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Birefnet Background Removal

bilateral reference framework (BiRefNet) for high-resolution dichotomous image segmentation (DIS)

Input

Fields marked * are required. Review the price before generating.

URL of the image to remove background from

Uploading a file needs an account. Create accountJPEG, PNG, WebP · up to 25 MB

Additional settings
Tips and requirements

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

The resolution to operate on. The higher the resolution, the more accurate the output will be for high res input images.

Whether to output the mask used to remove the background

Whether to refine the foreground using the estimated mask

The format of the output image

Price$0.28 / run

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API code for this input

Complete the required fields and fix input errors to see API code.

How to use Birefnet Background Removal

Getting started

Separate a foreground subject from its background. Keep the original and, when needed, request the mask so you can inspect or refine the selection.

Review your result

Check hair, transparent objects and low-contrast edges over different backgrounds. Foreground refinement can change edge detail; compare it with the original before compositing.

Inputs and settings

Provide image_url and select model and operating_resolution in the studio. These controls affect how the source is processed rather than describing a new scene.

Use output_mask to request the selection mask. Choose output_format and review refine_foreground before submitting.

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