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Remove Background Http V2
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"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/birefnet/v2", {
input: {
image_url: "https://storage.googleapis.com/falserverless/example_inputs/birefnet-input.jpeg"
},
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);The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_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.
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/birefnet/v2", {
input: {
image_url: "https://storage.googleapis.com/falserverless/example_inputs/birefnet-input.jpeg"
},
webhookUrl: "https://optional.webhook.url/for/results",
});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/birefnet/v2", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});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/birefnet/v2", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);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.
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.
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.
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);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.
model ModelEnumModel to use for background removal. The 'General Use (Light)' model is the original model used in the BiRefNet repository. The 'General Use (Light 2K)' model is the original model used in the BiRefNet repository but trained with 2K images. The 'General Use (Heavy)' model is a slower but more accurate model. The 'Matting' model is a model trained specifically for matting images. The 'Portrait' model is a model trained specifically for portrait images. The 'General Use (Dynamic)' model supports dynamic resolutions from 256x256 to 2304x2304. The 'General Use (Light)' model is recommended for most use cases.
The corresponding models are as follows:
"General Use (Light)"Possible enum values: General Use (Light), General Use (Light 2K), General Use (Heavy), Matting, Portrait, General Use (Dynamic)
operating_resolution OperatingResolutionEnumThe resolution to operate on. The higher the resolution, the more accurate the output will be for high res input images. The '2304x2304' option is only available for the 'General Use (Dynamic)' model. Default value: "1024x1024"
Possible enum values: 1024x1024, 2048x2048, 2304x2304
output_mask booleanWhether to output the mask used to remove the background
refine_foreground booleanWhether to refine the foreground using the estimated mask Default value: true
sync_mode booleanIf True, the media will be returned as a data URI and the output data won't be available in the request history.
image_url string* requiredURL of the image to remove background from
output_format OutputFormatEnumThe format of the output image Default value: "png"
Possible enum values: webp, png, gif
mask_only booleanWhether to return only the segmentation mask without applying it to the image. When set to True, only the mask will be returned and foreground refinement will be skipped. Useful for reducing computation and data transfer when only the mask is needed.
{
"model": "General Use (Light)",
"operating_resolution": "1024x1024",
"refine_foreground": true,
"image_url": "https://storage.googleapis.com/falserverless/example_inputs/birefnet-input.jpeg",
"output_format": "png"
}Image with background removed. When mask_only is True, this contains the segmentation mask instead.
Mask used to remove the background
{
"image": {
"url": "https://storage.googleapis.com/falserverless/example_outputs/birefnet-output.png",
"width": 1024,
"content_type": "image/png",
"height": 1024,
"file_name": "birefnet-output.png"
}
}url string* requiredThe URL where the file can be downloaded from.
content_type stringThe mime type of the file.
file_name stringThe name of the file. It will be auto-generated if not provided.
file_size integerThe size of the file in bytes.
width integerThe width of the image
height integerThe height of the image