FASHN Virtual Try-On | Image to Image

fashn/tryon

FASHN delivers precise virtual try-on capabilities, accurately rendering garment details like text and patterns at 576x864 resolution from both on-model and flat-lay photo references.

Inference

Commercial use

Partner

Schema

Playground API Examples

About

Generate Image

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Please check the migration guide for more information.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fashn/tryon", {
  input: {
    model_image: "https://utfs.io/f/wXFHUNfTHmLj4prvqbRMQ6JXFyUr3IT0avK2HSOmZWiAsxg9",
    garment_image: "https://utfs.io/f/wXFHUNfTHmLjtkhepmqOUnkr8XxZbNIFmRWldShDLu320TeC",
    category: "tops"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fashn/tryon", {
  input: {
    model_image: "https://utfs.io/f/wXFHUNfTHmLj4prvqbRMQ6JXFyUr3IT0avK2HSOmZWiAsxg9",
    garment_image: "https://utfs.io/f/wXFHUNfTHmLjtkhepmqOUnkr8XxZbNIFmRWldShDLu320TeC",
    category: "tops"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("fashn/tryon", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("fashn/tryon", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

model_image string* required

URL or base64 of the model image

garment_image string* required

URL or base64 of the garment image

category CategoryEnum* required

Category of the garment to try-on.

Possible enum values: tops, bottoms, one-pieces

garment_photo_type GarmentPhotoTypeEnum

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 value: "auto"

Possible enum values: auto, model, flat-lay

nsfw_filter boolean

Runs NSFW content filter on inputs. Default value: true

cover_feet boolean

Allows long garments to cover the feet/shoes or change their appearance.

adjust_hands boolean

Allow to change the appearance of the model’s hands. Example use-cases: Remove gloves, get hands out of pockets, long sleeves that should cover hands.

restore_background boolean

Apply additional steps to preserve the original background. Runtime will be slower. Not needed for simple backgrounds.

restore_clothes boolean

Apply additional steps to preserve the appearance of clothes that weren’t swapped (e.g. keep pants if trying-on top).

long_top boolean

Adjusts internal parameters for better performance on long tops such as: Longline shirts, tunics, coats, etc.

guidance_scale float

Higher guidance scales can help with preserving garment detail, but risks oversaturated colors. Default value: 2

timesteps integer

Determines how many steps the diffusion model will take to generate the image. For simple try-ons, steps can be reduced for faster runtime. Default value: 50

seed integer

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 value: 42

num_samples integer

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 value: 1

{
  "model_image": "https://utfs.io/f/wXFHUNfTHmLj4prvqbRMQ6JXFyUr3IT0avK2HSOmZWiAsxg9",
  "garment_image": "https://utfs.io/f/wXFHUNfTHmLjtkhepmqOUnkr8XxZbNIFmRWldShDLu320TeC",
  "category": "tops",
  "garment_photo_type": "auto",
  "nsfw_filter": true,
  "guidance_scale": 2,
  "timesteps": 50,
  "seed": 42,
  "num_samples": 1
}

Output#

images list<Image>* required

URLs of the generated images

{
  "images": [
    {
      "url": "",
      "content_type": "image/png",
      "file_name": "z9RV14K95DvU.png",
      "file_size": 4404019,
      "width": 1024,
      "height": 1024
    }
  ]
}

Other types#

Image#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

file_data string

File data

width integer

The width of the image in pixels.

height integer

The height of the image in pixels.

FASHN Virtual Try-On Image to Image

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

Image#