FASHN Virtual Try-On v1.6

Virtual Try-On v1.6 is a fast, lightweight virtual try-on model optimized for real-time e-commerce integrations.

Lifecycle: Stable · Released: June 25, 2025

Need studio-grade realism?

Try-On Max is our recommended virtual try-on endpoint, offering higher resolution (up to 4K), broader item support (shoes, hats, jewelry, bags), and prompt-based customization. Use v1.6 when speed and cost are your primary concerns.

Request body

Every call uses the universal POST /v1/run request body:

Field	Type	Required	Description
`model_name`	string	Yes	Must be `tryon-v1.6`.
`inputs`	object	Yes	Contains the endpoint-specific parameters documented under Input parameters.

Place all endpoint parameters inside inputs; they are not top-level request fields.

Request

Generate a virtual try-on by submitting your model and garment images to the universal /v1/run endpoint:

POST`https://api.fashn.ai/v1/run`

Request examples

curl -X POST https://api.fashn.ai/v1/run \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer YOUR_API_KEY" \
     -d '{
           "model_name": "tryon-v1.6",
           "inputs": {
             "model_image": "http://example.com/path/to/model.jpg",
             "garment_image": "http://example.com/path/to/garment.jpg"
           }
         }'

Response

Returns a prediction ID for status polling:

{
  "id": "123a87r9-4129-4bb3-be18-9c9fb5bd7fc1-u1",
  "error": null
}

Input parameters

`model_image`
Required
image URL | data URI

Primary image of the person on whom the virtual try-on will be performed.

`garment_image`
Required
image URL | data URI

Reference image of the clothing item to be tried on the model_image.

`category`'auto' | 'tops' | 'bottoms' | 'one-pieces'

Use auto to enable automatic classification of the garment type. For flat-lay or ghost mannequin images, the system detects the garment type automatically. For on-model images, full-body shots default to a full outfit swap. For focused shots (upper or lower body), the system selects the most likely garment type (tops or bottoms).

Default: auto

`segmentation_free`boolean

Direct garment fitting without clothing segmentation, enabling bulkier garment try-ons with improved preservation of body shape and skin texture. Set to false if original garments are not removed properly.

Default: true

`moderation_level`'conservative' | 'permissive' | 'none'

Sets the content moderation level for garment images.

-conservative enforces stricter modesty standards suitable for culturally sensitive contexts. Blocks underwear, swimwear, and revealing outfits.

-permissive allows swimwear, underwear, and revealing garments, while still blocking explicit nudity.

-none disables all content moderation

Default: permissive

Responsible Use Notice

This technology is designed for ethical virtual try-on applications. Misuse—such as generating inappropriate imagery without consent—violates our Terms of Service ↗.

Setting moderation_level: none does not remove your responsibility for ethical and lawful use. Violations may result in service denial.

`garment_photo_type`auto | flat-lay | model

Specifies the type of garment photo to optimize internal parameters for better performance. 'model' is for photos of garments on a model, 'flat-lay' is for flat-lay or ghost mannequin images, and 'auto' attempts to automatically detect the photo type.

Default: auto

`mode`performance | balanced | quality

Specifies the mode of operation.

-performance mode is faster but may compromise quality

-balanced mode is a perfect middle ground between speed and quality

-quality mode is slower, but delivers the highest quality results.

Default: balanced

`seed`int

Sets random operations to a fixed state. Use the same seed to reproduce results with the same inputs, or different seed to force different results.

Default: 42 · Range: 0 to 2^32 - 1

`num_samples`int

Number of images to generate in a single run. Image generation has a random element in it, so trying multiple images at once increases the chances of getting a good result.

Default: 1 · Range: 1 to 4

`output_format`'png' | 'jpeg'

Specifies the desired output image format.

-png: Delivers the highest quality image, ideal for use cases such as content creation where quality is paramount.

-jpeg: Provides a faster response with a slightly compressed image, more suitable for real-time applications like consumer virtual try-on experiences.

Default: png

`return_base64`boolean

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 according to the output_format (e.g., data:image/png;base64,... or data:image/jpeg;base64,...).

Privacy benefit: Outputs are available for up to 60 minutes instead of the standard three-day window. Request history contains only a <base64> placeholder.

Default: false

Technical limitations

Input images are processed at 864 × 1296 px.

Credit cost

Virtual Try-On v1.6 costs 1 credit per output image. The num_samples value determines the number of outputs and therefore the total cost.

Processing time

`mode`	Typical processing time
`performance`	About 5 seconds
`balanced`	About 8 seconds
`quality`	About 12–17 seconds, depending on the input resolution

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 virtual try-on completes successfully, the status endpoint will return:

{
  "id": "123a87r9-4129-4bb3-be18-9c9fb5bd7fc1-u1",
  "status": "completed",
  "output": [
    "https://cdn.fashn.ai/123a87r9-4129-4bb3-be18-9c9fb5bd7fc1-u1/output_0.png"
  ],
  "error": null
}

The output array contains URLs to your generated try-on images showing the model wearing the specified garment. The number of images depends on the num_samples parameter (default: 1).

Runtime errors

Try-On shares the common runtime errors in Error Handling. Additional endpoint-specific errors:

Name	Cause	Solution
`PoseError`	The pipeline was unable to detect a body pose in either the model image or the garment image (when `garment_photo_type: "model"`).	Improve model or garment photo quality following the model photo guidelines ↗.

For detailed implementation guidance and best practices specific to Virtual Try-On:

Try-On Parameters Guide - Detailed parameter optimization for try-on models
Python Quickstart Guide - Complete Python implementation examples
JavaScript Quickstart Guide - Frontend integration patterns
Image Preprocessing Best Practices - Optimize your input images for better results
Data Retention & Privacy - Understand how FASHN handles your data

FASHN Virtual Try-On v1.6

POSThttps://api.fashn.ai/v1/run

Response

model_imageRequiredimage URL | data URI

garment_imageRequiredimage URL | data URI

category'auto' | 'tops' | 'bottoms' | 'one-pieces'

segmentation_freeboolean

moderation_level'conservative' | 'permissive' | 'none'

garment_photo_typeauto | flat-lay | model

modeperformance | balanced | quality

seedint

num_samplesint

output_format'png' | 'jpeg'

return_base64boolean

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