try-on | Image to Image | API Documentation

fal-ai/cat-vton

Image based Virtual Try-On

Inference

Research only

Schema

About

Run

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("fal-ai/cat-vton", {
  input: {
    human_image_url: "https://storage.googleapis.com/falserverless/catvton/man5.jpg",
    garment_image_url: "https://storage.googleapis.com/falserverless/catvton/tshirt.jpg",
    cloth_type: "upper"
  },
  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("fal-ai/cat-vton", {
  input: {
    human_image_url: "https://storage.googleapis.com/falserverless/catvton/man5.jpg",
    garment_image_url: "https://storage.googleapis.com/falserverless/catvton/tshirt.jpg",
    cloth_type: "upper"
  },
  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("fal-ai/cat-vton", {
  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("fal-ai/cat-vton", {
  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#

human_image_url string* required

Url for the human image.

garment_image_url string* required

Url to the garment image.

cloth_type ClothTypeEnum* required

Type of the Cloth to be tried on.

Options: upper: Upper body cloth lower: Lower body cloth overall: Full body cloth inner: Inner cloth, like T-shirt inside a jacket outer: Outer cloth, like a jacket over a T-shirt

Possible enum values: upper, lower, overall, inner, outer

image_size ImageSize | Enum

The size of the generated image. Default value: portrait_4_3

Possible enum values: square_hd, square, portrait_4_3, portrait_16_9, landscape_4_3, landscape_16_9

Note: For custom image sizes, you can pass the width and height as an object:

"image_size": {
  "width": 1280,
  "height": 720
}

num_inference_steps integer

The number of inference steps to perform. Default value: 30

guidance_scale float

The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt when looking for a related image to show you. Default value: 2.5

seed integer

The same seed and the same input given to the same version of the model will output the same image every time.

{
  "human_image_url": "https://storage.googleapis.com/falserverless/catvton/man5.jpg",
  "garment_image_url": "https://storage.googleapis.com/falserverless/catvton/tshirt.jpg",
  "cloth_type": "upper",
  "image_size": "portrait_4_3",
  "num_inference_steps": 30,
  "guidance_scale": 2.5
}

Output#

image Image* required

The output image.

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