BiRefNet (birefnet)

birefnet (BiRefNet) separates the foreground subject from the background with hair-level detail. It returns either an RGBA cutout of the subject, or the 8-bit grayscale alpha matte on its own.

Model ID	birefnet (default model of the endpoint)
Model	BiRefNet, general model (Peng Zheng et al.)
Weights	Official general model (ZhengPeng7/BiRefNet)
Use cases	Background removal
Endpoints	POST /v1/remove-background
Pool	Paid: billed from the balance
Billing units	image. Units and prices: Billing
Licence	MIT

How it works

A call is one forward pass of a segmentation network over the whole image, followed by packing the result as an image.

Prediction. The image is resized to 1024 × 1024 pixels and normalised, and BiRefNet predicts for every pixel how much of it belongs to the foreground subject. The network keeps high-resolution detail, which is why thin structures such as hair keep soft, partly transparent edges. The prediction is scaled back to the size of the image and stored as 8-bit values: 255 is the subject, 0 is the background, and the values between are partly transparent pixels. This is the alpha matte.

Output. With options.output set to matte, the matte is returned as it is, as a grayscale image. With cutout (default), the colours of the input image and the matte as the alpha channel form an RGBA image, so the background is transparent. Both outputs come from the same pass; they differ only in how the result is packed. The image is then encoded as PNG or WebP, as options.format says.

Every call runs the full model on its own image. There is no reusable encoding, so there is no cache discount, and a call is billed as one image at the same price for the cutout and the matte.

In each use case

Background removal

• birefnet is the default model of POST /v1/remove-background. One image per call.
• options.output returns a cutout (default) or a matte, and options.format encodes it as png (default) or webp; see Output formats.
• The long side of the result is the smaller of the image’s long side and options.size (an integer from 64 to 4096, default 2048). The aspect ratio is kept, and an image is never enlarged. The result is returned inline as base64 in data.

Use case guide and example call: Background removal.

Limits and notes

• One image per call. Every call runs the full model; there is no cache discount.
• Image limits: see Images. When the image is larger than options.size, the result is scaled down on its long side to that value.
• Latency: the recorded call had timing.processingMs of 882 ms. The figure depends on the image, on queueing and on node load, and it is not a guarantee.