Birefnet Background Removal

fal-ai/birefnet
Inference
Commercial use

1. Calling the API#

Install the client#

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

npm install --save @fal-ai/serverless-client

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 * as fal from "@fal-ai/serverless-client";

const result = await fal.subscribe("fal-ai/birefnet", {
  input: {
    image_url: "https://fal.media/files/panda/K5Rndvzmn1j-OI1VZXDVd.jpeg"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});

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 * as fal from "@fal-ai/serverless-client";

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

3. 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 * as fal from "@fal-ai/serverless-client";

// Upload a file (you can get a file reference from an input element or a drag-and-drop event)
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

// Use the URL in your request
const result = await fal.subscribe("fal-ai/birefnet", { image_url: url });

Read more about file handling in our file upload guide.

4. Schema#

Input#

image_url*string

URL of the image to remove background from

modelModelEnum

Model to use for background removal. The 'General Use (Light)' model is the original model used in the BiRefNet repository. The 'General Use (Heavy)' model is a slower but more accurate model. The 'Portrait' model is a model trained specifically for portrait images. The 'General Use (Light)' model is recommended for most use cases.

The corresponding models are as follows:

  • 'General Use (Light)': BiRefNet-DIS_ep580.pth
  • 'General Use (Heavy)': BiRefNet-massive-epoch_240.pth
  • 'Portrait': BiRefNet-portrait-TR_P3M_10k-epoch_120.pth Default value: "General Use (Light)"

Possible values: "General Use (Light)", "General Use (Heavy)", "Portrait"

{
  "image_url": "https://fal.media/files/panda/K5Rndvzmn1j-OI1VZXDVd.jpeg",
  "model": "General Use (Light)"
}

Output#

image*Image

Image with background removed

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