Product to Model
Powered by best-in-class image editing AI, the Product to Model endpoint transforms product images into people wearing those products, with optional guidance from an inspiration image, background, or face reference.
This endpoint is designed specifically for wearable fashion items such as clothing, shoes, hats, jewelry, bags, and accessories.
- Model Name:
product-to-model - Lifecycle: preview
- Processing Time: 10s–55s (
+20swithface_reference, see below) - Output Formats: PNG, JPEG
- Delivery Methods: URL or Base64 encoding
- Credits: 1-5 per output image depending on
resolutionandgeneration_mode(+3per output image withface_reference)
To place a product on an existing person image while preserving that image's
pose and background, use the tryon-max endpoint
with model_image. On Product to Model, a model's identity is passed via
face_reference instead, and the endpoint generates a new image of that
person wearing the product; it does not replace the clothing or keep the pose
and background of the reference photo.
Request
Generate product-to-model images by submitting your product image to the universal /v1/run endpoint:
Request Parameters
product_imageRequiredimage URL | base64
URL or base64 encoded image of the product to be worn. Supports clothing, accessories, shoes, and other wearable fashion items.
image_promptimage URL | base64
Optional URL or base64 encoded inspiration image that guides pose, environment, and lighting while keeping the product centered in the final output.
Default: None
face_referenceimage URL | base64
Optional face identity reference to guide who the generated person should look like. When provided, the pipeline refines identity to match the reference while keeping product fidelity. Using a face reference typically adds around 20 seconds to the processing time.
Default: None
face_reference_mode'match_base' | 'match_reference'
Controls how the identity from face_reference influences pose and expression.
-match_reference favors the reference face’s pose and expression for maximum resemblance.
-match_base gives more weight to the prompt (or system default prompt if omitted) when generating the person's pose and expression.
Default: match_reference
promptstring
Additional instructions for person appearance, styling preferences, or background.
Examples: "man with tattoos", "tucked-in", "open jacket", "rolled-up sleeves", "studio background".
Default: None
aspect_ratiostring
Desired aspect ratio for the output image. If omitted, the generation inherits the aspect ratio from the most specific image supplied (background_reference → image_prompt → product_image). Provide an explicit ratio to override that default even when using these image references.
Supported ratios: "1:1", "3:4", "4:3", "9:16", "16:9", "2:3", "3:2", "4:5", "5:4"
Default: Aspect ratio of the most specific image supplied
resolution'1k' | '2k' | '4k'
Output resolution tier. '1k' produces ~1 megapixel output, '2k' ~4 megapixels, and '4k' ~16 megapixels. Exact output dimensions depend on this tier and the image aspect ratio.
Default: '1k'
generation_mode'fast' | 'balanced' | 'quality'
Sets the generation quality level. 'quality' produces the most detailed and
realistic output but takes longer to process and costs more credits. 'fast'
prioritizes speed and lower cost. If omitted, FASHN selects generation_mode
automatically. For product-to-model, omitted generation_mode is currently
billed as 'fast' at 1k and as 'balanced' at 2k or 4k.
background_referenceimage URL | base64
Background image used as the backdrop for generation. Ensures location consistency across generations. If a person appears in the image, they will be ignored and only the background will be used.
When provided alongside image_prompt, image_prompt governs the model's appearance, pose, and styling, while background_reference anchors the scene.
Default: None
seedinteger
Seed for reproducible results. Must be between 0 and 2^32-1.
Default: 42
num_imagesinteger
Number of images to generate in a single request. Must be between 1 and 4. Additional images consume more compute (and credits) and can increase processing time.
Default: 1
output_formatstring
Output image format.
"png"- PNG format, original quality"jpeg"- JPEG format, smaller file size
Default: "png"
return_base64boolean
When set to true, the API will return the generated image as a base64-encoded string instead of a CDN URL. The base64 string will be prefixed data:image/png;base64,....
This option offers enhanced privacy as user-generated outputs are not stored on our servers when return_base64 is enabled.
Default: false
Credit Cost
generation_mode \ resolution | 1k | 2k | 4k |
|---|---|---|---|
fast | 1 | 2 | 3 |
balanced | 2 | 3 | 4 |
quality | 3 | 4 | 5 |
Additional pricing rules:
face_referenceadds+3credits per output image.num_imagesmultiplies the total cost by the number of outputs requested.- If
generation_modeis omitted, automatic pricing applies.
Processing Time
Processing time depends on both resolution and generation_mode. The fastest configuration (fast + 1k) typically completes in around 10 seconds, balanced at 2k finishes in around 25 seconds, and the most intensive combination (quality + 4k) typically takes around 55 seconds. Using face_reference typically adds around 20 seconds on top of any configuration. Actual latency may vary with current server load.
Response Polling
After submitting your request, poll the status endpoint using the returned prediction ID. See API Fundamentals for complete polling details.
Successful Response
When your product-to-model generation completes successfully, the status endpoint will return:
Runtime Errors
Runtime errors for Product to Model use the shared set in Error Handling.
Related Guides
- Prompting in FASHN ↗ - Learn how to write effective prompts for best results
- Image Preprocessing Best Practices - Optimize your input images for better results
- Data Retention & Privacy - Understand how FASHN handles your data