, colors and materials. Warm-white sweep, even light, no extra accessories or claims, horizontal 4:3. ``` Replace “reference” with the product, model, or scene you upload. ### 8\. Lifestyle banner Add brand atmosphere while reserving clean copy space. ``` Wide lifestyle still life with the exact reference supplement bottle in the right third on a pale kitchen shelf, beside a clear water glass only. Preserve the bottle, cap and label layout. Natural morning light, generous uncluttered negative space on the left, no pills in motion, medical props, added text or benefit claims, horizontal 16:9. ``` Replace “reference” with the product, model, or scene you upload. ## What is different about this vertical | Challenge | Safe prompt boundary | Required human check | | --- | --- | --- | | Label text | Preserve layout; do not invent wording | Composite or verify approved artwork | | Claims and certifications | Explicitly forbid additions | Confirm required markets and channel policies | | Product form | Show only supplied capsule, gummy or powder references | Confirm shape, color, scale and count | | Ingredients | Include only approved supplied ingredients | Check that the image does not imply formulation or benefit | | Bundles | Name exact products and quantity | Match the actual offer and relative package sizes | ## How to use these prompts 1. Collect front, side, back, closure, seal and product-form references plus final approved artwork. 2. Generate and approve the primary bottle packshot before any ingredient or lifestyle scene. 3. Copy one prompt and replace every generic product noun with verified reference facts. 4. Reject invented seals, pills, ingredients, claims, medical imagery or label details. 5. Finish with artwork, legal and channel review appropriate to the markets where the image will appear. For the general coverage framework, see [E-commerce Product Photography Prompts](https://www.astria.ai/articles/ecommerce-product-photography-prompts/). As a public lighting reference only, inspect the [Beauty workspace](https://www.astria.ai/w/beauty). ## Frequently asked questions ### Can AI generate final supplement labels accurately? Do not rely on generated text. Verify or composite approved artwork and run the appropriate market and channel review. ### What references should a supplement prompt use? Supply every label panel, closure and seal, the actual product form and only approved ingredient references. ### What belongs in a supplement product image set? Cover the front, alternate label views, closure, product form, ingredients, bundle and a clean lifestyle frame. --- # Wedding Dress Photography Prompts: A Bridal Lookbook Shot List Source: https://www.astria.ai/articles/wedding-dress-photography-prompts/ Bridal imagery has two simultaneous jobs: sell the feeling and prove the dress. An airy image that erases lace, shortens the train or moves the waistline is not a product image. A useful prompt names the construction first, then directs pose, environment and emotion. The public [Wedding Dress workspace](https://www.astria.ai/w/wedding-dress) and [Wedding Dress template](https://www.astria.ai/p/wedding-dress) provide a runnable starting point. ## Reference → prompt → result Bridal ### Start with a full-length construction proof A quiet front three-quarter pose shows silhouette, waist, hem and train before movement introduces ambiguity. - Full length - 2:3 - Soft daylight Reference inputs ![Wedding dress reference](https://www.astria.ai/articles/img/prompt-recipes/bridal-dress-reference.webp) Reference · approved dress Generated result [![Model wearing the referenced wedding dress](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.webp)](https://www.astria.ai/prompts/37146748?ws=126) Public result · bridal hero **Copy-ready prompt**Replace “reference” with your uploaded product or model. ``` Full-length front three-quarter bridal portrait of the model wearing the exact reference wedding dress. Preserve the neckline, sleeve shape, bodice construction, waist position, lace placement, skirt volume, hem and train length. The model stands still with arms clear of the bodice. Soft side daylight, protected white highlights, pale stone interior, realistic fabric weight, vertical 2:3. ``` **The challenge** White-on-light scenes hide construction, while movement can invent lace, volume and train length. **Things to notice** - Confirm neckline and waist before judging the face. - Protect texture in the brightest lace and satin. - Keep hands away from closures and bodice details. - Compare front and back images for one consistent train length. Reference and result images come from the linked public Astria example. ## Eight copy-ready bridal prompts ### 1\. Full-front proof Document the complete construction before directing a story. ``` Full-length straight-on bridal portrait of the model wearing the exact reference wedding dress. Preserve the neckline, sleeves, bodice, waist, seams, lace placement, skirt volume, hem and train. Arms relaxed away from the dress, feet together, soft directional daylight, pale neutral studio, protected white highlights, vertical 2:3. ``` Replace “reference” with the product, model, or scene you upload. ### 2\. Back and train Show closures and the feature most often hidden in a front hero. ``` Full-length rear three-quarter bridal portrait of the same model and exact reference dress. Preserve the back neckline, buttons or closure, seam placement, skirt volume and full train length. Arms clear of the back, train arranged naturally on pale stone, soft window light, vertical 2:3. ``` Replace “reference” with the product, model, or scene you upload. ### 3\. Side silhouette Prove bodice depth, waist position and skirt profile. ``` Full-length exact side-profile portrait of the same model in the reference wedding dress. Preserve bodice structure, waist position, skirt projection, hem and train. Upright relaxed posture, hands lightly joined below the waist without covering the dress, soft side light, vertical 2:3. ``` Replace “reference” with the product, model, or scene you upload. ### 4\. Bodice and lace detail Pause the story to document workmanship. ``` Shoulder-to-waist bridal detail of the exact reference dress, focused on neckline, corsetry, seams, embroidery and lace placement. Arms remain outside the bodice. Soft raking daylight reveals white-on-white texture without grey cast or clipped highlights, shallow background, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 5\. Controlled movement Introduce life while limiting fabric invention. ``` Full-length bridal photograph of the same model taking one slow step in the exact reference dress. Preserve all construction and train length; allow gentle movement only at the outer skirt and veil. Keep the bodice sharp, hands clear, realistic fabric weight, soft courtyard daylight, vertical 2:3. ``` Replace “reference” with the product, model, or scene you upload. ### 6\. Staircase composition Use architecture to reveal layers and train shape. ``` Full-length bridal portrait of the same model paused three steps up a broad pale-stone staircase in the exact reference dress. Arrange the train down the steps without changing its length or volume. Preserve lace and closures, eye-level camera, diffused skylight, vertical 2:3. ``` Replace “reference” with the product, model, or scene you upload. ### 7\. Seated quiet frame Add intimacy while showing believable fabric compression. ``` Three-quarter seated portrait of the same model in the exact reference wedding dress on the edge of a pale bench. Torso upright, hands together beside the hip, bodice and waist unobstructed. Preserve construction and realistic folds; do not add skirt volume, soft window light, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ### 8\. Closing environmental wide End the lookbook with atmosphere rather than another catalog pose. ``` Wide environmental bridal photograph of the same model in the exact reference dress standing beneath a tall stone arch. Show the complete silhouette and train at a smaller scale with generous architecture. Soft late-afternoon daylight, subtle warm-neutral grade, no added bouquet or accessories, vertical 4:5. ``` Replace “reference” with the product, model, or scene you upload. ## Bridal-specific review checklist - **Construction:** neckline, sleeve, waist, closure, seams and lace stay in the same locations. - **White detail:** embroidery and satin retain texture without turning grey or clipping to blank white. - **Train:** length and volume agree across front, side, back and movement frames. - **Hands:** fingers look natural and never cover the product feature the shot should explain. - **Accessories:** veil, gloves, bouquet and jewelry appear only when supplied or intentionally specified. - **Sequence:** alternate proof, detail, movement and environment instead of repeating full-length poses. ## How to run the shot list 1. Upload front, back, profile and close construction references. 2. Generate the full-front proof and approve the dress before moving on. 3. Copy prompts in order, keeping dress, cast, light family and grade invariant. 4. Compare train length and waist placement across the contact sheet. 5. Remove any beautiful frame that represents a different dress. For sequencing and art direction, continue with the [AI Lookbook Guide](https://www.astria.ai/articles/ai-lookbook-guide/). For more fashion prompt structures, see [AI Fashion Photography Prompts](https://www.astria.ai/articles/ai-fashion-photography-prompts/). ## Frequently asked questions ### What shots should a wedding dress lookbook include? Cover front, back, side, construction detail, controlled movement, environment and a closing frame. ### How do you preserve bridal lace and construction? Name each construction fact explicitly and compare every result with clear garment references. ### How do you prompt white fabric without losing detail? Ask for soft directional light, protected highlights and visible texture across dress and background. --- # Astria Case Study: Pretty Ballerinas Source: https://www.astria.ai/case-studies/pretty-ballerinas Retail Case Study #### Cutting visual production costs by 90% using generative AI. ![Pretty Ballerinas Black Booties Lifestyle](https://a.astria.ai/assets/case-studies/ecommerce/52692_001_onfeet-f2f207d73744fb76c71f9ad32b18e217dea253e91fec15fe0b74b73cbbd46dfe.avif) Generated by Astria before product arrival. Product:[Pat Bootie](https://www.prettyballerinas.co.il/product/pat-52692-001/) ### The Paradox of Premium Retail For over a decade,**Pretty Ballerinas Israel**has brought Spanish craftsmanship to the Israeli market. But for high-end retail importers, there is a persistent logistical paradox:**you cannot sell what you cannot show.** Traditionally, the team received simple packshots from Spain. While accurate, these images lack the emotional context needed to drive desire. The crucial "on-foot" visuals could only be created once the physical shipment arrived, creating a bottleneck that delayed launches by 7–10 days. > "Our main challenges were logistics and cost. We had to wait weeks until the physical shoes arrived. Now we can create those visuals before the collection even arrives." ### The Solution: Digital Assets on the Fly Pretty Ballerinas Israel turned to**Astria**to transition to a digital-first visual workflow. The goal was controlled scalability: generating hyper-realistic on-foot imagery that respected specific materials without waiting for boxes to land in the warehouse. #### The Transformation: Suede Loafers Input: The Packshot![Green Loafer Packshot](https://a.astria.ai/assets/case-studies/ecommerce/48917_004_pers-ec68160621b0f19ee005a97e028e79869990c0ce16c33fd713c9cc9236c3feca.avif) Output: Astria Lifestyle![Green Loafer On Feet](https://a.astria.ai/assets/case-studies/ecommerce/48917_004_onfeet-06c37c004ff7079b40dc39bdad478341c2c5047351294d72ccb8175ecb54e4af.avif) Product:[Josephine Loafer](https://www.prettyballerinas.co.il/product/josephine-48917-004/) ### The Execution: A New Standard for Speed For their latest collection, the brand utilized Astria to generate one pristine on-foot image for every single SKU. Instead of a chaotic "shooting day," the team used Astria's fine-tuned templates to define lighting, skin tones, and floor textures, scaling it across the entire range. #### The Transformation: Slip-On Loafers Input: The Packshot![Black Slip-on Packshot](https://a.astria.ai/assets/case-studies/ecommerce/49964_002_pers-430abe44fce3c62bb9dea66bd58b84e4058f32ae31cca1a9f877afb2ae08f2db.avif) Output: Astria Lifestyle![Black Slip-on On Feet](https://a.astria.ai/assets/case-studies/ecommerce/49964_002_onfeet-dfd7b5b2613b7257d6ec3404d4839d23f4cb442b22f9fa10af36e676537c1229.avif) Product:[Charlize Loafer](https://www.prettyballerinas.co.il/product/charlize-49964-002/) ### The Impact: 90% Savings The shift to Astria didn't just save time, it fundamentally changed the unit economics of their content production. | Metric | Traditional Photoshoot | Astria AI Workflow | | --- | --- | --- | | Cost | High (Models, Studio) | ~90% Reduction | | Time-to-Market | 7–10 Days_after_arrival | Ready before arrival | | Flexibility | Rigid | Infinite Iterations | ### Elevating the E-commerce Experience Below is an example of the high-quality, on-foot visuals that drive better engagement than simple packshots. ![Tan and White Loafer Lifestyle](https://a.astria.ai/assets/case-studies/ecommerce/51926_002_onfeet-8eccf164e1e6dd3aeb042af252fa3164d856ea1af4cb229c77a1cd5b3c335a08.avif) Product:[Terry Loafer](https://www.prettyballerinas.co.il/product/terry-51926-002/) ### Advice to Retail Leaders For Creative Directors looking to make the leap, the team offers this advice: > "Define your visual standard, then let AI scale it. The shift from photoshoots to AI is easier than expected... It takes a few cycles to understand how to brief the system, but once the workflow is set, it becomes fast and very reliable." ### Ready to streamline your visual production? See how Astria can replicate your brand's look and feel. [Create Your First Template](https://www.astria.ai/gallery/packs/Ecommerce) --- # Astria Creators — Start with your craft Source: https://www.astria.ai/creators Noizz × CI898Noizz Campaign 417 × CI898417 Skincare Sebo × CI898Sebo Campaign Astria Explorer Fund Unlock up to [current pricing](https://www.astria.ai/pricing) in Astria creator credits: [current pricing](https://www.astria.ai/pricing) after each monthly publication milestone, up to 10; your completed first brief counts as milestone one. [Apply for the Explorer Fund](https://www.astria.ai/creators#apply) Creator fund [current pricing](https://www.astria.ai/pricing) Unlock in Astria creator credits First project 6–10 / 1 Images / reusable template Target 30 days From onboarding Publication 1 guaranteed Completed first brief published on Astria Ownership 100% Original work remains creator-owned The [current pricing](https://www.astria.ai/pricing) Astria Explorer Fund ## Make one signature project. **Get it published.** Choose one first assignment: a 6–10-image signature collection or one reusable template. The suggested pace is 30 days from onboarding. Complete the agreed brief and Astria publishes the work under your Astria Creative Partner · Explorer identity. Your original work remains yours. [Apply for the Explorer Fund](https://www.astria.ai/creators#apply)[See the activation steps](https://www.astria.ai/creators#activation) Invited directly by Astria? Continue from the private onboarding link in your invitation email. 01 ### Personal onboarding A one-to-one working session, an Explorer workspace and starter setup, and creative planning around an agreed first-project brief. 02 ### Monthly publication milestones Earn [current pricing](https://www.astria.ai/pricing) in Astria credits after each qualifying monthly Astria publication, for up to 10 milestones. Your completed first brief counts as milestone one. 03 ### Publishing baseline Complete the agreed brief and your collection or template is published on Astria under your Creative Partner · Explorer identity. 04 ### Quality-based amplification Astria reviews completed work for social reposts, showcase placement, and editorial features. These additional placements are selected for quality and fit, not guaranteed. The activation sequence 1. 01 ### Accept your place Confirm your Explorer acceptance by email. 2. 02 ### Onboard Meet one-to-one, open your Explorer workspace, and agree the project plan. 3. 03 ### Create Complete a 6–10-image collection or one reusable template, ideally within 30 days. 4. 04 ### Publish Astria publishes the completed brief under your Creative Partner · Explorer identity, completing your first [current pricing](https://www.astria.ai/pricing) publication milestone. 5. 05 ### Graduate to Pro Activate your first paying brand; when it crosses [current pricing](https://www.astria.ai/pricing) 12% applies to its full eligible spend. Want an immediate referral path? Explorers can separately join Astria’s existing [affiliate program](https://www.astria.ai/affiliate). It pays 20% of template-owner revenue after card-processing and GPU infrastructure costs, or 20% of the Photoshop plugin price. Attribution lasts 30 days; each qualifying purchase earns a one-time commission, not subscription revenue. This is separate from Pro brand-spend rev share. Featured Templates ## Best **performing.** Curated collections with unique brand aesthetics, crafted by our creators, powered by Astria.[See all templates](https://www.astria.ai/gallery/workspaces) [![The Language of Form By Angela Fraser](https://a.astria.ai/assets/creators/featured/angela-fraser-9709c6d7fad52b4c38f012d7e7c6320437314f2a8350b485098e9e406939a871.avif) By Angela FraserThe Language of FormA visual system translating the Simone Rocha SS26 silhouette into a reusable editorial template.Explore Collection ](https://www.astria.ai/w/angela-fraser?ws=14)[![Etro: The New Archive By Shreya Jain](https://a.astria.ai/assets/creators/featured/shreya-jain-a47b13f39baaca7424d90d953848f343719ef3f93e8fb63595b494ef69738862.avif) By Shreya JainEtro: The New ArchiveItalian luxury reimagined — timeless craftsmanship meets contemporary silhouette.Explore Collection ](https://www.astria.ai/w/shreya-jain?ws=14)[![Urban Elements By Uliana Horodnycha](https://a.astria.ai/assets/creators/featured/uliana-horodnycha-d128d76276f08176b5f2026aa51cfe31920a6b5c7f6f01ef7d819bd38df11df9.avif) By Uliana HorodnychaUrban ElementsPrecision-tailored streetwear — raw silhouettes meet advanced textiles for AW'24.Explore Collection ](https://www.astria.ai/w/uliana-horodnycha?ws=14) Our Network ## Most _Experienced_ [![CI898](https://a.astria.ai/assets/creators/ci898-3baee0a5984293aecf4f5ecea61cad48525499b70c202621e4def63f371effb2.avif) CI898Fashion · Editorial Featured](https://www.astria.ai/w/ci898?ws=3) ![Sivan Aharon](https://a.astria.ai/assets/creators/sivan-aharon-c1de9edf5ac51b29ce28b86272267dbda20aca252dda394b3226d83347b78a81.avif) Sivan AharonPortrait · Lifestyle ![Ori Roterman](https://a.astria.ai/assets/creators/ori-roterman-d00bc57da809bda4e9ff2175bf7c715ba04c9fb1e417d6280f8f0d8693bc7ae2.avif) Ori RotermanCommercial · Product The Three Tiers ## One _path,_ three stages. Explorer is the first-project activation tier. Pro begins with a paying brand; Elite remains invite-only for partners building sustained books of business on Astria. Tier 01ExplorerThe craft-first starting tier. Portfolio and project idea matter; existing brand contacts do not. Publication creditsUp to [current pricing](https://www.astria.ai/pricing)_10 qualifying publication months × [current pricing](https://www.astria.ai/pricing)_ First assignment6–10-image signature collection or one reusable template Suggested window30 days from onboarding Completion outcomePublished under Astria Creative Partner · Explorer identity Rev shareUnlocks at Pro How to advanceActivate your first paying brand Tier 02ProActive practice. You're auto-promoted the day your first brand activates. Rev share12% of full brand spend_unlocks after [current pricing](https://www.astria.ai/pricing); the first [current pricing](https://www.astria.ai/pricing) is included_ Public profileYour own page on Astria_with portfolio and a "Work with me" CTA_ How to stay1+ active brand in trailing 90 days Tier 03EliteInvite-only. Reserved for our most active creative partners — there's no application path. A small group of creators who've built real books of business on Astria. Elite isn't something you apply for — it's how we recognize the partners most invested in the program. If you're doing the work, we'll find you. [Talk to us](mailto:sales@astria.ai) Ownership & economics ## Your work stays yours. **Pro adds revenue.** Astria publishes the completed Explorer brief without taking ownership of your original creative work. Brand-spend rev share begins when you graduate to Pro. 01 — Creator ownership ### Keep your creative identity Your original work stays yoursbefore, during, and after Explorer Astria provides the platform, support, and publishing surface. It does not replace your authorship or take ownership of the original collection or template you create. 02 — Pro rev share ### A share of every eligible dollar 12% of full brand spendunlocks after [current pricing](https://www.astria.ai/pricing) · first [current pricing](https://www.astria.ai/pricing) included · 24-month window The [current pricing](https://www.astria.ai/pricing) is an unlock threshold, not a deductible. Once a paying brand crosses it, 12% applies to that brand's full eligible spend from its first dollar, including the first [current pricing](https://www.astria.ai/pricing) for 24 months measured from that first dollar. Earnings are never clawed back. Pro payouts land in your Astria account — apply them toward platform usage, or withdraw by sending an invoice to [payout@astria.ai](mailto:payout@astria.ai). FAQ ## Questions & answers Explorer is for promising AI-fashion photographers, stylists, art directors, and visual creators with a strong point of view. You do not need an existing brand network. We evaluate your portfolio and the collection or reusable template you want to make. Submit your portfolio and first-project idea. Highlight links, brand experience, and contacts are optional. We review applications within five business days. You can earn up to [current pricing](https://www.astria.ai/pricing) in Astria account credits: [current pricing](https://www.astria.ai/pricing) after each month in which one qualifying Explorer collection, reusable template, or agreed creative drop is published on Astria, for up to 10 qualifying months. Your completed first brief counts as month one. A month without a qualifying publication earns no credit and does not roll over. Complete the first-project brief agreed during onboarding and Astria publishes the collection or template under your Astria Creative Partner · Explorer identity. Social reposts, showcase placement, and editorial features receive a separate quality and fit review, so those additional placements are not guaranteed. It pays 20% of template-owner revenue after card-processing and GPU infrastructure costs for qualifying headshot or template purchases, and 20% of the Photoshop plugin price. Attribution lasts 30 days from referral. Each qualifying purchase earns a one-time commission; it does not continue after that purchase. The [current pricing](https://www.astria.ai/pricing) is an unlock threshold, not a deductible. Once a paying brand crosses it, you earn 12% of that brand’s full eligible spend from its first dollar, including the first [current pricing](https://www.astria.ai/pricing) for 24 months measured from that first dollar. Earnings are never clawed back. Your Explorer identity appears with the work you publish. When you graduate to Pro, your full creator page goes live with your portfolio, published templates, tier badge, and a "Work with me" CTA for custom work. Yes. You retain full ownership of your original work. Your page, your portfolio, and your creative identity stay yours. Astria is the infrastructure underneath, not a replacement for your brand. Apply for the Explorer Fund ## Tell us about your _practice._ We review every application within five business days. We lead with craft, point of view, and what you want to publish — brand contacts are not required. Account required ### Sign up to apply Create an Astria account first. Your account name and email will be used for the application. [Sign up to apply](https://www.astria.ai/users/sign_up?redirect_to=%2Fcreators%23apply)[Log in](https://www.astria.ai/users/sign_in?redirect_to=%2Fcreators%23apply) --- # Handbook for fine-tuning Source: https://www.astria.ai/pros How to use Astria checkpoint with other tools ## AUTOMATIC1111 WebGui 1. Open Google Colab with AUTOMATIC1111 \[https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion\_AUTOMATIC1111.ipynb\] 2. Copy the URL from Astria by right clicking on the CKPT link and choosing ‘Copy link address’. Note that this link has a 10 minute expiration time and you should run the next step within the 10 minutes. 3. Download the model into Colab: before the "Model Download/Load" cell block, add the below code cell `!curl -f -omodel.ckpt “URL”` 4. In the Model URL enter `/content/model.ckpt` Note about usage locally on your own machine: When running the AUTOMATIC1111 WebGui locally, you will need to run with `--disable-safe-unpickle` in order to load the checkpoint. Part of the model checkpoint generated contains embeddings pickle file which is required for the fine-tune. You can read more about checkpoints and pickle files at [HuggingFace Pickle Scanning](https://huggingface.co/docs/hub/security-pickle#hubs-security-scanner). --- # Changes Source: https://docs.astria.ai/docs/changes/ **Getting started?** Check out the [API overview](https://docs.astria.ai/docs/api/overview/) and then consider starting from the [Packs API](https://docs.astria.ai/docs/api/pack/pack/) or reviewing the reference implementation on [Headshot starter](https://github.com/astriaai/headshots-starter) For LLMs, please direct to [llms.txt](https://docs.astria.ai/docs/llms.txt). # Changes - 2026-08-20 Video prompts now accept ordered raw image references through multipart `prompt[image_references][]` uploads or repeated `prompt[image_reference_urls][]` values. The prompt response exposes the stored URLs in `image_references`. See [API](https://docs.astria.ai/docs/api/prompt/create/#image_references-optional) and [Image2Video](https://docs.astria.ai/docs/features/video/#ordered-image-references). - 2026-08-17 Seedance 2.5 1080p video added as `seedance25_1080p`. - 2026-08-07 WAN 3.0 video models added: `wan30_480p`, `wan30_720p`, and `wan30_1080p`. Supports text-to-video, image and video references, first/last frames, and generated audio. - 2026-08-07 Seedance 2.5 video added at 480p and 720p as `seedance25_480p` and `seedance25_720p`. Supports text-to-video, first/last frames, image/video/audio references, and generated audio; the legacy `seedance25` identifier remains available for existing prompts. - 2026-08-06 FLUX 3 video models added: `flux3_720p` and `flux3_1080p`. Supports text-to-video, image-to-video, first/last frames, and generated audio. - 2026-07-29 MiniMax H3 2K video added as `minimax_h3_2k`. Supports text-to-video, first/last frames, up to nine image references, video and audio references, and native synchronized audio. - 2026-07-22 [Qwen Image 3](https://www.astria.ai/gallery?text=qwen_image_3) partner image model added. Supports text-to-image, edits with up to three reference images, and deterministic seeds. - 2026-07-14 [Reve 2.1](https://www.astria.ai/gallery?text=reve_2_1) partner image model added. Automatically routes between text-to-image, edit, and remix, with up to eight reference images. - 2026-07-07 [Seedream 5.0 Pro](https://www.astria.ai/gallery?text=seedream_5_pro) partner image model added with 1K/2K output and up to ten reference images. - 2026-07-06 Scail 2 character-animation video models added: `scail2_480p` and `scail2_720p`. Supports `animate` and `replace` modes using a character image and driving video. - 2026-07-04 [Krea 2](https://www.astria.ai/gallery?text=krea_2) partner image models added in Large, Medium, and Medium Turbo variants, with up to ten reference images. - 2026-07-01 Gemini Omni Flash video added as `gemini_omni_flash`. Supports text-to-video, image-to-video, reference-to-video, video editing, and native synchronized audio. - 2026-07-01 [Nano Banana 2 Lite](https://www.astria.ai/gallery?text=nano_banana_2_lite) (Gemini 3.1 Flash Lite) image model added as a high-throughput, 1K-only option. - 2026-06-29 [Ideogram V4](https://www.astria.ai/gallery?text=ideogram_v4) partner image model added with low/medium/high quality and 1K/2K resolution tiers. - 2026-06-29 Video post-processing added with SeedVR2 upscaling (`seedvr2`, 720p/1080p/2K/4K targets) and frame-rate enhancement (`video_fps_increaser`). - 2026-06-28 Seedance 2 Fast 1080p added as `seedance2_fast_1080p`. - 2026-06-26 Seedance 2 4K models added as `seedance2_4k` and `seedance2_fast_4k`. - 2026-06-12 Luma Ray 3.2 video models added for text/image-to-video (`ray32_540p`, `ray32_720p`, `ray32_1080p`) and video editing (`ray32_edit_540p`, `ray32_edit_720p`, `ray32_edit_1080p`). - 2026-06-08 InfiniteTalk talking-avatar video models added as `infinitetalk_480p` and `infinitetalk_720p`, using a character image and audio reference. - 2026-05-15 [Recraft V4.1](https://www.astria.ai/gallery?text=recraft_v4_1) partner text-to-image model added. - 2026-05-08 [Grok Imagine](https://www.astria.ai/gallery?text=grok_imagine) partner image model added for text-to-image and image editing. - 2026-04-29 [Astria Claude Skills](https://github.com/astriaai/astria-claude-skills) — open-sourced Claude Code skills for working with the Astria API (prompt writing, packs guide, navigation, partner-model integration, and more). - 2026-04-29 Video parameters are now first-class form fields: `prompt[video_model]`, `prompt[video_prompt]`, `prompt[video_duration]`. The pre-existing `--video --video_model … --video_prompt "…" --duration N` flags inside `text` continue to work — fully backward compatible. See [API](https://docs.astria.ai/docs/api/prompt/create/#video) and [Image2Video](https://docs.astria.ai/docs/features/video/). - 2026-04-29 [HappyHorse](https://www.astria.ai/gallery?text=happyhorse) (Alibaba) video models added: `happyhorse_720p`, `happyhorse_1080p`, and `happyhorse_motion_control`. Supports image-to-video, reference-to-video, text-to-video, and video-edit motion control. [Docs](https://docs.astria.ai/docs/features/video/). - 2026-04-24 Kling 4K image-to-video model added (`kling30_4k`). - 2026-04-21 [GPT Image 2](https://www.astria.ai/gallery?text=gpt_image_2) partner image model added with low/medium/high quality and 1K/2K/4K resolution options. Native mask support, OpenAI-direct routing. - 2026-04-13 Veo 3.1 Fast 4K and Veo 3.1 Lite video models added. - 2026-04-10 [Seedance 2](https://www.astria.ai/gallery?text=seedance2) video model added (480p / 720p / 1080p / 2K, plus fast variants). Supports text-to-video, reference-to-video, audio reference, first/last frame. - 2026-04-05 Grok 2 Image partner text-to-image model added. - 2026-04-04 [GPT Image 1.5](https://www.astria.ai/gallery?text=gpt_image_15) partner image model added via WavespeedApi. Supports low/medium/high quality and 1024×1024, 1024×1536, and 1536×1024 output sizes. - 2026-04-04 WAN 2.7 1080p image-to-video added. - 2026-04-02 [WAN 2.7](https://www.astria.ai/gallery?text=wan27) and WAN 2.7 Pro partner image models added. - 2026-03-29 WAN 2.6 video model added. - 2026-03-27 LTX 2.3 video model added (720p/1080p). - 2026-03-14 [Flux Klein 9B](https://www.astria.ai/gallery?text=flux_klein) partner image model added with up to three reference images. - 2026-03-13 [Recraft V4](https://www.astria.ai/gallery?text=recraft_v4) and Recraft V4 Pro partner image models added. - 2026-02-24 [Seedream 5.0 Lite](https://www.astria.ai/gallery?text=seedream_5) partner image model added. - 2026-02-15 Kling 3 Pro and Kling 3 Motion Control video models added. - 2026-02-10 DreamActor M2 video model added (motion control). - 2026-02-07 Kling 3 video model added. - 2026-01-25 Kling 2.6 image-to-video and Wan Animate video models added via Fal. - 2026-01-08 P-Image Edit partner model added via Replicate. - 2026-01-07 [Flux 2 Turbo](https://www.astria.ai/gallery?text=flux2_turbo) partner image model added with support for up to four input images. - 2026-01-07 LTX 2 video model added. - 2025-12-28 Seedance v1.5 video model added (480p/720p, with audio variants). - 2025-12-03 [Seedream 4.5](https://www.astria.ai/gallery?text=seedream_4_5) partner image model added with a minimum output size of 3686400 pixels. - 2025-11-29 [Flux 2 Pro](https://www.astria.ai/gallery?text=flux2_pro) partner image model added with support for 2K (4MP) output. - 2025-10-30 Added support for Super-resolution, `--upscale_v4`, film grain to Gemini Nano-Banana and Seedream. - 2025-10-27 [Seedance Pro Fast](https://www.astria.ai/gallery?text=seedance) video model added. [Docs](https://docs.astria.ai/docs/features/video/) - 2025-10-23 [Hyperrealism](https://www.astria.ai/gallery?text=hyperrealism) partner image model added. - 2025-10-16 Veo 3.1 video model added (Vertex routing). - 2025-10-19 Added new upscaling `--upscale_v4`. See [gallery](https://www.astria.ai/gallery?text=--upscale_v4) - 2025-10-19 [Aspect-ratio](https://docs.astria.ai/docs/api/prompt/create/#aspect_ratio-optional) added to prompts - 2025-10-15 [Riverflow 1](https://www.astria.ai/gallery/tunes/3449219/prompts) and [Riverflow 1 Mini](https://www.astria.ai/gallery/tunes/3449218/prompts) support great precision in image-editing, with benchmarks surpassing nano-banana. Additonally available in the [Photoshop plugin](https://www.astria.ai/nano-banana-photoshop). - 2025-10-08 [Ovi](https://www.astria.ai/gallery?text=video_model%20ovi) video model added allowing talking Avatars with text and expressive voice. - 2025-09-25 Kling 2.5 and WAN 2.5 video models added. - 2025-09-18 [Reve](https://www.astria.ai/gallery?text=reve) partner image model added. - 2025-09-15 [Photoshop plugin](https://www.astria.ai/nano-banana-photoshop) for Nano Banana Gemini 2.5 and Seedream 4.0 is now available. - 2025-08-05 Veo 3 Fast video model added. - 2025-08-03 WAN 2.2 Fast video model added (480p/580p/720p). - 2025-07-30 WAN 2.2 video model added. - 2025-08-28 [Seedream 4.0](https://www.astria.ai/gallery/tunes/3225353/prompts) has been integrated. Allows 4K image edits. - 2025-08-26 [Nano Banana Gemini 2.5](https://www.astria.ai/gallery/tunes/3159068/prompts) has been integrated for consistent image editing. - 2025-08-05 [Krea Flux1.dev](https://www.astria.ai/gallery/tunes/3063697/prompts) added as an option for fine-tuning and inference, however, ee do currently do not recommend this. - 2025-06-22 Seedance video model added - higher quality than VEO3, and easier UX using the new video button. - 2025-06-09 Flux Kontext added - 2025-04-28 Updated Flux controlnets to the latest and added better default conditioning scales and end conditioning. - 2025-03-20 [Image2Video](https://docs.astria.ai/docs/features/video/) available - 2025-03-02 Added `auto_extend` for [tune](https://docs.astria.ai/docs/api/tune/create/) so that only specific tunes can be extended. - 2025-02-18 Added video tutorial on how to [create a pack](https://docs.astria.ai/docs/api/pack/pack/) and [how to fine-tune](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) - 2025-02-01 [Multiperson](https://docs.astria.ai/docs/features/multiperson/) for Flux in BETA - 2025-01-20 [Flux Pro 1.1 + Ultra](https://www.astria.ai/gallery/tunes?branch=flux-pro1) with fine-tuning support. - 2025-01-17 `focus` [preset](https://docs.astria.ai/docs/use-cases/flux-finetuning/#training-presets) for training faces, products, and objects with Flux. - 2025-01-13 Added `--remove_background` for output images. - 2024-12-20 [Outpainting](https://docs.astria.ai/docs/features/outpainting/) syntax for extending image, creating more cinematic images or longer portraits with high quality faces. - 2024-12-15 `portrait` flux training preset is available for [fine-tuning](https://docs.astria.ai/docs/use-cases/flux-finetuning/#training-presets) for better portrait results. - 2024-11-27 [FaceID](https://docs.astria.ai/docs/features/faceid-flux/) for Flux is now available. - 2024-11-27 Flux fine-tuning allows uploading .TXT file captions for multi concept fine-tuning. - 2024-11-24 Added film grain and color grading support for Flux Backend V0. - 2024-11-20 [Virtual try-on](https://docs.astria.ai/docs/use-cases/virtual-try-on/) with Flux is now in BETA. - 2024-11-15 [Face inpainting](https://docs.astria.ai/docs/features/face-inpainting/) with Flux and improved super-resolution - 2024-10-27 Added [Theme API](https://docs.astria.ai/docs/api/themes/create/) to allow users to easily create variation and write their own ideas while augmenting them and adding variation. - 2024-10-12 Added [Image inspect](https://docs.astria.ai/docs/api/images/inspect/) API to 1) filter/warn about bad training images 2) detect characteristics of the training images for templating with `characteristics`. - 2024-10-09 Flux controlnet pose is available with Backend V0 but is still not recommended for production use. - 2024-09-26 Added `prompts_callback` for [creating a tune with a pack](https://docs.astria.ai/docs/api/pack/tunes/create/) - 2024-09-03 [Flux fine-tuning](https://docs.astria.ai/docs/use-cases/flux-finetuning/) docs and [Flux API usage](https://docs.astria.ai/docs/api/flux-api/) - 2024-07-29 Added [Styles](https://docs.astria.ai/docs/features/styles/) option for prompt - 2024-06-26 [Tiled upscale V2](https://docs.astria.ai/docs/features/tiled-upscale/) preserves identity and similarity while adding details and skin tones. - 2024-06-26 Added controlnet `relight` for SD15 with Backend V1 to allow better composition and blending for [background replacement](https://docs.astria.ai/docs/use-cases/product-shots/). - 2024-05-26 Added `--improve_similarity 0.5` flag for Checkpoint/LoRA models which dramatically improves similarity/identity. - 2024-05-22 Improved SDXL FaceID - 2024-05-22 Added Controlnet Style for SDXL1 - 2024-05-10 Added Controlnet Lineart for SDXL1 - 2024-05-06 [Interior design](https://docs.astria.ai/docs/use-cases/room-redesign/) - improved "Segment Room" (`segroom`) controlnet to handle empty rooms. - 2024-05-05 [Packs](https://docs.astria.ai/docs/api/pack/pack/) + [Likes](https://docs.astria.ai/docs/api/like/create/) API - Control prompts from within Astria and experiment faster. - 2024-04-20 [Interior design](https://docs.astria.ai/docs/use-cases/room-redesign/) - Added "Segment Room" controlnet for Backend V1, and MLSD for SDXL to be used. - 2024-04-04 Added support for face-inpainting, hires-fix and tiled upscale for faceid on Backend V1. - 2024-04-04 Lightning schedulers for Backend V1 to support lightning models such as [RealVis XL Lightning](https://www.astria.ai/gallery/tunes/1194447/prompts) - 2024-04-04 V1: Lightning schedulers to support lightning models such as [RealVis XL Lightning](https://www.astria.ai/gallery/tunes/1194447/prompts) - 2024-03-27 Upgraded [Themes](https://www.astria.ai/themes) to use Claude Opus and adjust for both SDXL and SD15 prompting. - 2024-03-20 [Free Upscale](https://astria.ai/upscale) tool - 2024-03-17 Controlnet `composition` support for Backend V1. Examples in the [gallery](https://www.astria.ai/gallery?controlnet=composition) - 2024-03-10 New [Backend version v1](https://docs.astria.ai/docs/features/backend-v1/) for inference - Higher quality, better samplers, faster inference, and more features. - 2024-03-05 [TCD](https://github.com/jabir-zheng/TCD) sampler gets better results than LCM in 4-8 steps for SDXL models and is available on the samplers dropdown in the Advanced settings of the prompt. - 2024-02-16 [FaceID Portrait](https://docs.astria.ai/docs/features/faceid/) has better color balance and similarity and is well suited for portraits. - 2024-02-08 Deprecate prompt aspect-ratio. Use width and height instead for better results (backward compatability available). - 2024-02-08 Added `--fix_bindi` to remove dot on the forehead. - 2024-01-30 Deprecated prompt expansion. - 2024-01-25 [Upscale only](https://docs.astria.ai/docs/use-cases/upscale/) Allows you to upscale an existing image. - 2024-01-17 [FaceID](https://docs.astria.ai/docs/features/faceid/) Preserve identity without fine-tuning. - 2024-01-03 [Tiled upscale](https://docs.astria.ai/docs/features/tiled-upscale/) improved upscaling. - 2023-12-14 [Prompt masking](https://docs.astria.ai/docs/features/prompt-masking/) feature added - to support [product shots](https://docs.astria.ai/docs/use-cases/product-shots/) and [Masked Portraits](https://docs.astria.ai/docs/use-cases/masked-portraits/) - 2023-11-27 [LCM](https://docs.astria.ai/docs/features/lcm/) (Latent Consistency Models) scheduler allows inference in 6 steps - 2023-10-29 [Face-Swap](https://docs.astria.ai/docs/features/face-swap/) feature added - uses training images to improve inference time similarity - 2023-10-22 [Themes](https://www.astria.ai/themes) - Have ChatGPT create 10 prompts for you from a short 2-4 word theme description. --- # Face Inpainting Source: https://docs.astria.ai/docs/features/face-inpainting/ ## Overview Face inpainting is a feature that maintains facial similarity mainly in long-shot and full-body generations by performing additional facial enhancement. When enabled, it automatically improves facial consistency regardless of the shot distance. Face Inpainting with Flux ![source.png](https://docs.astria.ai/assets/images/face_inpainting_w_flux-e687f3cc9742f3097b00c99497f8fe41.png) Zoom on face - Before ![source.png](https://docs.astria.ai/assets/images/flux-face-inpainting-cropped-before-77a3f6b092bda64b02b1102c8e151621.jpg) Zoom on face - After ![source.png](https://docs.astria.ai/assets/images/flux-face-inpainting-cropped-after-fe29b78ca5cbbb5400f999a4d9b8d136.jpg) Full body longshot - before face inpainting ![source.png](https://docs.astria.ai/assets/images/flux-face-inpainting-before-f7f98f5297d4ab8597fe2f16e2de2e8f.jpg) Full body longshot - after face inpainting ![source.png](https://docs.astria.ai/assets/images/flux-face-inpainting-after-2e1ded443aa5056439ec23b130553389.jpg) See examples in our [gallery](https://www.astria.ai/gallery?inpaint_faces=true) ## Enabling Face Inpainting ### Playground To enable face inpainting, open the ‘Advanced’ settings when generating an image, and click the Inpaint faces toggle. Notice that this will turn on the super resolution toggle as well, which is necessary for this feature. ### API Use `inpaint_faces` and `super_resolution` in your prompt request. #### Example Request ```bash POST https://api.astria.ai/generate { "prompt": " Surrounded by the natural beauty of a serene, wooded environment, the ohwx woman exudes a sense of tranquility and connection with her surroundings. She wears a flowing, organic-inspired dress in earthy tones, her face framed by loose, windblown hair. The lighting is soft and diffused, creating a soothing, meditative atmosphere that invites the viewer to pause and connect with the moment.", "num_images": 1, "super_resolution": true, "inpaint_faces": true, "w": 896, "h": 1152, "scheduler": "euler_a", "ar": "1:1" } ``` ## Notes When face inpainting is enabled the following occurs: - An additional inference cycle - This affects the total cost per generation - see our [pricing page](https://www.astria.ai/pricing) for details - Generation time will be a bit longer Face inpainting Works with all Flux-based models, as well as SDXL and SD15 --- # FaceID for Flux Source: https://docs.astria.ai/docs/features/faceid-flux/ [FaceID](https://www.astria.ai/gallery?text=faceid&branch=flux1) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-flux-1-f3f12906fda7b314452f03151c5345ae.jpg) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-flux-2-b65bcd975d60e06c8b4b03ce2ca5e468.jpg) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-flux-3-ae5a9d8dfd3a7497e062184c3c43317d.jpg) ![faceid-output.png](https://docs.astria.ai/assets/images/faceid-flux-4-980af874c9608220fdf738b6468a07e9.jpg) See example prompts in the [gallery](https://www.astria.ai/gallery?text=faceid&branch=flux1) ## Overview FaceID is a model adapter allowing to generate image while preserving a person identity without fine-tuning. Input images can be as few as just one image. The adapter was trained on human faces and cannot be used for pets or other subjects. ## Usage 1. Generate a placeholder fine-tune dataset which will contain the person images. The fine-tune will not go through training and will be immediately ready. ![faceid-new-fine-tune.png](https://docs.astria.ai/assets/images/faceid-new-fine-tune-cc128107ac919832509e97a8ca45918b.png) 2. Generate the image using the FaceID adapter using a syntax similar to LoRA - `` ## Notes 1. For realistic images, please consider enabling face-correct to improve facial features. ## API FaceID is an adapter loaded on top of a base model. As such the inference needs to take place on a model from the gallery. The fine-tune's `trained_at` is set upon creation and there is no training time. As such no callback is needed. ### Step 1: Create fine-tune #### POST /tunes ```bash # With images as multipart/form-data # Hard coded tune id of Realistic Vision v5.1 from the gallery - https://www.astria.ai/gallery/tunes # https://www.astria.ai/gallery/tunes/690204/prompts curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes \ -F tune[title]="John Doe - UUID - 1234-6789-1234-56789" \ -F tune[name]=man \ -F tune[model_type]="faceid" \ -F tune[callback]="https://optional-callback-url.com/webhooks/astria?user_id=1&tune_id=1" \ -F tune[base_tune_id]=690204 \ -F "tune[images][]=@1.jpg" \ -F "tune[images][]=@2.jpg" \ -F "tune[images][]=@3.jpg" \ -F "tune[images][]=@4.jpg" # With image_urls as form-data curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes \ -F tune[title]="John Doe - UUID - 1234-6789-1234-56789" \ -F tune[name]=man \ -F tune[model_type]="faceid" \ -F tune[callback]="https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1" \ -F tune[base_tune_id]=690204 \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" \ -F "tune[image_urls][]=https://i.imgur.com/HLHBnl9.jpeg" # As JSON cat > data.json <<- EOM { "tune": { "title": "John Doe - UUID - 1234-6789-1234-56789", "name": "man", "model_type": "faceid", "callback": "https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1", "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ] } } EOM curl -X POST -H"Content-Type: application/json" -H "Authorization: Bearer $API_KEY" --data @data.json https://api.astria.ai/tunes ``` ```javascript // NodeJS 16 // With image_urls and fetch() // For NodeJS 18 - do NOT import the below as it is built-in import fetch from "node-fetch"; const API_KEY = 'sd_XXXXXX'; const DOMAIN = 'https://api.astria.ai'; function createTune() { let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY, 'Content-Type': 'application/json' }, body: JSON.stringify({ tune: { "title": 'John Doe - UUID - 1234-6789-1234-56789', // Hard coded tune id of Realistic Vision v5.1 from the gallery - https://www.astria.ai/gallery/tunes // https://www.astria.ai/gallery/tunes/690204/prompts "base_tune_id": 690204, "name": "man", "model_type": "faceid", "image_urls": [ "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg", "https://i.imgur.com/HLHBnl9.jpeg" ] } }) }; return fetch(DOMAIN + '/tunes', options) .then(r => r.json()) .then(r => console.log(r)) } createTune() /// With form-data, fetch() // For NodeJS 18 - do NOT import the two below as they are built-in import fetch from "node-fetch"; import FormData from 'form-data'; import fs from 'fs'; const API_KEY = 'sd_XXXX'; const DOMAIN = 'https://api.astria.ai'; function createTune() { let formData = new FormData(); formData.append('tune[title]', 'John Doe - UUID - 1234-6789-1234-56789'); formData.append('tune[model_type]', 'faceid'); // Hard coded tune id of Realistic Vision v5.1 from the gallery - https://www.astria.ai/gallery/tunes // https://www.astria.ai/gallery/tunes/690204/prompts formData.append('tune[base_tune_id]', 690204); formData.append('tune[name]', 'man'); // Load all JPGs from ./samples directory and append to FormData let files = fs.readdirSync('./samples'); files.forEach(file => { if(file.endsWith('.jpg')) { formData.append('tune[images][]', fs.createReadStream(`./samples/${file}`), file); } }); formData.append('tune[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?user_id=1&tune_id=1'); let options = { method: 'POST', headers: { 'Authorization': 'Bearer ' + API_KEY }, body: formData }; return fetch(DOMAIN + '/tunes', options) .then(r => r.json()) .then(r => console.log(r)); } createTune(); ``` ```python import requests headers = {'Authorization': f'Bearer {API_KEY}'} def load_image(file_path): with open(file_path, "rb") as f: return f.read() # Assuming `tune.images` are already defined in your context # If not, you should define them before the below code image_data = load_image("assets/image.jpeg") data = { "tune[title]": "John Doe - UUID - 1234-6789-1234-56789", "tune[name]": "man", "tune[base_tune_id]": 690204, "tune[model_type]": "faceid", "tune[token]": "ohwx" } files = [] for image in tune.images: image_data = load_image(image) # Assuming image is a file path files.append(("tune[images][]", image_data)) API_URL = 'https://api.astria.ai/tunes' response = requests.post(API_URL, data=data, files=files, headers=headers) response.raise_for_status() ``` #### Response ```json [ { "id": 1, "title": "John Doe", "name": "woman", "token": "ohwx", "base_tune_id": null, "args": null, "steps": null, "face_crop": null, "training_face_correct": false, "ckpt_url": null, "ckpt_urls": [], "eta": "2023-10-02T14:32:40.363Z", "trained_at": "2023-10-02T14:32:40.363Z", "started_training_at": "2023-10-02T14:32:05.229Z", "expires_at": "2023-11-01T14:32:40.363Z", "created_at": "2023-10-02T14:32:05.067Z", "branch": "sd15", "model_type": "faceid", "updated_at": "2023-10-02T14:32:40.363Z", "url": "https://www.astria.ai/tunes/788416.json", "orig_images": [ "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock" ] }, { "id": 775459, "title": "Marry Jane", "name": null, "is_api": false, "token": "ohwx", "base_tune_id": null, "args": null, "steps": null, "face_crop": null, "training_face_correct": null, "ckpt_url": "https://sdbooth2-production.s3.amazonaws.com/mock", "ckpt_urls": [ "https://sdbooth2-production.s3.amazonaws.com/mock" ], "eta": "2023-09-23T16:07:49.137Z", "trained_at": "2023-09-23T16:07:49.137Z", "started_training_at": "2023-09-23T16:07:37.334Z", "expires_at": "2023-10-23T16:07:49.137Z", "created_at": "2023-09-23T16:07:36.606Z", "branch": "sd15", "model_type": "faceid", "updated_at": "2023-09-23T16:07:49.138Z", "url": "https://www.astria.ai/tunes/775459.json", "orig_images": [ "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock", "https://sdbooth2-production.s3.amazonaws.com/mock" ] } ] ``` ### Step 2: Create prompts #### POST /tunes/:id/prompts ```bash # Note the hard-coded 150494 which is the tune_id of Realistic Vision v5.1 from the gallery curl -X POST -H "Authorization: Bearer $API_KEY" https://api.astria.ai/tunes/690204/prompts \ -F prompt[text]=" woman trekking in the alps" \ -F prompt[negative_prompt]="" \ -F prompt[super_resolution]=true \ -F prompt[face_correct]=true \ -F prompt[face_swap]=true \ -F prompt[callback]="https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1" ``` ```javascript const fetch = require('node-fetch'); const FormData = require('form-data'); // Note the hard-coded 690204 which is the tune_id of Realistic Vision v5.1 from the gallery const API_URL = 'https://api.astria.ai/tunes/690204/prompts'; const API_KEY = 'YOUR_API_KEY'; // Replace with your actual API key const headers = { Authorization: `Bearer ${API_KEY}` } const form = new FormData(); form.append('prompt[text]', ' woman trekking in the alps'); form.append('prompt[negative_prompt]', ''); form.append('prompt[super_resolution]', true); form.append('prompt[face_correct]', true); form.append('prompt[face_swap]', true); form.append('prompt[callback]', 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1'); fetch(API_URL, { method: 'POST', headers: headers, body: form }).then(response => response.json()) ``` ```python import requests # Note the hard-coded 690204 which is the tune_id of Realistic Vision v5.1 from the gallery API_URL = 'https://api.astria.ai/tunes/690204/prompts' API_KEY = 'YOUR_API_KEY' # Replace with your actual API key headers = { 'Authorization': f'Bearer {API_KEY}' } data = { 'prompt[text]': ' woman trekking in the alps', 'prompt[negative_prompt]': '', 'prompt[super_resolution]': True, 'prompt[face_correct]': True, 'prompt[face_swap]': True, 'prompt[callback]': 'https://optional-callback-url.com/to-your-service-when-ready?prompt_id=1' } files = [] files.append((f"tune[prompts_attributes][{i}][input_image]", load_image(prompt['input_image']))) response = requests.post(API_URL, headers=headers, data=data) ``` --- # Image2Video Source: https://docs.astria.ai/docs/features/video/ Image2Video See example prompts in the [gallery](https://www.astria.ai/gallery?branch=flux1&is_video=true). [Pricing](https://www.astria.ai/pricing/video) for video generation. ### Overview Astria Image2Video supports a wide catalog of image-to-video, text-to-video, reference-to-video, and motion-control video models. Pick a `video_model` and pass a `video_prompt` describing the motion; Astria renders the first frame using the prompt's image stage (the chosen tune + `text`) and animates it. ### Form fields Pass these alongside `text` on `POST /tunes/:id/prompts`: #### `video_model` (required) The model to animate with. See the [enum table](https://docs.astria.ai/docs/features/video/#models) below. #### `video_prompt` (required) Describes camera movement, scene, and object interactions. Keep image-stage LoRA tokens in `text`. For Seedance 2 reference-to-video, put reference tune tokens directly in `video_prompt` using ` TUNE_NAME` (or the corresponding `lora` token) so Astria can supply those tunes' images to the video model. Example: ` ohwx woman hiking in the alps` (in `text`) + `Woman looking at the camera, smiling, puts hands on her hips, confident` (in `video_prompt`). #### `image_references` (optional) Ordered array of multipart image uploads for reference-to-video models. Repeat `prompt[image_references][]` for each image. #### `image_reference_urls` (optional) Ordered array of public image URLs, as an alternative to `image_references`. Repeat `prompt[image_reference_urls][]` for each URL. Order is preserved within each array. #### `video_duration` (optional) Integer seconds. Allowed values depend on the chosen model — see the table. #### `video_first_frame` (optional) Multipart image upload. When provided it overrides the image-stage render and `text` is no longer required. #### `video_first_frame_url` (optional) URL alternative to `video_first_frame`. #### `video_last_frame` (optional) Multipart image upload for first+last keyframe models. #### `video_last_frame_url` (optional) URL alternative to `video_last_frame`. #### `source_image_url` (optional) URL of an existing generated image to animate. This skips the image-generation stage. An explicit `video_first_frame` takes precedence. #### `input_video` (optional) Multipart video upload. Seedance 2 uses it as a reference video; motion-control models require it as their driving video. #### `input_video_url` (optional) URL alternative to `input_video`. #### `audio_reference` (optional) Multipart reference-audio upload. Supported by `seedance2_*`. Set `video_audio=true` so the output includes audio. You can refer to the track as `@Audio1`; Astria adds that token on provider paths that require it when the prompt has no `@Audio` token. #### `audio_reference_url` (optional) URL alternative to `audio_reference`. #### `video_audio` (optional) Boolean. Generate an audio track when supported by the model. Defaults to `false`. Set it to `true` when using `audio_reference`. #### `aspect_ratio` (optional) Forwarded to the video model when it supports an aspect-ratio knob. Seedance 2 accepts `16:9`, `9:16`, `1:1`, `4:3`, `3:4`, `21:9`, or `adaptive`. For image-to-video the aspect ratio is derived from the input image. ### Example ```bash curl -X POST -H "Authorization: Bearer $API_KEY" \ https://api.astria.ai/tunes/$TUNE_ID/prompts \ -F 'prompt[text]= A highly detailed image of thoughtful ohwx woman exploring a hidden urban garden' \ -F 'prompt[video_model]=seedance_v15_720p' \ -F 'prompt[video_prompt]=Woman looking at the camera, smiling, puts hands on her hips, confident' \ -F 'prompt[video_duration]=5' \ -F 'prompt[aspect_ratio]=16:9' ``` ### Text-to-video Models in the text-to-video group accept a `video_prompt` with no first frame and no `text` — the model generates the video directly. Currently: `seedance2_*`, `happyhorse_720p`, `happyhorse_1080p`. ```bash curl -X POST -H "Authorization: Bearer $API_KEY" \ https://api.astria.ai/tunes/$TUNE_ID/prompts \ -F 'prompt[video_model]=seedance2_fast_720p' \ -F 'prompt[video_prompt]=A lone explorer walks across endless dunes at sunrise, cinematic.' \ -F 'prompt[video_duration]=5' \ -F 'prompt[aspect_ratio]=16:9' \ -F 'prompt[video_audio]=true' ``` ### Seedance 2 reference media Seedance 2 can condition a video on multiple tune images, one reference video, and one audio reference. Mention existing tunes in `video_prompt` using the same token and class-name syntax as image generation. Up to nine reference images are sent in total (up to three per tune). ```bash curl -X POST -H "Authorization: Bearer $API_KEY" \ https://api.astria.ai/tunes/$TUNE_ID/prompts \ -F 'prompt[video_model]=seedance2_fast_720p' \ -F 'prompt[video_prompt]= woman walks onto the stage in time with @Audio1 while the camera follows' \ -F 'prompt[video_duration]=8' \ -F 'prompt[aspect_ratio]=16:9' \ -F 'prompt[input_video]=@/path/to/movement-reference.mp4' \ -F 'prompt[audio_reference]=@/path/to/music-reference.mp3' \ -F 'prompt[video_audio]=true' ``` Use the corresponding `*_url` field instead when the file is already publicly accessible. Do not combine Seedance 2 or Seedance 2.5 first/last frames with `image_references`, `image_reference_urls`, `input_video`, or `audio_reference`; these are separate media modes. ### Ordered image references Use `image_references` when each frame should be conditioned directly by a sequence of images rather than tune training images. The same field works with any compatible reference-to-video model; model-specific image limits apply. ```bash curl -X POST -H "Authorization: Bearer $API_KEY" \ https://api.astria.ai/tunes/$TUNE_ID/prompts \ -F 'prompt[video_model]=seedance2_fast_720p' \ -F 'prompt[video_prompt]=Begin on the back detail, orbit around the model, and end on the front view' \ -F 'prompt[video_duration]=8' \ -F 'prompt[image_references][]=@/path/to/back.jpg' \ -F 'prompt[image_references][]=@/path/to/front.jpg' ``` For hosted images, send repeated `prompt[image_reference_urls][]` fields instead. The prompt response exposes the stored references as an ordered `image_references` array of URLs. On models with exclusive media modes, image references cannot be combined with first/last frames. ### Motion control Motion-control models replay the motion from `input_video` while restyling the subject. Models: `kling30_motion_control`, `kling30_motion_control_pro`, `wan_animate_720p`, `dreamactor_m2`, `happyhorse_motion_control`. ```bash curl -X POST -H "Authorization: Bearer $API_KEY" \ https://api.astria.ai/tunes/$TUNE_ID/prompts \ -F 'prompt[text]=ohwx man ' \ -F 'prompt[video_model]=kling30_motion_control_pro' \ -F 'prompt[video_prompt]=match the dance moves' \ -F 'prompt[video_duration]=10' \ -F 'prompt[input_video]=@/path/to/reference.mp4' ``` ### Models Cost is the per-prompt charge in cents at the base duration listed for the model; videos longer than the base scale linearly. `_audio` variants generate an audio track. Use the current vendor pricing pages for rates and allowances. Astria pricing: [pricing](https://www.astria.ai/pricing). ### Capability matrix - **Text-to-video** (no first frame required): `seedance2_*`, `happyhorse_720p`, `happyhorse_1080p`. - **Multi-reference images** (`image_references[]` / `image_reference_urls[]`): `seedance2_*`, `seedance25*`, `minimax_h3_2k`, `wan30_*`, `happyhorse_720p`, `happyhorse_1080p`, `ray32_*`, `gemini_omni_flash`. Limits vary by model (nine for Seedance 2 and MiniMax H3, ten for WAN 3.0, and up to thirty for Seedance 2.5). - **Reference video** (`input_video` / `input_video_url`): `seedance2_*` and motion-control models. - **Reference audio** (`audio_reference` / `audio_reference_url`): `seedance2_*`. - **Generated audio** (`video_audio=true`): `seedance2_*`. - **First+last keyframe** (`video_last_frame`): `seedance_v15_*`, `seedance2_*`, `wan21_*`, `wan26_*`, `wan27_*`, `wan_fast_*`, `ltx23_*`, `kling*`, `veo31_*`, `hailuo*`. - **Motion control** (requires `input_video`): `kling30_motion_control*`, `wan_animate_720p`, `dreamactor_m2`, `happyhorse_motion_control`. ### Backwards compatibility The pre-2026-04 syntax embedding flags inside `text` is still accepted and promoted to the new columns server-side: ```text ohwx woman hiking in the alps --video --video_model seedance_v15_720p --duration 5 --video_prompt "Woman looking at the camera, smiling, confident" ``` New integrations should prefer the dedicated form fields. --- # LoRA Source: https://docs.astria.ai/docs/features/lora/ LoRA (low ranking adaption) is a type of model fine-tuning which takes an additional set of model weights (or parameters) and trains those on top while not changing the existing model. For this reason LoRA training is faster comparing to full checkpoint training as it only trains a small amount of weights comparing to the full model. Additionally, LoRA can be loaded on top of any base model, and multiple LoRA can be combined. The LoRA ecosystem contains many different types of weight structures with different ranks. For this reason some LoRA might not be compatible in different UIs or platforms. LoRAs can be used to enhance the quality of the image or deepen a specific style that is desired. Astria provides a [LoRA gallery](https://www.astria.ai/gallery/tunes?model_type=lora) and allows importing external LoRAs. To use a LoRA go to the [generate tab](https://www.astria.ai/prompts) and use the LoRA syntax as such: ``. For example, a very common usage `` - will load a Noise offset lora at strength 0.5 which will turn the image a bit darker and deepen the contrast. See example prompts in the [gallery](https://www.astria.ai/gallery?text=lora) You can also combine LoRAs as such: ```text ``` danger Note that LoRA can reduce the similarity of trained subjects when used together. To avoid this use low-strength for the lora. Some LoRAs may be trained on trigger words which are required to show in the prompt text. Check the LoRA information by clicking on the Website link and reading its docs and trigger words. --- # Styles Source: https://docs.astria.ai/docs/features/styles/ Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. Animated ![Animated](https://docs.astria.ai/assets/images/animated-62fe824fb999578365bbd12effeb9a58.jpg) Comic book ![Comic book](https://docs.astria.ai/assets/images/comics-9e9ca83560bb6ad202705575d4105df5.jpg) Line art ![Lineart](https://docs.astria.ai/assets/images/line-art-713a5f6c7775999c1dce49442d0917b8.jpg) Enhance ![Enhance](https://docs.astria.ai/assets/images/enhance-f07356dfdb1b319137c06d946dcdf55a.jpg) Fantasy art ![Fantasy art](https://docs.astria.ai/assets/images/fantasy-art-a6b7634d213ad38cb62116ea00a51cf4.jpg) Lowpoly ![Lowpoly](https://docs.astria.ai/assets/images/lowpoly-9455be144ee82ed1fa460c94a501f737.jpg) Neonpunk ![Neonpunk](https://docs.astria.ai/assets/images/neonpunk-b39d73b89c4b0a83c2f166d903fed3dc.jpg) Photographic ![Photographic](https://docs.astria.ai/assets/images/photographic-3beffd9b18f7ab73359f712bd4a417cc.jpg) ## Usage Styles can be used in the GUI style dropdown or in the [API](https://docs.astria.ai/docs/api/prompt/create/#backend_version-optional) using the `style` attribute. When using a style template, provide a short prompt text describing the main subject and scene, while leaving the generic style keywords to the template. --- # 2 docs tagged with "flux" Source: https://docs.astria.ai/docs/tags/flux/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Flux API usage ](https://docs.astria.ai/docs/api/flux-api/) For an overview of Flux fine-tuning, see Flux fine-tuning [ ## Flux training ](https://docs.astria.ai/docs/use-cases/flux-finetuning/) Overview --- # 2 docs tagged with "lora" Source: https://docs.astria.ai/docs/tags/lora/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Flux API usage ](https://docs.astria.ai/docs/api/flux-api/) For an overview of Flux fine-tuning, see Flux fine-tuning [ ## Flux training ](https://docs.astria.ai/docs/use-cases/flux-finetuning/) Overview --- # 2 docs tagged with "style" Source: https://docs.astria.ai/docs/tags/style/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## LoRA ](https://docs.astria.ai/docs/features/lora/) LoRA [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # 2 docs tagged with "training" Source: https://docs.astria.ai/docs/tags/training/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Flux API usage ](https://docs.astria.ai/docs/api/flux-api/) For an overview of Flux fine-tuning, see Flux fine-tuning [ ## Flux training ](https://docs.astria.ai/docs/use-cases/flux-finetuning/) Overview --- # 2 docs tagged with "virtual try-on" Source: https://docs.astria.ai/docs/tags/virtual-try-on/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Flux Outpainting ](https://docs.astria.ai/docs/features/outpainting/) LoRA [ ## Virtual Try-on for Flux ](https://docs.astria.ai/docs/use-cases/virtual-try-on/) Garment / Clothing - with a model - Recommended --- # 4 docs tagged with "fine-tuning" Source: https://docs.astria.ai/docs/tags/fine-tuning/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## AI Photoshoot - fine-tuning for professional headshots ](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) Overview [ ## Flux API usage ](https://docs.astria.ai/docs/api/flux-api/) For an overview of Flux fine-tuning, see Flux fine-tuning [ ## Flux training ](https://docs.astria.ai/docs/use-cases/flux-finetuning/) Overview [ ## LoRA ](https://docs.astria.ai/docs/features/lora/) LoRA --- # API Source: https://docs.astria.ai/docs/category/api/ [ ## 📄️ Overview Billing ](https://docs.astria.ai/docs/api/overview/) [ ## 🗃️ Tunes 5 items ](https://docs.astria.ai/docs/category/tunes/) [ ## 🗃️ Prompts 6 items ](https://docs.astria.ai/docs/category/prompts/) [ ## 🗃️ Packs 4 items ](https://docs.astria.ai/docs/category/packs/) [ ## 🗃️ Likes 2 items ](https://docs.astria.ai/docs/category/likes/) [ ## 🗃️ Images 1 items ](https://docs.astria.ai/docs/category/images/) [ ## 📄️ Flux API usage For an overview of Flux fine-tuning, see Flux fine-tuning ](https://docs.astria.ai/docs/api/flux-api/) --- # Features Source: https://docs.astria.ai/docs/category/features/ [ ## 📄️ Face Inpainting Overview ](https://docs.astria.ai/docs/features/face-inpainting/) [ ## 📄️ FaceID for Flux FaceID ](https://docs.astria.ai/docs/features/faceid-flux/) [ ## 📄️ LoRA LoRA ](https://docs.astria.ai/docs/features/lora/) [ ## 📄️ Styles Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. ](https://docs.astria.ai/docs/features/styles/) [ ## 📄️ Image2Video Image2Video ](https://docs.astria.ai/docs/features/video/) --- # Images Source: https://docs.astria.ai/docs/category/images/ [ ## 📄️ Inspect image Inspects an image to extract attributes both for filtering/warning about bad training set images and as characteristics to be added to the fine tune. ](https://docs.astria.ai/docs/api/images/inspect/) --- # Likes Source: https://docs.astria.ai/docs/category/likes/ [ ## 📄️ Create a like Adds a like to a prompt. ](https://docs.astria.ai/docs/api/like/create/) [ ## 📄️ Delete a like Delete a like from a prompt. ](https://docs.astria.ai/docs/api/like/delete/) --- # One doc tagged with "AI avatars" Source: https://docs.astria.ai/docs/tags/ai-avatars/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## AI Photoshoot - fine-tuning for professional headshots ](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) Overview --- # One doc tagged with "AI headshots" Source: https://docs.astria.ai/docs/tags/ai-headshots/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## AI Photoshoot - fine-tuning for professional headshots ](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) Overview --- # One doc tagged with "AI photoshoot" Source: https://docs.astria.ai/docs/tags/ai-photoshoot/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## AI Photoshoot - fine-tuning for professional headshots ](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) Overview --- # One doc tagged with "animated" Source: https://docs.astria.ai/docs/tags/animated/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "api" Source: https://docs.astria.ai/docs/tags/api/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Flux API usage ](https://docs.astria.ai/docs/api/flux-api/) For an overview of Flux fine-tuning, see Flux fine-tuning --- # One doc tagged with "avatars" Source: https://docs.astria.ai/docs/tags/avatars/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## AI Photoshoot - fine-tuning for professional headshots ](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) Overview --- # One doc tagged with "backend-v0" Source: https://docs.astria.ai/docs/tags/backend-v-0/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Virtual Try-on for Flux ](https://docs.astria.ai/docs/use-cases/virtual-try-on/) Garment / Clothing - with a model - Recommended --- # One doc tagged with "background replacement" Source: https://docs.astria.ai/docs/tags/background-replacement/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Product placement ](https://docs.astria.ai/docs/use-cases/product-shots/) Background replacement --- # One doc tagged with "cinematic" Source: https://docs.astria.ai/docs/tags/cinematic/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Flux Outpainting ](https://docs.astria.ai/docs/features/outpainting/) LoRA --- # One doc tagged with "clarity" Source: https://docs.astria.ai/docs/tags/clarity/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Tiled upscale ](https://docs.astria.ai/docs/features/tiled-upscale/) Super-Resolution --- # One doc tagged with "clothing" Source: https://docs.astria.ai/docs/tags/clothing/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Virtual Try-on for Flux ](https://docs.astria.ai/docs/use-cases/virtual-try-on/) Garment / Clothing - with a model - Recommended --- # One doc tagged with "comic book" Source: https://docs.astria.ai/docs/tags/comic-book/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "creative upscaler" Source: https://docs.astria.ai/docs/tags/creative-upscaler/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Tiled upscale ](https://docs.astria.ai/docs/features/tiled-upscale/) Super-Resolution --- # One doc tagged with "e-commerce" Source: https://docs.astria.ai/docs/tags/e-commerce/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Virtual Try-on for Flux ](https://docs.astria.ai/docs/use-cases/virtual-try-on/) Garment / Clothing - with a model - Recommended --- # One doc tagged with "ecommerce" Source: https://docs.astria.ai/docs/tags/ecommerce/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Product placement ](https://docs.astria.ai/docs/use-cases/product-shots/) Background replacement --- # One doc tagged with "enhance" Source: https://docs.astria.ai/docs/tags/enhance/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "fantasy art" Source: https://docs.astria.ai/docs/tags/fantasy-art/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "fashion" Source: https://docs.astria.ai/docs/tags/fashion/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Virtual Try-on for Flux ](https://docs.astria.ai/docs/use-cases/virtual-try-on/) Garment / Clothing - with a model - Recommended --- # One doc tagged with "garment" Source: https://docs.astria.ai/docs/tags/garment/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Virtual Try-on for Flux ](https://docs.astria.ai/docs/use-cases/virtual-try-on/) Garment / Clothing - with a model - Recommended --- # One doc tagged with "headshots" Source: https://docs.astria.ai/docs/tags/headshots/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## AI Photoshoot - fine-tuning for professional headshots ](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) Overview --- # One doc tagged with "image enhancement" Source: https://docs.astria.ai/docs/tags/image-enhancement/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Tiled upscale ](https://docs.astria.ai/docs/features/tiled-upscale/) Super-Resolution --- # One doc tagged with "line art" Source: https://docs.astria.ai/docs/tags/line-art/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "lineart" Source: https://docs.astria.ai/docs/tags/lineart/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## LoRA ](https://docs.astria.ai/docs/features/lora/) LoRA --- # One doc tagged with "LoRA" Source: https://docs.astria.ai/docs/tags/lo-ra/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## LoRA ](https://docs.astria.ai/docs/features/lora/) LoRA --- # One doc tagged with "lowpoly" Source: https://docs.astria.ai/docs/tags/lowpoly/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "magnific" Source: https://docs.astria.ai/docs/tags/magnific/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Tiled upscale ](https://docs.astria.ai/docs/features/tiled-upscale/) Super-Resolution --- # One doc tagged with "neonpunk" Source: https://docs.astria.ai/docs/tags/neonpunk/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "noise offset" Source: https://docs.astria.ai/docs/tags/noise-offset/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## LoRA ](https://docs.astria.ai/docs/features/lora/) LoRA --- # One doc tagged with "oil painting" Source: https://docs.astria.ai/docs/tags/oil-painting/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## LoRA ](https://docs.astria.ai/docs/features/lora/) LoRA --- # One doc tagged with "Outpainting" Source: https://docs.astria.ai/docs/tags/outpainting/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Flux Outpainting ](https://docs.astria.ai/docs/features/outpainting/) LoRA --- # One doc tagged with "photo optimization" Source: https://docs.astria.ai/docs/tags/photo-optimization/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Product placement ](https://docs.astria.ai/docs/use-cases/product-shots/) Background replacement --- # One doc tagged with "photographic" Source: https://docs.astria.ai/docs/tags/photographic/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "product photo" Source: https://docs.astria.ai/docs/tags/product-photo/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Product placement ](https://docs.astria.ai/docs/use-cases/product-shots/) Background replacement --- # One doc tagged with "products" Source: https://docs.astria.ai/docs/tags/products/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Product placement ](https://docs.astria.ai/docs/use-cases/product-shots/) Background replacement --- # One doc tagged with "prompt styles" Source: https://docs.astria.ai/docs/tags/prompt-styles/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "Shopify" Source: https://docs.astria.ai/docs/tags/shopify/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Product placement ](https://docs.astria.ai/docs/use-cases/product-shots/) Background replacement --- # One doc tagged with "social profiles" Source: https://docs.astria.ai/docs/tags/social-profiles/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## AI Photoshoot - fine-tuning for professional headshots ](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) Overview --- # One doc tagged with "super-resolution" Source: https://docs.astria.ai/docs/tags/super-resolution/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Tiled upscale ](https://docs.astria.ai/docs/features/tiled-upscale/) Super-Resolution --- # One doc tagged with "templates" Source: https://docs.astria.ai/docs/tags/templates/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Styles ](https://docs.astria.ai/docs/features/styles/) Prompt style attribute allow to easily generate an image according to a preconfigured set of styles. The styles are essentially a set of weights that are applied to the model during the generation process. --- # One doc tagged with "upscaling" Source: https://docs.astria.ai/docs/tags/upscaling/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Tiled upscale ](https://docs.astria.ai/docs/features/tiled-upscale/) Super-Resolution --- # One doc tagged with "virtual influencers" Source: https://docs.astria.ai/docs/tags/virtual-influencers/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## AI Photoshoot - fine-tuning for professional headshots ](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) Overview --- # One doc tagged with "VTON" Source: https://docs.astria.ai/docs/tags/vton/ [View All Tags](https://docs.astria.ai/docs/tags/) [ ## Virtual Try-on for Flux ](https://docs.astria.ai/docs/use-cases/virtual-try-on/) Garment / Clothing - with a model - Recommended --- # Pack tunes Source: https://docs.astria.ai/docs/category/pack-tunes/ [ ## 📄️ Create a tune from a pack Creates a new fine-tune model from training images according to the pack base tune id, model type and adds prompts to generate images. ](https://docs.astria.ai/docs/api/pack/tunes/create/) --- # Packs Source: https://docs.astria.ai/docs/category/packs/ [ ## 📄️ The pack object A pack is a collection of prompts. A pack also contains the configuration for fine-tuning such as using LoRA/FaceID/Checkpoint, base model and preset. ](https://docs.astria.ai/docs/api/pack/pack/) [ ## 📄️ List all packs Parameters ](https://docs.astria.ai/docs/api/pack/list/) [ ## 📄️ Retrieve a pack Parameters ](https://docs.astria.ai/docs/api/pack/get/) [ ## 🗃️ Pack tunes 1 items ](https://docs.astria.ai/docs/category/pack-tunes/) --- # Prompts Source: https://docs.astria.ai/docs/category/prompts/ [ ## 📄️ The prompt object The prompt object is usually used as a nested resource of tune as prompts are generated using a fine-tune model. A prompt is a text that is used to generate images using a fine-tune model. The prompt object contains the generated images. ](https://docs.astria.ai/docs/api/prompt/prompt/) [ ## 📄️ Create a prompt Creates a new fine-tune model from training images which in turn will be used to create prompts and generate images. ](https://docs.astria.ai/docs/api/prompt/create/) [ ## 📄️ Retrieve a prompt Parameters ](https://docs.astria.ai/docs/api/prompt/retrieve/) [ ## 📄️ Duplicate a prompt Duplicates a prompt, and allows overriding any of its attributes. ](https://docs.astria.ai/docs/api/prompt/duplicate/) [ ## 📄️ List all prompts Parameters ](https://docs.astria.ai/docs/api/prompt/list/) [ ## 📄️ Delete a prompt Deletes a specific prompt associated with a tune. ](https://docs.astria.ai/docs/api/prompt/delete/) --- # Tags Source: https://docs.astria.ai/docs/tags/ ## A - [AI avatars1](https://docs.astria.ai/docs/tags/ai-avatars/) - [AI headshots1](https://docs.astria.ai/docs/tags/ai-headshots/) - [AI photoshoot1](https://docs.astria.ai/docs/tags/ai-photoshoot/) - [animated1](https://docs.astria.ai/docs/tags/animated/) - [api1](https://docs.astria.ai/docs/tags/api/) - [avatars1](https://docs.astria.ai/docs/tags/avatars/) * * * ## B - [backend-v01](https://docs.astria.ai/docs/tags/backend-v-0/) - [background replacement1](https://docs.astria.ai/docs/tags/background-replacement/) * * * ## C - [cinematic1](https://docs.astria.ai/docs/tags/cinematic/) - [clarity1](https://docs.astria.ai/docs/tags/clarity/) - [clothing1](https://docs.astria.ai/docs/tags/clothing/) - [comic book1](https://docs.astria.ai/docs/tags/comic-book/) - [creative upscaler1](https://docs.astria.ai/docs/tags/creative-upscaler/) * * * ## E - [e-commerce1](https://docs.astria.ai/docs/tags/e-commerce/) - [ecommerce1](https://docs.astria.ai/docs/tags/ecommerce/) - [enhance1](https://docs.astria.ai/docs/tags/enhance/) * * * ## F - [fantasy art1](https://docs.astria.ai/docs/tags/fantasy-art/) - [fashion1](https://docs.astria.ai/docs/tags/fashion/) - [fine-tuning4](https://docs.astria.ai/docs/tags/fine-tuning/) - [flux2](https://docs.astria.ai/docs/tags/flux/) * * * ## G - [garment1](https://docs.astria.ai/docs/tags/garment/) * * * ## H - [headshots1](https://docs.astria.ai/docs/tags/headshots/) * * * ## I - [image enhancement1](https://docs.astria.ai/docs/tags/image-enhancement/) * * * ## L - [line art1](https://docs.astria.ai/docs/tags/line-art/) - [lineart1](https://docs.astria.ai/docs/tags/lineart/) - [lora2](https://docs.astria.ai/docs/tags/lora/) - [LoRA1](https://docs.astria.ai/docs/tags/lo-ra/) - [lowpoly1](https://docs.astria.ai/docs/tags/lowpoly/) * * * ## M - [magnific1](https://docs.astria.ai/docs/tags/magnific/) * * * ## N - [neonpunk1](https://docs.astria.ai/docs/tags/neonpunk/) - [noise offset1](https://docs.astria.ai/docs/tags/noise-offset/) * * * ## O - [oil painting1](https://docs.astria.ai/docs/tags/oil-painting/) - [Outpainting1](https://docs.astria.ai/docs/tags/outpainting/) * * * ## P - [photo optimization1](https://docs.astria.ai/docs/tags/photo-optimization/) - [photographic1](https://docs.astria.ai/docs/tags/photographic/) - [product photo1](https://docs.astria.ai/docs/tags/product-photo/) - [products1](https://docs.astria.ai/docs/tags/products/) - [prompt styles1](https://docs.astria.ai/docs/tags/prompt-styles/) * * * ## S - [Shopify1](https://docs.astria.ai/docs/tags/shopify/) - [social profiles1](https://docs.astria.ai/docs/tags/social-profiles/) - [style2](https://docs.astria.ai/docs/tags/style/) - [super-resolution1](https://docs.astria.ai/docs/tags/super-resolution/) * * * ## T - [templates1](https://docs.astria.ai/docs/tags/templates/) - [training2](https://docs.astria.ai/docs/tags/training/) * * * ## U - [upscaling1](https://docs.astria.ai/docs/tags/upscaling/) * * * ## V - [virtual influencers1](https://docs.astria.ai/docs/tags/virtual-influencers/) - [virtual try-on2](https://docs.astria.ai/docs/tags/virtual-try-on/) - [VTON1](https://docs.astria.ai/docs/tags/vton/) * * * --- # Tunes Source: https://docs.astria.ai/docs/category/tunes/ [ ## 📄️ The tune object Tune (or Fine-tune) represents a model that is created using training images to learn a new concept or subject. At its core a fine-tune is a neural-network weights file (usually weights 2GB) and contains the information of the trained images. ](https://docs.astria.ai/docs/api/tune/) [ ## 📄️ Create a tune Creates a new fine-tune model from training images which in turn will be used to create prompts and generate images. ](https://docs.astria.ai/docs/api/tune/create/) [ ## 📄️ Retrieve a tune Parameters ](https://docs.astria.ai/docs/api/tune/retrieve/) [ ## 📄️ List all tunes Parameters ](https://docs.astria.ai/docs/api/tune/list/) [ ## 📄️ Delete a tune Deletes a specific tune by its ID, and associated prompts in case of a checkpoint. ](https://docs.astria.ai/docs/api/tune/delete/) --- # Use cases Source: https://docs.astria.ai/docs/category/use-cases/ [ ## 📄️ AI Photoshoot - fine-tuning for professional headshots Overview ](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) [ ## 📄️ Flux training Overview ](https://docs.astria.ai/docs/use-cases/flux-finetuning/) [ ## 📄️ Inpainting and masking Input image ](https://docs.astria.ai/docs/use-cases/inpainting-and-masking/) --- # AI Photoshoot Source: https://docs.astria.ai/docs/use-cases/ai-photoshoot/ ## Overview The term "AI Photoshoot", "AI Headshots" or "AI Avatars" refers to the process of creating a generative AI model from around 16 images of a person. This model can then be used to create professional photography imagery of the person, without requiring high-end camera equipment, lighting, or wardrobe. AI headshots are particularly useful for social profile photos, such as those used on LinkedIn or Facebook, as well as on dating sites. Another recent trend is the creation of virtual social influencers - consistent characters that do not exist in reality, but can be generated consistently in different scenes and everyday life. Training images ![Fine-tuning training images](https://docs.astria.ai/assets/images/ai-photoshoot-input-66e224267554a6902bfaa5e6103ae27f.jpg) Output images ![Generated images](https://docs.astria.ai/assets/images/ai-photoshoot-output-79da8a3cef6fc255171431261d38c892.jpg) ## Steps ### 1\. Create a model At this stage, we will create a 2GB numeric (AI) model file that contains the "features" of a person. This is not the part where we generate new images, but only train a dedicated AI model. To begin, go to the [New tune](https://www.astria.ai/tunes/new) page. **Title** - Enter the person's name, e.g. `Elon Musk`. Or choose whatever title that fits your needs. Choosing a title is not a part of the actual training of the model. **Class Name** - Enter `man` or `woman`, or possibly `boy, girl, cat`, or `dog`. This is highly important as it is a part of the actual technical training of your model. We automatically generate images of the "class" while training, and by comparing them to your images (the training set), the model 'learns' your subject's unique features. **Base tune** - Select a baseline model on which you would like to train. For best quality we recommend using [Flux1.dev](https://www.astria.ai/gallery/tunes/1504944/prompts). Other settings are optional and can be changed to your preference. ### 2\. Generate images Even before the model is ready, you can prepare a queue of prompts (texts) that will be fed into the model and used to generate models. ## Tips for training images 1. Upload both portrait and full body shots of the person 2. Use 4-8 pictures of your subject, portrait shoulders up or waist up. 3. Optional: Use 4 photos of full body or entire object. Make sure these are high quality. 4. Variation is key - Change body pose for every picture, use pictures from different days backgrounds and lighting. Every picture of your subject should introduce new info about your subject. 5. Avoid pictures taken at the same hour/day. For example few pictures with the same shirt will make the model learn the shirt as well as part of the subject. 6. Always pick a new background. 7. Do not upload pictures mixed with other people 8. Do not upload upload funny faces ## Tips for inference 1. Use `ohwx woman/man` at the _beginning_ of the sentence. 2. Textual inversion can reduce similarity. Try to avoid it. Specifically TIs such as `ng_deepnegative_v1_75t` or `CyberRealistic`. 3. Use of [LoRA](https://docs.astria.ai/docs/features/lora/) for styles can reduce similarity. Keep LoRA strength low around 0.2-0.3. --- # Flux training Source: https://docs.astria.ai/docs/use-cases/flux-finetuning/ ## Overview _Flux_ is a family of generative image models created by [Black Forest Labs](https://blackforestlabs.ai/announcing-black-forest-labs/) making use of some of the latest AI advancement such as diffusion, transformer (MMDiT), Flow-matching and a large T5 text encoder. - The large text encoder allows the text2img model to better align the image with the given prompt and create complex images with a single prompt. - The model base resolution is 1024x1024 however unlike previous models, was trained on a variety of aspect ratios its neural-network architecture is able to better cope with different aspect ratios both in training and inference. - Flux1.dev requires commercial licensing which is provided to Astria customers. Input training images![generated.png](https://docs.astria.ai/assets/images/ai-photoshoot-input-66e224267554a6902bfaa5e6103ae27f.jpg) Output images![generated.png](https://docs.astria.ai/assets/images/ai-photoshoot-output-79da8a3cef6fc255171431261d38c892.jpg) ## Training presets Astria uses training presets to evolve its training strategies and provide better results while also providing a stable API for its customers. info Our current recommendation is to use the `portrait` preset for training Flux models. 1. **Focus** - API `flux-lora-focus` - Focus preset automatically balances different types of training, allowing it to focus on faces, products as well as styles. 2. **Portrait** - API `flux-lora-portrait` - This preset is generally recommended for any kind of training including non portraits, except for `style` training. With the latest `portrait` we now use more precise hyper-parameters for training, improved pre-processing, better handling of blurred/low-res images, face-cropping and focusing on learning face features, less overfitting to clothing and background. You will get more diverse images with better prompt alignment while better preserving identity and face. For this preset we default to 27 steps per image for training, with minimum steps of 300. You may override the number of steps depending on price and quality requirements. 3. **Fast** API `flux-lora-fast` - Legacy preset. Provides an alternative for the high preset, with faster training and lower cost. Similarly to the `portrait` preset, it uses 27 steps per image for training, with minimum steps of 300. 4. **High** API `null` - High steps, low-learning rate. Should be used for `style` training or training sets with 20+ images. With the high preset, we default to 100 steps per image for training, with minimum steps of 300. You may override the number of steps depending on price and quality requirements. ![Training preset screenshot in new fine-tune](https://docs.astria.ai/assets/images/new-finetune-presets-53c9a515f3bddb36bbac49aaa00141bb.png) ## Training tips Default token for SDXL should be `ohwx` and will be set automatically if none is specified 1. As with other models, a good training set is critical to getting great results. See [AI photoshoot guide](https://docs.astria.ai/docs/use-cases/ai-photoshoot/) 2. Astria currently defaults to 100 steps per image for training a Flux lora for the high preset, and 27 steps per image for the fast preset. Fast preset is recommended when training on people or headshots. 3. You may opt to override the number of training steps in order to reduce costs and processing time. Reducing the steps can produce good results when the target inference images are similar to the input training images (like headshots). Example lower steps could be 600, 1000 or 1200 (roughly 50-70 \* number of training images ) ## Inference tips 1. **Dense prompts** - Flux works best when the prompt is descriptive and detailed, but as in most models, be concise. Overdoing the description can create conflicts in the inference. 2. \*_No negatives_ - Flux doesn’t work with negatives, instead we suggest using positive prompts. For example, instead of writing ‘cartoon’ in the negative, write ‘realistic settings and characters’ in the prompt. 3. **No weighted prompts** - using weights or parentheses () to emphasis certain parts of a prompt doesn’t work with Flux. Instead, make sure the important parts at the beginning of the prompt. 4. **Adding text in the image** - One of the advantages of Flux is the ability to add text to an image. There are a few ways to do that, like “ohwx man holding a sign with the words ‘Hello’“. 5. Looking for prompt ideas? Try the [Themes](https://www.astria.ai/themes) feature. Themes uses Claude GPT to help write prompts for the specific model. You can add a theme and write any topic on your mind, and themes will produce 10 prompts on it. ## API usage See here for [API usage](https://docs.astria.ai/docs/api/flux-api/) --- # Inpainting and masking Source: https://docs.astria.ai/docs/use-cases/inpainting-and-masking/ Input image ![source.png](https://docs.astria.ai/assets/images/inpaint-input-58e7521dea463626a153c16a63c4e26d.png) Mask ![generated.png](https://docs.astria.ai/assets/images/inpaint-mask-20057dc63fcc851126231618ccf0884d.png) Output ![generated.png](https://docs.astria.ai/assets/images/inpaint-output-dd16b5c0ad5e2fe412cdcbbcac28641c.jpg) ## Overview Inpainting means re-painting of a specific area of an image defined by a mask image. The mask image is a binary image where the area to be inpainted is white and the rest is black. The inpainting process will fill the white area with the content of the surrounding area. Astria has features for automatically creating masks for inpainting - see [Prompt Masking](https://docs.astria.ai/docs/features/prompt-masking/). --- # A lookbook is a rhythm, not twenty unrelated good images. Source: https://www.astria.ai/articles/features/lookbook-photos/ [← All feature guides](https://www.astria.ai/articles/features/) Shoot photos / Lookbook The reader should feel one afternoon passing, one model moving, one collection unfolding. That coherence comes from repeating the right things—cast, place, light, camera distance—while letting the clothes and gestures change. [Make it, step by step](https://www.astria.ai/articles/features/lookbook-photos/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/lookbook-photos/#prompts) ![Bridal fashion lookbook photograph on a terrace](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.jpg) Location, light and camera language hold the world together while the looks change. The point of view ## Sequence before you generate. Write the contact sheet first: hero, full look, 45-degree, movement, seated, detail, back, and one wide breath. The battery creates pacing and guarantees the practical views are not forgotten after a beautiful first frame. Choose one location sentence and reuse it verbatim. “A sun-washed limestone terrace above a calm sea at late afternoon” is a set. Twelve slightly different Mediterranean descriptions are twelve sets. Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Choose the visual world Write one paragraph for location, light, camera and mood. Keep it stable across the sequence. 2. 02 ### Cast and dress the pilot Use one approved model and two or three representative looks before committing the full collection. 3. 03 ### Build the contact sheet Alternate wide, full, medium and detail. Give movement a reason—a doorway, a turn, a breeze—not a generic “dynamic pose.” 4. 04 ### Promote the winner to a pack Save the final scene language and shot battery as templates. Swap products without rewriting the photographer’s eye. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Fashion lookbook location master on a limestone terrace](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.jpg) Example result Location master · 4:5 woman wearing dress on a sun-washed limestone terrace above a calm sea at late afternoon. Full-length still pose, body turned 20 degrees, gaze just past camera. 85mm fashion photograph, pale warm stone, soft directional sunlight, subtle Portra-like grain, natural skin and fabric. Preserve the dress silhouette, neckline and hem. **Why this works**This is the anchor frame: one place, one light, one complete look. ![Fashion model in a natural movement frame](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-motion.webp) Example result Movement beat · 4:5 woman in dress crossing the same sun-washed limestone terrace above a calm sea at late afternoon. She turns slightly as the hem catches a light breeze. 50mm full-length photograph, same pale stone palette, same soft directional sunlight, natural motion, dress details exact. **Why this works**Repeating the location phrase and palette helps the second frame belong to the first. ![Quiet close detail in a fashion sequence](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-seedream-close.webp) Example result Quiet detail · 3:2 Close crop from shoulder to hip of woman wearing dress on the same limestone terrace. Her hand rests naturally at the side seam. Show fabric texture, closure and construction clearly. Same late-afternoon light, 85mm lens, restrained film grain. **Why this works**A practical product detail becomes a pause in the visual rhythm. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. Nano Banana / Gemini ### Look-by-look reference composition Good when each frame combines the same cast with changing garments. Seedream 5 ### Location and layered outfit references Useful when the world and look both need reference support. Seedance 2 ### Motion after still approval Animate an approved hero; do not ask video generation to invent the collection’s look from scratch. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## The valuable output is the next look, not just the hero. When the location paragraph and contact sheet become a reusable pack, a collection extension can inherit the same photographic day without rebuilding it. - One pilot before full-assortment spend - A shot list that serves editorial and product needs - Still-to-motion paths from approved frames ![Editorial bridal lookbook result](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.jpg) See the work ## Open the lookbook as a working template. The Chapter 2 workspace and template show how a fashion story can remain editable—cast, clothing and scene are inputs, not secrets. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### store-photoshoot Turns a visual direction into a shot battery and reusable pack. [Open the skill](https://github.com/astriaai/skills/tree/main/store-photoshoot) ### templatize-page Turns a rights-cleared existing lookbook page into reusable pose references and swap prompts. [Open the skill](https://github.com/astriaai/skills/tree/main/templatize-page) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) If the contact sheet feels like one photographer stayed for the whole day, the lookbook is working. [Start a production](https://www.astria.ai/ecommerce) --- # Approve the photograph. Then let time into it. Source: https://www.astria.ai/articles/features/product-video/ [← All feature guides](https://www.astria.ai/articles/features/) Generate videos / Fashion & product The fastest route to a coherent product film is not a giant video prompt. It is a still frame whose cast, product and art direction are already right—followed by one clear movement the camera can believe. [Make it, step by step](https://www.astria.ai/articles/features/product-video/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/product-video/#prompts) ![Fashion campaign still ready to become a short motion sequence](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.jpg) A strong first frame carries wardrobe, cast, light and location into motion. Video only has to solve time. The point of view ## Motion should reveal something. A model crosses the frame so the garment drape becomes visible. A shoe turns so the heel construction appears. A bottle rotates so the label and side profile both read. “Cinematic movement” is not an action. Keep a short film to three beats: enter, hold, leave; closed, quarter-turn, back; still water, ripple, settle. A static camera and continuous motion often preserve the product better than a prompt full of cuts and lens moves. Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Approve the first frame Do not animate a compromise. Fix product, face, hands, light and background while they are still easy to inspect. 2. 02 ### Choose one useful motion Name what the movement shows about fit, volume, construction or mood. 3. 03 ### Write three time beats Give the beginning, middle and end a physical action. Keep camera behavior explicit and restrained. 4. 04 ### Review frame by frame Check product geometry, logos, hands, face and garment continuity across the full clip—not only the poster frame. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Approved fashion first frame ready for motion](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.jpg) Approved first frame Lookbook loop · 8 seconds · 16:9 Use the approved fashion still as the first-frame reference. 0–2s: the model walks in from the left at an easy natural pace, garment moving with her body. 2–5s: she stops in the center, shifts her weight and looks just past camera so the full outfit reads. 5–8s: she walks past camera and exits right. Static camera, one continuous take, same face, body, outfit, light and location. No cuts, no garment deformation, no extra people. **Why this works**Three physical beats make an edit-friendly loop and keep the model from improvising a new commercial. ![Approved product frame ready for turntable motion](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.jpg) Approved first frame Product turntable · 8 seconds · 1:1 The referenced shoe rotates slowly and smoothly through one full 360-degree turn over 8 seconds on a fixed studio turntable. 0s strict side view, 2s front three-quarter, 4s opposite side, 6s rear three-quarter, 8s returns exactly to starting side view. Static level camera, light grey seamless background, contact shadow stays fixed. Preserve sole, heel, stitching, panels, material and logo. No foot, no hands, no shape change, no fast spin. **Why this works**Angle checkpoints give the rotation a measurable job and make review straightforward. ![Approved beauty still ready for subtle campaign motion](https://www.astria.ai/articles/img/prompt-recipes/beauty-still-life-result.jpg) Approved first frame Quiet campaign motion · 6 seconds · 9:16 Use the approved beauty still as the first frame. The bottle remains perfectly still and label-forward. A narrow ribbon of water enters from frame left, circles the base once, then settles into small ripples. Camera locked, reflections and lighting unchanged, luxurious restrained pace. Preserve bottle geometry, cap, label typography and color. No camera move, no text change, no extra objects. **Why this works**The product is the anchor; motion belongs to the environment. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. Seedance 2 ### Reference-to-video and fashion movement Astria’s current video workflow supports reference images and timed motion briefs; start from the approved still. Seedance Fast ### Iteration and coverage tests Use faster passes to test motion logic, then choose the delivery model and resolution after the movement works. Video variation ### Updating an existing clip When motion and timing are already good, vary only the targeted wardrobe or product rather than regenerating the performance. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## One approved still can become more than a poster. Turn the same direction into PDP motion, a social loop and a campaign beat. The saving comes from reusing art direction and references—not from making a longer film than the idea needs. - Still-to-motion without a second visual reset - Short loops for product pages and social - Targeted variations of approved movement ![Approved fashion still used as a video anchor](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.jpg) See the work ## Start from a photograph with a real source. Use a finished lookbook or packshot as the motion anchor, then read the fashion-film guide for shot and delivery choices. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### storyboard Turns an ordered set of still references into a text-only cinematic sequence before generation. [Open the skill](https://github.com/astriaai/skills/tree/main/storyboard) ### astria-api Documents current video models, reference inputs, duration and variation commands. [Open the skill](https://github.com/astriaai/skills/tree/main/astria-api) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) If the still already knows who, what and where, the video prompt only has to answer: what changes over the next eight seconds? [Start a production](https://www.astria.ai/ecommerce) --- # Build a cast with a point of view—not a folder of interchangeable faces. Source: https://www.astria.ai/articles/features/fashion-models/ [← All feature guides](https://www.astria.ai/articles/features/) Create models / Fashion A memorable model is not “beautiful, editorial, 25.” It is a face with decisions in it. Cast two to five candidates, choose the one who belongs in the brand, and give that choice an identity the next photograph can call by name. [Make it, step by step](https://www.astria.ai/articles/features/fashion-models/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/fashion-models/#prompts) ![Close fashion portrait from an Astria identity benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-seedream-close.webp) Start close enough to see character. Add the clothes and the world after the cast is approved. The point of view ## Good casting is editing. Write exact age, skin and facial detail, eyes, nose, face structure, hair and one distinguishing feature. Vary the traits across the casting board deliberately. “Diverse” is an outcome of real choices, not a prompt you can paste at the end. Keep the first portraits neutral. When every candidate arrives with a different outfit, lens and location, you are choosing art direction instead of a face. The clean headshot makes comparison honest. Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Name the role Who carries this collection: quiet observer, restless downtown lead, precise minimalist, warm host? Translate that role into expression and visible traits. 2. 02 ### Generate a small board Use Recraft 4.1, square format, two images per prompt. Create two to five genuinely different candidates rather than tiny variations of one type. 3. 03 ### Register the winner Create a FaceID/reference from the selected synthetic portrait. Use that explicit identity token in every production prompt. 4. 04 ### Prove four distances Test close, full-length, seated and motion. A face that only works in a beauty crop is not yet a production cast. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Close editorial fashion casting portrait from an Astria model benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-seedream-close.webp) Example result Editorial woman · Recraft 4.1 · 1:1 Close-up studio headshot of a fictional 42-year-old East Asian woman with light olive skin, monolid dark-brown eyes, a straight narrow nose, high cheekbones, a small scar through the right eyebrow, and black hair pulled into a low knot. Bare shoulders, no clothing visible, no jewelry. Quiet assured expression, looking at camera. Clean white background #fff, large north-facing soft light, natural realistic skin texture, casting photograph. **Why this works**Age, structure and one imperfect detail create somebody specific enough to remember. ![Character-led studio fashion portrait from an Astria model benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-gpt-close.webp) Example result Characterful man · Recraft 4.1 · 1:1 Close-up studio headshot of a fictional 31-year-old Middle Eastern man with warm olive skin, close-set amber-brown eyes, a prominent aquiline nose, angular jaw, a faint dimple in the left cheek, and thick dark wavy hair brushed back. Bare shoulders, no jewelry. Open thoughtful expression, looking at camera. Clean white background #fff, soft clamshell studio light, natural pores and beard shadow, honest casting photograph. **Why this works**The prompt avoids celebrity shorthand and builds the face from visible traits. ![Fashion model identity tested in a full-body movement frame](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-motion.webp) Example result Production identity test · 4:5 woman in four consistent fashion moments: full-length standing, close portrait, seated three-quarter view, and walking mid-step. Same face, age, hair, body proportions and understated expression in every frame. Minimal black tailoring, white daylight studio, 50mm lens, natural skin and fabric. No face drift, no hairstyle change, no body reshaping. **Why this works**The four-distance test exposes drift before the model is attached to a hundred products. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. Recraft 4.1 ### Casting without a reference The Astria skill’s first choice for original face generation. It is a casting tool here, not the entire production pipeline. Nano Banana 2 ### Reference-led fashion scenes Useful once the cast and product references exist and need to meet inside a new photograph. Model benchmark ### Choosing for the actual shot Close portrait, full-length, motion and product typography stress different abilities. Route the brief instead of declaring one permanent winner. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## The first face is an experiment. The approved cast is an asset. The cost comes back when the same cast carries new SKUs, reshoots, crops and seasons without restarting the identity search. - Two-to-five-face casting board - One explicit reusable identity - Four-angle stress test before production ![Seated fashion model identity test](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-seated.webp) See the work ## See identity tested, not merely claimed. Open the model benchmark and compare the same brief across close, full, seated and motion views. That is a more useful test than one immaculate portrait. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### avatar Provides the exact casting template and Recraft 4.1 command. [Open the skill](https://github.com/astriaai/skills/tree/main/avatar) ### prompt-writing Explains how to turn the chosen output into a referenced, repeatable model. [Open the skill](https://github.com/astriaai/skills/tree/main/prompt-writing) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) The model is not the adjective list. The model is the face you chose—and the discipline to keep choosing it in every frame. [Start a production](https://www.astria.ai/ecommerce) --- # Give the product a world—but make sure it still comes home as itself. Source: https://www.astria.ai/articles/features/campaign-editorial/ [← All feature guides](https://www.astria.ai/articles/features/) Shoot photos / Campaign Campaign is where the photograph earns desire. It is also where products get lost under atmosphere. Start from an approved cast and verified product image, then add one coherent location, one camera language and one reason for the person to be there. [Make it, step by step](https://www.astria.ai/articles/features/campaign-editorial/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/campaign-editorial/#prompts) ![Beauty campaign still life created with Astria](https://www.astria.ai/articles/img/prompt-recipes/beauty-still-life-result.jpg) Atmosphere works when the object remains countable: silhouette, label, finish and scale still matter. The point of view ## Write the scene like a photographer briefs a crew. Use labeled decisions—location, foreground, subject, background, camera, light, mood—then write the final prompt as one coherent photograph. Specificity is not more adjectives; it is fewer contradictions. Reuse the same location paragraph across the campaign. Change framing and action, not the entire universe. The result should feel like a shoot day with coverage, not a feed assembled from unrelated fantasies. Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Start from approved truth Bring the final cast, product references and one verified clean frame from the catalog workflow. 2. 02 ### Choose one scene grammar Define location, time, palette, lens, light and texture. Remove any phrase that points toward a different visual world. 3. 03 ### Make three coverage frames Create hero wide, model/product medium and tactile detail. This is enough to test whether the world can hold both story and object. 4. 04 ### Branch by crop Once the hero passes, derive 9:16, 1:1 and 16:9 with protected negative space rather than rebuilding the idea for each channel. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Fashion campaign hero in a coherent location](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.jpg) Example result Fashion campaign hero · 4:5 woman wearing coat moving through a crowded hotel bar after dark. She turns toward the lens while everyone else falls softly out of focus. Hard direct on-camera flash, 35mm lens, deep burgundy and tobacco palette, honest skin texture, slightly imperfect motion. Preserve the coat’s exact color, closure, lapels and length. No extra logos, no garment redesign. **Why this works**One action, place and light give the scene a reason to exist while product guards keep it sellable. ![Beauty product campaign still-life result](https://www.astria.ai/articles/img/prompt-recipes/beauty-still-life-result.jpg) Example result Beauty still-life hero · 4:5 bottle standing upright on honed rose travertine with a thin pool of water and two soft ripples. Label faces camera and remains fully legible. Warm sunrise edge light from behind, cool soft fill from front, precise glass reflections, restrained blush and stone palette, luxury campaign still life. Preserve bottle shape, cap, label typography and liquid color. **Why this works**The atmosphere is specific, but the label and construction remain first-class instructions. ![Editorial jewelry macro result](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-result.jpg) Example result Editorial detail · 3:2 Tight 100mm macro of ring worn on a relaxed hand resting on dark green velvet. Preserve band profile, stone count, setting, metal finish and realistic scale. One narrow hard-light highlight, deep shadow, visible skin texture, cinematic editorial crop. No extra stones, no altered prongs, no yellow cast. **Why this works**A detail frame can be sensual and still give the reviewer a checklist. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. Seedream 5 ### Rich, reference-led campaign scenes Useful when cast, product and a location or set reference all matter. Nano Banana / Gemini ### Campaign iteration and controlled edits Good for moving an approved photograph into a nearby crop or scene while retaining references. GPT Image 2 ### Text and packaging-sensitive scenes A model to test when legible labels or graphic elements dominate; compare with the product benchmark before committing. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## Campaign becomes coverage, not a single expensive miracle. Approve one world, then produce the wide, medium, detail and channel crops it owes the launch. The creative investment travels farther without diluting the direction. - One scene grammar across the set - Product truth carried into atmosphere - Channel crops derived from an approved hero ![Editorial jewelry macro result](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-result.jpg) See the work ## Open the product-led worlds. Astria workspaces for jewelry and beauty show how a source object can travel from clean reference into campaign light. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### store-photoshoot Includes the labeled campaign prompt structure and anti-CGI realism checks. [Open the skill](https://github.com/astriaai/skills/tree/main/store-photoshoot) ### prompt-writing Helps keep essential references explicit as the scene grows more ambitious. [Open the skill](https://github.com/astriaai/skills/tree/main/prompt-writing) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) A campaign image can be strange, tender or loud. It cannot be careless about the thing the customer will buy. [Start a production](https://www.astria.ai/ecommerce) --- # Kids fashion models: make childhood—not a grown-up campaign made smaller. Source: https://www.astria.ai/articles/features/kids-models/ [← All feature guides](https://www.astria.ai/articles/features/) Create models / Kids The best kids image has an easy truth to it: the child looks their age, the clothes can move, and nothing feels over-directed. Start with a synthetic cast, build a small world around the collection, then keep the same young character from the first product page to the last lookbook frame. [Make it, step by step](https://www.astria.ai/articles/features/kids-models/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/kids-models/#prompts) ![A child fashion model wearing a brown sweatshirt, grey skirt, and boots in a catalog photograph](https://www.astria.ai/articles/img/features/kids-catalog-result.webp) Three product references became one complete look. The next look can keep the same cast and light. The point of view ## Cast a character a parent would recognize. Write down an exact age, a natural expression, hair, a few visible facial traits, and the energy of the brand. “Cool kid” is too vague. “An eight-year-old with a thoughtful half-smile, springy dark curls and a small gap between the front teeth” gives the casting image a personhood without borrowing a real child’s identity. Keep the styling childlike and ordinary at the casting stage: a plain crew-neck T-shirt, clean daylight, no jewelry, no adult makeup, no glamour pose. The clothing references and art direction arrive after the face is approved. That separation makes it easier to judge the cast instead of being seduced by a finished campaign image. Before you make anything ## Use synthetic identities for children. Astria’s service is not directed to children under 16 and its privacy policy says it does not knowingly collect their personal information. For this workflow, create a fictional child model rather than uploading a real child’s photographs. An adult brand team should own the account, the brief, and every approval. [Read Astria’s privacy policy](https://www.astria.ai/privacy) Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Write the casting card Choose exact age, visible features, hair, expression, and brand energy. Describe heritage only when it comes from the brief; do not guess it from where the brand is based. 2. 02 ### Cast in Recraft 4.1 Generate two square, clean studio portraits with no identity reference. Pick the face for character and age—not for a perfect finish. The Astria avatar skill uses Recraft 4.1 for this first casting pass. 3. 03 ### Turn the winner into the cast Register the selected synthetic portrait as a reusable face reference. From here on, the face has an ID; your prompts should not try to reinvent it with adjectives. 4. 04 ### Build a short shot battery Start with front, 45-degree, back, and one movement frame. Keep the same age and height/proportion description in every on-model prompt, then check hands, shoes, hems, logos, and whether the expression still feels natural. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Synthetic child casting portrait on a white studio background](https://www.astria.ai/articles/img/features/kids-casting-portrait.webp) Example result Synthetic casting portrait · Recraft 4.1 · 1:1 Close-up studio casting portrait of a fictional 8-year-old girl with warm brown skin, deep-set hazel eyes, a softly rounded face, a small gap between her front teeth, and springy dark curls tied back. Wearing a plain cream crew-neck T-shirt. Relaxed curious expression, looking at camera. Clean white background #fff, large soft window light, natural age-appropriate skin detail, honest children’s casting photograph. No makeup, no jewelry, no adult styling. **Why this works**It fixes age and memorable traits while keeping clothing and campaign styling out of the casting decision. ![Child model wearing a complete referenced catalog outfit](https://www.astria.ai/articles/img/features/kids-catalog-result.webp) Example result Catalog look · 3:4 girl, 8 years old, natural child proportions, wearing sweatshirt, skirt and boots. Full-length front catalog photograph, relaxed shoulders, feet comfortably apart, soft curious expression. Pale warm-grey seamless studio, diffused daylight, true garment colors, realistic fabric drape. No makeup, no adult pose, no jewelry, no beauty retouching. **Why this works**The reusable face and product references do the factual work. The text protects age, posture, light, and garment behavior. ![Child model walking naturally through a painted schoolyard](https://www.astria.ai/articles/img/features/kids-lookbook-movement.webp) Example result Lookbook movement · 4:5 boy, 11 years old, natural tween proportions, wearing jacket and trousers. He walks across a quiet painted schoolyard, caught mid-step while glancing toward a friend outside frame. Late-afternoon overcast light, 50mm documentary fashion photograph, fabric moving naturally, gentle film grain, playful but not performative. Keep the child’s face, age, body proportions and outfit exact. **Why this works**A small action and an implied friend create life without forcing an adult fashion pose. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. Recraft 4.1 ### Fresh synthetic casting faces Use it without a person reference for the first square portrait. Astria added Recraft V4.1 as a partner text-to-image model in May 2026. Nano Banana / Gemini ### Putting approved cast and products together Use references for the face and each essential garment. Keep the reference stack lean; add accessories in a second pass if they compete. Seedream 5 ### Reference-heavy scenes Useful when a look needs several controlled ingredients. More references still require stricter review; capacity is not a reason to attach everything at once. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## The return is a cast you do not have to recast for every SKU. Approve one fictional child identity, one background, and four useful poses on a small pilot. Once those choices hold, a late colorway or wholesale crop becomes continuation work—not a second production day. - One casting decision across a collection - A reusable four-angle catalog battery - Campaign and social frames from the same visual world ![Kids fashion catalog result created from several product references](https://www.astria.ai/articles/img/features/kids-catalog-result.webp) See the work ## Open a real kids template, then make it yours. The Kids Lookbook template exposes the working parts—pose, face, dress, shoes, background and output settings—so the example is a starting system, not a locked mood board. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### avatar Turns a trait-dense brief into original casting portraits with Recraft 4.1. Adapt the adult headshot recipe for children by using an age-appropriate T-shirt and styling. [Open the skill](https://github.com/astriaai/skills/tree/main/avatar) ### prompt-writing Keeps face and garment references explicit, the background repeatable, and the prompt readable. [Open the skill](https://github.com/astriaai/skills/tree/main/prompt-writing) ### store-photoshoot Builds the avatar, SKU references, shot battery, QA pass, and reusable pack as one production workflow. [Open the skill](https://github.com/astriaai/skills/tree/main/store-photoshoot) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) Start with one fictional cast, two real products, and four frames. If the child still feels like the same child—and the garments still feel like the garments—you have something worth scaling. [Start a production](https://www.astria.ai/ecommerce) --- # Make the catalog dependable before you make it beautiful. Source: https://www.astria.ai/articles/features/ecommerce-catalog/ [← All feature guides](https://www.astria.ai/articles/features/) Shoot photos / E-commerce Catalog photography earns trust through repetition: the same scale, background, angle and honest product detail from one tile to the next. The creative move is deciding those rules well once. [Make it, step by step](https://www.astria.ai/articles/features/ecommerce-catalog/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/ecommerce-catalog/#prompts) ![Volumetric dress packshot created from a garment reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.jpg) A product reference becomes a useful packshot only after shape, seams, hem and color survive review. The point of view ## Consistency is part of the product information. Choose the background hex, crop, camera height, scale and shadow before running the assortment. For an on-model catalog, add one cast and one approved stance by category. Create batteries by item type. Tops need front, 45-degree, back and detail; footwear needs side elevation, front, rear three-quarter, overhead and stitching macro. One generic “product photography” prompt cannot see the questions a shopper asks. Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Set the house frame Fix aspect ratio, background color, crop, camera height and shadow behavior. 2. 02 ### Build one category battery Make prompts around the information needed for tops, dresses, footwear, bags or jewelry. 3. 03 ### Pilot difficult SKUs Choose prints, black-on-black, shine, layered construction and unusual closures—not only the easiest product. 4. 04 ### QA, then batch Compare every output to the source for shape, color, print, scale, seams, logo and missing parts before the pack reaches the full catalog. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Volumetric dress packshot result](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.jpg) Example result Volumetric dress packshot · 4:5 dress as a high-end volumetric ghost-mannequin packshot, straight front view, natural three-dimensional body volume without a visible person or mannequin. Preserve exact neckline, sleeves, waist seam, closure, hem and fabric color. Seamless light grey background #f2f2f2, soft contact shadow, centered consistent scale, clean catalog lighting. No extra accessories, no body parts, no redesigned garment --background\_color #f2f2f2 **Why this works**It separates the desired volume from any visible model and pins the catalog background. ![Clean product photography result from an Astria model benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/general-gpt-image-2.webp) Example result Footwear side elevation · 4:5 shoe, strict side elevation with toe pointing right and heel left, camera level with the shoe centerline. Preserve sole thickness, heel shape, stitching, panels, laces, material and logo placement. Seamless light neutral grey studio background #f2f2f2, realistic contact shadow beneath product, natural scale. No foot, no model, no extra objects, no compression or squashing. **Why this works**Directional and camera language makes the required product view unambiguous. ![Full-length on-model PDP catalog result](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.jpg) Example result On-model PDP · 3:4 woman wearing top and trousers. Full-length front PDP photograph, neutral comfortable stance, top untucked, trousers reach the shoes. Same crop and scale as the catalog master, plain background #f5f5f5, even softbox light, true garment color --background\_color #f5f5f5 **Why this works**The same house frame can serve every SKU while the references change. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. Nano Banana / Gemini ### On-model catalog and controlled edits Useful for combining model and product references while keeping the house frame. Seedream 5 ### Reference-heavy products A candidate for layered looks and complex product context; keep QA itemized. Post-processing background color ### Exact catalog backdrop Use Astria’s background-color option when a precise hex matters. Prompt wording alone can drift. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## Templates move the cost from every SKU into the first good decision. Track approved cost per SKU—including corrections and rejection—not just generation price. The gain is a lower repeat cost after the category battery passes. - Fixed background and scale - Category-specific shot batteries - Pilot QA before high-volume spend ![Finished 3D dress packshot](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.jpg) See the work ## See the garment and the packshot together. The 3D Packshots workspace exposes the source dress, the resulting volume, and a reusable dress template. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### store-photoshoot Contains the item-by-item shot batteries and pilot QA checklist. [Open the skill](https://github.com/astriaai/skills/tree/main/store-photoshoot) ### packs-guide Explains how repeatable templates become a pack non-technical teammates can run. [Open the skill](https://github.com/astriaai/skills/tree/main/packs-guide) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) A reliable catalog looks quiet because the system did the hard thinking before the customer arrived. [Start a production](https://www.astria.ai/ecommerce) --- # Plus-size fashion models: photograph the fit you chose. Do not prompt the body away. Source: https://www.astria.ai/articles/features/plus-size-models/ [← All feature guides](https://www.astria.ai/articles/features/) Create models / Plus size A plus-size image should not be a standard fashion prompt with one adjective added. The body is part of the casting decision, the drape is part of the product truth, and neither should quietly change as you move from front view to campaign. [Make it, step by step](https://www.astria.ai/articles/features/plus-size-models/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/plus-size-models/#prompts) ![Original full-length synthetic plus-size fashion casting master in a neutral studio](https://www.astria.ai/articles/img/features/plus-size-casting-master.webp) An original neutral casting master for this guide. Identity is only half the brief: approve body proportions before the clothes arrive. The point of view ## Respect begins with specificity. “Plus size” covers many bodies. Cast the person you actually want to photograph: age, height range, shoulder and hip relationship, posture, face, hair, and presence. Use direct, neutral language. Avoid euphemisms, before-and-after framing, or instructions that shrink the waist, lengthen the legs, hide the arms, or “flatter” the body into somebody else. A headshot can hold a face; it cannot prove a body. Approve a full-length body reference or a first full-length master frame before producing the shot battery. Then repeat the same proportion language and reference across front, side, back, seated, and movement views. Before you make anything ## A generated photograph is not a fit claim. Use it to show styling, silhouette and visual direction. Keep measured fit guidance, garment dimensions and size charts grounded in verified product data and real fit testing. Review every image for changed seams, stretch, hem length, closures and scale. Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Write the person, not the category Specify exact adult age, visible facial features, hair, expression and presence. Make diversity a casting choice, not a random adjective generator. 2. 02 ### Create the face Use Recraft 4.1 to generate clean square casting portraits with hair pulled back and one distinguishing feature. Approve character before styling. 3. 03 ### Approve a body master Make one neutral full-length studio image that clearly shows the chosen proportions and stance. This becomes the body truth for the collection; a face-only reference is not enough. 4. 04 ### Test the garment in four views Run front, 45-degree, back and seated or walking. Compare shoulder seams, armholes, waist placement, fabric tension, hem length and print scale against the source product before making campaign images. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Synthetic adult plus-size fashion casting portrait on white](https://www.astria.ai/articles/img/features/plus-size-casting-portrait.webp) Example result Synthetic casting portrait · Recraft 4.1 · 1:1 Close-up studio headshot of a fictional 34-year-old Black woman with deep neutral-brown skin, almond-shaped dark eyes, a broad softly rounded nose, a full oval face, a faint beauty mark beside her left cheek, and tightly coiled black hair in a low pulled-back bun. Bare shoulders, no clothing visible, no jewelry. Calm self-possessed expression, looking at camera. Clean white background #fff, broad softbox lighting, natural realistic skin detail, honest casting photograph. **Why this works**This follows the Astria avatar recipe: precise traits, clean light, one memorable feature, and no wardrobe noise. ![Full-length neutral body master for a plus-size fashion model](https://www.astria.ai/articles/img/features/plus-size-casting-master.webp) Example result Body master · 3:4 woman, 34 years old, plus-size body with full hips, soft midsection, broad upper arms and naturally proportioned legs. Full-length neutral casting photograph, relaxed upright stance, arms resting comfortably at her sides. Wearing a simple fitted black T-shirt and straight black trousers. White seamless studio, level camera at waist height, soft even light. Preserve her exact face, body shape and proportions. No waist slimming, no elongated legs, no contouring pose, no beauty filter. **Why this works**A neutral master makes the body decision visible before clothing, cropping and campaign light complicate it. ![Plus-size fashion model wearing a teal midi dress in a clean PDP photograph](https://www.astria.ai/articles/img/features/plus-size-pdp-example.webp) Example result PDP garment view · 3:4 woman, preserve the exact approved face and plus-size body proportions from the model reference. Wearing dress; keep neckline, sleeve opening, waist seam, print scale and hem length faithful to the product reference. Full-length front PDP photograph, comfortable natural stance, hands relaxed, pale grey seamless background #f5f5f5, large diffused softbox, true color, realistic fabric tension and drape. No body reshaping, no hidden arms, no pinched waist, no garment redesign. **Why this works**It names the places where garment fidelity often fails and explicitly protects the approved body. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. Recraft 4.1 ### Original face casting Strong for making the first synthetic casting sheet. Keep this pass face-led; body approval happens separately. Nano Banana / Gemini ### Face + body + garment composition Use the approved face/body master and the product reference. A lean, well-labeled stack usually beats a pile of weak references. Seedream 5 ### Complex looks and scene continuity Use when the outfit has multiple garments or the set must also be referenced. Review the body and every layer independently. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## A reusable cast makes representation operational. The economic win is not “one plus-size image.” It is a cast and fit language the team can keep through a whole assortment, without treating inclusive imagery as a special campaign that happens once a year. - One approved body master across front, side and back - Fewer recasts when colorways arrive late - The same model in PDP, lookbook and campaign crops ![Fashion identity consistency benchmark in a movement pose](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-motion.webp) See the work ## Stress-test consistency before you scale. Astria’s identity benchmark uses the same model across full-length, close, seated and motion prompts. Treat your body master the same way: make the hard views early, then decide whether the cast is production-ready. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### avatar Creates original adult casting portraits from visible traits, then presents a small candidate board instead of settling for the first face. [Open the skill](https://github.com/astriaai/skills/tree/main/avatar) ### prompt-writing Keeps identity tokens, garment tokens and the exact background instruction clear enough to review. [Open the skill](https://github.com/astriaai/skills/tree/main/prompt-writing) ### store-photoshoot Turns the chosen cast into a controlled shot battery and stops for sign-off before high-volume generation. [Open the skill](https://github.com/astriaai/skills/tree/main/store-photoshoot) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) Approve the person first, the body master second, and the garment battery third. Scaling before those three truths agree only makes the corrections more expensive. [Start a production](https://www.astria.ai/ecommerce) --- # Put the real product on the model. Keep the evidence in the frame. Source: https://www.astria.ai/articles/features/product-to-model/ [← All feature guides](https://www.astria.ai/articles/features/) Shoot photos / On model The source garment is not a mood board. It is the contract. Photograph it clearly, name the parts that must survive, and build the pose and light around those facts. [Make it, step by step](https://www.astria.ai/articles/features/product-to-model/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/product-to-model/#prompts) ![On-model fashion image created from separate garment references](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.jpg) Shirt, skirt and shoes arrive as references; pose, cast and light turn them into one photograph. The point of view ## Begin where the customer will look. List the details that make the SKU itself: silhouette, color, print, trim, closure, sleeve length, hem, logo and construction. If the reference cannot show a detail, the generated image cannot verify it. Use one clean product reference per essential item and one approved model reference. Ask for fit and layering in plain language—shirt untucked, trousers covering the shoe, top two buttons open—instead of burying the garment in aesthetic adjectives. Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Prepare the source Use clean front and back product views when available. Separate colorways so a faithful generation does not become a faithful rendering of the wrong variant. 2. 02 ### Name each reference A ring is a ring, not “jewelry.” A dress is a dress, not “product.” Clear classes make the prompt and review legible. 3. 03 ### Make the front master Generate a straight, full-length front view first. Lock identity, background and outfit before adding movement or location. 4. 04 ### Complete the battery Add 45-degree, back, waist/detail and movement. Check every new view against the source, not against the previous generation. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Clean full-length on-model catalog result](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.jpg) Example result Clean front view · 3:4 woman wearing shirt and trousers. Full-length front catalog photograph, arms relaxed, shirt untucked, trousers reach the shoes. Plain background #f5f5f5, large softbox, true color, natural fabric drape --background\_color #f5f5f5 **Why this works**The prompt is short because the references carry the product. It spends words on fit and the repeatable set. ![Layered reference fashion result from an Astria model benchmark](https://www.astria.ai/articles/img/model-benchmarks/2026-09/complex-nano-banana-2.webp) Example result Layered look · 3:4 woman wearing jacket open over top with skirt. Three-quarter standing view, jacket fully visible, top hem visible, skirt waistband unobstructed. Soft off-white studio, camera at waist height, accurate garment shape, trims and colors. Avoid copied room backgrounds, changed closures, extra layers or missing products. **Why this works**Visibility clauses stop one garment from quietly swallowing another. ![Full-length fashion model in motion](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-motion.webp) Example result Movement frame · 4:5 woman wearing dress and shoes, walking toward camera at an easy pace. Keep the exact neckline, sleeves, print, hem and shoe shape from the references. White daylight studio, 50mm lens, fabric moving naturally, same model identity and body proportions. **Why this works**Motion is added only after product and identity are locked. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. Nano Banana / Gemini ### Reference composition and edits A strong default when a model and several product references need to meet in one image. Seedream 5 ### More complex reference stacks Useful for layered outfits, but still split overloaded looks into passes when details compete. Recraft 4.1 ### Casting only Use it upstream to create an original model face, then bring that approved identity into the product workflow. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## The front master buys down the rest of the collection. Solve one representative SKU before launching the whole assortment. Once cast, background and fit grammar are approved, every neighboring SKU starts closer to finished. - Fewer full-look reshoots for late samples - One treatment across product and lookbook - Reusable fit instructions by garment class ![America Basics full-look result](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.jpg) See the work ## Inspect the inputs beside the result. America Basics shows the product references, a finished full look, and the reusable template that joins them. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### store-photoshoot Ingests SKU references, maps garment classes and builds shot batteries. [Open the skill](https://github.com/astriaai/skills/tree/main/store-photoshoot) ### prompt-writing Documents identity and product token syntax plus repeatable background control. [Open the skill](https://github.com/astriaai/skills/tree/main/prompt-writing) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) Make the clean front frame boringly correct. Once it is, the interesting photographs have something true to build on. [Start a production](https://www.astria.ai/ecommerce) --- # Same person. New photograph. No identity séance. Source: https://www.astria.ai/articles/features/consistent-models/ [← All feature guides](https://www.astria.ai/articles/features/) Create models / Consistency Consistency is not repeating the same seed and hoping. It is an approved identity reference, a small set of truths you refuse to renegotiate, and a test that reaches beyond the easy beauty crop. [Make it, step by step](https://www.astria.ai/articles/features/consistent-models/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/consistent-models/#prompts) ![Fashion model in a movement identity consistency test](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-motion.webp) Motion is where identity systems earn trust. Test it before the campaign depends on it. The point of view ## Lock fewer things, more clearly. Give the person one explicit FaceID/reference. Repeat age, hair and body proportions only when the shot needs reinforcement; do not describe a different face around the identity token. Treat hairstyle, makeup and body shape as art-direction decisions with their own approved master. If they change, call it a new look on purpose—not a side effect of a longer prompt. Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Approve the casting portrait Choose a close, neutral image with a clear face and no accessory or hairstyle that will contaminate every future scene. 2. 02 ### Register one identity Turn the winner into a FaceID/reference and use the exact token every time. Names alone carry no identity. 3. 03 ### Create the four-view test Render close, full-length, seated and walking with the same styling. Compare face geometry, age, hair and proportions. 4. 04 ### Save the passing setup Move the reference, prompts, background and settings into a template or pack. Consistency improves when the team repeats the system, not just the prose. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Full-length fashion model identity result](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-full.webp) Example result Four-view proof · 4:5 woman shown as a coherent four-image casting test: close portrait, full-length standing, seated three-quarter view, and walking mid-step. Same exact face, age, pulled-back hair, body proportions and calm expression in every image. Simple black T-shirt and trousers, white studio, soft daylight, natural skin. No face drift, no age change, no body reshaping. **Why this works**One compact battery tests the distances and poses that usually reveal drift. ![Same fashion identity in a new seated scene](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-seated.webp) Example result New scene, same person · 3:2 woman leaning against a tiled café counter in early morning light, wearing coat. Preserve the exact approved face, age, hairstyle and body proportions. Candid 50mm fashion photograph, soft window light, muted city palette, realistic skin and coat texture. **Why this works**The model stays factual while the setting is allowed to change. ![Close crop preserving the same fashion identity](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-close.webp) Example result New crop, same look · 4:5 Waist-up crop of woman from the approved full-length look, wearing blazer. Keep the same face, hair, makeup, body proportions, blazer fit, light direction and grey background. Camera moves closer; nothing else changes. **Why this works**It frames a derivative crop as a camera change, not a new generation brief. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. FaceID / reference ### The identity anchor This is the durable part of the system. The text cannot substitute for the actual chosen face. Nano Banana 2 ### Reference-led scene changes Test identity across the four-view battery before making it your default for a production. Seedream 5 ### Multi-reference continuity A candidate when model, outfit and location all need references; compare against the same identity test. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## Consistency turns a good image into a castable production asset. Once a model survives close, full, seated and motion views, new products and crops stop reopening the most subjective decision in the shoot. - Fewer identity corrections - One recognizable cast across channels - Templates that survive team handoffs ![Full-length identity benchmark photograph](https://www.astria.ai/articles/img/model-benchmarks/2026-09/identity-nano-full.webp) See the work ## Compare the hard views. The Astria benchmark puts current image models through the same identity sequence, so teams can choose from evidence rather than a leaderboard. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### prompt-writing Explains explicit reference tokens and how to diagnose competing face references. [Open the skill](https://github.com/astriaai/skills/tree/main/prompt-writing) ### avatar Creates the clean source portrait that becomes the identity anchor. [Open the skill](https://github.com/astriaai/skills/tree/main/avatar) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) Do not ask whether the face looks good twice. Ask whether it is unmistakably the same person four times. [Start a production](https://www.astria.ai/ecommerce) --- # Save the decisions—not just the files. Source: https://www.astria.ai/articles/features/production-workspaces/ [← All feature guides](https://www.astria.ai/articles/features/) Scale / Workspaces & templates The most expensive knowledge in a shoot is usually trapped in people’s heads: which model, which grey, which lens, which fit language, which reference was approved. A workspace makes those decisions visible; a template makes them runnable. [Make it, step by step](https://www.astria.ai/articles/features/production-workspaces/#make-it)[Copy the prompts](https://www.astria.ai/articles/features/production-workspaces/#prompts) ![Jewelry result produced from a reusable Astria workflow](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-result.jpg) The deliverable is not only this ring photograph. It is the setup the next ring can inherit. The point of view ## A reusable system still has an author. Templates do not remove taste. They record it: source references, cast, background, pose, prompt and settings, plus the outputs that passed review. A teammate can then repeat the photograph without reverse-engineering a filename. Organize by brand and production question. The workspace holds the visual world; the pack holds a product formula and shot battery; the template holds one repeatable frame. Keep experiments nearby, but promote only approved work. Make it ## From blank page to a production-ready first set. Work small until the cast, product and photograph agree. Then make the system repeatable. 1. 01 ### Create the brand workspace Collect product references, model identities, locations, prompts, outputs and review in one place. 2. 02 ### Pilot one item formula Choose a representative top, dress, shoe or jewelry item and finish its complete shot battery. 3. 03 ### Promote the winners Turn the approved prompts and settings into templates, then group related templates into a pack. 4. 04 ### Scale with a ledger Track SKU, reference ID, template, output, review state and final delivery. Use the API or CLI only after the pilot has signed off. Prompt book ## Prompts you can actually start from. Replace the placeholder IDs with your Astria references. Keep the factual product and identity instructions; change the creative language until it sounds like your brand. ![Reusable on-model template result](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.jpg) Example result Template brief Create a reusable PDP template for women’s tops. Keep model , front relaxed stance, crop from head to mid-thigh, pale grey background #f5f5f5, even softbox light and true color fixed. Expose one replaceable top reference. Preserve neckline, sleeves, hem, print and fit. Output 3:4 at delivery resolution with exact background color. **Why this works**A good template separates the fixed photographic decisions from the one product input that changes. ![Reusable product pack result](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.jpg) Example result Pack brief Build a six-template tops pack: full front, 45-degree front-side, full back, waist detail, sleeve/neckline detail, and walking frame. Keep the same model identity, crop family, grey background and light across all six. Each template accepts one top reference and repeats the same fidelity checklist. **Why this works**The pack is a coverage promise for an item type, not a bag of unrelated prompts. ![Reviewed jewelry workflow result](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-result.jpg) Example result Review brief Review every generated image against its source reference for silhouette, exact color, print scale, trims, closures, sleeve and hem length, logo, garment layering, model identity, anatomy, background hex and crop. Mark pass, correct or reject; do not scale the pack until two difficult pilot SKUs pass every required view. **Why this works**A reusable workflow needs a reusable definition of done. Choose the tool by the photograph ## There is no single “best model.” There is a best next move. Templates and packs ### Repeat the approved frame Expose only the inputs teammates should change. Freeze the photographer’s quiet rules. Boards and workspace ### Keep context and review together The source, output and discussion should be visible beside one another. Astria CLI / API ### Scale after sign-off Batching is the last step. Automating an unapproved prompt only produces corrections faster. Model availability changes. Astria added Recraft V4.1 in May 2026; check the current model catalog before production. [See Astria changes](https://docs.astria.ai/docs/changes/) Show me the money ## The money is in not reconstructing the shoot. A late SKU, a new crop or a second operator can inherit the approved cast, light and checks. That is where creative operations compound. - Less setup duplicated across teams - Approval criteria attached to the work - Batch production only after a passing pilot ![Reusable 3D packshot workflow result](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.jpg) See the work ## Walk through public production systems. Each workspace below holds a different photographic problem and links to templates designed to be reused. Build it with Astria Skills ## The craft can live in your workflow. The open-source Astria skills turn these methods into guided work with the Astria CLI. Read the skill, adapt the judgment, and keep every generation reviewable. ### packs-guide Explains how creators author packs and how production users run them with their own references. [Open the skill](https://github.com/astriaai/skills/tree/main/packs-guide) ### store-photoshoot Provides the end-to-end store audit, cast, SKU ingestion, template battery and QA workflow. [Open the skill](https://github.com/astriaai/skills/tree/main/store-photoshoot) ### astria-api Provides the CLI for tunes, prompts, packs, image generation, video and variation. [Open the skill](https://github.com/astriaai/skills/tree/main/astria-api) [Explore all Astria Skills on GitHub](https://github.com/astriaai/skills) A folder proves that work happened. A workspace and a passing template make the next piece of work easier. [Start a production](https://www.astria.ai/ecommerce) --- # Your brand is not a preset. It is a way of seeing. Source: https://www.astria.ai/articles/features/ Astria for fashion and product teams A photographer gives a collection one world: the same eye, the same light, the same quiet rules. Astria lets your team keep that world—then bring every product, crop, channel, and late arrival into it. [Walk through a real production](https://www.astria.ai/w/america-basics)[Start with your products](https://www.astria.ai/ecommerce) See the inputs, the reusable treatment, and the finished photographs. No mystery gallery. No “imagine if.” ![Fashion model wearing the products from the reference photographs](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.jpg) The finished frame ![Brown sweatshirt product reference](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.jpg) Shirt ![Grey pleated skirt product reference](https://www.astria.ai/articles/img/prompt-recipes/fashion-skirt-reference.jpg) Skirt ![Shoe product reference](https://www.astria.ai/articles/img/prompt-recipes/fashion-shoes-reference.jpg) Shoes Three products. One photograph. A direction the next look can reuse. Ten practical guides ## Start with the photograph you need to make. Not a dictionary of tiny SEO pages. Ten working guides—with prompts, model choices, templates, proof, and the production decision that pays for the next frame. 1. [01 Create models / Kids ### Kids fashion models: make childhood—not a grown-up campaign made smaller. **↗**](https://www.astria.ai/articles/features/kids-models/) 2. [02 Create models / Plus size ### Plus-size fashion models: photograph the fit you chose. Do not prompt the body away. **↗**](https://www.astria.ai/articles/features/plus-size-models/) 3. [03 Create models / Fashion ### Build a cast with a point of view—not a folder of interchangeable faces. **↗**](https://www.astria.ai/articles/features/fashion-models/) 4. [04 Shoot photos / On model ### Put the real product on the model. Keep the evidence in the frame. **↗**](https://www.astria.ai/articles/features/product-to-model/) 5. [05 Create models / Consistency ### Same person. New photograph. No identity séance. **↗**](https://www.astria.ai/articles/features/consistent-models/) 6. [06 Shoot photos / Lookbook ### A lookbook is a rhythm, not twenty unrelated good images. **↗**](https://www.astria.ai/articles/features/lookbook-photos/) 7. [07 Shoot photos / E-commerce ### Make the catalog dependable before you make it beautiful. **↗**](https://www.astria.ai/articles/features/ecommerce-catalog/) 8. [08 Shoot photos / Campaign ### Give the product a world—but make sure it still comes home as itself. **↗**](https://www.astria.ai/articles/features/campaign-editorial/) 9. [09 Generate videos / Fashion & product ### Approve the photograph. Then let time into it. **↗**](https://www.astria.ai/articles/features/product-video/) 10. [10 Scale / Workspaces & templates ### Save the decisions—not just the files. **↗**](https://www.astria.ai/articles/features/production-workspaces/) What changes ## The photograph stops being an event. It becomes an asset the brand can return to. Add a colorway after launch. Give wholesale a wider crop. Turn the approved campaign still into a short film. The taste stays human; the repetition does not have to be. ## First find the look. Then make it useful. This is closer to running a shoot than ordering images from a vending machine. The difference is that the approved setup survives the day. 1. 1 ### Bring the products Start with the flat lays, packshots, product pages, or reference photographs you already have. The garment is evidence, not inspiration. 2. 2 ### Find the photograph Choose the cast, light, distance, setting, and attitude. Work from a template, a rights-cleared reference, or the visual language already inside the brand. 3. 3 ### Approve the world once Review the first look until it feels true. The useful deliverable is not one lucky image—it is the treatment the next SKU can inherit. 4. 4 ### Let the collection enter it Run the approved direction across the assortment, then branch the same visual world into PDP, lookbook, campaign, social, and motion. ## One visual world, wherever the product has to go. Clean enough for the product page. Alive enough for the campaign. Recognizable enough to belong to the same brand. ![Fashion campaign image from an Astria workflow](https://www.astria.ai/articles/img/prompt-recipes/bridal-terrace-result.jpg) **Campaign**Give the collection somewhere to live. ![Generated fashion catalog image](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.jpg) **Lookbook**Keep the cast and treatment across the sequence. ![Generated volumetric dress packshot](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.jpg) **Product page**Make the product easy to inspect. ![Generated jewelry macro photograph](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-result.jpg) **Detail**Bring the eye close without losing the object. ![Generated beauty campaign still life](https://www.astria.ai/articles/img/prompt-recipes/beauty-still-life-result.jpg) **Social and launch**Branch the art direction into new formats. Put your own numbers in ## The expensive part is doing the same thinking twice. A shoot budget is not only the day on set. It is the casting conversation, the lighting decision, the rounds of approval, and then doing it all again because three products arrived late. Astria turns that approved thinking into something the next product can inherit. [See the full cost-per-SKU model](https://www.astria.ai/articles/product-photography-cost-per-sku/) Products in a dropDrops each yearCurrent approved cost per SKU$Reduction worth testing% Your current annual production envelope **[current pricing](https://www.astria.ai/pricing)** A 25% improvement would return**[current pricing](https://www.astria.ai/pricing)**to the year’s creative budget. The prefilled figures are illustrative. This is a planning model, not a savings promise. Use your real approved-SKU cost and include setup, review, rejected work, corrections, and delivery. ## Don’t take the feature list on faith. Open the work. The useful examples show more than a beautiful last frame. They let you see the source product, the repeatable setup, and the neighboring outputs. ![Sweatshirt reference for America Basics](https://www.astria.ai/articles/img/prompt-recipes/fashion-shirt-reference.jpg)![America Basics full-look result](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.jpg) ### America Basics Several products enter one full-look photograph. Study the inputs, then open the template that holds the treatment together. ![Dress product reference](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-reference.jpg)![Generated dress packshot](https://www.astria.ai/articles/img/prompt-recipes/packshot-dress-result.jpg) ### 3D ghost packshots Begin with the real garment. Build front, side, three-quarter, detail, and motion assets around it—then review anything the source image could not prove. ![Ring product reference](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-reference.jpg)![Generated ring macro](https://www.astria.ai/articles/img/prompt-recipes/jewelry-ring-result.jpg) ### Jewelry, close enough to count Atmosphere comes after accuracy. Check the band, setting, stone count, scale, and finish before the photograph earns its campaign lighting. ## Everything the work remembers Products, casting, locations, poses, references, prompts, approved templates, and the images and films that came from them—kept together so the next person does not have to reconstruct the photographer’s intent from a folder name. - **Models and casting**Keep a recognizable cast across scenes and seasons. - **Products and references**Build from the real details customers will receive. - **Templates**Save the visual decisions worth repeating. - **Boards and review**Discuss the photograph beside the photograph. - **Current image models**Choose the model for the brief, not the other way around. - **Image, video, and API**Explore by hand; repeat with a production workflow. ![Fashion model in a finished Astria campaign photograph](https://www.astria.ai/articles/img/prompt-recipes/fashion-catalog-result.jpg) ## Bring us one product. Leave with a way to photograph the next hundred. Start from a proven template, inspect a public workspace, or bring the visual language your brand already owns. [Create with Astria](https://www.astria.ai/ecommerce)[Browse real workspaces](https://www.astria.ai/gallery/workspaces) --- # Astria - AI Lookbooks Source: https://www.astria.ai/gallery/workspaces Public Workspaces Curated collections from the Astria community Lookbook ## LeFrenchBulldog By Astria [Explore Collection](https://www.astria.ai/w/lefrenchbulldog) [![LeFrenchBulldog](https://mp.astria.ai/o1nduwdea7r6n4rnt2l55kq8bl58)](https://www.astria.ai/w/lefrenchbulldog) [![LeFrenchBulldog](https://mp.astria.ai/um74f731of5v80w3cs1hn54wclnk)](https://www.astria.ai/w/lefrenchbulldog)[![LeFrenchBulldog](https://mp.astria.ai/9tfcg0l216yrnolqwripkb8h330f)](https://www.astria.ai/w/lefrenchbulldog) Lookbook ## Handbag formation By Astria [Explore Collection](https://www.astria.ai/w/handbag-formation) [![Handbag formation](https://mp.astria.ai/ow77zio41zlnycq38syphfx39zn2)](https://www.astria.ai/w/handbag-formation) [![Handbag formation](https://mp.astria.ai/i9usofkuu9xumbct1fywvapuhy9c)](https://www.astria.ai/w/handbag-formation)[![Handbag formation](https://mp.astria.ai/venilwvbj9x0sox3q6ppf2j4tv1q)](https://www.astria.ai/w/handbag-formation) Lookbook ## Seestarz Lookbook By Stav Almon [Explore Collection](https://www.astria.ai/w/stav-almon) [![Seestarz Lookbook](https://mp.astria.ai/f23pncsz0risr6gj6cbwwtxzhk1j)](https://www.astria.ai/w/stav-almon) [![Seestarz Lookbook](https://mp.astria.ai/osg7w04xa1alqhunbsnv4yd92gq9)](https://www.astria.ai/w/stav-almon)[![Seestarz Lookbook](https://mp.astria.ai/nkr8doei3z84d70ngv1f69hc1by8)](https://www.astria.ai/w/stav-almon) Lookbook ## Crocs By Astria [Explore Collection](https://www.astria.ai/w/crocs) [![Crocs](https://mp.astria.ai/fbeu6uc77hbu5wl02y2igdbabnqy)](https://www.astria.ai/w/crocs) [![Crocs](https://mp.astria.ai/ckx2thcqfya8asik8bfcajwbhdub)](https://www.astria.ai/w/crocs)[![Crocs](https://mp.astria.ai/iqsa67hthmqdkjrh1ahk1rn9ctmk)](https://www.astria.ai/w/crocs) Lookbook ## Beauty By Astria [Explore Collection](https://www.astria.ai/w/beauty) [![Beauty ](https://mp.astria.ai/3s0gdyuho4u7ic786zmhjehjf36d)](https://www.astria.ai/w/beauty) [![Beauty ](https://mp.astria.ai/l17slusni3jk0bpvfr834txpz539)](https://www.astria.ai/w/beauty)[![Beauty ](https://mp.astria.ai/qr2upy7j2boocb8ol5s4i38jdooe)](https://www.astria.ai/w/beauty) Lookbook ## UNTOUCHABLE By tammy montag studio [Explore Collection](https://www.astria.ai/w/tammy-montag) [![UNTOUCHABLE](https://mp.astria.ai/paf64jeh9lxj1fijiyekbrejootq)](https://www.astria.ai/w/tammy-montag) [![UNTOUCHABLE](https://mp.astria.ai/pwesgiqrv2v1ypjnl0tf55nx3lam)](https://www.astria.ai/w/tammy-montag)[![UNTOUCHABLE](https://mp.astria.ai/m2f8kvgozecp7sgl9y3rggeywoon)](https://www.astria.ai/w/tammy-montag) Lookbook ## Concept Photography By Astria [Explore Collection](https://www.astria.ai/w/match-day) [![Concept Photography](https://mp.astria.ai/a7182r5e7csso8gqtgkqpdj48cpd)](https://www.astria.ai/w/match-day) [![Concept Photography](https://mp.astria.ai/884wjn4m51jkockp6c5df5w2pwgu)](https://www.astria.ai/w/match-day)[![Concept Photography](https://mp.astria.ai/qgoq82gnuuobwrmv1ronmmfw2dp2)](https://www.astria.ai/w/match-day) Lookbook ## Fashion 3d-packshots By Astria [Explore Collection](https://www.astria.ai/w/fashion-3d-packshots) [](https://www.astria.ai/w/fashion-3d-packshots) [](https://www.astria.ai/w/fashion-3d-packshots)[](https://www.astria.ai/w/fashion-3d-packshots) Lookbook ## Dark swimwear By Astria [Explore Collection](https://www.astria.ai/w/dark-swimwear) [![Dark swimwear](https://mp.astria.ai/f386lyf3q7zwzoco8zy0npsbf9m4)](https://www.astria.ai/w/dark-swimwear) [![Dark swimwear](https://mp.astria.ai/ieyk2kp3793b96tfvap2rcfezm4q)](https://www.astria.ai/w/dark-swimwear)[![Dark swimwear](https://mp.astria.ai/g9fo1hzopyn8gwsbpb25zd204pcg)](https://www.astria.ai/w/dark-swimwear) Lookbook ## Swimwear By Astria [Explore Collection](https://www.astria.ai/w/swimwear) [![Swimwear](https://mp.astria.ai/1gekyjjz9qx54ycsiaibo2d3mwtg)](https://www.astria.ai/w/swimwear) [![Swimwear](https://mp.astria.ai/9tb509sasjlcouzzlpwm022lv45t)](https://www.astria.ai/w/swimwear)[![Swimwear](https://mp.astria.ai/av8phe1im0gxwf8hhws4xjexfh5z)](https://www.astria.ai/w/swimwear) Lookbook ## The Language of Form By Angela Fraser [Explore Collection](https://www.astria.ai/w/angela-fraser) [![The Language of Form](https://mp.astria.ai/vpxvd74ifkricdi9rddhkaoqc2ts)](https://www.astria.ai/w/angela-fraser) [![The Language of Form](https://mp.astria.ai/bmo6ddgiovt2pjg4mnw0h3u5q5ls)](https://www.astria.ai/w/angela-fraser)[![The Language of Form](https://mp.astria.ai/9oers9c63x0ijbgksfmad8w3n9c8)](https://www.astria.ai/w/angela-fraser) Lookbook ## Jewelry product photography By Astria [Explore Collection](https://www.astria.ai/w/jewelry-product-photography) [![Jewelry product photography](https://mp.astria.ai/0vpydvyn2uq402goi0rbsn8uqtyc)](https://www.astria.ai/w/jewelry-product-photography) [![Jewelry product photography](https://mp.astria.ai/dh9pqtphb536hj8ejwbbvw1djky2)](https://www.astria.ai/w/jewelry-product-photography)[![Jewelry product photography](https://mp.astria.ai/0rs2b5m36gw0bdkeqkg6umuj7rhn)](https://www.astria.ai/w/jewelry-product-photography) Lookbook ## 3D ghost packshots By Astria [Explore Collection](https://www.astria.ai/w/3d-packshots) [](https://www.astria.ai/w/3d-packshots) [](https://www.astria.ai/w/3d-packshots) Lookbook ## Shreya Jain By Shreya Jain [Explore Collection](https://www.astria.ai/w/shreya-jain) [![Shreya Jain](https://mp.astria.ai/d6d5y2zpq5zus2c2csphxtg15clw)](https://www.astria.ai/w/shreya-jain) [![Shreya Jain](https://mp.astria.ai/efvbc9agwcxm2chotj5bucikpo8k)](https://www.astria.ai/w/shreya-jain)[![Shreya Jain](https://mp.astria.ai/smxw80lfnkme5i0g6kqvxkxmunf3)](https://www.astria.ai/w/shreya-jain) Lookbook ## Adi Harari - Luxury Editorial Lingerie Film By Adi Harari [Explore Collection](https://www.astria.ai/w/adi-harari) [](https://www.astria.ai/w/adi-harari) [](https://www.astria.ai/w/adi-harari)[](https://www.astria.ai/w/adi-harari) Lookbook ## Ariadna Arnes By ariadna.arnes@gmail.com [Explore Collection](https://www.astria.ai/w/ariadna-arnes) [![Ariadna Arnes](https://mp.astria.ai/60sxs9npkli6j21ewzx89rx8g08o)](https://www.astria.ai/w/ariadna-arnes) [![Ariadna Arnes](https://mp.astria.ai/pol4ra66v0b40obmu42q8hcjon5c)](https://www.astria.ai/w/ariadna-arnes)[![Ariadna Arnes](https://mp.astria.ai/tnn3avt5oblsl0a2ck2jkighhi3o)](https://www.astria.ai/w/ariadna-arnes) Lookbook ## SHIZUKA YUZEN By AI Portraits Studio [Explore Collection](https://www.astria.ai/w/shizuka_yuzen) [![ SHIZUKA YUZEN](https://mp.astria.ai/9r1q48j1rpjm6yte8qcgqcyew9xf)](https://www.astria.ai/w/shizuka_yuzen) [![ SHIZUKA YUZEN](https://mp.astria.ai/zhfjtwon2aq7bdroxzflhjl99cyp)](https://www.astria.ai/w/shizuka_yuzen)[![ SHIZUKA YUZEN](https://mp.astria.ai/00bbq59w5bdt2jwftjb75adhp3db)](https://www.astria.ai/w/shizuka_yuzen) Lookbook ## Uliana Horodnycha By Uliana Horodnycha [Explore Collection](https://www.astria.ai/w/uliana-horodnycha) [![Uliana Horodnycha](https://mp.astria.ai/l0w4s6fkfm6dlhsxp4xut840k0km)](https://www.astria.ai/w/uliana-horodnycha) [![Uliana Horodnycha](https://mp.astria.ai/mb0n6mvjzjwzmps78tdahmxcdakb)](https://www.astria.ai/w/uliana-horodnycha)[![Uliana Horodnycha](https://mp.astria.ai/hntu6ho8wll14ku460glvuo6fy20)](https://www.astria.ai/w/uliana-horodnycha) Lookbook ## Anastasiia Naidanova By Anastasiia Naidanova [Explore Collection](https://www.astria.ai/w/anastasiia-naidanova) [![Anastasiia Naidanova](https://mp.astria.ai/uc4pmyy778s9tdchsz2el0x7g19l)](https://www.astria.ai/w/anastasiia-naidanova) [![Anastasiia Naidanova](https://mp.astria.ai/fbl9xg1xze1hkjf4das35jwmj8f2)](https://www.astria.ai/w/anastasiia-naidanova)[![Anastasiia Naidanova](https://mp.astria.ai/q3qg4yo9kqg94chj3k5b7l63rppe)](https://www.astria.ai/w/anastasiia-naidanova) Lookbook ## Guido (GFXS) Fuà By Guido Fuà [Explore Collection](https://www.astria.ai/w/guido-gfxs-fua) [![Guido (GFXS) Fuà](https://mp.astria.ai/9i3bjt9a9obq715x7kvnclah8lq5)](https://www.astria.ai/w/guido-gfxs-fua) [![Guido (GFXS) Fuà](https://mp.astria.ai/440wrkn8rmwxp5m99h340sm82y9y)](https://www.astria.ai/w/guido-gfxs-fua)[![Guido (GFXS) Fuà](https://mp.astria.ai/w4rzge241uv35jt2gmww0npuw0mq)](https://www.astria.ai/w/guido-gfxs-fua) Lookbook ## Douglas Manzanares By dmanzanaresa94@gmail.com [Explore Collection](https://www.astria.ai/w/douglas-manzanares) [![Douglas Manzanares](https://mp.astria.ai/r8fshvg3hlril56dte8o3hfhzhav)](https://www.astria.ai/w/douglas-manzanares) [![Douglas Manzanares](https://mp.astria.ai/vfd6k8hkm4io0uqgqw4kex4iw6yn)](https://www.astria.ai/w/douglas-manzanares)[![Douglas Manzanares](https://mp.astria.ai/b3zdybvvaay0bh5cv4c9bt3rr04h)](https://www.astria.ai/w/douglas-manzanares) Lookbook ## Wedding dress AI photoshoot By Astria [Explore Collection](https://www.astria.ai/w/wedding-dress) [](https://www.astria.ai/w/wedding-dress) [](https://www.astria.ai/w/wedding-dress)[](https://www.astria.ai/w/wedding-dress) Lookbook ## Great sports By Astria [Explore Collection](https://www.astria.ai/w/great-sports) [![Great sports](https://mp.astria.ai/dbvhvsrg2up4nx8jlpx4q6gui0bs)](https://www.astria.ai/w/great-sports) [![Great sports](https://mp.astria.ai/mkj1pdtvelpgwukwn6psrpkh6fzx)](https://www.astria.ai/w/great-sports)[![Great sports](https://mp.astria.ai/yua7id4jfotgooazkxpvekdz1lu5)](https://www.astria.ai/w/great-sports) Lookbook ## America Basics By Astria [Explore Collection](https://www.astria.ai/w/america-basics) [![America Basics](https://mp.astria.ai/6wb9j8m2gyimz9df3j5rqv2ttmym)](https://www.astria.ai/w/america-basics) [![America Basics](https://mp.astria.ai/z6p8zipl9zk0anmv5gmlwt3twaza)](https://www.astria.ai/w/america-basics)[![America Basics](https://mp.astria.ai/etetei0w9qogxtv3abvwu195yf20)](https://www.astria.ai/w/america-basics) Lookbook ## Maison Urban By Astria [Explore Collection](https://www.astria.ai/w/maison-urban) [![Maison Urban](https://mp.astria.ai/mmpj3dw4wzjd24kph4ydnk1hue6f)](https://www.astria.ai/w/maison-urban) [![Maison Urban](https://mp.astria.ai/62bz4glxtkcf8zgzfqzn3a6sqs4r)](https://www.astria.ai/w/maison-urban)[![Maison Urban](https://mp.astria.ai/o6kosu70vraoeo7vueh35yth1maj)](https://www.astria.ai/w/maison-urban) Lookbook ## CI898. CREATIVE INTELLIGENCE FOR FASHION BRANDS By CI898 [Explore Collection](https://www.astria.ai/w/ci898) [![CI898. CREATIVE INTELLIGENCE FOR FASHION BRANDS](https://mp.astria.ai/sh8chmaeiiwmbmxboav279nd9j8c)](https://www.astria.ai/w/ci898) [![CI898. CREATIVE INTELLIGENCE FOR FASHION BRANDS](https://mp.astria.ai/h61cckq6kd2lwmy1vyf7hm3razsk)](https://www.astria.ai/w/ci898)[![CI898. CREATIVE INTELLIGENCE FOR FASHION BRANDS](https://mp.astria.ai/9fp8mixmqprt028burubta875wri)](https://www.astria.ai/w/ci898) --- # Astria - AI Lookbooks Source: https://www.astria.ai/gallery/workspaces/featured Brand Lookbooks Curated collections with unique brand aesthetics Editorial ## Maison Urbane Sophisticated fashion editorial. Elegant on-model photoshoots for contemporary style. [Explore Collection](https://www.astria.ai/w/zara) [![Maison Urbane](https://a.astria.ai/assets/workspaces/featured/editorial-hero-7f32eec900bb8315dfe7239b1e0c39d9fbf488fdecf336e1c00bb31a38668dee.avif)](https://www.astria.ai/w/zara) [![Editorial pants look](https://a.astria.ai/assets/workspaces/featured/editorial-lookbook-3fc85cc9863abdcd7f7be577a21fddeb60ba5a9efdc529d2a6cba8f6c8ededf0.avif)](https://www.astria.ai/w/zara)[![Editorial shirt look](https://a.astria.ai/assets/workspaces/featured/editorial-shirt-33ee1a40d27c5c51d6154b4fa9c44fe88a394f74a5a0874618accaf22f5ad4a0.avif)](https://www.astria.ai/w/zara) [![Sport Jacket](https://a.astria.ai/assets/workspaces/featured/sports-jacket-410a4e6817866f80b48bc060e17dc21194f96b2dac8abd972fa2225d475f10d8.avif)](https://www.astria.ai/w/great-sports) [![Sport Shoes](https://a.astria.ai/assets/workspaces/featured/sports-shoes-f3edf54f0a7511a9091efe334081a6649929a8c6d7f8357c68b1cdb5df4453b7.avif)](https://www.astria.ai/w/great-sports)[![Sport Shirt](https://a.astria.ai/assets/workspaces/featured/sports-shirt-b4966fa25b864ff99c8b2167658048bb9fcfa4326dfe4db39127b0bda32de62b.avif)](https://www.astria.ai/w/great-sports) Athletic ## Great Sports Performance athletic wear. Bold sportswear imagery for active lifestyles. [View Lookbook](https://www.astria.ai/w/great-sports) Minimal ## America Basics Clean everyday essentials. Quality basics designed for comfort and effortless style. [View Collection](https://www.astria.ai/w/america-basics) [![Basic Pants](https://a.astria.ai/assets/workspaces/featured/basics-pants-502693cd02584516f74fa5cf210f41d3a95ebae8b677e64f7f6714f641c11285.avif)](https://www.astria.ai/w/america-basics) [![Basic Jacket](https://a.astria.ai/assets/workspaces/featured/basics-jacket-7f37aeeda4f8e9fb2524f18b13a49a5edb23e10c509e960b8a23c8ee71a608d5.avif)](https://www.astria.ai/w/america-basics) [![Basic Shirt](https://a.astria.ai/assets/workspaces/featured/basics-shirt-a5dc5684e6c047198c61cdacce919acdeda7b949b4849b0471f486120bfff121.avif)](https://www.astria.ai/w/america-basics) [![Dark Swimwear resort look](https://a.astria.ai/assets/workspaces/featured/dark-swimwear-hero-3bf1cf4fe05bdc190f062b8bb5b0216a1d58b2efc436f25a2cc89d281fda4b42.avif)](https://www.astria.ai/w/dark-swimwear) [![Dark Swimwear editorial one piece](https://a.astria.ai/assets/workspaces/featured/dark-swimwear-detail-193f3c3aa2b49a966d46505444d95e3ec502340432bc26dafcd9541af22b70c5.avif)](https://www.astria.ai/w/dark-swimwear)[![Dark Swimwear red bikini look](https://a.astria.ai/assets/workspaces/featured/dark-swimwear-look-e502349e4110c4353b1ba961c2c2c7e1d974444dc5a6b1efb558450ae7f95bc2.avif)](https://www.astria.ai/w/dark-swimwear) Resort ## Dark Swimwear Polished resort swimwear. Minimal silhouettes with a refined editorial edge. [Explore Collection](https://www.astria.ai/w/dark-swimwear) --- # Model gallery Source: https://www.astria.ai/gallery/tunes Astria model catalog Find the right model, explore what it makes, and start generating. ## Featured A curated starting point for most projects. [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/03tosz081sat5rdngsjqa996xhre)](https://www.astria.ai/gallery/tunes/4180298/prompts) ### Nano Banana 2.1 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/ylawognez77a1eg1ofh1x5mianzs)](https://www.astria.ai/gallery/tunes/5236038/prompts) ### Seedream 5.0 Pro [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/1tvpiacnh6zxcgyqwwhrq9faz8uc)](https://www.astria.ai/gallery/tunes/5605622/prompts) ### Muse Image [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/7w2fa0t64a4yp3corimyw9rdf39c)](https://www.astria.ai/gallery/tunes/5634510/prompts) ### GPT Image 2.5 Sunburst [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/d0e8zmed28tuzxr2i1kqk5ksyimc)](https://www.astria.ai/gallery/tunes/5634511/prompts) ### GPT Image 2.5 Flare [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/dhhvmmodj2blwn3v5jyhiuuy70pk)](https://www.astria.ai/gallery/tunes/5811585/prompts) ### Flux 3 ## All models Every image model currently available in the Composer. [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/j8ui3sn5dirztc384sy7lkgzsvx3)](https://www.astria.ai/gallery/tunes/1504944/prompts) ### Flux.1 dev [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/mtr0qyqbz2ohhbs1w635rlo1crto)](https://www.astria.ai/gallery/tunes/3159068/prompts) ### Gemini 2.5 Flash - Nano Banana [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/4t78wnq0scpff0xbgogkh4pxuv2f)](https://www.astria.ai/gallery/tunes/3225353/prompts) ### Seedream 4.0 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/97sd5b6gbajvhtqtz4prm5dhc63k)](https://www.astria.ai/gallery/tunes/3672206/prompts) ### Flux 2 Pro [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/aekqmwysyjhv3fen2y6rpd0chjoy)](https://www.astria.ai/gallery/tunes/3691308/prompts) ### Seedream 4.5 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/q5g019k066cywaqvj3dewge5jwj7)](https://www.astria.ai/gallery/tunes/3879785/prompts) ### Flux 2 Turbo [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/8wbhgcsmx07eupr7kpfvghmavjn4)](https://www.astria.ai/gallery/tunes/3884682/prompts) ### P-Image Edit [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/br7iqwabnhhtnx34na0265u6t2fl)](https://www.astria.ai/gallery/tunes/4160332/prompts) ### Seedream 5.0 Lite [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/dxwxwxdwgmfnrzkr047rsk5q1exl)](https://www.astria.ai/gallery/tunes/4313230/prompts) ### Recraft V4 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/jd7g6v5ix3xjp73zza0ep1qxaucr)](https://www.astria.ai/gallery/tunes/4313228/prompts) ### Recraft V4 Pro [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/wskgmuozw5v8oskzmbk45rvb2j9f)](https://www.astria.ai/gallery/tunes/4329255/prompts) ### Flux Klein 9B [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/rq7840aob9m4ce42fvnck9itga71)](https://www.astria.ai/gallery/tunes/4495283/prompts) ### WAN 2.7 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/15kbbjy06ex9paqqfgncuyws9jcw)](https://www.astria.ai/gallery/tunes/4495299/prompts) ### WAN 2.7 Pro [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/x4qt3703ndysu66s3bjd79aozqxv)](https://www.astria.ai/gallery/tunes/4665564/prompts) ### GPT Image 2 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/jlj2vbftlv8w9ve9mulgtf0bpgrt)](https://www.astria.ai/gallery/tunes/4815694/prompts) ### Grok Imagine [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/4acyc3271evvjd54rmfbcrkrtzuz)](https://www.astria.ai/gallery/tunes/4906493/prompts) ### Recraft V4.1 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/l8oote10rn23tq30a3ikxcbqqlrn)](https://www.astria.ai/gallery/tunes/5195670/prompts) ### Ideogram V4 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/gm056epby1hpg7h4j0gdz6dsq8k4)](https://www.astria.ai/gallery/tunes/5205178/prompts) ### Nano Banana 2 Lite [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/batfrij1n8hfqqjxyc8860r10i3g)](https://www.astria.ai/gallery/tunes/5218808/prompts) ### Krea 2 Medium [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/x7xqq9mw9tfkme50kgxqfycr6zo9)](https://www.astria.ai/gallery/tunes/5218807/prompts) ### Krea 2 Large [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/ebcnk9uyq4qmo9lzapqp416yissq)](https://www.astria.ai/gallery/tunes/5218809/prompts) ### Krea 2 Medium Turbo [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/94wiaatczx73gvs6wu1s3k1391mn)](https://www.astria.ai/gallery/tunes/5276161/prompts) ### Reve 2.1 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/ffzz9uuo1q2k8mdtqhbgnyfnfgcd)](https://www.astria.ai/gallery/tunes/5543175/prompts) ### MoGe 2 Geometry [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/q86k74vmhpzgfr6tgdifhki8qcu7)](https://www.astria.ai/gallery/tunes/5603706/prompts) ### MAI Image 2.6 Flash [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/ms0x5ng9y0ojo0ktfdrlh0xop5f6)](https://www.astria.ai/gallery/tunes/5605362/prompts) ### MAI Image 2.6 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/ylawognez77a1eg1ofh1x5mianzs)](https://www.astria.ai/gallery/tunes/5671219/prompts) ### Layer separation [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/h0ij8tdcxmesxqri4ji4uuhfj3vt)](https://www.astria.ai/gallery/tunes/5851889/prompts) ### Grok Imagine Image 2.0 [![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/xtuzfkh7acybekqorv9p85ihbwxn)](https://www.astria.ai/gallery/tunes/5879476/prompts) ### Ideogram V4.5 --- # Prompts gallery Source: https://www.astria.ai/gallery Prompts made by the Astria team Filters - - [Image edits](https://www.astria.ai/gallery?has_input_image=true) - [No edits](https://www.astria.ai/gallery?has_input_image=false) - [Video edits](https://www.astria.ai/gallery?is_input_video=true) - [Today](https://www.astria.ai/gallery?today=true) - - Explore every image model in the [Models Gallery](https://www.astria.ai/gallery/tunes). - - Explore every image model in the [Models Gallery](https://www.astria.ai/gallery/tunes). - [Liked](https://www.astria.ai/gallery?sort=liked) ![woman, bag, bar, men](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/tymdcvnkpwfjx278v6ty5fvmxv3a) woman, bag, bar, men ![woman, bag, bar, men](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/z6biyl3899r07cnd4qkg5l1bvzbc) woman, bag, bar, men ![woman, bag, bar, men](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/iie2imb3xm2lvwsghv3u819z0d8v) woman, bag, bar, men ![woman, bag, bar, men](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/izk2rqqwshx2cckygfv65fhrasoq) woman, bag, bar, men ![woman, bag, bar, men](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/0n7z6x3v36b9mooqcjz7guywgr4e) woman, bag, bar, men ![Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe.](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/rtdwmzk0remmhdrrn4e7kbzij2kt) Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe. ![Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe.](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/8al1c83g8nmhcwx4glx8rv7bkus4) Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe. ![Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe.](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/w3a7wmdf2mpugy5h1r67s9cg899b) Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe. ![Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe.](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/eoddzwykczgozkahbd4713wogklc) Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe. ![Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe.](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/fab6fytbch30ylwuq4zmbscmu8y6) Dress hero shot. An off-kilter, flash-lit photograph of woman in a dynamic, almost off-balance leaning pose against a dark wooden bar counter or pillar at night. Her body creates a diagonal line. She wears the reference dress and holds the reference bag . The strong flash casts a sharp shadow. Blurred shelves with bottles and bar lights define the out-of-focus background. Cool, edgy vibe. ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/zsba5p9c1xyx8h0h8ch2bhn1055x) ![](https://mp.astria.ai/qbnxiidm1f4wafhejz7214r251ha) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/3p1jgq5v6sdszf6a4smpxtfby5bl) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/gy2zie42azy1qcw7vcrzdmhp4873) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/xhf65fk1ancu8velu4r06x12wufs) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/sclgkb6iux2mng88easv0wop2o08) ![](https://mp.astria.ai/enx88lx0mwsdns3hilg9ibtxjjwu) ![](https://mp.astria.ai/mqh7o87wsvnf4tssx91r1hhr01u0) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/r0dopyrudbs3kpsr16vlm5mqwzqi) ![](https://mp.astria.ai/k3vhs1fzl0s54pjjuyu6abeo0prk) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/ofi9w5fehyanpjacvoz1y2aqk03r) ![](https://mp.astria.ai/q6my7e2r8svv3gwo3hfyshkvbz1w) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/m6z4ju1xci5x8mvutjzivkqgv5bs) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/s1cp1qixqdtfchdtjhgvwkrdbfwb) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/er8hx6uew089yrku2j0fclhoublz) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/rjry0kza6tm6ai1hg28llq2bwxjz) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/isrvj4ezz7t9red90vc9j7oqg56e) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/vbhyr7v2e5xv2plsc1hoy1d0ocij) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/3jhkter8xi20k5r1938h2aixer1v) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/4nhy7qotjbr4hegslvgldqybwbqa) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/os286ovkgc1uck3vmpvflqmmnin2) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/ei5j46jsii7bxcrcdpypikum5172) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/2rp4wja2wr5v3z644rq7efywcktz) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/zz1caed2g8sp8i2vjome54pjukyf) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/goq4d7omfdfyh50142v36qc41l35) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/mscshfmrbo4s3z799mffakxmvbyl) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/tg2w7vcoydl8mbik5ix72yjk9m3b) --- # Prompts gallery Source: https://www.astria.ai/gallery/video Prompts made by the Astria team Filters - - [Image edits](https://www.astria.ai/gallery?has_input_image=true&is_video=true) - [No edits](https://www.astria.ai/gallery?has_input_image=false&is_video=true) - [Video edits](https://www.astria.ai/gallery?is_input_video=true&is_video=true) - [Today](https://www.astria.ai/gallery?is_video=true&today=true) - - Explore every image model in the [Models Gallery](https://www.astria.ai/gallery/tunes). - - Explore every image model in the [Models Gallery](https://www.astria.ai/gallery/tunes). - [Liked](https://www.astria.ai/gallery?is_video=true&sort=liked) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/zsba5p9c1xyx8h0h8ch2bhn1055x) ![](https://mp.astria.ai/qbnxiidm1f4wafhejz7214r251ha) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/r5e5abc7mclxmc0ef4a7l7pvkzmr) ![](https://mp.astria.ai/2d5z9cghunspfyqk7ydus26zics9) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/shab5r53qj36rgew4wyz7ew1iex4) ![](https://mp.astria.ai/cln80wcpxxakmv40559vbni2668r) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/0zi57q5j130oioohim6lskie4p74) ![](https://mp.astria.ai/5gbtk9awu7ww6e11pv8qw2mit3ju) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/2i60h08nkeeqb9pqxp57bq7rwh2j) ![](https://mp.astria.ai/72g1giso6o6jk4efs20rv1wv18lw) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/1d2wianx9ma5dmdee4beo609voen) ![](https://mp.astria.ai/4rd5bay1p9dn7en2u4lpvqv5w6wz) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/c8augemoe4mt4n1irv3szr5ew3xr) ![](https://mp.astria.ai/0qqf4yzdq7nyakqolg8tumj53tej) ![ shoes Create a clean 3D product video storyboard of a single shoe rotating in place on a seamless neutral studio background. The shoe is displayed as a premium e-commerce hero product, fully visible at all times, centered in the frame, with accurate proportions, realistic materials, crisp edges, and refined studio lighting. Camera: Static camera, straight-on product view, 50mm lens feel, no camera movement, no zoom, no distortion. Lighting: Soft diffused studio lighting from the front and slightly above, gentle shadow under the shoe, subtle floor reflection, clean high-end catalog look. Background: Seamless light gray studio background, minimal, clean, no props, no text, no logo, no hands, no model. Rotation speed: Slow and smooth 360-degree rotation over 8 seconds. The motion should feel premium and controlled, not fast. Consistent speed throughout the rotation, no sudden jumps. Storyboard: 00:00-00:01 Front view of the shoe. The toe box faces directly toward the camera. The shoe is centered, stable, and fully visible. 00:01-00:02 Front three-quarter view. The shoe rotates slowly to reveal the front shape, side profile, sole thickness, and material texture. 00:02-00:03 Full side profile view. The shoe is shown from a clean 90-degree side angle. The silhouette, heel height, sole line, and upper structure are clearly visible. 00:03-00:04 Back three-quarter view. The shoe continues rotating smoothly, revealing the heel construction, back curve, stitching, and rear details. 00:04-00:05 Back view. The heel faces directly toward the camera. The shoe remains centered and fully visible, with clean symmetry. 00:05-00:06 Opposite back three-quarter view. The rotation continues at the same slow speed, showing the opposite side details and rear shape. 00:06-00:07 Opposite full side profile view. The shoe reaches a clean 90-degree side angle from the other side. The side silhouette, sole, and upper are clearly readable. 00:07-00:08 Front three-quarter view returning to front. The shoe completes the 360-degree rotation and returns smoothly to the original front hero angle. Style: Premium 3D product animation, luxury footwear campaign, realistic materials, sharp focus, smooth motion, clean e-commerce presentation, high-end studio catalog quality. Negative prompt: avoid fast spinning, avoid shaky camera, avoid zooming, avoid changing shoe shape, avoid deformation, avoid cropped shoe, avoid extra shoes, avoid model, avoid feet, avoid hands, avoid text, avoid logo, avoid busy background, avoid dramatic shadows, avoid low resolution, avoid motion blur, avoid unstable rotation](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/vcso7haqkv0ccp51q1va2okj7eyu) shoes Create a clean 3D product video storyboard of a single shoe rotating in place on a seamless neutral studio background. The shoe is displayed as a premium e-commerce hero product, fully visible at all times, centered in the frame, with accurate proportions, realistic materials, crisp edges, and refined studio lighting. Camera: Static camera, straight-on product view, 50mm lens feel, no camera movement, no zoom, no distortion. Lighting: Soft diffused studio lighting from the front and slightly above, gentle shadow under the shoe, subtle floor reflection, clean high-end catalog look. Background: Seamless light gray studio background, minimal, clean, no props, no text, no logo, no hands, no model. Rotation speed: Slow and smooth 360-degree rotation over 8 seconds. The motion should feel premium and controlled, not fast. Consistent speed throughout the rotation, no sudden jumps. Storyboard: 00:00-00:01 Front view of the shoe. The toe box faces directly toward the camera. The shoe is centered, stable, and fully visible. 00:01-00:02 Front three-quarter view. The shoe rotates slowly to reveal the front shape, side profile, sole thickness, and material texture. 00:02-00:03 Full side profile view. The shoe is shown from a clean 90-degree side angle. The silhouette, heel height, sole line, and upper structure are clearly visible. 00:03-00:04 Back three-quarter view. The shoe continues rotating smoothly, revealing the heel construction, back curve, stitching, and rear details. 00:04-00:05 Back view. The heel faces directly toward the camera. The shoe remains centered and fully visible, with clean symmetry. 00:05-00:06 Opposite back three-quarter view. The rotation continues at the same slow speed, showing the opposite side details and rear shape. 00:06-00:07 Opposite full side profile view. The shoe reaches a clean 90-degree side angle from the other side. The side silhouette, sole, and upper are clearly readable. 00:07-00:08 Front three-quarter view returning to front. The shoe completes the 360-degree rotation and returns smoothly to the original front hero angle. Style: Premium 3D product animation, luxury footwear campaign, realistic materials, sharp focus, smooth motion, clean e-commerce presentation, high-end studio catalog quality. Negative prompt: avoid fast spinning, avoid shaky camera, avoid zooming, avoid changing shoe shape, avoid deformation, avoid cropped shoe, avoid extra shoes, avoid model, avoid feet, avoid hands, avoid text, avoid logo, avoid busy background, avoid dramatic shadows, avoid low resolution, avoid motion blur, avoid unstable rotation ![ shoes Create a clean 3D product video storyboard of a single shoe rotating in place on a seamless neutral studio background. The shoe is displayed as a premium e-commerce hero product, fully visible at all times, centered in the frame, with accurate proportions, realistic materials, crisp edges, and refined studio lighting. Camera: Static camera, straight-on product view, 50mm lens feel, no camera movement, no zoom, no distortion. Lighting: Soft diffused studio lighting from the front and slightly above, gentle shadow under the shoe, subtle floor reflection, clean high-end catalog look. Background: Seamless light gray studio background, minimal, clean, no props, no text, no logo, no hands, no model. Rotation speed: Slow and smooth 360-degree rotation over 8 seconds. The motion should feel premium and controlled, not fast. Consistent speed throughout the rotation, no sudden jumps. Storyboard: 00:00-00:01 Front view of the shoe. The toe box faces directly toward the camera. The shoe is centered, stable, and fully visible. 00:01-00:02 Front three-quarter view. The shoe rotates slowly to reveal the front shape, side profile, sole thickness, and material texture. 00:02-00:03 Full side profile view. The shoe is shown from a clean 90-degree side angle. The silhouette, heel height, sole line, and upper structure are clearly visible. 00:03-00:04 Back three-quarter view. The shoe continues rotating smoothly, revealing the heel construction, back curve, stitching, and rear details. 00:04-00:05 Back view. The heel faces directly toward the camera. The shoe remains centered and fully visible, with clean symmetry. 00:05-00:06 Opposite back three-quarter view. The rotation continues at the same slow speed, showing the opposite side details and rear shape. 00:06-00:07 Opposite full side profile view. The shoe reaches a clean 90-degree side angle from the other side. The side silhouette, sole, and upper are clearly readable. 00:07-00:08 Front three-quarter view returning to front. The shoe completes the 360-degree rotation and returns smoothly to the original front hero angle. Style: Premium 3D product animation, luxury footwear campaign, realistic materials, sharp focus, smooth motion, clean e-commerce presentation, high-end studio catalog quality. Negative prompt: avoid fast spinning, avoid shaky camera, avoid zooming, avoid changing shoe shape, avoid deformation, avoid cropped shoe, avoid extra shoes, avoid model, avoid feet, avoid hands, avoid text, avoid logo, avoid busy background, avoid dramatic shadows, avoid low resolution, avoid motion blur, avoid unstable rotation](https://mp.astria.ai/ue7b1x856kgwtszdj2otkrshric3) shoes Create a clean 3D product video storyboard of a single shoe rotating in place on a seamless neutral studio background. The shoe is displayed as a premium e-commerce hero product, fully visible at all times, centered in the frame, with accurate proportions, realistic materials, crisp edges, and refined studio lighting. Camera: Static camera, straight-on product view, 50mm lens feel, no camera movement, no zoom, no distortion. Lighting: Soft diffused studio lighting from the front and slightly above, gentle shadow under the shoe, subtle floor reflection, clean high-end catalog look. Background: Seamless light gray studio background, minimal, clean, no props, no text, no logo, no hands, no model. Rotation speed: Slow and smooth 360-degree rotation over 8 seconds. The motion should feel premium and controlled, not fast. Consistent speed throughout the rotation, no sudden jumps. Storyboard: 00:00-00:01 Front view of the shoe. The toe box faces directly toward the camera. The shoe is centered, stable, and fully visible. 00:01-00:02 Front three-quarter view. The shoe rotates slowly to reveal the front shape, side profile, sole thickness, and material texture. 00:02-00:03 Full side profile view. The shoe is shown from a clean 90-degree side angle. The silhouette, heel height, sole line, and upper structure are clearly visible. 00:03-00:04 Back three-quarter view. The shoe continues rotating smoothly, revealing the heel construction, back curve, stitching, and rear details. 00:04-00:05 Back view. The heel faces directly toward the camera. The shoe remains centered and fully visible, with clean symmetry. 00:05-00:06 Opposite back three-quarter view. The rotation continues at the same slow speed, showing the opposite side details and rear shape. 00:06-00:07 Opposite full side profile view. The shoe reaches a clean 90-degree side angle from the other side. The side silhouette, sole, and upper are clearly readable. 00:07-00:08 Front three-quarter view returning to front. The shoe completes the 360-degree rotation and returns smoothly to the original front hero angle. Style: Premium 3D product animation, luxury footwear campaign, realistic materials, sharp focus, smooth motion, clean e-commerce presentation, high-end studio catalog quality. Negative prompt: avoid fast spinning, avoid shaky camera, avoid zooming, avoid changing shoe shape, avoid deformation, avoid cropped shoe, avoid extra shoes, avoid model, avoid feet, avoid hands, avoid text, avoid logo, avoid busy background, avoid dramatic shadows, avoid low resolution, avoid motion blur, avoid unstable rotation ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/348bxygfh2t3aiimyzhhvpcvxv89) ![](https://mp.astria.ai/d91ki5ozzpmxtt46yjrte8wo2pbw) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/i989exa5wiewi7ekixr6xbcw4ljy) ![](https://mp.astria.ai/k6l07194r6gbc45z6cpsi0f064bv) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/iqq52xwa7cbkeazzp0yi9kam0hxe) ![](https://mp.astria.ai/wq7y0rliabn0pln9yr8urmx2abro) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/sclgkb6iux2mng88easv0wop2o08) ![](https://mp.astria.ai/enx88lx0mwsdns3hilg9ibtxjjwu) ![](https://mp.astria.ai/mqh7o87wsvnf4tssx91r1hhr01u0) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/r0dopyrudbs3kpsr16vlm5mqwzqi) ![](https://mp.astria.ai/k3vhs1fzl0s54pjjuyu6abeo0prk) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/ofi9w5fehyanpjacvoz1y2aqk03r) ![](https://mp.astria.ai/q6my7e2r8svv3gwo3hfyshkvbz1w) ![](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/0ke0sgmu56d5510oenxg9mj2d46n) ![](https://mp.astria.ai/bz7635w14mmcqgkvzho04zmkur5a) ![A direct flash "paparazzi" style shot of woman moving through the crowded bar. She is looking straight into the camera lens with an intense or surprised expression. She wears the dress and the bag on her shoulder. The background is dark and out of focus. High contrast, sharp flash. --video --video_model cinematic_video --duration 10 --video_prompt "Shot 1: She enters the room, standing looking around, fixing her hair Shot 2: She swiftly moves through the crowd Shot 3: She comes over to the bar Shot 3: Barman pours a drink Shot 4: She leans back at the back, smiling, winking, flying a kiss The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman. Bass music in the background with electronic synths playing Atomic"](https://mp.astria.ai/zl99rbxno1yid5jt8e4fk00mc5kg) A direct flash "paparazzi" style shot of woman moving through the crowded bar. She is looking straight into the camera lens with an intense or surprised expression. She wears the dress and the bag on her shoulder. The background is dark and out of focus. High contrast, sharp flash. --video --video\_model cinematic\_video --duration 10 --video\_prompt "Shot 1: She enters the room, standing looking around, fixing her hair Shot 2: She swiftly moves through the crowd Shot 3: She comes over to the bar Shot 3: Barman pours a drink Shot 4: She leans back at the back, smiling, winking, flying a kiss The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman. Bass music in the background with electronic synths playing Atomic" ![A direct flash "paparazzi" style shot of woman moving through the crowded bar. She is looking straight into the camera lens with an intense or surprised expression. She wears the dress and the bag on her shoulder. The background is dark and out of focus. High contrast, sharp flash. --video --video_model cinematic_video --duration 10 --video_prompt "Shot 1: She enters the room, standing looking around, fixing her hair Shot 2: She swiftly moves through the crowd Shot 3: She comes over to the bar Shot 3: Barman pours a drink Shot 4: She leans back at the back, smiling, winking, flying a kiss The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman. Bass music in the background with electronic synths playing Atomic"](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/3lf7m87ettn8c7hnkgzmz4zmgcdw) A direct flash "paparazzi" style shot of woman moving through the crowded bar. She is looking straight into the camera lens with an intense or surprised expression. She wears the dress and the bag on her shoulder. The background is dark and out of focus. High contrast, sharp flash. --video --video\_model cinematic\_video --duration 10 --video\_prompt "Shot 1: She enters the room, standing looking around, fixing her hair Shot 2: She swiftly moves through the crowd Shot 3: She comes over to the bar Shot 3: Barman pours a drink Shot 4: She leans back at the back, smiling, winking, flying a kiss The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman. Bass music in the background with electronic synths playing Atomic" ![Zara editorial style dynamic photograph capturing woman walking through a narrow vintage hotel lobby corridor. She wears the reference jacket zipped midway (half-closed) for a relaxed drape, over shirt , skirt , and shoes . Caught mid-stride on the red patterned carpet runner. Flash freezes the motion. Background of wallpapered walls and wall lights. High film grain. --video --video_model cinematic_video --duration 10 --video_prompt "Shot 1: She walk into the corridor, passing the camera Shot 2: Shoulder view behind her, she paces forward opens the door Shot 3: Eagle eye view of the room full of drinks as she enters the room The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman"](https://mp.astria.ai/58l5poi5hqn2bab6dayfebj1z7za) Zara editorial style dynamic photograph capturing woman walking through a narrow vintage hotel lobby corridor. She wears the reference jacket zipped midway (half-closed) for a relaxed drape, over shirt , skirt , and shoes . Caught mid-stride on the red patterned carpet runner. Flash freezes the motion. Background of wallpapered walls and wall lights. High film grain. --video --video\_model cinematic\_video --duration 10 --video\_prompt "Shot 1: She walk into the corridor, passing the camera Shot 2: Shoulder view behind her, she paces forward opens the door Shot 3: Eagle eye view of the room full of drinks as she enters the room The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman" ![Zara editorial style dynamic photograph capturing woman walking through a narrow vintage hotel lobby corridor. She wears the reference jacket zipped midway (half-closed) for a relaxed drape, over shirt , skirt , and shoes . Caught mid-stride on the red patterned carpet runner. Flash freezes the motion. Background of wallpapered walls and wall lights. High film grain. --video --video_model cinematic_video --duration 10 --video_prompt "Shot 1: She walk into the corridor, passing the camera Shot 2: Shoulder view behind her, she paces forward opens the door Shot 3: Eagle eye view of the room full of drinks as she enters the room The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman"](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/e1r8v9vw28vuafynihidb5a4jie6) Zara editorial style dynamic photograph capturing woman walking through a narrow vintage hotel lobby corridor. She wears the reference jacket zipped midway (half-closed) for a relaxed drape, over shirt , skirt , and shoes . Caught mid-stride on the red patterned carpet runner. Flash freezes the motion. Background of wallpapered walls and wall lights. High film grain. --video --video\_model cinematic\_video --duration 10 --video\_prompt "Shot 1: She walk into the corridor, passing the camera Shot 2: Shoulder view behind her, she paces forward opens the door Shot 3: Eagle eye view of the room full of drinks as she enters the room The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman" ![A direct flash "paparazzi" style shot of woman moving through the crowded bar. She is looking straight into the camera lens with an intense or surprised expression. She wears the dress and the bag on her shoulder. The background is dark and out of focus. High contrast, sharp flash. --video --video_model cinematic_video --duration 10 --video_prompt "Shot 1: She enters the part, fixing her hair Shot 2: She comes over to the bar Shot 3: Barman pours a drink Shot 4: She leans back at the back, smiling, winking, flying a kiss The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman"](https://mp.astria.ai/fmbz3h36nxmp9r80ulmxdgctnuel) A direct flash "paparazzi" style shot of woman moving through the crowded bar. She is looking straight into the camera lens with an intense or surprised expression. She wears the dress and the bag on her shoulder. The background is dark and out of focus. High contrast, sharp flash. --video --video\_model cinematic\_video --duration 10 --video\_prompt "Shot 1: She enters the part, fixing her hair Shot 2: She comes over to the bar Shot 3: Barman pours a drink Shot 4: She leans back at the back, smiling, winking, flying a kiss The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman" ![A direct flash "paparazzi" style shot of woman moving through the crowded bar. She is looking straight into the camera lens with an intense or surprised expression. She wears the dress and the bag on her shoulder. The background is dark and out of focus. High contrast, sharp flash. --video --video_model cinematic_video --duration 10 --video_prompt "Shot 1: She enters the part, fixing her hair Shot 2: She comes over to the bar Shot 3: Barman pours a drink Shot 4: She leans back at the back, smiling, winking, flying a kiss The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman"](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/7zqbbv4ftb32mxfc7od02820hz3o) A direct flash "paparazzi" style shot of woman moving through the crowded bar. She is looking straight into the camera lens with an intense or surprised expression. She wears the dress and the bag on her shoulder. The background is dark and out of focus. High contrast, sharp flash. --video --video\_model cinematic\_video --duration 10 --video\_prompt "Shot 1: She enters the part, fixing her hair Shot 2: She comes over to the bar Shot 3: Barman pours a drink Shot 4: She leans back at the back, smiling, winking, flying a kiss The energy is raw and electric, full of grit and joy, capturing the essence of the beautiful woman" ![Editorial full-body photograph of woman wearing coat walking through a minimalist concrete plaza at dusk. The coat is worn open, flowing naturally as she moves. Paired with tailored pants and leather boots. Background shows clean modern architecture with soft ambient city lights beginning to glow. Shot from a low angle to emphasize the coat's silhouette and movement. Natural twilight tones with cool blue undertones. Kodak Portra 400 film grain. Cinematic depth of field. --video --video_model cinematic_video --duration 10 --video_prompt "Shot 1: She walk into the corridor, passing the camera Shot 2: Shoulder view behind her, she paces forward opens the door Shot 3: Eagle eye view of the room full of drinks as she enters the room"](https://mp.astria.ai/0gfexibgbs5z6v7rdrfkhqj5j1c8) Editorial full-body photograph of woman wearing coat walking through a minimalist concrete plaza at dusk. The coat is worn open, flowing naturally as she moves. Paired with tailored pants and leather boots. Background shows clean modern architecture with soft ambient city lights beginning to glow. Shot from a low angle to emphasize the coat's silhouette and movement. Natural twilight tones with cool blue undertones. Kodak Portra 400 film grain. Cinematic depth of field. --video --video\_model cinematic\_video --duration 10 --video\_prompt "Shot 1: She walk into the corridor, passing the camera Shot 2: Shoulder view behind her, she paces forward opens the door Shot 3: Eagle eye view of the room full of drinks as she enters the room" ![Editorial full-body photograph of woman wearing coat walking through a minimalist concrete plaza at dusk. The coat is worn open, flowing naturally as she moves. Paired with tailored pants and leather boots. Background shows clean modern architecture with soft ambient city lights beginning to glow. Shot from a low angle to emphasize the coat's silhouette and movement. Natural twilight tones with cool blue undertones. Kodak Portra 400 film grain. Cinematic depth of field. --video --video_model cinematic_video --duration 10 --video_prompt "Shot 1: She walk into the corridor, passing the camera Shot 2: Shoulder view behind her, she paces forward opens the door Shot 3: Eagle eye view of the room full of drinks as she enters the room"](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/ij1v5fbf5c6a1370mwpmcd23vr69) Editorial full-body photograph of woman wearing coat walking through a minimalist concrete plaza at dusk. The coat is worn open, flowing naturally as she moves. Paired with tailored pants and leather boots. Background shows clean modern architecture with soft ambient city lights beginning to glow. Shot from a low angle to emphasize the coat's silhouette and movement. Natural twilight tones with cool blue undertones. Kodak Portra 400 film grain. Cinematic depth of field. --video --video\_model cinematic\_video --duration 10 --video\_prompt "Shot 1: She walk into the corridor, passing the camera Shot 2: Shoulder view behind her, she paces forward opens the door Shot 3: Eagle eye view of the room full of drinks as she enters the room" ![ sweater model wearing a reference sweater, a woman with a light skin tone dressed in a soft beige pleated skirt that complements the knit texture, standing in a relaxed pose with one leg slightly bent and one hand resting casually at her side, gazing softly toward the camera, set against a light neutral grey studio background with a soft cool tone, illuminated by diffused side light that creates gentle contrast and depth, photographed with an 85mm lens for a refined editorial composition. --video --video_model veo31_fast_1080p --video_prompt "Turning around, showing her sweater"](https://mp.astria.ai/cdn-cgi/image/width=640,fit=scale-down,format=auto,quality=82,metadata=none,onerror=redirect/w67lhnjikgjqp42qllfayqgghsyn) sweater model wearing a reference sweater, a woman with a light skin tone dressed in a soft beige pleated skirt that complements the knit texture, standing in a relaxed pose with one leg slightly bent and one hand resting casually at her side, gazing softly toward the camera, set against a light neutral grey studio background with a soft cool tone, illuminated by diffused side light that creates gentle contrast and depth, photographed with an 85mm lens for a refined editorial composition. --video --video\_model veo31\_fast\_1080p --video\_prompt "Turning around, showing her sweater" ![ sweater model wearing a reference sweater, a woman with a light skin tone dressed in a soft beige pleated skirt that complements the knit texture, standing in a relaxed pose with one leg slightly bent and one hand resting casually at her side, gazing softly toward the camera, set against a light neutral grey studio background with a soft cool tone, illuminated by diffused side light that creates gentle contrast and depth, photographed with an 85mm lens for a refined editorial composition. --video --video_model veo31_fast_1080p --video_prompt "Turning around, showing her sweater"](https://mp.astria.ai/noc0enfrddtum4vb9nutvma9yu3s) sweater model wearing a reference sweater, a woman with a light skin tone dressed in a soft beige pleated skirt that complements the knit texture, standing in a relaxed pose with one leg slightly bent and one hand resting casually at her side, gazing softly toward the camera, set against a light neutral grey studio background with a soft cool tone, illuminated by diffused side light that creates gentle contrast and depth, photographed with an 85mm lens for a refined editorial composition. --video --video\_model veo31\_fast\_1080p --video\_prompt "Turning around, showing her sweater" --- # Templates gallery Source: https://www.astria.ai/gallery/packs Start with a proven creative workflow, then adapt the references and prompts to your project. Filters - - [Popular](https://www.astria.ai/gallery/packs?tags=category%3APopular) - [New](https://www.astria.ai/gallery/packs?tags=category%3ANew) - [Photoshoot](https://www.astria.ai/gallery/packs?tags=category%3APhotoshoot) - [Professional](https://www.astria.ai/gallery/packs?tags=category%3AProfessional) - [Photography](https://www.astria.ai/gallery/packs?tags=category%3APhotography) - [Holiday](https://www.astria.ai/gallery/packs?tags=category%3AHoliday) - [Fashion](https://www.astria.ai/gallery/packs?tags=category%3AFashion) - [Kids](https://www.astria.ai/gallery/packs?tags=category%3AKids) - [Themed](https://www.astria.ai/gallery/packs?tags=category%3AThemed) - [Artistic](https://www.astria.ai/gallery/packs?tags=category%3AArtistic) - [Cultural](https://www.astria.ai/gallery/packs?tags=category%3ACultural) - [Events](https://www.astria.ai/gallery/packs?tags=category%3AEvents) - [Trending](https://www.astria.ai/gallery/packs?tags=category%3ATrending) - [Pets](https://www.astria.ai/gallery/packs?tags=category%3APets) - [Cards](https://www.astria.ai/gallery/packs?tags=category%3ACards) [API docs](https://docs.astria.ai/docs/api/pack/pack/) ## [Popular](https://www.astria.ai/gallery/packs?tags=category%3APopular) [![](https://mp.astria.ai/nehw708vhz47lbgtt1jwboi7svto)](https://www.astria.ai/p/corporate-headshots) ## Corporate Headshots [![](https://mp.astria.ai/vheha0qzfjpih446l7t14fcloigv)](https://www.astria.ai/p/dating) ## Dating [![](https://mp.astria.ai/ddbgy8vgbq8rcr13i7u56zm672vm)](https://www.astria.ai/p/j-crew) ## J. Crew [![](https://mp.astria.ai/yyo9snql0yp0v5nmrificaxtnw20)](https://www.astria.ai/p/natural-headshots) ## Natural looks ## [New](https://www.astria.ai/gallery/packs?tags=category%3ANew) [![](https://mp.astria.ai/3vp2xglmzly61mkxkgbw8p80v6su)](https://www.astria.ai/p/january-winters) ## Winters Fashion [![](https://mp.astria.ai/43l70idw39xyv80lv3nwys9mtp7h)](https://www.astria.ai/p/popular-packs) ## Dating Photos [![](https://mp.astria.ai/3pqpvvhfahftzk6v7gq0gga9n38z)](https://www.astria.ai/p/personal-branding-2025) ## Personal Brand photos [![](https://mp.astria.ai/vqqiuo2lc2m73jp8ennlktfynmve)](https://www.astria.ai/p/office-outfits) ## Office Outfits ## [Photoshoot](https://www.astria.ai/gallery/packs?tags=category%3APhotoshoot) [![](https://mp.astria.ai/p0getoqvbcdsx8w3vfvwv5p8oxnw)](https://www.astria.ai/p/amichai-ai) ## Styled for Success [![](https://mp.astria.ai/hwejdtrt82iz50yhwhfmid6rlok9)](https://www.astria.ai/p/elegant-street-style) ## Elegant Street Style [![](https://mp.astria.ai/6nl8m7snki2630f44cexmg5b5mkp)](https://www.astria.ai/p/modelsempire) ## MODELSWORLD [![](https://mp.astria.ai/uaecwxpwpzu3jf5rs5drfdzca3fu)](https://www.astria.ai/p/portraits_vibrant) ## Vibrant ## [Professional](https://www.astria.ai/gallery/packs?tags=category%3AProfessional) [![](https://mp.astria.ai/m9qx8b49kjtrw6l1zk5qvffrudjy)](https://www.astria.ai/p/speaker) ## Speaker [![](https://mp.astria.ai/7o9p83819li2kkmb8slvhkla9qxr)](https://www.astria.ai/p/realtor) ## Realtor [![](https://mp.astria.ai/pi5s3kjod6qobpyczid3n3yceh4r)](https://www.astria.ai/p/partners-headshots) ## Partner's headshots [![](https://mp.astria.ai/fxc4pv99xu0j9wbuzvr3tpn0r390)](https://www.astria.ai/p/effortless-professionalism) ## Effortless Professionalism ## [Photography](https://www.astria.ai/gallery/packs?tags=category%3APhotography) [![](https://mp.astria.ai/awnf1qpkasm2waq6gce00fyassiu)](https://www.astria.ai/p/nine-moons) ## NINE MOONS [![](https://mp.astria.ai/9j0mztvbvcavxiq9g4l120w32kua)](https://www.astria.ai/p/bat-mitzvah) ## AI Bat Mitzvah photoshoot [![](https://mp.astria.ai/9rwhcyxkevfl56woqcc4oy5mfwyx)](https://www.astria.ai/p/bridal-reverie) ## Bridal Reverie ## [Fashion](https://www.astria.ai/gallery/packs?tags=category%3AFashion) [![](https://mp.astria.ai/mfvprnc42w1bsawi8gwc5hcmjl5w)](https://www.astria.ai/p/portraits_minimalist) ## Minimalist [![](https://mp.astria.ai/fau0iwrg6ll36qnjuke2vad5apih)](https://www.astria.ai/p/neutral-muse) ## Neutral Muse [![](https://mp.astria.ai/qvbhpnfk00l4e40gffwjxdemr6bv)](https://www.astria.ai/p/tennis_club_-74) ## Tennis Club ‘74 [![](https://mp.astria.ai/5ocu8judvq8vuus2jbgz4yx8ggen)](https://www.astria.ai/p/gs-autumn-fashion) ## Autumn Outfits ## [Kids](https://www.astria.ai/gallery/packs?tags=category%3AKids) [![](https://mp.astria.ai/kedhltwssf5zegl9vqdw9hmeu56a)](https://www.astria.ai/p/kids-bday) ## Kids Birthdays [![](https://mp.astria.ai/3xx62ebzl0cwocvai7lhc6yrc6uc)](https://www.astria.ai/p/tiny-dreamers) ## Tiny Dreamers [![](https://mp.astria.ai/e38tynkpd7fkik6njwvdj6x8b0gc)](https://www.astria.ai/p/baby-diy-magic) ## Baby DIY Magic [![](https://mp.astria.ai/byyiqp21hnb5sbhocmefitwz6bez)](https://www.astria.ai/p/kids) ## Childhood album ## [Themed](https://www.astria.ai/gallery/packs?tags=category%3AThemed) [![](https://mp.astria.ai/lpvkmisse8a6yj29de13dikzm9ll)](https://www.astria.ai/p/mythical-creatures) ## Mythical Creatures [![](https://mp.astria.ai/j9e5636ly2gk3dox3rgdqlpmu85p)](https://www.astria.ai/p/vikings) ## Vikings [![](https://mp.astria.ai/qog3fe09gu0r4nqtq5z9zb72499h)](https://www.astria.ai/p/americana) ## Americana [![](https://mp.astria.ai/oq0pjf7bovc17pkf07eik02b9247)](https://www.astria.ai/p/dressed-in-fantasy) ## Dressed in Fantasy ## [Artistic](https://www.astria.ai/gallery/packs?tags=category%3AArtistic) [![](https://mp.astria.ai/23o4jwjonzpowl82318x6ez8sbk9)](https://www.astria.ai/p/botanical-illustration) ## Botanical illustration [![](https://mp.astria.ai/lxxjg34cpafi3r3lylo03p565h4o)](https://www.astria.ai/p/me-iconic) ## Me Iconic [![](https://mp.astria.ai/jwu04zzt8ltuonif6hr3madg9iag)](https://www.astria.ai/p/collage-yourself) ## Collage Yourself ## [Pets](https://www.astria.ai/gallery/packs?tags=category%3APets) [![](https://mp.astria.ai/i3rynf4m5pocxuseo1h07nqnvimt)](https://www.astria.ai/p/furry-memories) ## Furry Memories [![](https://mp.astria.ai/0lrioa4u0b74jnmy7x7p89n025sr)](https://www.astria.ai/p/furry-tales) ## Furry Tales [![](https://mp.astria.ai/p8rp3sykq8r4iapbjf63voy6szzs)](https://www.astria.ai/p/paws-spa) ## Paws & smiles [Show all](https://www.astria.ai/gallery/packs?all=true&) --- # Archive Source: https://www.astria.ai/articles/archive/ ### 2026 - [August 1 - Astria vs Botika (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/) - [August 1 - Astria vs Ayna (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/) - [August 2 - Astria vs Rawshot (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/) - [August 2 - Astria vs Caimera (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/) - [August 3 - Astria vs FASHN (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/) - [August 4 - Astria vs Tellos (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/) - [August 4 - Astria vs Looklet (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/) - [August 5 - Astria vs Pixelz (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/) - [August 5 - Astria vs Graswald (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/) - [August 6 - Astria vs Browzwear (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-browzwear-fashion-ai/) - [August 7 - Astria vs Tolstoy (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-tolstoy-fashion-ai/) - [August 7 - Astria vs DRESSX (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/) - [August 8 - Astria vs FLORA (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-flora-fashion-ai/) - [August 8 - Astria vs Figma Weave (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/) - [August 9 - Astria vs Aiuta (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-aiuta-fashion-ai/) - [August 10 - Astria vs Stoodio (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-stoodio-fashion-ai/) - [August 10 - Astria vs Pletor (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-pletor-fashion-ai/) - [August 11 - Astria vs Stylic (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-stylic-fashion-ai/) - [August 11 - Astria vs Lookgen (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-lookgen-fashion-ai/) - [August 12 - Astria vs Fermat AI: Pricing & Fashion Review (2026)](https://www.astria.ai/articles/astria-vs-fermat-fashion-ai/) - [August 13 - Astria vs Lumoo: Pricing & AI Fashion Review (2026)](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/) - [August 13 - Astria vs FashionLab (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-fashionlab-fashion-ai/) - [August 14 - Virtual Try-On for Fashion Brands (2026): What It Actually Means](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) - [August 14 - Astria vs Emilia AI Studio (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-emilia-fashion-ai/) - [August 14 - AI Team Photos: Consistent Headshots for Remote Teams](https://www.astria.ai/articles/ai-headshots-for-teams/) - [August 15 - Flat Lay to On-Model: Put Clothes on an AI Model](https://www.astria.ai/articles/flat-lay-to-on-model/) - [August 15 - How to Keep the Same AI Model Across a Whole Collection](https://www.astria.ai/articles/consistent-ai-fashion-models/) - [August 16 - How to Create a Lookbook: 8 Steps + Layout Checklist](https://www.astria.ai/articles/how-to-create-a-lookbook/) - [August 17 - Product Photography Cost Per SKU: Formula + Budget Example](https://www.astria.ai/articles/product-photography-cost-per-sku/) - [August 17 - What a Fashion Photoshoot Actually Costs (2026 Breakdown)](https://www.astria.ai/articles/fashion-photoshoot-cost/) - [August 18 - AI vs Traditional Photoshoot: An Honest Cost Comparison](https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/) - [August 19 - AI Jewelry Photography: Where Generic Product Tools Fall Apart](https://www.astria.ai/articles/ai-jewelry-photography/) - [August 19 - AI Footwear Photography: Packshots, On-Foot, and the Shoes That Fight Back](https://www.astria.ai/articles/ai-footwear-photography/) - [August 20 - AI Fashion Film: Turning a Campaign Into Motion](https://www.astria.ai/articles/ai-fashion-film-guide/) - [August 21 - Nano Banana in Photoshop: Generative Editing Without Leaving the Document](https://www.astria.ai/articles/nano-banana-photoshop-guide/) - [August 21 - Botika Alternatives (2026): What to Look at Instead, and Why](https://www.astria.ai/articles/botika-alternatives/) - [August 22 - Best AI Fashion Photography Platforms (2026): A Buyer's Shortlist](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/) - [August 22 - AI Lookbook Guide: From Product Photos to a Collection](https://www.astria.ai/articles/ai-lookbook-guide/) - [August 23 - Astria vs WearView: Pricing & AI Fashion Review (2026)](https://www.astria.ai/articles/astria-vs-wearview-fashion-ai/) - [August 23 - Astria vs StyTrix: Pricing & Fashion Workflow Review (2026)](https://www.astria.ai/articles/astria-vs-stytrix-fashion-ai/) - [August 23 - Astria vs Rewarx Studio (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-rewarx-fashion-ai/) - [August 23 - AI Photoshoots for Fashion Brands: The Complete Guide (2026)](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/) - [August 29 - 3D Packshots for Ecommerce: What They Are and When to Use Them](https://www.astria.ai/articles/3d-packshots-ecommerce/) - [August 30 - Lumoo Alternatives (2026): A Shortlist for Fashion Catalogs](https://www.astria.ai/articles/lumoo-alternatives/) - [August 30 - Ghost Mannequin Photography for Ecommerce: The Complete Apparel Guide](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/) - [August 31 - Vitamin and Supplement Photography Prompts for E-commerce](https://www.astria.ai/articles/vitamin-supplement-photography-prompts/) - [August 31 - StyTrix Alternatives (2026): Design, Sampling, or Photoshoots?](https://www.astria.ai/articles/stytrix-alternatives/) - [August 31 - Shopify Product Media: Images, Video, 3D Models, and the Right Order](https://www.astria.ai/articles/shopify-product-media-guide/) - [September 1 - Wedding Dress Photography Prompts: A Bridal Lookbook Shot List](https://www.astria.ai/articles/wedding-dress-photography-prompts/) - [September 1 - Apparel Product Video: A Production Guide for Ecommerce PDPs](https://www.astria.ai/articles/apparel-product-video-guide/) - [September 1 - Amazon Product Images for Apparel: Main-Image Rules, Detail Views, and Video](https://www.astria.ai/articles/amazon-product-images-apparel/) - [September 2 - Jewelry Photography Prompts: Rings, Necklaces and Earrings](https://www.astria.ai/articles/jewelry-photography-prompts/) - [September 2 - Fashion Video Ads: Build and Test Creative from One Collection](https://www.astria.ai/articles/fashion-video-ads-guide/) - [September 2 - Best 3D Packshot Services for Ecommerce (2026): What to Buy](https://www.astria.ai/articles/best-3d-packshot-services-ecommerce/) - [September 3 - Ghost Mannequin Alternatives: Which Workflow to Choose (2026)](https://www.astria.ai/articles/ghost-mannequin-alternative/) - [September 3 - Beauty Product Photography Prompts: 8 Copy-Ready Setups](https://www.astria.ai/articles/beauty-product-photography-prompts/) - [September 3 - AI Product Photography vs a Packshot Studio: The Hybrid Workflow](https://www.astria.ai/articles/ai-product-photography-vs-studio/) - [September 4 - AI Lookbook Prompts: Build a Cohesive Fashion Sequence](https://www.astria.ai/articles/ai-lookbook-prompts/) - [September 4 - AI Fashion Models: A Casting Guide for Brands](https://www.astria.ai/articles/ai-fashion-models/) - [September 5 - Ecommerce Product Photography Prompts for PDP, Packshots and Campaigns](https://www.astria.ai/articles/ecommerce-product-photography-prompts/) - [September 5 - Astria vs Higgsfield (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-higgsfield-fashion-ai/) - [September 6 - How We Benchmark AI Image Models for Fashion and Product Work](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) - [September 6 - Best AI Image Models for Fashion and Product Photography (2026 Benchmark)](https://www.astria.ai/articles/best-ai-image-models-fashion/) - [September 6 - Which AI Image Model Should You Use in Astria? (2026 Guide)](https://www.astria.ai/articles/astria-image-models/) - [September 6 - AI Fashion Photography Prompts: A Production-Ready Guide](https://www.astria.ai/articles/ai-fashion-photography-prompts/) - [September 7 - AI Product Photography for Ecommerce: Workflow & QA](https://www.astria.ai/articles/ai-product-photography/) - [September 8 - One Live Workspace Brief Across Seven AI Video Models](https://www.astria.ai/articles/video-model-comparison-calibration/) - [September 8 - MAI-Image-2.6 Review: Reference-Led Fashion and Product Editing](https://www.astria.ai/articles/mai-image-2-6-review/) - [September 9 - Nano Banana 2 for Fashion and Product Photography: Tested Guide](https://www.astria.ai/articles/nano-banana-2-for-fashion/) - [September 9 - GPT Image 2.5 Review: Sunburst vs Flare for Reference-Led Fashion](https://www.astria.ai/articles/gpt-image-2-5-review/) - [September 10 - GPT Image 2 for Product Photography, Beauty, and Text: Tested Guide](https://www.astria.ai/articles/gpt-image-2-for-product-photography/) - [September 11 - Seedream 5 for Fashion Photography: Texture, Identity, and Swimwear Test](https://www.astria.ai/articles/seedream-5-for-fashion/) - [September 12 - Muse Image Model Review: Multi-Reference Fashion and Product Tests](https://www.astria.ai/articles/muse-image-model-review/) - [October 4 - FLUX 3 Image vs Nano Banana 2, GPT Image 2.5, and Seedream 5](https://www.astria.ai/articles/flux-3-image-review/) - [October 7 - Nano Banana 2.1 for Fashion, Ecommerce, and Marketing: Comparison](https://www.astria.ai/articles/nano-banana-2-1-fashion-ecommerce-marketing/) ### 2024 - [April 4 - AI Headshots and Portraits Without Training a Model](https://www.astria.ai/articles/ai-headshots-without-model-training/) - [September 18 - Fine-Tuning FLUX.1 on Astria: A Developer's Guide to the API](https://www.astria.ai/articles/fine-tuning-flux/) --- # Astria documentation Source: https://docs.astria.ai/ Fine-tuning and high quality image generation --- # Authors Source: https://www.astria.ai/articles/authors/ - [![Astria Team](https://www.astria.ai/articles/img/logo@2x.webp)](https://www.astria.ai/) [ ## Astria Team ](https://www.astria.ai/)77 AI fashion production --- # Comparisons Source: https://www.astria.ai/articles/tags/comparisons/ The Astria blog Head-to-head looks at Astria and other AI fashion platforms. [ ![](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/hero-portrait-nano-banana-2-1.webp) ](https://www.astria.ai/articles/nano-banana-2-1-fashion-ecommerce-marketing/) [Models](https://www.astria.ai/articles/tags/models/)26 min read ## [Nano Banana 2.1 for Fashion, Ecommerce, and Marketing: Comparison](https://www.astria.ai/articles/nano-banana-2-1-fashion-ecommerce-marketing/) 31 reference-led outputs of Nano Banana 2.1, 2.0, Pro, Sunburst, Flare and Seedream Pro: garment fidelity, identity, ad copy, campaigns and Arena… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamOct 7, 2026 [ ![](https://www.astria.ai/articles/img/covers/flux-3-image-review.webp) ](https://www.astria.ai/articles/flux-3-image-review/) [Models](https://www.astria.ai/articles/tags/models/)16 min read ## [FLUX 3 Image vs Nano Banana 2, GPT Image 2.5, and Seedream 5](https://www.astria.ai/articles/flux-3-image-review/) A reference-led FLUX 3 Image review comparing quality, prompt adherence, editing, references, resolution, speed, pricing approach, and best use cases. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamOct 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/gpt-image-2-5-review.webp) ](https://www.astria.ai/articles/gpt-image-2-5-review/) [Models](https://www.astria.ai/articles/tags/models/)9 min read ## [GPT Image 2.5 Review: Sunburst vs Flare for Reference-Led Fashion](https://www.astria.ai/articles/gpt-image-2-5-review/) A hands-on GPT Image 2.5 comparison of Sunburst and Flare using the same Sloane identity, yellow dress, gold bag, prompt, aspect ratio, and output… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 9, 2026 [ ![](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-jacket-edit.webp) ](https://www.astria.ai/articles/mai-image-2-6-review/) [Models](https://www.astria.ai/articles/tags/models/)11 min read ## [MAI-Image-2.6 Review: Reference-Led Fashion and Product Editing](https://www.astria.ai/articles/mai-image-2-6-review/) Compare MAI-Image-2.6 with Nano Banana 2, Seedream 5, and Muse using supplied product references, direct edits, and fidelity-focused fashion tests. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 8, 2026 [ ![](https://www.astria.ai/articles/img/covers/best-ai-image-models-fashion.webp) ](https://www.astria.ai/articles/best-ai-image-models-fashion/) [Models](https://www.astria.ai/articles/tags/models/)20 min read ## [Best AI Image Models for Fashion and Product Photography (2026 Benchmark)](https://www.astria.ai/articles/best-ai-image-models-fashion/) A reference-led Astria comparison of Nano Banana 2, GPT Image 2, Seedream 5, Muse, GPT Image 2.5 Sunburst and Flare, and MAI-Image-2.6. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-higgsfield-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-higgsfield-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)19 min read ## [Astria vs Higgsfield (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-higgsfield-fashion-ai/) Astria vs Higgsfield for fashion brands: compare a collection-scale production system with a broad AI image, product-content, video, and filmmaking… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 5, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-product-photography-vs-studio.webp) ](https://www.astria.ai/articles/ai-product-photography-vs-studio/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [AI Product Photography vs a Packshot Studio: The Hybrid Workflow](https://www.astria.ai/articles/ai-product-photography-vs-studio/) Decide what fashion brands should photograph physically, what AI can produce from approved references, and how to run a hybrid packshot-to-campaign… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/ghost-mannequin-alternative.webp) ](https://www.astria.ai/articles/ghost-mannequin-alternative/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Ghost Mannequin Alternatives: Which Workflow to Choose (2026)](https://www.astria.ai/articles/ghost-mannequin-alternative/) Compare flat lay, hanger, live-model and AI on-model alternatives to ghost mannequin photography. Decide what to replace, what to keep, and how to… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/best-3d-packshot-services-ecommerce.webp) ](https://www.astria.ai/articles/best-3d-packshot-services-ecommerce/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)16 min read ## [Best 3D Packshot Services for Ecommerce (2026): What to Buy](https://www.astria.ai/articles/best-3d-packshot-services-ecommerce/) Compare managed packshot studios, automated 360 capture, true 3D model production, and 3D asset delivery by input, output, QA, and operating model. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/stytrix-alternatives.webp) ](https://www.astria.ai/articles/stytrix-alternatives/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)8 min read ## [StyTrix Alternatives (2026): Design, Sampling, or Photoshoots?](https://www.astria.ai/articles/stytrix-alternatives/) Compare StyTrix alternatives by the work you need: Fermat for design iteration, Browzwear for 3D sampling, and Astria for product imagery. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 31, 2026 --- # Comparisons Source: https://www.astria.ai/articles/tags/comparisons/page/2/ The Astria blog Head-to-head looks at Astria and other AI fashion platforms. [ ![](https://www.astria.ai/articles/img/covers/lumoo-alternatives.webp) ](https://www.astria.ai/articles/lumoo-alternatives/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)9 min read ## [Lumoo Alternatives (2026): A Shortlist for Fashion Catalogs](https://www.astria.ai/articles/lumoo-alternatives/) Compare Lumoo Air alternatives for fashion catalogs: WearView, FASHN, and Astria, with switching criteria, credit pitfalls, and a practical product… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 30, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-rewarx-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-rewarx-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)16 min read ## [Astria vs Rewarx Studio (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-rewarx-fashion-ai/) Astria vs Rewarx Studio for fashion brands: a 20-industry AI product-photography suite metered in credits versus a fashion production system built… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 23, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-stytrix-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-stytrix-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)6 min read ## [Astria vs StyTrix: Pricing & Fashion Workflow Review (2026)](https://www.astria.ai/articles/astria-vs-stytrix-fashion-ai/) StyTrix pricing, credits and workflow reviewed against Astria. Compare design tools, reusable assets, team limits and the cost of producing a… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 23, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-wearview-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-wearview-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)6 min read ## [Astria vs WearView: Pricing & AI Fashion Review (2026)](https://www.astria.ai/articles/astria-vs-wearview-fashion-ai/) WearView pricing and credits reviewed against Astria: monthly plans, HD to 4K image costs, video, rollover, and a practical test for lookbooks and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 23, 2026 [ ![](https://www.astria.ai/articles/img/covers/best-ai-fashion-photography-platforms.webp) ](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)12 min read ## [Best AI Fashion Photography Platforms (2026): A Buyer's Shortlist](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/) Choose the best AI fashion model generator for one-off images, collection production, virtual try-on, listing cleanup, or managed delivery. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 22, 2026 [ ![](https://www.astria.ai/articles/img/covers/botika-alternatives.webp) ](https://www.astria.ai/articles/botika-alternatives/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)8 min read ## [Botika Alternatives (2026): What to Look at Instead, and Why](https://www.astria.ai/articles/botika-alternatives/) Comparing Botika alternatives for AI fashion photography — production systems, try-on tools, and listing generators, sorted by the problem each one… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 21, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-emilia-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-emilia-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)15 min read ## [Astria vs Emilia AI Studio (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-emilia-fashion-ai/) Astria vs Emilia AI Studio for fashion brands: a general ecommerce product-photography tool with plan-capped casting versus a fashion production… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 14, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-fashionlab-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-fashionlab-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs FashionLab (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-fashionlab-fashion-ai/) Astria vs FashionLab for fashion brands: a talent-marketplace content platform versus a production system for collections, categories, motion, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 13, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-lumoo-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)7 min read ## [Astria vs Lumoo: Pricing & AI Fashion Review (2026)](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/) Lumoo Air pricing and workflow reviewed against Astria: euro plans, image and try-on allowances, collaboration, and a practical collection-production… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 13, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-fermat-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-fermat-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)7 min read ## [Astria vs Fermat AI: Pricing & Fashion Review (2026)](https://www.astria.ai/articles/astria-vs-fermat-fashion-ai/) Fermat AI pricing and features reviewed against Astria: Pro and Studio credits, design tools, photography workflows, team access and enterprise… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 12, 2026 --- # Comparisons Source: https://www.astria.ai/articles/tags/comparisons/page/3/ The Astria blog Head-to-head looks at Astria and other AI fashion platforms. [ ![](https://www.astria.ai/articles/img/covers/astria-vs-lookgen-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-lookgen-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Lookgen (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-lookgen-fashion-ai/) Astria vs Lookgen for fashion brands: styled outfit generation with PIM publishing versus a full production system for catalog, campaign, social, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 11, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-stylic-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-stylic-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Stylic (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-stylic-fashion-ai/) Astria vs Stylic for fashion brands: a low-cost marketplace-seller photo tool versus a production system professional photographers and studios… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 11, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-pletor-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-pletor-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)12 min read ## [Astria vs Pletor (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-pletor-fashion-ai/) Astria vs Pletor for fashion brands: an ad-production engine for marketing teams versus a fashion production system for catalog, lookbook, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 10, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-stoodio-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-stoodio-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Stoodio (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-stoodio-fashion-ai/) Astria vs Stoodio for fashion brands: licensed real-model digital twins and guided onboarding versus a self-directed production workspace built for… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 10, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-aiuta-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-aiuta-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Aiuta (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-aiuta-fashion-ai/) Astria vs Aiuta for fashion brands: an enterprise try-on vendor with a QA-delivered studio service versus a production workspace your own creative… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 9, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-figma-weave-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Figma Weave (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/) Astria vs Figma Weave for fashion brands: a general node-based AI canvas versus a fashion-specific production system for catalog, lookbook, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 8, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-flora-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-flora-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs FLORA (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-flora-fashion-ai/) Astria vs FLORA for fashion brands: design-stage ideation and a broad model canvas versus a production system built for collections, casting, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 8, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-dressx-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs DRESSX (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/) Astria vs DRESSX for fashion brands: shopper-facing virtual try-on versus AI production of the catalog, lookbook, campaign, and social imagery that… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 7, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-tolstoy-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-tolstoy-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Tolstoy (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-tolstoy-fashion-ai/) Astria vs Tolstoy for fashion brands: a bundled commerce platform with an AI content module versus a fashion-specific production system built for… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 7, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-browzwear-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-browzwear-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Browzwear (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-browzwear-fashion-ai/) Astria vs Browzwear for fashion brands: 3D design and fit validation versus AI production of catalog, lookbook, and campaign imagery — and where each… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 6, 2026 --- # Comparisons Source: https://www.astria.ai/articles/tags/comparisons/page/4/ The Astria blog Head-to-head looks at Astria and other AI fashion platforms. [ ![](https://www.astria.ai/articles/img/covers/astria-vs-graswald-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)15 min read ## [Astria vs Graswald (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/) Astria vs Graswald for fashion brands: compare brand avatars, category coverage, collaboration, external handoff, campaign work, and collection-scale… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 5, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-pixelz-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Pixelz (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/) Astria vs Pixelz for fashion brands: compare managed post-production against an owned AI production workflow — turnaround, cost stacking, control,… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 5, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-looklet-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Looklet (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/) Astria vs Looklet for fashion brands: compare studio capture requirements, digitized models, creative direction, channel coverage, collaboration, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-tellos-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Tellos (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/) Astria vs Tellos for fashion brands: compare AI fashion video, shoppable storefront content, art direction, catalog production, collaboration, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-fashn-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs FASHN (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/) Astria vs FASHN for fashion brands: compare virtual try-on, art direction, catalog production, collaboration, video, and running a collection end to… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-caimera-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)12 min read ## [Astria vs Caimera (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/) Astria vs Caimera for fashion brands: compare creative control, product fidelity, team workflows, video, pricing transparency, and catalog production. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-rawshot-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Rawshot (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/) Astria vs Rawshot for fashion brands: compare art direction, casting, catalog consistency, video, collaboration, and production at collection scale. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-ayna-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Ayna (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/) Astria vs Ayna for fashion brands: compare creative direction, product fidelity, listing automation, video, collaboration, and collection-scale… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-botika-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)12 min read ## [Astria vs Botika (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/) Astria vs Botika for fashion brands: compare professional art direction, product fidelity, templates, video, collaboration, and catalog production. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 1, 2026 --- # Economics Source: https://www.astria.ai/articles/tags/economics/ The Astria blog What fashion and product imagery actually costs, per shoot and per SKU. [ ![](https://www.astria.ai/articles/img/covers/ai-vs-traditional-photoshoot-cost.webp) ](https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/) [Economics](https://www.astria.ai/articles/tags/economics/)9 min read ## [AI vs Traditional Photoshoot: An Honest Cost Comparison](https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/) Comparing generation rates with full shoot budgets is misleading. A fully-loaded model of both approaches, where the break-even sits, and when a… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 18, 2026 [ ![](https://www.astria.ai/articles/img/covers/fashion-photoshoot-cost.webp) ](https://www.astria.ai/articles/fashion-photoshoot-cost/) [Economics](https://www.astria.ai/articles/tags/economics/)9 min read ## [What a Fashion Photoshoot Actually Costs (2026 Breakdown)](https://www.astria.ai/articles/fashion-photoshoot-cost/) A line-by-line look at fashion photoshoot costs — crew, studio, samples, post — why quotes vary so widely, and the hidden items that blow up budgets. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 17, 2026 [ ![](https://www.astria.ai/articles/img/covers/product-photography-cost-per-sku.webp) ](https://www.astria.ai/articles/product-photography-cost-per-sku/) [Economics](https://www.astria.ai/articles/tags/economics/)10 min read ## [Product Photography Cost Per SKU: Formula + Budget Example](https://www.astria.ai/articles/product-photography-cost-per-sku/) Calculate product photography cost per SKU with a worked budget, per-image comparison, colourway counting rules, and a checklist for comparing studio… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 17, 2026 --- # Engineering Source: https://www.astria.ai/articles/tags/engineering/ The Astria blog Fine-tuning, APIs, and tooling for developers building on Astria. [ O ](https://www.astria.ai/articles/video-model-comparison-calibration/) [Models](https://www.astria.ai/articles/tags/models/)6 min read ## [One Live Workspace Brief Across Seven AI Video Models](https://www.astria.ai/articles/video-model-comparison-calibration/) A transparent calibration of seven video models using a reference-aware runway brief from Astria's Live workspace, with disclosed Kling and Veo… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 8, 2026 [ ![](https://www.astria.ai/articles/img/covers/how-we-benchmark-ai-image-models.webp) ](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) [Models](https://www.astria.ai/articles/tags/models/)9 min read ## [How We Benchmark AI Image Models for Fashion and Product Work](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) The prompts, references, selection rule, limitations, and disclosure behind Astria's 2026 fashion image-model comparison. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/nano-banana-photoshop-guide.webp) ](https://www.astria.ai/articles/nano-banana-photoshop-guide/) [Engineering](https://www.astria.ai/articles/tags/engineering/)7 min read ## [Nano Banana in Photoshop: Generative Editing Without Leaving the Document](https://www.astria.ai/articles/nano-banana-photoshop-guide/) How the Astria Photoshop plugin brings Nano Banana, Seedream, and Flux into a live document — selection-based edits, layer workflow, and when to use… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 21, 2026 [ ![](https://www.astria.ai/articles/img/covers/fine-tuning-flux.webp) ](https://www.astria.ai/articles/fine-tuning-flux/) [Engineering](https://www.astria.ai/articles/tags/engineering/)10 min read ## [Fine-Tuning FLUX.1 on Astria: A Developer's Guide to the API](https://www.astria.ai/articles/fine-tuning-flux/) Train a FLUX.1 LoRA on your own images through Astria's API — key setup, a working Python fine-tune and generation script, and what changed by 2026. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 18, 2024 --- # Guides Source: https://www.astria.ai/articles/tags/guides/ The Astria blog How to plan, direct, and run AI fashion photoshoots that stay on brand. [ ![](https://www.astria.ai/articles/img/covers/muse-image-model.webp) ](https://www.astria.ai/articles/muse-image-model-review/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [Muse Image Model Review: Multi-Reference Fashion and Product Tests](https://www.astria.ai/articles/muse-image-model-review/) A hands-on Muse Image review for jewelry, complex references, product fidelity, identity, fashion imagery, constraints, and production routing in… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 12, 2026 [ ![](https://www.astria.ai/articles/img/covers/seedream-5-fashion.webp) ](https://www.astria.ai/articles/seedream-5-for-fashion/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [Seedream 5 for Fashion Photography: Texture, Identity, and Swimwear Test](https://www.astria.ai/articles/seedream-5-for-fashion/) A hands-on Seedream 5 Pro guide for fashion, fabric texture, saturated color, identity, swimwear, product references, and model routing in Astria. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 11, 2026 [ ![](https://www.astria.ai/articles/img/covers/gpt-image-2-fashion.webp) ](https://www.astria.ai/articles/gpt-image-2-for-product-photography/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [GPT Image 2 for Product Photography, Beauty, and Text: Tested Guide](https://www.astria.ai/articles/gpt-image-2-for-product-photography/) A hands-on GPT Image 2 review for beauty campaigns, packaging, typography, fashion, jewelry, references, and production routing in Astria. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 10, 2026 [ ![](https://www.astria.ai/articles/img/covers/nano-banana-2-fashion.webp) ](https://www.astria.ai/articles/nano-banana-2-for-fashion/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [Nano Banana 2 for Fashion and Product Photography: Tested Guide](https://www.astria.ai/articles/nano-banana-2-for-fashion/) See where Nano Banana 2 wins for fashion imagery, where Seedream, GPT Image, Muse, or MAI may be better, and how to prompt it with references. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 9, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-product-photography.webp) ](https://www.astria.ai/articles/ai-product-photography/) [Guides](https://www.astria.ai/articles/tags/guides/)14 min read ## [AI Product Photography for Ecommerce: Workflow & QA](https://www.astria.ai/articles/ai-product-photography/) AI product photography turns product references into catalog, on-model, lifestyle, and campaign images. Learn the workflow, inputs, quality checks,… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 7, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-fashion-photography-prompts.webp) ](https://www.astria.ai/articles/ai-fashion-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)16 min read ## [AI Fashion Photography Prompts: A Production-Ready Guide](https://www.astria.ai/articles/ai-fashion-photography-prompts/) Write AI fashion photography prompts for catalog, editorial, movement, detail and lookbook images—with 15 copyable examples and public templates. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-image-models.webp) ](https://www.astria.ai/articles/astria-image-models/) [Models](https://www.astria.ai/articles/tags/models/)8 min read ## [Which AI Image Model Should You Use in Astria? 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A brand guide to casting, product accuracy, usage rights, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-lookbook-prompts.webp) ](https://www.astria.ai/articles/ai-lookbook-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)9 min read ## [AI Lookbook Prompts: Build a Cohesive Fashion Sequence](https://www.astria.ai/articles/ai-lookbook-prompts/) Copy AI lookbook prompts for openers, full looks, movement, details and closing frames, with a repeatable sequence system and public Astria examples. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 4, 2026 --- # Guides Source: https://www.astria.ai/articles/tags/guides/page/2/ The Astria blog How to plan, direct, and run AI fashion photoshoots that stay on brand. [ ![](https://www.astria.ai/articles/img/covers/beauty-product-photography-prompts.webp) ](https://www.astria.ai/articles/beauty-product-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)8 min read ## [Beauty Product Photography Prompts: 8 Copy-Ready Setups](https://www.astria.ai/articles/beauty-product-photography-prompts/) Copy beauty product photography prompts for packshots, texture, hands, routines and campaigns, with visual references and public Astria examples. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/fashion-video-ads-guide.webp) ](https://www.astria.ai/articles/fashion-video-ads-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [Fashion Video Ads: Build and Test Creative from One Collection](https://www.astria.ai/articles/fashion-video-ads-guide/) Build fashion video ads from approved collection assets: a two-hook test, a 15-second edit brief, placement checks, product proof, and cost per… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/jewelry-photography-prompts.webp) ](https://www.astria.ai/articles/jewelry-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)7 min read ## [Jewelry Photography Prompts: Rings, Necklaces and Earrings](https://www.astria.ai/articles/jewelry-photography-prompts/) Copy AI jewelry photography prompts for macro packshots, on-body scale and campaign images, with visual references and public Astria examples. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/amazon-product-images-apparel.webp) ](https://www.astria.ai/articles/amazon-product-images-apparel/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [Amazon Product Images for Apparel: Main-Image Rules, Detail Views, and Video](https://www.astria.ai/articles/amazon-product-images-apparel/) How to build Amazon apparel image sets around a compliant main image, useful alternate views, product-accuracy review, and video that shows the… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/apparel-product-video-guide.webp) ](https://www.astria.ai/articles/apparel-product-video-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [Apparel Product Video: A Production Guide for Ecommerce PDPs](https://www.astria.ai/articles/apparel-product-video-guide/) Plan apparel product videos around buying questions: shot lists, filmed versus AI motion, garment accuracy, production costs, and a measurable PDP… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/wedding-dress-photography-prompts.webp) ](https://www.astria.ai/articles/wedding-dress-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)7 min read ## [Wedding Dress Photography Prompts: A Bridal Lookbook Shot List](https://www.astria.ai/articles/wedding-dress-photography-prompts/) Copy wedding dress photography prompts for front, back, train, lace and movement images, with a visual example and public Astria links. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/shopify-product-media-guide.webp) ](https://www.astria.ai/articles/shopify-product-media-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [Shopify Product Media: Images, Video, 3D Models, and the Right Order](https://www.astria.ai/articles/shopify-product-media-guide/) A practical Shopify product media guide: current image, video, and 3D limits; 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What the formats do, where they earn their place, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 29, 2026 --- # Guides Source: https://www.astria.ai/articles/tags/guides/page/3/ The Astria blog How to plan, direct, and run AI fashion photoshoots that stay on brand. [ ![](https://www.astria.ai/articles/img/covers/ai-fashion-photoshoot-guide.webp) ](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [AI Photoshoots for Fashion Brands: The Complete Guide (2026)](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/) How AI fashion photoshoots actually work in production — casting, treatments, collection scale, cost, and the failure modes nobody demos. A practical… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 23, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-lookbook-guide.webp) ](https://www.astria.ai/articles/ai-lookbook-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)13 min read ## [AI Lookbook Guide: From Product Photos to a Collection](https://www.astria.ai/articles/ai-lookbook-guide/) Build an AI fashion lookbook from product photos. Learn what to upload, how to keep models consistent, and what to check before using generated… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 22, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-fashion-film-guide.webp) ](https://www.astria.ai/articles/ai-fashion-film-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)8 min read ## [AI Fashion Film: Turning a Campaign Into Motion](https://www.astria.ai/articles/ai-fashion-film-guide/) Fashion video is where AI production gets hardest and most valuable. Shot structure, continuity across cuts, what still breaks, and a realistic… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 20, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-footwear-photography.webp) ](https://www.astria.ai/articles/ai-footwear-photography/) [Guides](https://www.astria.ai/articles/tags/guides/)8 min read ## [AI Footwear Photography: Packshots, On-Foot, and the Shoes That Fight Back](https://www.astria.ai/articles/ai-footwear-photography/) Footwear has its own conventions — three-quarter hero, sole shot, on-foot context. What AI handles well, where it fails, and how to review shoe… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 19, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-jewelry-photography.webp) ](https://www.astria.ai/articles/ai-jewelry-photography/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [AI Jewelry Photography: Where Generic Product Tools Fall Apart](https://www.astria.ai/articles/ai-jewelry-photography/) Jewelry breaks most AI product photography. Why metal, stones, and scale are uniquely hard, what actually works, and how to review output before it… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 19, 2026 [ ![](https://www.astria.ai/articles/img/covers/how-to-create-a-lookbook.webp) ](https://www.astria.ai/articles/how-to-create-a-lookbook/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [How to Create a Lookbook: 8 Steps + Layout Checklist](https://www.astria.ai/articles/how-to-create-a-lookbook/) Make a fashion lookbook from brief to delivery: choose looks, build a shot list, plan the page order, add wholesale details, and export a useful PDF… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 16, 2026 [ ![](https://www.astria.ai/articles/img/covers/consistent-ai-fashion-models.webp) ](https://www.astria.ai/articles/consistent-ai-fashion-models/) [Guides](https://www.astria.ai/articles/tags/guides/)9 min read ## [How to Keep the Same AI Model Across a Whole Collection](https://www.astria.ai/articles/consistent-ai-fashion-models/) Casting drift is the reason AI catalogs look wrong. Why consistency breaks, the four kinds of consistency you actually need, and how to hold all of… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 15, 2026 [ ![](https://www.astria.ai/articles/img/covers/flat-lay-to-on-model.webp) ](https://www.astria.ai/articles/flat-lay-to-on-model/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [Flat Lay to On-Model: Put Clothes on an AI Model](https://www.astria.ai/articles/flat-lay-to-on-model/) Turn a flat lay or garment photo into an on-model image with AI. Learn the source-image requirements, product-to-model workflow, accuracy checks, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 15, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-headshots-for-teams.webp) ](https://www.astria.ai/articles/ai-headshots-for-teams/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [AI Team Photos: Consistent Headshots for Remote Teams](https://www.astria.ai/articles/ai-headshots-for-teams/) Create AI team photos with matching lighting, backgrounds and crops. A practical workflow for remote headshots, group composites, approvals and new… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 14, 2026 [ ![](https://www.astria.ai/articles/img/covers/virtual-try-on-for-fashion-brands.webp) ](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) [Guides](https://www.astria.ai/articles/tags/guides/)9 min read ## [Virtual Try-On for Fashion Brands (2026): What It Actually Means](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) Two different technologies share the name virtual try-on. Only one of them solves a brand's imagery problem — here's how to tell them apart and what… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 14, 2026 --- # Guides Source: https://www.astria.ai/articles/tags/guides/page/4/ The Astria blog How to plan, direct, and run AI fashion photoshoots that stay on brand. [ ![](https://www.astria.ai/articles/img/covers/face-id.webp) ](https://www.astria.ai/articles/ai-headshots-without-model-training/) [Guides](https://www.astria.ai/articles/tags/guides/)7 min read ## [AI Headshots and Portraits Without Training a Model](https://www.astria.ai/articles/ai-headshots-without-model-training/) Create consistent AI headshots and portraits from one reference photo without training a custom model. Learn when the instant approach works and when… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamApr 4, 2024 --- # Models Source: https://www.astria.ai/articles/tags/models/ The Astria blog Model selection, benchmarks, strengths, and production tradeoffs for Astria image models. [ ![](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/hero-portrait-nano-banana-2-1.webp) ](https://www.astria.ai/articles/nano-banana-2-1-fashion-ecommerce-marketing/) [Models](https://www.astria.ai/articles/tags/models/)26 min read ## [Nano Banana 2.1 for Fashion, Ecommerce, and Marketing: Comparison](https://www.astria.ai/articles/nano-banana-2-1-fashion-ecommerce-marketing/) 31 reference-led outputs of Nano Banana 2.1, 2.0, Pro, Sunburst, Flare and Seedream Pro: garment fidelity, identity, ad copy, campaigns and Arena… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamOct 7, 2026 [ ![](https://www.astria.ai/articles/img/covers/flux-3-image-review.webp) ](https://www.astria.ai/articles/flux-3-image-review/) [Models](https://www.astria.ai/articles/tags/models/)16 min read ## [FLUX 3 Image vs Nano Banana 2, GPT Image 2.5, and Seedream 5](https://www.astria.ai/articles/flux-3-image-review/) A reference-led FLUX 3 Image review comparing quality, prompt adherence, editing, references, resolution, speed, pricing approach, and best use cases. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamOct 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/muse-image-model.webp) ](https://www.astria.ai/articles/muse-image-model-review/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [Muse Image Model Review: Multi-Reference Fashion and Product Tests](https://www.astria.ai/articles/muse-image-model-review/) A hands-on Muse Image review for jewelry, complex references, product fidelity, identity, fashion imagery, constraints, and production routing in… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 12, 2026 [ ![](https://www.astria.ai/articles/img/covers/seedream-5-fashion.webp) ](https://www.astria.ai/articles/seedream-5-for-fashion/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [Seedream 5 for Fashion Photography: Texture, Identity, and Swimwear Test](https://www.astria.ai/articles/seedream-5-for-fashion/) A hands-on Seedream 5 Pro guide for fashion, fabric texture, saturated color, identity, swimwear, product references, and model routing in Astria. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 11, 2026 [ ![](https://www.astria.ai/articles/img/covers/gpt-image-2-fashion.webp) ](https://www.astria.ai/articles/gpt-image-2-for-product-photography/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [GPT Image 2 for Product Photography, Beauty, and Text: Tested Guide](https://www.astria.ai/articles/gpt-image-2-for-product-photography/) A hands-on GPT Image 2 review for beauty campaigns, packaging, typography, fashion, jewelry, references, and production routing in Astria. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 10, 2026 [ ![](https://www.astria.ai/articles/img/covers/gpt-image-2-5-review.webp) ](https://www.astria.ai/articles/gpt-image-2-5-review/) [Models](https://www.astria.ai/articles/tags/models/)9 min read ## [GPT Image 2.5 Review: Sunburst vs Flare for Reference-Led Fashion](https://www.astria.ai/articles/gpt-image-2-5-review/) A hands-on GPT Image 2.5 comparison of Sunburst and Flare using the same Sloane identity, yellow dress, gold bag, prompt, aspect ratio, and output… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 9, 2026 [ ![](https://www.astria.ai/articles/img/covers/nano-banana-2-fashion.webp) ](https://www.astria.ai/articles/nano-banana-2-for-fashion/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [Nano Banana 2 for Fashion and Product Photography: Tested Guide](https://www.astria.ai/articles/nano-banana-2-for-fashion/) See where Nano Banana 2 wins for fashion imagery, where Seedream, GPT Image, Muse, or MAI may be better, and how to prompt it with references. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 9, 2026 [ ![](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-jacket-edit.webp) ](https://www.astria.ai/articles/mai-image-2-6-review/) [Models](https://www.astria.ai/articles/tags/models/)11 min read ## [MAI-Image-2.6 Review: Reference-Led Fashion and Product Editing](https://www.astria.ai/articles/mai-image-2-6-review/) Compare MAI-Image-2.6 with Nano Banana 2, Seedream 5, and Muse using supplied product references, direct edits, and fidelity-focused fashion tests. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 8, 2026 [ O ](https://www.astria.ai/articles/video-model-comparison-calibration/) [Models](https://www.astria.ai/articles/tags/models/)6 min read ## [One Live Workspace Brief Across Seven AI Video Models](https://www.astria.ai/articles/video-model-comparison-calibration/) A transparent calibration of seven video models using a reference-aware runway brief from Astria's Live workspace, with disclosed Kling and Veo… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 8, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-image-models.webp) ](https://www.astria.ai/articles/astria-image-models/) [Models](https://www.astria.ai/articles/tags/models/)8 min read ## [Which AI Image Model Should You Use in Astria? (2026 Guide)](https://www.astria.ai/articles/astria-image-models/) Choose the right Astria image model for reference-led fashion, beauty, swimwear, jewelry, and product editing, with a controlled benchmark and MAI… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 --- # Models Source: https://www.astria.ai/articles/tags/models/page/2/ The Astria blog Model selection, benchmarks, strengths, and production tradeoffs for Astria image models. [ ![](https://www.astria.ai/articles/img/covers/best-ai-image-models-fashion.webp) ](https://www.astria.ai/articles/best-ai-image-models-fashion/) [Models](https://www.astria.ai/articles/tags/models/)20 min read ## [Best AI Image Models for Fashion and Product Photography (2026 Benchmark)](https://www.astria.ai/articles/best-ai-image-models-fashion/) A reference-led Astria comparison of Nano Banana 2, GPT Image 2, Seedream 5, Muse, GPT Image 2.5 Sunburst and Flare, and MAI-Image-2.6. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/how-we-benchmark-ai-image-models.webp) ](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) [Models](https://www.astria.ai/articles/tags/models/)9 min read ## [How We Benchmark AI Image Models for Fashion and Product Work](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) The prompts, references, selection rule, limitations, and disclosure behind Astria's 2026 fashion image-model comparison. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 --- # Notes on AI fashion production Source: https://www.astria.ai/articles/ The Astria blog Comparisons, techniques, and field notes for teams producing on-brand fashion imagery with AI. [Models](https://www.astria.ai/articles/tags/models/)26 min read ## [Nano Banana 2.1 for Fashion, Ecommerce, and Marketing: Comparison](https://www.astria.ai/articles/nano-banana-2-1-fashion-ecommerce-marketing/) 31 reference-led outputs of Nano Banana 2.1, 2.0, Pro, Sunburst, Flare and Seedream Pro: garment fidelity, identity, ad copy, campaigns and Arena ranking. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamOct 7, 2026 [ ![](https://www.astria.ai/articles/img/model-benchmarks/2026-10/nb21/hero-portrait-nano-banana-2-1.webp) ](https://www.astria.ai/articles/nano-banana-2-1-fashion-ecommerce-marketing/) [ ![](https://www.astria.ai/articles/img/covers/flux-3-image-review.webp) ](https://www.astria.ai/articles/flux-3-image-review/) [Models](https://www.astria.ai/articles/tags/models/)16 min read ## [FLUX 3 Image vs Nano Banana 2, GPT Image 2.5, and Seedream 5](https://www.astria.ai/articles/flux-3-image-review/) A reference-led FLUX 3 Image review comparing quality, prompt adherence, editing, references, resolution, speed, pricing approach, and best use cases. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamOct 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/muse-image-model.webp) ](https://www.astria.ai/articles/muse-image-model-review/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [Muse Image Model Review: Multi-Reference Fashion and Product Tests](https://www.astria.ai/articles/muse-image-model-review/) A hands-on Muse Image review for jewelry, complex references, product fidelity, identity, fashion imagery, constraints, and production routing in… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 12, 2026 [ ![](https://www.astria.ai/articles/img/covers/seedream-5-fashion.webp) ](https://www.astria.ai/articles/seedream-5-for-fashion/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [Seedream 5 for Fashion Photography: Texture, Identity, and Swimwear Test](https://www.astria.ai/articles/seedream-5-for-fashion/) A hands-on Seedream 5 Pro guide for fashion, fabric texture, saturated color, identity, swimwear, product references, and model routing in Astria. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 11, 2026 [ ![](https://www.astria.ai/articles/img/covers/gpt-image-2-fashion.webp) ](https://www.astria.ai/articles/gpt-image-2-for-product-photography/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [GPT Image 2 for Product Photography, Beauty, and Text: Tested Guide](https://www.astria.ai/articles/gpt-image-2-for-product-photography/) A hands-on GPT Image 2 review for beauty campaigns, packaging, typography, fashion, jewelry, references, and production routing in Astria. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 10, 2026 [ ![](https://www.astria.ai/articles/img/covers/gpt-image-2-5-review.webp) ](https://www.astria.ai/articles/gpt-image-2-5-review/) [Models](https://www.astria.ai/articles/tags/models/)9 min read ## [GPT Image 2.5 Review: Sunburst vs Flare for Reference-Led Fashion](https://www.astria.ai/articles/gpt-image-2-5-review/) A hands-on GPT Image 2.5 comparison of Sunburst and Flare using the same Sloane identity, yellow dress, gold bag, prompt, aspect ratio, and output… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 9, 2026 [ ![](https://www.astria.ai/articles/img/covers/nano-banana-2-fashion.webp) ](https://www.astria.ai/articles/nano-banana-2-for-fashion/) [Models](https://www.astria.ai/articles/tags/models/)5 min read ## [Nano Banana 2 for Fashion and Product Photography: Tested Guide](https://www.astria.ai/articles/nano-banana-2-for-fashion/) See where Nano Banana 2 wins for fashion imagery, where Seedream, GPT Image, Muse, or MAI may be better, and how to prompt it with references. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 9, 2026 [ ![](https://www.astria.ai/articles/img/model-benchmarks/2026-09/mai-image-2-6-jacket-edit.webp) ](https://www.astria.ai/articles/mai-image-2-6-review/) [Models](https://www.astria.ai/articles/tags/models/)11 min read ## [MAI-Image-2.6 Review: Reference-Led Fashion and Product Editing](https://www.astria.ai/articles/mai-image-2-6-review/) Compare MAI-Image-2.6 with Nano Banana 2, Seedream 5, and Muse using supplied product references, direct edits, and fidelity-focused fashion tests. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 8, 2026 [ O ](https://www.astria.ai/articles/video-model-comparison-calibration/) [Models](https://www.astria.ai/articles/tags/models/)6 min read ## [One Live Workspace Brief Across Seven AI Video Models](https://www.astria.ai/articles/video-model-comparison-calibration/) A transparent calibration of seven video models using a reference-aware runway brief from Astria's Live workspace, with disclosed Kling and Veo… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 8, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-product-photography.webp) ](https://www.astria.ai/articles/ai-product-photography/) [Guides](https://www.astria.ai/articles/tags/guides/)14 min read ## [AI Product Photography for Ecommerce: Workflow & QA](https://www.astria.ai/articles/ai-product-photography/) AI product photography turns product references into catalog, on-model, lifestyle, and campaign images. Learn the workflow, inputs, quality checks,… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 7, 2026 --- # Notes on AI fashion production Source: https://www.astria.ai/articles/page/2/ The Astria blog Comparisons, techniques, and field notes for teams producing on-brand fashion imagery with AI. [ ![](https://www.astria.ai/articles/img/covers/ai-fashion-photography-prompts.webp) ](https://www.astria.ai/articles/ai-fashion-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)16 min read ## [AI Fashion Photography Prompts: A Production-Ready Guide](https://www.astria.ai/articles/ai-fashion-photography-prompts/) Write AI fashion photography prompts for catalog, editorial, movement, detail and lookbook images—with 15 copyable examples and public templates. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-image-models.webp) ](https://www.astria.ai/articles/astria-image-models/) [Models](https://www.astria.ai/articles/tags/models/)8 min read ## [Which AI Image Model Should You Use in Astria? (2026 Guide)](https://www.astria.ai/articles/astria-image-models/) Choose the right Astria image model for reference-led fashion, beauty, swimwear, jewelry, and product editing, with a controlled benchmark and MAI… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/best-ai-image-models-fashion.webp) ](https://www.astria.ai/articles/best-ai-image-models-fashion/) [Models](https://www.astria.ai/articles/tags/models/)20 min read ## [Best AI Image Models for Fashion and Product Photography (2026 Benchmark)](https://www.astria.ai/articles/best-ai-image-models-fashion/) A reference-led Astria comparison of Nano Banana 2, GPT Image 2, Seedream 5, Muse, GPT Image 2.5 Sunburst and Flare, and MAI-Image-2.6. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/how-we-benchmark-ai-image-models.webp) ](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) [Models](https://www.astria.ai/articles/tags/models/)9 min read ## [How We Benchmark AI Image Models for Fashion and Product Work](https://www.astria.ai/articles/how-we-benchmark-ai-image-models/) The prompts, references, selection rule, limitations, and disclosure behind Astria's 2026 fashion image-model comparison. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-higgsfield-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-higgsfield-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)19 min read ## [Astria vs Higgsfield (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-higgsfield-fashion-ai/) Astria vs Higgsfield for fashion brands: compare a collection-scale production system with a broad AI image, product-content, video, and filmmaking… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 5, 2026 [ ![](https://www.astria.ai/articles/img/covers/ecommerce-product-photography-prompts.webp) ](https://www.astria.ai/articles/ecommerce-product-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)19 min read ## [Ecommerce Product Photography Prompts for PDP, Packshots and Campaigns](https://www.astria.ai/articles/ecommerce-product-photography-prompts/) Write ecommerce product photography prompts for clean PDP coverage, packshots, details and campaigns—with copyable examples and public templates. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 5, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-fashion-models.webp) ](https://www.astria.ai/articles/ai-fashion-models/) [Guides](https://www.astria.ai/articles/tags/guides/)14 min read ## [AI Fashion Models: A Casting Guide for Brands](https://www.astria.ai/articles/ai-fashion-models/) Choose between fictional AI fashion models, licensed digital twins, and real talent. A brand guide to casting, product accuracy, usage rights, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-lookbook-prompts.webp) ](https://www.astria.ai/articles/ai-lookbook-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)9 min read ## [AI Lookbook Prompts: Build a Cohesive Fashion Sequence](https://www.astria.ai/articles/ai-lookbook-prompts/) Copy AI lookbook prompts for openers, full looks, movement, details and closing frames, with a repeatable sequence system and public Astria examples. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-product-photography-vs-studio.webp) ](https://www.astria.ai/articles/ai-product-photography-vs-studio/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [AI Product Photography vs a Packshot Studio: The Hybrid Workflow](https://www.astria.ai/articles/ai-product-photography-vs-studio/) Decide what fashion brands should photograph physically, what AI can produce from approved references, and how to run a hybrid packshot-to-campaign… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/beauty-product-photography-prompts.webp) ](https://www.astria.ai/articles/beauty-product-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)8 min read ## [Beauty Product Photography Prompts: 8 Copy-Ready Setups](https://www.astria.ai/articles/beauty-product-photography-prompts/) Copy beauty product photography prompts for packshots, texture, hands, routines and campaigns, with visual references and public Astria examples. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 3, 2026 --- # Notes on AI fashion production Source: https://www.astria.ai/articles/page/3/ The Astria blog Comparisons, techniques, and field notes for teams producing on-brand fashion imagery with AI. [ ![](https://www.astria.ai/articles/img/covers/ghost-mannequin-alternative.webp) ](https://www.astria.ai/articles/ghost-mannequin-alternative/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Ghost Mannequin Alternatives: Which Workflow to Choose (2026)](https://www.astria.ai/articles/ghost-mannequin-alternative/) Compare flat lay, hanger, live-model and AI on-model alternatives to ghost mannequin photography. Decide what to replace, what to keep, and how to… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/best-3d-packshot-services-ecommerce.webp) ](https://www.astria.ai/articles/best-3d-packshot-services-ecommerce/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)16 min read ## [Best 3D Packshot Services for Ecommerce (2026): What to Buy](https://www.astria.ai/articles/best-3d-packshot-services-ecommerce/) Compare managed packshot studios, automated 360 capture, true 3D model production, and 3D asset delivery by input, output, QA, and operating model. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/fashion-video-ads-guide.webp) ](https://www.astria.ai/articles/fashion-video-ads-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [Fashion Video Ads: Build and Test Creative from One Collection](https://www.astria.ai/articles/fashion-video-ads-guide/) Build fashion video ads from approved collection assets: a two-hook test, a 15-second edit brief, placement checks, product proof, and cost per… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/jewelry-photography-prompts.webp) ](https://www.astria.ai/articles/jewelry-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)7 min read ## [Jewelry Photography Prompts: Rings, Necklaces and Earrings](https://www.astria.ai/articles/jewelry-photography-prompts/) Copy AI jewelry photography prompts for macro packshots, on-body scale and campaign images, with visual references and public Astria examples. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/amazon-product-images-apparel.webp) ](https://www.astria.ai/articles/amazon-product-images-apparel/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [Amazon Product Images for Apparel: Main-Image Rules, Detail Views, and Video](https://www.astria.ai/articles/amazon-product-images-apparel/) How to build Amazon apparel image sets around a compliant main image, useful alternate views, product-accuracy review, and video that shows the… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/apparel-product-video-guide.webp) ](https://www.astria.ai/articles/apparel-product-video-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [Apparel Product Video: A Production Guide for Ecommerce PDPs](https://www.astria.ai/articles/apparel-product-video-guide/) Plan apparel product videos around buying questions: shot lists, filmed versus AI motion, garment accuracy, production costs, and a measurable PDP… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/wedding-dress-photography-prompts.webp) ](https://www.astria.ai/articles/wedding-dress-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)7 min read ## [Wedding Dress Photography Prompts: A Bridal Lookbook Shot List](https://www.astria.ai/articles/wedding-dress-photography-prompts/) Copy wedding dress photography prompts for front, back, train, lace and movement images, with a visual example and public Astria links. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/shopify-product-media-guide.webp) ](https://www.astria.ai/articles/shopify-product-media-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [Shopify Product Media: Images, Video, 3D Models, and the Right Order](https://www.astria.ai/articles/shopify-product-media-guide/) A practical Shopify product media guide: current image, video, and 3D limits; theme and variant constraints; and how to order a PDP gallery around… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 31, 2026 [ ![](https://www.astria.ai/articles/img/covers/stytrix-alternatives.webp) ](https://www.astria.ai/articles/stytrix-alternatives/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)8 min read ## [StyTrix Alternatives (2026): Design, Sampling, or Photoshoots?](https://www.astria.ai/articles/stytrix-alternatives/) Compare StyTrix alternatives by the work you need: Fermat for design iteration, Browzwear for 3D sampling, and Astria for product imagery. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 31, 2026 [ ![](https://www.astria.ai/articles/img/covers/vitamin-supplement-photography-prompts.webp) ](https://www.astria.ai/articles/vitamin-supplement-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)7 min read ## [Vitamin and Supplement Photography Prompts for E-commerce](https://www.astria.ai/articles/vitamin-supplement-photography-prompts/) Copy AI supplement photography prompts for bottles, labels, capsules, ingredients and bundles, with a transparent reference workflow and QA checklist. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 31, 2026 --- # Notes on AI fashion production Source: https://www.astria.ai/articles/page/4/ The Astria blog Comparisons, techniques, and field notes for teams producing on-brand fashion imagery with AI. [ ![](https://www.astria.ai/articles/img/covers/ghost-mannequin-photography-ecommerce.webp) ](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [Ghost Mannequin Photography for Ecommerce: The Complete Apparel Guide](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/) How ghost mannequin photography is captured and joined, which garment views it needs, where the technique works, and the QC checks that prevent… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 30, 2026 [ ![](https://www.astria.ai/articles/img/covers/lumoo-alternatives.webp) ](https://www.astria.ai/articles/lumoo-alternatives/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)9 min read ## [Lumoo Alternatives (2026): A Shortlist for Fashion Catalogs](https://www.astria.ai/articles/lumoo-alternatives/) Compare Lumoo Air alternatives for fashion catalogs: WearView, FASHN, and Astria, with switching criteria, credit pitfalls, and a practical product… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 30, 2026 [ ![](https://www.astria.ai/articles/img/covers/3d-packshots-ecommerce.webp) ](https://www.astria.ai/articles/3d-packshots-ecommerce/) [Guides](https://www.astria.ai/articles/tags/guides/)13 min read ## [3D Packshots for Ecommerce: What They Are and When to Use Them](https://www.astria.ai/articles/3d-packshots-ecommerce/) A 3D packshot is an interactive product model, not a spin video or a set of generated angles. What the formats do, where they earn their place, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 29, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-fashion-photoshoot-guide.webp) ](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [AI Photoshoots for Fashion Brands: The Complete Guide (2026)](https://www.astria.ai/articles/ai-fashion-photoshoot-guide/) How AI fashion photoshoots actually work in production — casting, treatments, collection scale, cost, and the failure modes nobody demos. A practical… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 23, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-rewarx-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-rewarx-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)16 min read ## [Astria vs Rewarx Studio (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-rewarx-fashion-ai/) Astria vs Rewarx Studio for fashion brands: a 20-industry AI product-photography suite metered in credits versus a fashion production system built… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 23, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-stytrix-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-stytrix-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)6 min read ## [Astria vs StyTrix: Pricing & Fashion Workflow Review (2026)](https://www.astria.ai/articles/astria-vs-stytrix-fashion-ai/) StyTrix pricing, credits and workflow reviewed against Astria. Compare design tools, reusable assets, team limits and the cost of producing a… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 23, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-wearview-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-wearview-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)6 min read ## [Astria vs WearView: Pricing & AI Fashion Review (2026)](https://www.astria.ai/articles/astria-vs-wearview-fashion-ai/) WearView pricing and credits reviewed against Astria: monthly plans, HD to 4K image costs, video, rollover, and a practical test for lookbooks and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 23, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-lookbook-guide.webp) ](https://www.astria.ai/articles/ai-lookbook-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)13 min read ## [AI Lookbook Guide: From Product Photos to a Collection](https://www.astria.ai/articles/ai-lookbook-guide/) Build an AI fashion lookbook from product photos. Learn what to upload, how to keep models consistent, and what to check before using generated… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 22, 2026 [ ![](https://www.astria.ai/articles/img/covers/best-ai-fashion-photography-platforms.webp) ](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)12 min read ## [Best AI Fashion Photography Platforms (2026): A Buyer's Shortlist](https://www.astria.ai/articles/best-ai-fashion-photography-platforms/) Choose the best AI fashion model generator for one-off images, collection production, virtual try-on, listing cleanup, or managed delivery. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 22, 2026 [ ![](https://www.astria.ai/articles/img/covers/botika-alternatives.webp) ](https://www.astria.ai/articles/botika-alternatives/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)8 min read ## [Botika Alternatives (2026): What to Look at Instead, and Why](https://www.astria.ai/articles/botika-alternatives/) Comparing Botika alternatives for AI fashion photography — production systems, try-on tools, and listing generators, sorted by the problem each one… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 21, 2026 --- # Notes on AI fashion production Source: https://www.astria.ai/articles/page/5/ The Astria blog Comparisons, techniques, and field notes for teams producing on-brand fashion imagery with AI. [ ![](https://www.astria.ai/articles/img/covers/nano-banana-photoshop-guide.webp) ](https://www.astria.ai/articles/nano-banana-photoshop-guide/) [Engineering](https://www.astria.ai/articles/tags/engineering/)7 min read ## [Nano Banana in Photoshop: Generative Editing Without Leaving the Document](https://www.astria.ai/articles/nano-banana-photoshop-guide/) How the Astria Photoshop plugin brings Nano Banana, Seedream, and Flux into a live document — selection-based edits, layer workflow, and when to use… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 21, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-fashion-film-guide.webp) ](https://www.astria.ai/articles/ai-fashion-film-guide/) [Guides](https://www.astria.ai/articles/tags/guides/)8 min read ## [AI Fashion Film: Turning a Campaign Into Motion](https://www.astria.ai/articles/ai-fashion-film-guide/) Fashion video is where AI production gets hardest and most valuable. Shot structure, continuity across cuts, what still breaks, and a realistic… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 20, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-footwear-photography.webp) ](https://www.astria.ai/articles/ai-footwear-photography/) [Guides](https://www.astria.ai/articles/tags/guides/)8 min read ## [AI Footwear Photography: Packshots, On-Foot, and the Shoes That Fight Back](https://www.astria.ai/articles/ai-footwear-photography/) Footwear has its own conventions — three-quarter hero, sole shot, on-foot context. What AI handles well, where it fails, and how to review shoe… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 19, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-jewelry-photography.webp) ](https://www.astria.ai/articles/ai-jewelry-photography/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [AI Jewelry Photography: Where Generic Product Tools Fall Apart](https://www.astria.ai/articles/ai-jewelry-photography/) Jewelry breaks most AI product photography. Why metal, stones, and scale are uniquely hard, what actually works, and how to review output before it… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 19, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-vs-traditional-photoshoot-cost.webp) ](https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/) [Economics](https://www.astria.ai/articles/tags/economics/)9 min read ## [AI vs Traditional Photoshoot: An Honest Cost Comparison](https://www.astria.ai/articles/ai-vs-traditional-photoshoot-cost/) Comparing generation rates with full shoot budgets is misleading. A fully-loaded model of both approaches, where the break-even sits, and when a… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 18, 2026 [ ![](https://www.astria.ai/articles/img/covers/fashion-photoshoot-cost.webp) ](https://www.astria.ai/articles/fashion-photoshoot-cost/) [Economics](https://www.astria.ai/articles/tags/economics/)9 min read ## [What a Fashion Photoshoot Actually Costs (2026 Breakdown)](https://www.astria.ai/articles/fashion-photoshoot-cost/) A line-by-line look at fashion photoshoot costs — crew, studio, samples, post — why quotes vary so widely, and the hidden items that blow up budgets. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 17, 2026 [ ![](https://www.astria.ai/articles/img/covers/product-photography-cost-per-sku.webp) ](https://www.astria.ai/articles/product-photography-cost-per-sku/) [Economics](https://www.astria.ai/articles/tags/economics/)10 min read ## [Product Photography Cost Per SKU: Formula + Budget Example](https://www.astria.ai/articles/product-photography-cost-per-sku/) Calculate product photography cost per SKU with a worked budget, per-image comparison, colourway counting rules, and a checklist for comparing studio… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 17, 2026 [ ![](https://www.astria.ai/articles/img/covers/how-to-create-a-lookbook.webp) ](https://www.astria.ai/articles/how-to-create-a-lookbook/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [How to Create a Lookbook: 8 Steps + Layout Checklist](https://www.astria.ai/articles/how-to-create-a-lookbook/) Make a fashion lookbook from brief to delivery: choose looks, build a shot list, plan the page order, add wholesale details, and export a useful PDF… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 16, 2026 [ ![](https://www.astria.ai/articles/img/covers/consistent-ai-fashion-models.webp) ](https://www.astria.ai/articles/consistent-ai-fashion-models/) [Guides](https://www.astria.ai/articles/tags/guides/)9 min read ## [How to Keep the Same AI Model Across a Whole Collection](https://www.astria.ai/articles/consistent-ai-fashion-models/) Casting drift is the reason AI catalogs look wrong. Why consistency breaks, the four kinds of consistency you actually need, and how to hold all of… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 15, 2026 [ ![](https://www.astria.ai/articles/img/covers/flat-lay-to-on-model.webp) ](https://www.astria.ai/articles/flat-lay-to-on-model/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [Flat Lay to On-Model: Put Clothes on an AI Model](https://www.astria.ai/articles/flat-lay-to-on-model/) Turn a flat lay or garment photo into an on-model image with AI. Learn the source-image requirements, product-to-model workflow, accuracy checks, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 15, 2026 --- # Notes on AI fashion production Source: https://www.astria.ai/articles/page/6/ The Astria blog Comparisons, techniques, and field notes for teams producing on-brand fashion imagery with AI. [ ![](https://www.astria.ai/articles/img/covers/ai-headshots-for-teams.webp) ](https://www.astria.ai/articles/ai-headshots-for-teams/) [Guides](https://www.astria.ai/articles/tags/guides/)11 min read ## [AI Team Photos: Consistent Headshots for Remote Teams](https://www.astria.ai/articles/ai-headshots-for-teams/) Create AI team photos with matching lighting, backgrounds and crops. A practical workflow for remote headshots, group composites, approvals and new… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 14, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-emilia-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-emilia-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)15 min read ## [Astria vs Emilia AI Studio (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-emilia-fashion-ai/) Astria vs Emilia AI Studio for fashion brands: a general ecommerce product-photography tool with plan-capped casting versus a fashion production… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 14, 2026 [ ![](https://www.astria.ai/articles/img/covers/virtual-try-on-for-fashion-brands.webp) ](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) [Guides](https://www.astria.ai/articles/tags/guides/)9 min read ## [Virtual Try-On for Fashion Brands (2026): What It Actually Means](https://www.astria.ai/articles/virtual-try-on-for-fashion-brands/) Two different technologies share the name virtual try-on. Only one of them solves a brand's imagery problem — here's how to tell them apart and what… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 14, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-fashionlab-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-fashionlab-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs FashionLab (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-fashionlab-fashion-ai/) Astria vs FashionLab for fashion brands: a talent-marketplace content platform versus a production system for collections, categories, motion, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 13, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-lumoo-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)7 min read ## [Astria vs Lumoo: Pricing & AI Fashion Review (2026)](https://www.astria.ai/articles/astria-vs-lumoo-fashion-ai/) Lumoo Air pricing and workflow reviewed against Astria: euro plans, image and try-on allowances, collaboration, and a practical collection-production… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 13, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-fermat-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-fermat-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)7 min read ## [Astria vs Fermat AI: Pricing & Fashion Review (2026)](https://www.astria.ai/articles/astria-vs-fermat-fashion-ai/) Fermat AI pricing and features reviewed against Astria: Pro and Studio credits, design tools, photography workflows, team access and enterprise… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 12, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-lookgen-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-lookgen-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Lookgen (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-lookgen-fashion-ai/) Astria vs Lookgen for fashion brands: styled outfit generation with PIM publishing versus a full production system for catalog, campaign, social, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 11, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-stylic-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-stylic-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Stylic (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-stylic-fashion-ai/) Astria vs Stylic for fashion brands: a low-cost marketplace-seller photo tool versus a production system professional photographers and studios… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 11, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-pletor-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-pletor-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)12 min read ## [Astria vs Pletor (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-pletor-fashion-ai/) Astria vs Pletor for fashion brands: an ad-production engine for marketing teams versus a fashion production system for catalog, lookbook, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 10, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-stoodio-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-stoodio-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Stoodio (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-stoodio-fashion-ai/) Astria vs Stoodio for fashion brands: licensed real-model digital twins and guided onboarding versus a self-directed production workspace built for… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 10, 2026 --- # Notes on AI fashion production Source: https://www.astria.ai/articles/page/7/ The Astria blog Comparisons, techniques, and field notes for teams producing on-brand fashion imagery with AI. [ ![](https://www.astria.ai/articles/img/covers/astria-vs-aiuta-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-aiuta-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Aiuta (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-aiuta-fashion-ai/) Astria vs Aiuta for fashion brands: an enterprise try-on vendor with a QA-delivered studio service versus a production workspace your own creative… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 9, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-figma-weave-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Figma Weave (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-figma-weave-fashion-ai/) Astria vs Figma Weave for fashion brands: a general node-based AI canvas versus a fashion-specific production system for catalog, lookbook, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 8, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-flora-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-flora-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs FLORA (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-flora-fashion-ai/) Astria vs FLORA for fashion brands: design-stage ideation and a broad model canvas versus a production system built for collections, casting, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 8, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-dressx-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs DRESSX (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-dressx-fashion-ai/) Astria vs DRESSX for fashion brands: shopper-facing virtual try-on versus AI production of the catalog, lookbook, campaign, and social imagery that… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 7, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-tolstoy-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-tolstoy-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Tolstoy (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-tolstoy-fashion-ai/) Astria vs Tolstoy for fashion brands: a bundled commerce platform with an AI content module versus a fashion-specific production system built for… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 7, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-browzwear-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-browzwear-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Browzwear (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-browzwear-fashion-ai/) Astria vs Browzwear for fashion brands: 3D design and fit validation versus AI production of catalog, lookbook, and campaign imagery — and where each… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-graswald-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)15 min read ## [Astria vs Graswald (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-graswald-fashion-ai/) Astria vs Graswald for fashion brands: compare brand avatars, category coverage, collaboration, external handoff, campaign work, and collection-scale… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 5, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-pixelz-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Pixelz (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-pixelz-fashion-ai/) Astria vs Pixelz for fashion brands: compare managed post-production against an owned AI production workflow — turnaround, cost stacking, control,… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 5, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-looklet-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Looklet (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-looklet-fashion-ai/) Astria vs Looklet for fashion brands: compare studio capture requirements, digitized models, creative direction, channel coverage, collaboration, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-tellos-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Tellos (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-tellos-fashion-ai/) Astria vs Tellos for fashion brands: compare AI fashion video, shoppable storefront content, art direction, catalog production, collaboration, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 4, 2026 --- # Notes on AI fashion production Source: https://www.astria.ai/articles/page/8/ The Astria blog Comparisons, techniques, and field notes for teams producing on-brand fashion imagery with AI. [ ![](https://www.astria.ai/articles/img/covers/astria-vs-fashn-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs FASHN (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-fashn-fashion-ai/) Astria vs FASHN for fashion brands: compare virtual try-on, art direction, catalog production, collaboration, video, and running a collection end to… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-caimera-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)12 min read ## [Astria vs Caimera (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-caimera-fashion-ai/) Astria vs Caimera for fashion brands: compare creative control, product fidelity, team workflows, video, pricing transparency, and catalog production. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-rawshot-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Astria vs Rawshot (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-rawshot-fashion-ai/) Astria vs Rawshot for fashion brands: compare art direction, casting, catalog consistency, video, collaboration, and production at collection scale. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-ayna-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [Astria vs Ayna (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-ayna-fashion-ai/) Astria vs Ayna for fashion brands: compare creative direction, product fidelity, listing automation, video, collaboration, and collection-scale… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/astria-vs-botika-fashion-ai.webp) ](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)12 min read ## [Astria vs Botika (2026): Which Is Better for Fashion Brands?](https://www.astria.ai/articles/astria-vs-botika-fashion-ai/) Astria vs Botika for fashion brands: compare professional art direction, product fidelity, templates, video, collaboration, and catalog production. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/fine-tuning-flux.webp) ](https://www.astria.ai/articles/fine-tuning-flux/) [Engineering](https://www.astria.ai/articles/tags/engineering/)10 min read ## [Fine-Tuning FLUX.1 on Astria: A Developer's Guide to the API](https://www.astria.ai/articles/fine-tuning-flux/) Train a FLUX.1 LoRA on your own images through Astria's API — key setup, a working Python fine-tune and generation script, and what changed by 2026. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 18, 2024 [ ![](https://www.astria.ai/articles/img/covers/face-id.webp) ](https://www.astria.ai/articles/ai-headshots-without-model-training/) [Guides](https://www.astria.ai/articles/tags/guides/)7 min read ## [AI Headshots and Portraits Without Training a Model](https://www.astria.ai/articles/ai-headshots-without-model-training/) Create consistent AI headshots and portraits from one reference photo without training a custom model. Learn when the instant approach works and when… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamApr 4, 2024 --- # Packshots Source: https://www.astria.ai/articles/tags/packshots/ The Astria blog Packshot formats, production workflows, and ecommerce product imagery. [ ![](https://www.astria.ai/articles/img/covers/ecommerce-product-photography-prompts.webp) ](https://www.astria.ai/articles/ecommerce-product-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)19 min read ## [Ecommerce Product Photography Prompts for PDP, Packshots and Campaigns](https://www.astria.ai/articles/ecommerce-product-photography-prompts/) Write ecommerce product photography prompts for clean PDP coverage, packshots, details and campaigns—with copyable examples and public templates. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 5, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-product-photography-vs-studio.webp) ](https://www.astria.ai/articles/ai-product-photography-vs-studio/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)13 min read ## [AI Product Photography vs a Packshot Studio: The Hybrid Workflow](https://www.astria.ai/articles/ai-product-photography-vs-studio/) Decide what fashion brands should photograph physically, what AI can produce from approved references, and how to run a hybrid packshot-to-campaign… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/ghost-mannequin-alternative.webp) ](https://www.astria.ai/articles/ghost-mannequin-alternative/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)14 min read ## [Ghost Mannequin Alternatives: Which Workflow to Choose (2026)](https://www.astria.ai/articles/ghost-mannequin-alternative/) Compare flat lay, hanger, live-model and AI on-model alternatives to ghost mannequin photography. Decide what to replace, what to keep, and how to… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/best-3d-packshot-services-ecommerce.webp) ](https://www.astria.ai/articles/best-3d-packshot-services-ecommerce/) [Comparisons](https://www.astria.ai/articles/tags/comparisons/)16 min read ## [Best 3D Packshot Services for Ecommerce (2026): What to Buy](https://www.astria.ai/articles/best-3d-packshot-services-ecommerce/) Compare managed packshot studios, automated 360 capture, true 3D model production, and 3D asset delivery by input, output, QA, and operating model. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/ghost-mannequin-photography-ecommerce.webp) ](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/) [Guides](https://www.astria.ai/articles/tags/guides/)12 min read ## [Ghost Mannequin Photography for Ecommerce: The Complete Apparel Guide](https://www.astria.ai/articles/ghost-mannequin-photography-ecommerce/) How ghost mannequin photography is captured and joined, which garment views it needs, where the technique works, and the QC checks that prevent… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 30, 2026 [ ![](https://www.astria.ai/articles/img/covers/3d-packshots-ecommerce.webp) ](https://www.astria.ai/articles/3d-packshots-ecommerce/) [Guides](https://www.astria.ai/articles/tags/guides/)13 min read ## [3D Packshots for Ecommerce: What They Are and When to Use Them](https://www.astria.ai/articles/3d-packshots-ecommerce/) A 3D packshot is an interactive product model, not a spin video or a set of generated angles. What the formats do, where they earn their place, and… ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 29, 2026 --- # Prompts Source: https://www.astria.ai/articles/tags/prompts/ The Astria blog Copy-ready prompts, visual examples, and production guidance for fashion and ecommerce imagery. [ ![](https://www.astria.ai/articles/img/covers/ai-fashion-photography-prompts.webp) ](https://www.astria.ai/articles/ai-fashion-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)16 min read ## [AI Fashion Photography Prompts: A Production-Ready Guide](https://www.astria.ai/articles/ai-fashion-photography-prompts/) Write AI fashion photography prompts for catalog, editorial, movement, detail and lookbook images—with 15 copyable examples and public templates. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 6, 2026 [ ![](https://www.astria.ai/articles/img/covers/ecommerce-product-photography-prompts.webp) ](https://www.astria.ai/articles/ecommerce-product-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)19 min read ## [Ecommerce Product Photography Prompts for PDP, Packshots and Campaigns](https://www.astria.ai/articles/ecommerce-product-photography-prompts/) Write ecommerce product photography prompts for clean PDP coverage, packshots, details and campaigns—with copyable examples and public templates. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 5, 2026 [ ![](https://www.astria.ai/articles/img/covers/ai-lookbook-prompts.webp) ](https://www.astria.ai/articles/ai-lookbook-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)9 min read ## [AI Lookbook Prompts: Build a Cohesive Fashion Sequence](https://www.astria.ai/articles/ai-lookbook-prompts/) Copy AI lookbook prompts for openers, full looks, movement, details and closing frames, with a repeatable sequence system and public Astria examples. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 4, 2026 [ ![](https://www.astria.ai/articles/img/covers/beauty-product-photography-prompts.webp) ](https://www.astria.ai/articles/beauty-product-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)8 min read ## [Beauty Product Photography Prompts: 8 Copy-Ready Setups](https://www.astria.ai/articles/beauty-product-photography-prompts/) Copy beauty product photography prompts for packshots, texture, hands, routines and campaigns, with visual references and public Astria examples. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 3, 2026 [ ![](https://www.astria.ai/articles/img/covers/jewelry-photography-prompts.webp) ](https://www.astria.ai/articles/jewelry-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)7 min read ## [Jewelry Photography Prompts: Rings, Necklaces and Earrings](https://www.astria.ai/articles/jewelry-photography-prompts/) Copy AI jewelry photography prompts for macro packshots, on-body scale and campaign images, with visual references and public Astria examples. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 2, 2026 [ ![](https://www.astria.ai/articles/img/covers/wedding-dress-photography-prompts.webp) ](https://www.astria.ai/articles/wedding-dress-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)7 min read ## [Wedding Dress Photography Prompts: A Bridal Lookbook Shot List](https://www.astria.ai/articles/wedding-dress-photography-prompts/) Copy wedding dress photography prompts for front, back, train, lace and movement images, with a visual example and public Astria links. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamSep 1, 2026 [ ![](https://www.astria.ai/articles/img/covers/vitamin-supplement-photography-prompts.webp) ](https://www.astria.ai/articles/vitamin-supplement-photography-prompts/) [Prompts](https://www.astria.ai/articles/tags/prompts/)7 min read ## [Vitamin and Supplement Photography Prompts for E-commerce](https://www.astria.ai/articles/vitamin-supplement-photography-prompts/) Copy AI supplement photography prompts for bottles, labels, capsules, ingredients and bundles, with a transparent reference workflow and QA checklist. ![](https://www.astria.ai/articles/img/logo@2x.webp)Astria TeamAug 31, 2026 --- # Tags Source: https://www.astria.ai/articles/tags/ ## C - [Comparisons39](https://www.astria.ai/articles/tags/comparisons/ "Head-to-head looks at Astria and other AI fashion platforms.") * * * ## E - [Economics3](https://www.astria.ai/articles/tags/economics/ "What fashion and product imagery actually costs, per shoot and per SKU.") - [Engineering4](https://www.astria.ai/articles/tags/engineering/ "Fine-tuning, APIs, and tooling for developers building on Astria.") * * * ## G - [Guides31](https://www.astria.ai/articles/tags/guides/ "How to plan, direct, and run AI fashion photoshoots that stay on brand.") * * * ## M - [Models12](https://www.astria.ai/articles/tags/models/ "Model selection, benchmarks, strengths, and production tradeoffs for Astria image models.") * * * ## P - [Packshots6](https://www.astria.ai/articles/tags/packshots/ "Packshot formats, production workflows, and ecommerce product imagery.") - [Prompts7](https://www.astria.ai/articles/tags/prompts/ "Copy-ready prompts, visual examples, and production guidance for fashion and ecommerce imagery.") * * * --- # Markdown page example Source: https://docs.astria.ai/markdown-page/ You don't need React to write simple standalone pages. --- # Content-Policy Source: https://www.astria.ai/content Last modified September 08 2026 This Content Policy applies to your use of Astria's generative AI platform and services. In your usage, you must adhere to our Content Policy: ## Create, upload, and share content responsibly. Non-sexual fashion, ecommerce, and editorial imagery featuring underwear, swimwear, or lingerie is permitted. These clothing categories and ordinary fashion poses are not, by themselves, prohibited sexual content. Do not attempt to create, upload, or share content in the prohibited categories below: - _Hate_: hateful symbols, negative stereotypes, comparing certain groups to animals/objects, or otherwise expressing or promoting hate based on identity. - _Harassment_: mocking, threatening, or bullying an individual. - _Violence_: violent acts and the suffering or humiliation of others. - _Self-harm_: suicide, cutting, eating disorders, and other attempts at harming oneself. - _Sexual_: pornography, nudity, sexual acts, sexual services, or content primarily intended to arouse sexual excitement. Sexualization or sexual exploitation of minors is prohibited. - _Shocking_: bodily fluids, obscene gestures, or other profane subjects that may shock or disgust. - _Illegal activity_: drug use, theft, vandalism, and other illegal activities. - _Deception_: major conspiracies or events related to major ongoing geopolitical events. - _Political_: politicians, ballot-boxes, protests, or other content that may be used to influence the political process or to campaign. - _Public and personal health_: the treatment, prevention, diagnosis, or transmission of diseases, or people experiencing health ailments. - _Spam_: unsolicited bulk content. Don’t mislead your audience about AI involvement. - When sharing your work, we encourage you to proactively disclose AI involvement in your work. ## Respect the rights of others. - Do not upload images of people without their consent. - Do not upload images to which you do not hold appropriate usage rights. Appropriate usage rights may come from ownership, a license, or another lawful basis; copyright ownership is not required. Those rights must cover your intended use, including the processing and any training you authorize under your agreement with Astria. - Do not create images of public figures. Please report any suspected violations of these rules to our team through our support email We will investigate and take action accordingly, up to and including terminating the violating account. --- # Privacy Source: https://www.astria.ai/privacy Last modified October 7 2026 We at Astria, (together with our affiliates, “Astria”, “we”, “our” or “us”) respect your privacy and are strongly committed to keeping secure any information we obtain from you or about you. This Privacy Policy describes our practices with respect to Personal Information we collect from or about you when you access Astria and its affiliates’ websites (collectively, the “Site”), or use Astria and its affiliates’ products and services, including application programming interfaces, associated software, tools, developer services, data and documentation (collectively, the “Services”). ## 1\. PERSONAL INFORMATION WE COLLECT We collect information that alone or in combination with other information in our possession could be used to identify you (“Personal Information”) as follows: Personal Information You Provide: We may collect Personal Information if you create an account to use our Services or communicate with us as follows. Communication Information: If you communicate with us, we may collect your name, contact information, and the contents of any messages you send (“Communication Information”). Connected Applications: When you connect Astria to an application such as ChatGPT, we process the account information, permissions, workspace membership and OAuth authorization records needed to identify your account and maintain authorized access. We process the inputs you choose to send through that application's Astria tools, which may include prompts, reference media URLs and generation settings. Login takes place through Astria's authorization flow; your password is not an Astria tool input. Personal Information We Collect Through Our Social Media Pages: We have pages on social media sites like Instagram, Facebook, Medium, Twitter, YouTube and LinkedIn (“Social Media Pages”). When you interact with our Social Media Pages, we will collect Personal Information that you elect to provide to us, such as your contact details (“Social Information”). In addition, the companies that host our Social Media Pages may provide us with aggregate information and analytics regarding the use of our Social Media Pages. Personal Information We Receive Automatically From Your Use of the Services: When you visit, use, and interact with the Services, we may receive certain information about your visit, use, or interactions (“Technical Information”), including the following: Log data: Information that your browser automatically sends whenever you visit the Site (“log data”). Log data includes your Internet Protocol address, browser type and settings, the date and time of your request, and how you interacted with the Site. Usage data: We may automatically collect information about your use of the Services, such as the types of content that you view or engage with, the features you use and the actions you take, as well as your time zone, country, the dates and times of access, user agent and version, type of computer or mobile device, computer connection, IP address, and the like. Device information: Includes name of the device, operating system, and browser you are using. Information collected may depend on the type of device you use and its settings. Cookies: We use cookies to operate and administer our Site and Services, and improve your experience on it. A “cookie” is a piece of information sent to your browser by a website you visit. We use the following types of cookies: - **Essential cookies**: These cookies are necessary for the Site to function and cannot be switched off. They include session cookies for authentication, CSRF protection tokens, and cookie consent preferences. These cookies do not require your consent as they are strictly necessary for the operation of the Site. - **Analytics cookies**: We use Google Analytics to help us understand how visitors use the Site. Google Analytics uses cookies to collect information about your use of the Site, including your IP address, which is transmitted to and stored by Google. We use this information to evaluate use of the Site, compile reports on activity, and provide other services relating to Site usage. For more information on how Google uses data, visit [Google's Privacy Policy](https://policies.google.com/privacy). You can opt out of Google Analytics by installing the [Google Analytics Opt-out Browser Add-on](https://tools.google.com/dlpage/gaoptout). For users in the European Economic Area (EEA), United Kingdom, and other jurisdictions that require consent for non-essential cookies, we will request your consent before placing analytics cookies on your device. You may accept or reject non-essential cookies via the cookie consent banner displayed on the Site. You can change your cookie preferences at any time by clearing your browser cookies and revisiting the Site. Online Tracking and Do Not Track Signals: We and our third party service providers may use cookies or other tracking technologies to collect information about your browsing activities over time and across different websites following your use of the Site. Our Site currently does not respond to “Do Not Track” (“DNT”) signals and operates as described in this Privacy Policy whether or not a DNT signal is received. ## 2\. HOW WE USE PERSONAL INFORMATION We may use Personal Information for the following purposes: To provide, administer, maintain, improve and/or analyze the Services; To perform operations you request through connected applications, check account and workspace permissions, deliver generation results and prevent duplicate generation submissions; To conduct research within the permissions and confidentiality limits in our Terms; published research must not disclose private Customer Content or identifiable Personal Information without separate lawful authorization; To communicate with you; To develop new programs and services; To prevent fraud, criminal activity, or misuses of our Services, and to ensure the security of our IT systems, architecture, and networks; and To comply with legal obligations and legal process and to protect our rights, privacy, safety, or property, and/or that of our affiliates, you, or other third parties. Aggregated Information. We may aggregate Personal Information to analyze the effectiveness of our Services, improve features and conduct research. We may publish or share aggregate statistics only where they do not identify individuals or disclose private Customer Content. We may collect this information through the Services, cookies and other means described in this Privacy Policy. Model training. Where our Terms or your agreement permits it, Astria may use submitted inputs and generated outputs to train and evaluate AI models for improved quality and performance. This is distinct from using your files as marketing assets or as assets, templates or references for another customer, which the training permission does not authorize. Access requires specific authorization for permitted duties. Where training involves Personal Information, Astria must also have an applicable lawful basis and comply with relevant notices, confidentiality, retention and individual-rights requirements. Selected model providers may have their own disclosed training conditions, subject to the agreement and applicable law. This Privacy Policy does not enlarge those permissions or override an applicable DPA. ## 3\. SHARING AND DISCLOSURE OF PERSONAL INFORMATION In certain circumstances we may share your Personal Information with third parties without further notice to you, unless required by the law, including without limitation in the situations below: Vendors and Service Providers: To assist us in meeting business operations needs and to perform certain services and functions, we may share Personal Information with vendors and service providers, including providers of hosting services, cloud services, and other information technology services providers, event management services, email communication software and email newsletter services, advertising and marketing services, and web analytics services. Pursuant to our instructions, these parties will access, process, or store Personal Information in the course of performing their duties to us. Connected Applications and Model Providers: We return requested account information, model or template details and authorized generation results, including media URLs, to the application you connected. Depending on the model you select, generation inputs are processed by the provider delivering that model's service. These disclosures remain subject to the confidentiality and model-training permissions in our Terms and any applicable agreement. The connected application's handling of information it receives is governed by its own privacy policy. Business Transfers: If we are involved in strategic transactions, reorganization, bankruptcy, receivership, or transition of service to another provider (collectively a “Transaction”), your Personal Information and other information may be shared in the diligence process with counterparties and others assisting with the Transaction and transferred to a successor or affiliate as part of that Transaction along with other assets. Legal Requirements: If required to do so by law or in the good faith belief that such action is necessary to (i) comply with a legal obligation, including to meet national security or law enforcement requirements, (ii) protect and defend our rights or property, (iii) prevent fraud, (iv) act in urgent circumstances to protect the personal safety of users of the Services, or the public, or (v) protect against legal liability. Affiliates: We may share Personal Information with our affiliates, meaning an entity that controls, is controlled by, or is under common control with Astria. Our affiliates may use the Personal Information we share in a manner consistent with this Privacy Policy. Other Users: certain actions you take may be visible to other users of the Service. ## 4\. HOW TO CONTACT US; UPDATING YOUR INFORMATION You can update, correct, or delete your Personal Information directly through your account settings on the Site, including deleting your account entirely. If you have any questions or concerns not already addressed in this Privacy Policy, please contact us at support@Astria.ai. ### RETENTION AND CONNECTED-APPLICATION CONTROLS We retain account information and Customer Content for the service and account relationship, subject to the deletion requests, subscription limits and other retention provisions described in our [Terms](https://www.astria.ai/terms) or your applicable agreement. Marking an item deleted requests permanent deletion of that item and its associated content without undue delay; archiving is not deletion. Custom-trained models may be deleted after 30 days under the applicable training service's terms. This is not a universal 30-day retention period for prompts, references or outputs. Minimal transaction records may remain where necessary for accounting or legal obligations. Our current operational retention periods are: - **MCP completion and failure events and their delivery records:** scheduled for deletion once they are seven days old. Expired event subscriptions are scheduled for deletion seven days after expiry. - **MCP submission records:** records used to prevent duplicate generation charges, including their saved tool results, are scheduled for deletion after 95 days. Deleting these operational records does not delete the underlying generation or media. - **Application logs in Datadog:** 15 days of indexed retention, with no additional Flex retention or Datadog log archive currently configured. - **Database recovery and snapshots:** continuous database protection currently provides a four-day rollback window. Manual database snapshots have no time-based expiry: up to 25 are retained, and the oldest is replaced when a new snapshot exceeds that limit, or snapshots are deleted explicitly. Restricted backup copies are not available for ordinary service use. - **OAuth authorization records:** token history is scheduled for deletion 30 days after the later of the authorization's absolute expiry or recorded revocation. Authorizations have a 90-day absolute lifetime, so ordinary token history becomes eligible about 120 days after initial issuance. Later revocation or replay detection can extend this audit window. Unused authorization codes become eligible 30 days after expiry or revocation; used codes remain while their linked token is retained. Expired temporary Photoshop login relays are scheduled for deletion after expiry. Active authorizations and the history required to detect refresh-token replay are retained throughout these windows. Scheduled deletion runs periodically, so eligible records may remain until the next successful cleanup. Retention can also be affected by an applicable agreement or legal obligation. You can end a connected application's access through its connection settings or contact support@astria.ai for help revoking authorization or requesting account-wide deletion. Ending authorization stops future access; it does not itself delete previously created Astria content or copies already returned to the connected application. ## 5\. CALIFORNIA PRIVACY RIGHTS The following disclosures are intended to provide additional information about (1) the categories of Personal Information we collect (as defined above), (2) the source of the Personal Information, (3) how we use each category of Personal Information, and (4) how we disclose Personal Information. These disclosures do not limit our ability to use or disclose information as described above.
Category of Personal InformationSources of Personal InformationUse of Personal InformationDisclosure of Personal Information
Social InformationWe may collect Social Information from you when you interact with our Social Media Pages.We may use Social Information to perform analytics and to communicate with you.We may disclose Social Information to our affiliates.
Communication InformationWe collect Communication Information directly from you.We use Communication Information for providing our Services and responding to you.We disclose Communication Information to our affiliates and communication services providers.
Technical InformationWe collect Technical Information from you.We use Technical Information for analytics and in some cases, for moderation and prevention of fraud and malicious activity by users of our Services.We disclose Technical Information to our affiliates and analytics provider(s).
To the extent provided for by law and subject to applicable exceptions, California residents have the following privacy rights in relation to the Personal Information we collect: The right to know what Personal Information we have collected and how we have used and disclosed that Personal Information; The right to request deletion of your Personal Information; and The right to be free from discrimination relating to the exercise of any of your privacy rights. We do not and will not sell your Personal Information. Exercising Your Rights: California residents can exercise the above privacy rights directly through their account settings on the Site, including updating personal information and deleting their account. Verification: in order to protect your Personal Information from unauthorized access or deletion, we may require you to verify your credentials before you can submit a request to know or delete Personal Information. If you do not have an account with us, or if we suspect fraudulent or malicious activity, we may ask you to provide additional Personal Information and proof of residency for verification. If we cannot verify your identity, we will not provide or delete your Personal Information. Authorized Agents: you may submit a request to know or a request to delete your Personal Information through an authorized agent. If you do so, the agent must present signed written permission to act on your behalf and you may also be required to independently verify your identity and submit proof of your residency with us. ## 6\. EUROPEAN ECONOMIC AREA, UNITED KINGDOM, AND SWISS PRIVACY RIGHTS If you are located in the European Economic Area ("EEA"), the United Kingdom ("UK"), or Switzerland, you have certain rights under the General Data Protection Regulation ("GDPR") and related data protection laws. **Legal Basis for Processing**: We process your Personal Information on the following legal bases: - **Consent**: Where you have given us consent to process your Personal Information for specific purposes, such as placing non-essential cookies on your device. You may withdraw your consent at any time. - **Contract**: Where processing is necessary for the performance of a contract with you, such as providing our Services when you create an account. - **Legitimate Interests**: Where processing is necessary for our legitimate interests, such as improving our Services, preventing fraud, and ensuring security, provided these interests are not overridden by your rights. - **Legal Obligation**: Where processing is necessary for compliance with a legal obligation to which we are subject. **Your Rights**: Subject to applicable law, you have the following rights: - **Access**: The right to request a copy of the Personal Information we hold about you. - **Rectification**: The right to request correction of inaccurate Personal Information. - **Erasure**: The right to request deletion of your Personal Information, subject to certain exceptions. - **Restriction**: The right to request that we restrict the processing of your Personal Information. - **Data Portability**: The right to receive your Personal Information in a structured, commonly used, and machine-readable format. - **Objection**: The right to object to processing of your Personal Information based on legitimate interests. - **Withdraw Consent**: The right to withdraw consent at any time where we rely on consent to process your Personal Information. You can exercise these rights directly through your account settings on the Site, including updating your information and deleting your account and associated data. **International Data Transfers**: Your Personal Information may be transferred to and processed in countries outside the EEA, UK, or Switzerland that may not provide the same level of data protection. When we transfer your data outside of these regions, we rely on appropriate safeguards such as Standard Contractual Clauses approved by the European Commission to ensure your Personal Information is adequately protected. ## 7\. CHILDREN Our Service is not directed to children who are under the age of 16. Astria does not knowingly collect Personal Information from children under the age of 16. If you have reason to believe that a child under the age of 16 has provided Personal Information to Astria through the Service please email us at support@astria.ai and we will endeavor to delete that information from our databases. ## 8\. LINKS TO OTHER WEBSITES The Service may contain links to other websites not operated or controlled by Astria, including social media services (“Third Party Sites”). The information that you share with Third Party Sites will be governed by the specific privacy policies and terms of service of the Third Party Sites and not by this Privacy Policy. By providing these links we do not imply that we endorse or have reviewed these sites. Please contact the Third Party Sites directly for information on their privacy practices and policies. ## 9\. SECURITY You use the Service at your own risk. We implement commercially reasonable technical, administrative, and organizational measures to protect Personal Information both online and offline from loss, misuse, and unauthorized access, disclosure, alteration, or destruction. However, no Internet or e-mail transmission is ever fully secure or error free. In particular, e-mail sent to or from us may not be secure. Therefore, you should take special care in deciding what information you send to us via the Service or e-mail. In addition, we are not responsible for circumvention of any privacy settings or security measures contained on the Service, or third party websites. ## 10\. INTERNATIONAL USERS By using our Service, you understand and acknowledge that your Personal Information will be transferred from your location to our facilities and servers. For users in the EEA, UK, or Switzerland, please refer to the "International Data Transfers" subsection in Section 6 above for information about the safeguards we use. ## 11\. YOUR CHOICES If you choose not to provide Personal Information that is needed to use some features of our Service, you may be unable to use those features. ## 12\. CHANGES TO THE PRIVACY POLICY We may change this Privacy Policy at any time. When we do we will post an updated version on this page, unless another type of notice is required by applicable law. By continuing to use our Service or providing us with Personal Information after we have posted an updated Privacy Policy, or notified you by other means, you consent to the revised Privacy Policy. --- # Terms and Conditions Source: https://www.astria.ai/terms Last modified September 12 2026 Thank you for your interest in Astria. By using Astria, you agree to these terms. ## Use of Astria Astria can generate images (“Generations”) based on text input you provide (“Prompts”), Combined with a set of pictures you upload (\`\`Training set”). ## Age Requirements By accessing the Services, you confirm that you’re at least 16 years old and meet the minimum age of digital consent in your country. If you are not old enough to consent to our Terms of use in your country, your parent or guardian must agree to this Agreement on your behalf. Please ask your parent or guardian to read these terms with you. If you’re a parent or legal guardian, and you allow your teenager to use the Services, then these terms also apply to you and you’re responsible for your teenager’s activity on the Services. No assurances are made as to the suitability of the Generations for you. ## Use of Images Subject to your compliance with these terms and our [Content Policy](https://www.astria.ai/content), you may use Generations for any legal purpose, including for commercial use. This means you may sell your rights to the Generations you create, incorporate them into works such as books, websites, and presentations, and otherwise commercialize them. ## Prompt Guidelines Your use must comply with our [Content Policy](https://www.astria.ai/content). You must have appropriate usage rights for submitted references and other materials, whether through ownership, a license or another lawful basis, covering your intended use and the processing you authorize. ## Buying Credits You may buy credits to create additional training, subject to the\*\* payment terms \*\*in our Terms of Use. Credits must be used within one year of purchase or they will expire. We may change our prices at any time by updating our pricing page. ## No Infringing or Harmful Use You must comply with our Content Policy, and you may not use Astria in a way that may harm a person or infringe their rights. For example, you may not submit Uploads for which you don’t have the necessary rights, images of people without their consent, or Prompts intended to generate harmful or illegal images. We may delete Prompts and Uploads, or suspend or ban your account for any violations ## Ownership of Generations To the extent allowed by law and as between you and Astria, you own your Prompts, Uploads and Generations, subject to the limited service-processing and model-training permissions below. Astria will not resell Generations that you have created or assert any copyright in such Generations against you or your end users, all provided that you comply with these terms and our Content Policy. If you violate our terms or Content Policy, you will lose rights to use Generations, but we will provide you written notice and a reasonable opportunity to fix your violation, unless it was clearly illegal or abusive. You understand and acknowledge that similar or identical Generations may be created by other people using their own Prompts, and your rights are only to the specific Generation that you have created. ## Service processing and model training Your prompts, instructions, references and other submitted materials ("Inputs"), together with generated results ("Outputs"), are "Customer Content". You authorize Astria and its authorized providers to process Customer Content as necessary to carry out your instructions and provide, store, deliver, support and secure your selected services. Separately, you permit Astria to train and evaluate AI models using Inputs and Outputs solely to improve model quality and performance, including resolution, texture, alignment and fidelity to instructions and references. If Astria undertakes such training, this permission includes only the internal dataset preparation and source copies necessary for that purpose. It does not permit Astria to publish or sell your private Customer Content, use it in marketing, or supply or reuse it as assets, templates or references for another customer. General improvements to model capabilities may benefit other users; this does not authorize deliberately reproducing your confidential content for them. Training remains subject to applicable rights, provider restrictions, confidentiality obligations, any applicable DPA and applicable law. Providers acting on Astria's behalf for training are subject to the same purpose and confidentiality limits. Providers supplying a selected model may separately train on Inputs and Outputs only under the applicable provider conditions disclosed for that model and to the extent consistent with these protections and applicable law. ## Confidentiality of Customer Content Astria will protect private Customer Content with reasonable care and use it only to provide your selected services and for the limited model-training purpose above. Access is limited to specifically authorized personnel and providers who need it for those duties and are subject to confidentiality obligations. These protections apply whether or not the content contains personal information. Your instructions to share or publish content authorize that sharing; the training permission itself does not. Disclosure required by law will be limited to what is required, with notice where legally permitted. These confidentiality obligations continue while retained content remains confidential. ## Storage policy 1. **Deletion at your request.** Marking an item as deleted in the application instructs Astria to permanently delete that item and its associated content within the scope of that deletion action, without undue delay. Archiving is not deletion. Minimal transaction records may be retained where necessary for accounting or legal obligations; this does not authorize retaining prompt or reference content merely because it is associated with a transaction. 2. **Custom-trained models.** Astria may delete custom-trained LoRA models and checkpoints after 30 days, as specified in the terms applicable to the selected training service. This is not a general 30-day deletion rule for all Customer Content. 3. **Subscription-specific retention.** Astria may delete references and their associated prompts and Outputs according to retention limits disclosed for the account's subscription or plan. The applicable limits will be made available before they apply, including when a subscription changes or ends, subject to any separately agreed storage commitments. Deletion includes relevant content in active systems, controlled caches and applicable provider copies. Restricted backups expire under the applicable retention schedule and must not restore deleted content to ordinary use. If Astria creates source copies for separately permitted training, those copies remain subject to applicable retention limits, deletion rights and any applicable DPA. Deleting a source file does not itself reverse completed model training. Contact support@astria.ai for account-wide deletion or questions about the scope of an item's deletion. ## No Guarantees We plan to continue to develop and improve Astria, but we make no guarantees or promises about how Astria operates or that it will function as intended, and your use of Astria is at your own risk. The Service is provided on an "AS IS" and "AS AVAILABLE" basis. The Service is provided without warranties of any kind, whether express or implied, including, but not limited to, implied warranties of merchantability, fitness for a particular purpose, non-infringement or course of performance. Astria do not warrant that a) the Service will function uninterrupted, secure or available at any particular time or location; b) any errors or defects will be corrected; c) the Service is free of viruses or other harmful components; or d) the results of using the Service will meet your requirements. ## Modification of Terms of Service At its sole discretion, Astria may modify or replace any of the Terms of Service, or change, suspend, or discontinue the Service (including without limitation, the availability of any feature, database, or content) at any time by posting a notice on the Astria or Service or by sending you an email. Astria may also impose limits on certain features and services or restrict your access to parts or all of the Service without notice or liability. It is your responsibility to check the Terms of Service periodically for changes. Your continued use of the Service following the posting of any changes to the Terms of Service constitutes acceptance of those changes. Choice of Law Should any dispute arise under this Terms of Use, the access or use of Astria web app, etc, the law of the Israel in all respects shall govern, without regard for the jurisdiction or forum in which the user is domiciled, resides, or located at the time of such access/use. Use of Astria without the user’s acceptance of this Terms of Use and all incorporated provisions constitutes unauthorised use for which the user disclaims any/all potential or actual right to relief against Astria, regardless of the legal jurisdiction of Astria or the user’s domicile, residence, or location. ## Limitation Of Liability In no event shall Astria nor its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from (i) your access to or use of or inability to access or use the Service; (ii) any conduct or content of any third party on the Service; (iii) any content obtained from the Service; and (iv) unauthorized access, use or alteration of your transmissions or content, whether based on warranty, contract, tort (including negligence) or any other legal theory, whether or not we have been informed of the possibility of such damage, and even if a remedy set forth herein is found to have failed of its essential purpose. ## Indemnity Clause Users, by use of Astria, hereby agree to indemnify and hold harmless Astria against any and all claims, demands, charges, complaints, controversies, and causes of action of any kind or nature whatsoever, both at law and in equity, known or unknown, suspected or unsuspected (from now on “Claims”), arising out of and relating in any way to intellectual property infringement claims made against Astria concerning your Content if such Claims inure to your benefit in any way and also to hold harmless and indemnify Astria against all Claims relating in any way to your use of Astria. This indemnity includes reimbursement to Astria for any applicable court costs and expenses of litigation, including but not limited to reasonable attorneys’ fees, and reimbursement for any losses and liabilities of Astria, including but not limited to, judgments, settlements, fees, costs, expenses, legal debts, legal obligations, and any third-party Claims against Astria. Astria makes no warranties or representations concerning user Content and is not responsible for unauthorised use. Users are responsible for using Astria only as authorised and in compliance with applicable laws of the jurisdictions in which such users are domiciled, reside, or are located at the time of such use. --- # AI Photoshoot Calculator - Price Your Whole Catalog in 30 Seconds Source: https://www.astria.ai/photoshoot-calculator Price your catalog, then turn the estimate into a ready-to-use photoshoot workspace. ≈ products · [try another URL](https://www.astria.ai/photoshoot-calculator#) Basic ### Studio angles Front ×2BackSide+1 spare Most popular Advanced ### Angles + outdoor Front ×2BackSide+1 spareOutdoor ×3 for 20%Video for 10% Campaign ### Studio + social Front ×2BackSide+1 spareOutdoor ×3 for 20%Video for 10%UGC & atmosphere ×2UGC video for 10% 2K resolution · 15-second 1080p video Your working space ## From catalog estimate to campaign Sign in to create a separate workspace with product references and a reusable starter template for this store. [Sign in to create workspace](https://www.astria.ai/users/sign_in?redirect_to=%2Fphotoshoot-calculator) --- # Astria - AI Photoshoots for Ecommerce and Headshots Source: https://www.astria.ai/ ![Fashion image generated with Astria](https://a.astria.ai/assets/workspaces/featured/editorial-hero-7f32eec900bb8315dfe7239b1e0c39d9fbf488fdecf336e1c00bb31a38668dee.avif) Astria creates AI product photos, fashion lookbooks and professional headshots from reference images. Build reusable photoshoot templates or generate through the API. [Ecommerce](https://www.astria.ai/ecommerce)[Headshots](https://www.astria.ai/gallery/tunes) [ ## Ecommerce Studio Create product photos, on-model fashion lookbooks and campaign images from your product references. Reuse models, scenes and photoshoot templates across your ecommerce catalog. Explore ![Generated ecommerce lookbook imagery](https://a.astria.ai/assets/workspaces/featured/zara-ecommerce-03e0ac209e1ae107e1314ae6f3ea61394ee3bd5aa0d82da3df3a3767c9b970ed.avif) ](https://www.astria.ai/ecommerce) ## Headshots Train a custom AI model from a person's photos, then generate professional headshots in different settings. Use ready-made photoshoot packs or integrate the Astria API. [Gallery](https://www.astria.ai/gallery/packs)[API](https://docs.astria.ai/docs/api/overview/) ![AI headshot generated with Astria](https://a.astria.ai/assets/workspaces/featured/headshot-api-202760fda531a6c11fc4f3ac8ee3118a1f1dd1da40bf5aa5da0d3e24cc17659e.avif) ![AI model in a flowing dress](https://a.astria.ai/assets/homepage/street-still-1fc6e182561fb8b5c82674f4864750f582a9f127c8cc597766e5a70d29a9af15.avif) ## Train. Generate. Deliver. Keep the same model across different scenes with a custom AI model. Change the setting, styling and composition to create a coordinated image collection. ## [Featured workspaces](https://www.astria.ai/gallery/workspaces/featured) [View all](https://www.astria.ai/gallery/workspaces) [![Maison Urbane workspace](https://a.astria.ai/assets/workspaces/featured/editorial-lookbook-3fc85cc9863abdcd7f7be577a21fddeb60ba5a9efdc529d2a6cba8f6c8ededf0.avif)Maison Urbane](https://www.astria.ai/w/zara)[![Great Sports workspace](https://a.astria.ai/assets/workspaces/featured/sports-jacket-410a4e6817866f80b48bc060e17dc21194f96b2dac8abd972fa2225d475f10d8.avif)Great Sports](https://www.astria.ai/w/great-sports)[![America Basics workspace](https://a.astria.ai/assets/workspaces/featured/basics-pants-502693cd02584516f74fa5cf210f41d3a95ebae8b677e64f7f6714f641c11285.avif)America Basics](https://www.astria.ai/w/america-basics)[![Generated ecommerce eyewear campaign image](https://a.astria.ai/assets/homepage/ecommerce-glasses-cover-fb6b1ae1272b9454c3497ea0608d9c808304f357ba0c042c94e5c702071849a6.avif)Ecommerce](https://www.astria.ai/ecommerce) ![](https://a.astria.ai/assets/workspaces/featured/editorial-hero-7f32eec900bb8315dfe7239b1e0c39d9fbf488fdecf336e1c00bb31a38668dee.avif) ## Ready when you are. [Ecommerce](https://www.astria.ai/ecommerce)[Headshots](https://www.astria.ai/gallery/packs) --- # Astria — Make AI videos in Claude, Codex, and Cursor Source: https://www.astria.ai/mcp Turn your ideas and images into videos, right inside ChatGPT, Claude, and your favorite AI tools. [Install in ChatGPT](https://chatgpt.com/plugins/plugins_6aa518f7d9608191aa8b89c668c2fbe1) Connect Astria to your assistant using `https://mcp.astria.ai/mcp`, then sign in and start creating. --- # Astria — The AI Studio for Your Brand Source: https://www.astria.ai/ecommerce AI-Powered [Explore ready-to-use templates](https://www.astria.ai/gallery/workspaces/featured)[Generate your own](https://www.astria.ai/prompts) ![](https://a.astria.ai/assets/ecommerce/hero/hero-1-5800081d69c9b673bf35667fc4453382caae22cf1e88a9adb98bd773855f358e.avif) ![](https://a.astria.ai/assets/ecommerce/hero/hero-2-8a9674b8d429548ae226ac266220708e1589696c05d55e8c65aa6330dcbfda59.avif) ![](https://a.astria.ai/assets/ecommerce/hero/hero-3-33f68d0ebee1e0f58225d8eea1ca2facbeb967b679b5b3dea655bb58645204d9.avif) ![](https://a.astria.ai/assets/ecommerce/hero/hero-4-e05c6d45348d7fc896b399b504b74ec61e3a4c1bf69d47445c148e3262fcc48e.avif) ![](https://a.astria.ai/assets/ecommerce/hero/hero-5-8526fad6f71474be63e7df26f134c0ad29e6678d720c195d5878f17de7b446a9.avif) ![](https://a.astria.ai/assets/ecommerce/hero/hero-6-c940ba2f09260aa1811b3db19d1322c944835b92e330468816cb6f0939ed18ad.avif) ![](https://a.astria.ai/assets/ecommerce/hero/hero-7-b4813ee898033180eb08fd5982d1c7528f1eca9cb3cd478a6a639e73f93c5dc3.avif) ![](https://a.astria.ai/assets/ecommerce/hero/hero-8-f1f43bad15f4190b3b68decac75d6fd82298f6a77d3a71293acf66ccf0eb8f4a.avif) ![](https://a.astria.ai/assets/ecommerce/hero/hero-9-a08d7606e229d8289d256e6fbdd8b8dbbccc9ad43feb180f2685f89e162bdc3d.avif) Trusted by leading brands [![Terminal X](https://media.terminalx.com/pub/media/logo/stores/1/banner_TerminalX.png)Terminal X](https://www.terminalx.com/) [![Renuar](https://renuar.co.il/cdn/shop/files/Logo_black_3f4a3793-1b44-46c6-89a8-7171f81cc6ba.svg)Renuar](https://renuar.co.il/) [![Fox](https://fox.co.il/cdn/shop/files/66f09c4a7a05e4ac1fcc302c485d4eb1_5ff8c165-c1c2-4740-9594-fb05002bc536.svg?width=300)Fox](https://fox.co.il/) [![Golf & Co](https://www.golfco.co.il/pub/media/idus/default/logo.png)Golf & Co](https://www.golfco.co.il/) [![Ronny Kobo](https://ronnykobo.com/cdn/shop/files/ronny_logo_220x.png?v=1628586173)Ronny Kobo](https://ronnykobo.com/) [![Nununu](https://nununu.com/cdn/shop/files/logo_png.png?v=1710926656)Nununu](https://nununu.com/) [![Sacks](https://sacksfashion.com/cdn/shop/files/logo-2.png?v=1686074498)Sacks](https://sacksfashion.com/) [![Nine West](https://ninewest.com/cdn/shop/files/NineWest_Logo.jpg?v=1613697835)Nine West](https://ninewest.com/) [![Femina](https://femina.co.il/cdn/shop/files/4066b126ac7f5ce81d622532210f7c5d.svg)Femina](https://femina.co.il/) [![Mania](https://www.maniajeans.co.il/pub/media/idus/default/logo_1.png)Mania](https://www.maniajeans.co.il/) [Ronen Chen](https://ronenchen.com/) [![Vadobag](https://www.vadobag.nl/media/site/1/Toplogo_portalcoresuite.png)Vadobag](https://www.vadobag.nl/) [![Urbanica](http://urbanica.co.il/wp-content/uploads/2016/10/LOGO-UR.png)Urbanica](http://urbanica.co.il/) [Sebo](https://sebo.co.il/) [![Gant](https://gant-dfs-production-us.sfdc-8tgtt5-ecom1.exp-delivery.com/mobify/bundle/1583/static/img/global/logo.png)Gant](https://www.gant.com/) [![B-Unique](https://b-unique.co.il/cdn/shop/files/Bunique_logo_yellow_ribbon-web.webp?v=1719664535)B-Unique](https://b-unique.co.il/) [![-417](https://417skincare.co.il/cdn/shop/files/logo_280x80_px.png?v=1771157911)\-417](https://www.minus417.com/) [![Lee Cooper](https://upload.wikimedia.org/wikipedia/commons/e/e6/Lee_cooper_logo.png)Lee Cooper](https://www.leecooper.com/) [FixThePhoto](https://fixthephoto.com/) The Process ## From Concept To Content 01 ### Select your template Browse hundreds of ready-to-use creative templates built for fashion, beauty, jewelry, and more. 02 ### Upload your items Drop in your product photos. Our AI takes it from there — no styling, no studio. 03 ### Generate at scale Get hundreds of on-brand, production-ready images in minutes, not weeks. Choose your pack[View demo lookbook →](https://www.astria.ai/w/zara) ![Lookbook](https://a.astria.ai/assets/ecommerce/process/pack-lookbook-2227358e9aca172043f0e808aa07846b483976daa0b2310e30baf9e4eead119d.avif) LookbookEditorial · On-model · Print ![Social](https://a.astria.ai/assets/ecommerce/process/pack-social-af8ec6642f0d46740cd4ddd745635e9f8bda96b9ef23bc4320124977aeabcfb4.avif) SocialStories · Posts · Reels ![Campaign](https://a.astria.ai/assets/ecommerce/process/pack-campaign-9cc39be3b1140afc522070f69d716d94cd2948ee0d72766485ebb6f1d58d03f9.avif) CampaignBanners · Ads · Hero ![Velvet blazer](https://a.astria.ai/assets/ecommerce/process/upload-blazer-401a0949330974a1072a8359e9d6b666271db578b21240e605ce0c56372c4955.avif)![Black boots](https://a.astria.ai/assets/ecommerce/process/upload-boots-ffc1d9cac4c2152979c3f0e68c155e570352ec25dbb83361681f1eb59468317d.avif)![Grey skirt](https://a.astria.ai/assets/ecommerce/process/upload-skirt-5c3d067ed96a02b58bf31346b9bda3c8f76c66003c5b5cc36f2eda0feb747f64.avif)![Cream shirt](https://a.astria.ai/assets/ecommerce/process/upload-shirt-1f7d694a1acdedeb4527658f378a523bcb6aa0d754533ac2c9cc1583ad612270.avif) blazer.jpg ![Generated result 1](https://a.astria.ai/assets/ecommerce/process/gen-1-6cd4cbbb3f4439f12dec2b064e0d59aaaa5873fd455e2d315bd52efef1fba01a.avif)![Generated result 2](https://a.astria.ai/assets/ecommerce/process/gen-2-af67af4663e757fc15dfbe91765098974d967053d25d2b447edeb4bda4ccfa12.avif)![Generated result 3](https://a.astria.ai/assets/ecommerce/process/gen-3-c4d07a7f1308cbc34195da4fc463c40a6f1b5a8ff234d8810a1f3191f2f56a86.avif)![Generated result 4](https://a.astria.ai/assets/ecommerce/process/gen-4-f8b8e4f364effd2cebdd622a68babed60276ab32504993f632592c799112f2e4.avif)![Generated result 5](https://a.astria.ai/assets/ecommerce/process/gen-5-edbbbecb27730dbb301adb997155ee23becdb41a9d64db65f69b157011a54f54.avif)![Generated result 6](https://a.astria.ai/assets/ecommerce/process/gen-6-a7baecfe3eba65b26ce34c240fd054bfbf05f374656797d68259086460d3315b.avif)![Generated result 7](https://a.astria.ai/assets/ecommerce/process/gen-7-9da4583ed02f748a7ba7488918837ca8c9ba0aa8733605a6acf66a066c1688c0.avif)![Generated result 8](https://a.astria.ai/assets/ecommerce/process/gen-8-0de219dfc54edc5c3090b1e3c083a8917ad163c4674f053817b6ff34ab5f414c.avif) Generated ## Results _That speak_ Join the brands that already made the shift from the exhausting photoshoots to full flexibility with AI, at a fraction of the price. ![Noizz](https://a.astria.ai/assets/ecommerce/brands/new-collection-9a7bc5c4d4ecdec2b3122d8c3f319a87a11c192bfd1eb6a283ffe9c37f33f516.avif) ![Noizz](https://a.astria.ai/assets/ecommerce/brands/noizz-logo-cc23ab94886b4bf5393a087df46fc14cfa4559590224cc3468d8be436d7f4f17.avif) ![Noizz](https://a.astria.ai/assets/ecommerce/brands/noizz-2-5bf47459231a074f1611cdea14025908873b7f042e44392bca19fc0098281b21.avif) ![Noizz](https://a.astria.ai/assets/ecommerce/brands/noizz-logo-cc23ab94886b4bf5393a087df46fc14cfa4559590224cc3468d8be436d7f4f17.avif) ![Ronny Kobo](https://a.astria.ai/assets/ecommerce/brands/ronny-kobo-0ece5af8c6bd66de96baf8400d4b74cdd2bde817c75333deba963e04472d86c5.avif) ![Ronny Kobo](https://ronnykobo.com/cdn/shop/files/ronny_logo_220x.png?v=1628586173)Ronny Kobo ![Ronny Kobo](https://a.astria.ai/assets/ecommerce/brands/ronny-kobo-2-7c94424042c9cf375b5f0751b76c94002c4f72729876a8462fc21e44e6fb1ae9.avif) ![Ronny Kobo](https://ronnykobo.com/cdn/shop/files/ronny_logo_220x.png?v=1628586173)Ronny Kobo ![Nununu](https://a.astria.ai/assets/ecommerce/brands/nununu-6f2bc50931606ccf15adc813eb0af6f8374e651bb3bc88a9d9744ca704d396f8.avif) ![Nununu](https://nununu.com/cdn/shop/files/logo_png.png?v=1710926656)Nununu ![Gant](https://a.astria.ai/assets/ecommerce/brands/gant-5c344a15a574aacb01dc3ccc93d1bd04b7efa03fa26a65a91922879876642ffa.avif) ![Gant](https://gant-dfs-production-us.sfdc-8tgtt5-ecom1.exp-delivery.com/mobify/bundle/1583/static/img/global/logo.png)Gant ![Vadobag](https://a.astria.ai/assets/ecommerce/brands/vadobag-f1f0946a305e94f90f577a926c88430cd1c02500b96f053ec87da6e6717ed974.avif) ![Vadobag](https://www.vadobag.nl/media/site/1/Toplogo_portalcoresuite.png)Vadobag ![-417](https://a.astria.ai/assets/ecommerce/brands/417-886aec8948e57868cb4f385275d9af2dc26dc5b8019a2717add2c09b7fab3aa3.avif) ![-417](https://417skincare.co.il/cdn/shop/files/logo_280x80_px.png?v=1771157911)\-417 ![Sacks](https://a.astria.ai/assets/ecommerce/brands/sacks-2-959b6a3035f599ab4e20b84068b0f48d91e5609dd02e14c58f073a1e0a235e98.avif) ![Sacks](https://sacksfashion.com/cdn/shop/files/logo-2.png?v=1686074498)Sacks ![Sacks](https://a.astria.ai/assets/ecommerce/brands/sacks-3d03467de5eafbfc8ced95598c8fdf7ed3b456e0cd00724ec736e349c805dd54.avif) ![Sacks](https://sacksfashion.com/cdn/shop/files/logo-2.png?v=1686074498)Sacks ![Lee Cooper](https://a.astria.ai/assets/ecommerce/brands/lee-cooper-f6aec7a2b0e274a9315b45f0fa0c576354439c3c443cea2641ce62b1f075d267.avif) ![Lee Cooper](https://upload.wikimedia.org/wikipedia/commons/e/e6/Lee_cooper_logo.png)Lee Cooper ![B-Unique](https://a.astria.ai/assets/ecommerce/brands/b-unique-945b41c5f9c980a01a974554e330810fbd9c34e67e93e0bc729ab243cd81d42c.avif) ![B-Unique](https://b-unique.co.il/cdn/shop/files/Bunique_logo_yellow_ribbon-web.webp?v=1719664535)B-Unique ![Sebo](https://a.astria.ai/assets/ecommerce/brands/sebo-44c1c9e2322d82ab0b8e24d9b4f4e06991ce553027a2800f1b28c101c87ae1e9.avif) Sebo ![Mania](https://a.astria.ai/assets/ecommerce/brands/mania-model-c2f7b6255245961ac4380b1aff6ec5ab29898b7f4c7fe9280aad519c290d9c0b.avif) ![Mania](https://www.maniajeans.co.il/pub/media/idus/default/logo_1.png)Mania ![Mania Jeans](https://a.astria.ai/assets/ecommerce/brands/mania-jeans-8464c539fca3b9e944add977f2f740baa75d9e24bed2464dc42b3783c07fc6a0.avif) ![Mania](https://www.maniajeans.co.il/pub/media/idus/default/logo_1.png)Mania ![Nine West](https://a.astria.ai/assets/ecommerce/brands/nine-west-6e1295cf989febee12f9d52b37998d8355fdb32fe3c00e02ad4b42269772921d.avif) ![Nine West](https://ninewest.com/cdn/shop/files/NineWest_Logo.jpg?v=1613697835)Nine West ## Why Brands Choose Us 01 ### Your Brand, Native in AI We build AI workflows around your brand's visual identity, so every image feels consistent, recognizable, and on-brand. 02 ### Photoshoots at Scale Create thousands of product images in hours instead of weeks, ideal for new drops, seasonal campaigns, and large catalogs. 03 ### Creative Control, Not Creative Guesswork Define your style with experienced artists and fine-tune the output until it matches your brand vision. 04 ### Pay Only for What You Create No large production costs or rigid retainers, just pay for the assets you actually generate. Featured Creators ## [Creators we're proud of](https://www.astria.ai/gallery/workspaces) [![CI898](https://a.astria.ai/assets/ecommerce/creators/ci898-3baee0a5984293aecf4f5ecea61cad48525499b70c202621e4def63f371effb2.avif) CI898 ](https://www.astria.ai/w/ci898)[![Sivan Aharon](https://a.astria.ai/assets/ecommerce/creators/sivan-aharon-701be3d2c2c70f34befaafe70e027308e1864941b8044c71b23b3a5cf4e4c5e1.avif) Sivan Aharon ](https://www.astria.ai/w/sivanAharon) ![Ori Roterman](https://a.astria.ai/assets/ecommerce/creators/ori-roterman-637d90de7c0d5fb58c7560baefb04c3bbb258f37dfa87d36fd9036e1d9892950.avif) Ori Roterman [![Douglas Manzanares](https://a.astria.ai/assets/ecommerce/creators/douglas-manzanares-3c4491c948e30e8ab30cd338870e4f133684eab8bcd61884ed8984fb5449d6d2.avif) Douglas Manzanares ](https://www.astria.ai/w/douglas-manzanares) Featured Templates ## [Work we're proud of](https://www.astria.ai/gallery/workspaces/featured) [![America Basics](https://a.astria.ai/assets/workspaces/featured/sports-shoes-f3edf54f0a7511a9091efe334081a6649929a8c6d7f8357c68b1cdb5df4453b7.avif) MinimalAmerica BasicsClean everyday essentials designed for comfort.View Collection ](https://www.astria.ai/w/america-basics)[![Great Sports](https://a.astria.ai/assets/workspaces/featured/basics-pants-502693cd02584516f74fa5cf210f41d3a95ebae8b677e64f7f6714f641c11285.avif) AthleticGreat SportsBold sportswear imagery for active lifestyles.View Lookbook ](https://www.astria.ai/w/great-sports)[![Maison Urbane](https://a.astria.ai/assets/workspaces/featured/editorial-hero-7f32eec900bb8315dfe7239b1e0c39d9fbf488fdecf336e1c00bb31a38668dee.avif) EditorialMaison UrbaneSophisticated fashion editorial for contemporary style.Explore Collection ](https://www.astria.ai/w/zara) ## Studio Quality Photos. With no studio. Want to know more? Feel free to talk to us. [Get Started](https://www.astria.ai/prompts)[Contact Sales](mailto:support@astria.ai?subject=E-Commerce%20AI%20Photoshoots%20Inquiry) FAQ ## Questions & answers Astria is an AI visual production platform that helps brands create high-quality product and campaign imagery without relying on traditional photoshoots for every asset. Astria is built for fashion, retail, and brand teams that need to create visual assets at scale while keeping their imagery consistent and on-brand. You provide your product inputs and brand direction, and Astria generates visual assets based on your brand's aesthetic, creative requirements, and production needs. No. Astria is designed for brand and creative teams, not just AI experts. The workflow is built to make production faster and easier without requiring deep technical knowledge. Yes. Astria is built to work around your brand language, so outputs can reflect your visual style, product presentation, and campaign direction more consistently than generic AI tools. You can create product imagery, campaign visuals, seasonal assets, collection updates, and other branded content for ecommerce, marketing, and digital channels. Yes. Astria is designed to help brands produce large volumes of imagery far faster than traditional production, especially for new drops, seasonal campaigns, and ongoing catalog needs. Astria is designed to be flexible, so brands can create what they need and pay based on usage rather than committing to the fixed overhead of a traditional production model. Not always. For many use cases, Astria can replace or reduce the need for traditional shoots. For others, it works best as a faster, more scalable extension of your existing content workflow. The best way to start is with a demo or a test run using your own products, so you can see how Astria fits your brand and workflow. --- # Model rankings Source: https://www.astria.ai/model-rankings Product & brand images. Ranked by people on Arena. | Rank | Model | Score | | --- | --- | --- | | 1 | gpt-image-2.5-sunburst | 1557 | | 2 | gpt-image-2.5-flare | 1503 | | 3 | gemini-nano-banana-2.1 | 1490 | | 4 | gpt-image-2 (medium) | 1484 | | 5 | seedream-5.0-pro | 1431 | | 6 | muse-image | 1422 | | 7 | gemini-3.1-flash-image (nano-banana-2) \[web-search\] | 1403 | | 8 | ideogram-4.5 | 1391 | | 9 | gemini-3-pro-image-2k (nano-banana-pro) | 1384 | | 10 | reve-2.0 | 1383 | | 11 | chatgpt-image-latest-high-fidelity (20251216) | 1376 | | 12 | gpt-image-1.5-high-fidelity | 1361 | | 13 | uni-1.1-max | 1329 | | 14 | gemini-3.1-flash-lite-image (nano-banana-2-lite) | 1307 | | 15 | uni-1.1 | 1302 | | 16 | flux-2-max | 1283 | | 17 | seedream-4.5 | 1282 | | 18 | reve-v1.1 | 1280 | | 19 | flux-2-flex | 1277 | | 20 | seedream-5.0-lite | 1277 | | 21 | flux-2-pro | 1269 | | 22 | gemini-2.5-flash-image-preview (nano-banana) | 1248 | | 23 | flux-2-dev | 1245 | | 24 | wan2.6-image | 1242 | | 25 | kling-image-o1 | 1226 | | 26 | flux-2-klein-9b | 1221 | | 27 | seedream-4-high-res-fal | 1203 | | 28 | wan2.5-i2i-preview | 1188 | | 29 | qwen-image-edit-2511 | 1175 | | 30 | flux-2-klein-4b | 1170 | | 31 | p-image-edit | 1141 | | 32 | gpt-image-1-mini | 1140 | [View on Arena](https://arena.ai/leaderboard/image/image-edit/multi-image-commercial-design) --- # Nano Banana Pro 2 — AI Photo Plugin for Photoshop Source: https://www.astria.ai/nano-banana-photoshop Nano Banana Pro 2 for Photoshop. Select any part of an image. Describe the change. Get a new, editable layer—powered by GPT Image 2.5, Nano Banana, Seedream and Flux. macOS and Windows · Photoshop 2025+ · Version 2.0.50. Select it. Describe it. Keep every result on its own layer. ## Select. Describe. Keep editing. No exporting. No flattened result. The familiar Photoshop workflow, with a much more capable edit command. 1. Select only the pixels you want to change. 2. Describe the edit in the plugin panel. 3. Keep working with real, editable Photoshop layers. ## A complete AI edit desk. ### The right model for every kind of edit. Move from a surgical masked replacement to an identity-aware campaign variation without leaving the panel. Nano Banana 2.1, Seedream 5.0 Pro, Muse Image, GPT Image 2.5 Sunburst, GPT Image 2.5 Flare, Flux 3. ### Your API key. Your choice. Use a single Astria key for every model, or bring your own Google AI Studio key for Gemini. Pay as you go. No subscription. See [current pricing](https://www.astria.ai/pricing). ### Always a new layer. The original stays intact. Mask, blend, grade or generate another take with the tools you already know. ## Say what changes. Keep what matters. ### Remove > Remove the phone in the foreground. Reconstruct the dress and hand naturally; keep the face, pose and crop unchanged. ### Replace > Replace the shoes with reference 2. Preserve identity, proportions, garment, floor shadows and camera angle. ### Render > Turn this sketch into a photoreal product shot. Brushed aluminum, soft window light, 50 mm lens, shallow depth of field. ### Restyle > Create a crimson editorial portrait with subtle shutter-drag trails; keep the central facial features sharp. ## Real workflows. Start to finish. Fashion, photography, product, space and retail edits—every original walkthrough, still here. - Fashion editing with references: swap wardrobe, jewelry and products while preserving the model. - AI-assisted bridal photography: remove distractions, redirect gaze and refine the final image. - From sketch to realism: turn sketches, CAD and Blender renders into finished photography. - Fashion and product design: explore clothing, accessories, expressions and new locations. - Real-estate enhancement: clear objects, repair light and improve the surrounding space. - Everyday photo editing: change hair, clothing, expressions and small details naturally. - Packaging and e-commerce: test patterns, typography, logos and retail environments. ## Good to know. ### What do I get? A CCX file that installs in a click and opens as a native Photoshop panel. ### Are generations included? Buying more credits is optional: top up when you need it, or use your own Google AI Studio API key for Gemini. Google bills direct API usage to your account. There is no subscription. See [current pricing and allowances](https://www.astria.ai/pricing). ### Can I bring my own Google API key? Yes. Paste a Google AI Studio key into the panel and route Gemini Nano Banana calls through your own Google quota. ### Do I need a GPU? No. The models run in the cloud, and the panel works on macOS and Windows. ### What version of Photoshop is required? Photoshop 2025, version 26 or later, is required for the CCX/UXP plugin. ### What is the refund policy? All sales are final because the plugin is delivered as a downloadable CCX file. ## A better way to finish the image. Install once. Edit with any supported model. Keep working in Photoshop. Instant download. 16,000 sales. No subscription. BYOK supported.