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Birefnet Background Removal V2

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

Input

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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 (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)': BiRefNet - 'General Use (Light 2K)': BiRefNet_lite-2K - 'General Use (Heavy)': BiRefNet_lite - 'Matting': BiRefNet-matting - 'Portrait': BiRefNet-portrait - 'General Use (Dynamic)': BiRefNet_dynamic

Tips and requirements

The 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.

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

Tips and requirements

Whether 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.

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 V2

Getting started

Separate a subject from its background with BiRefNet V2. Choose a variant for general images, portraits or matting before refining the cutout.

Review your result

Check hair, glass and gaps against light and dark backgrounds. Compare the selected foreground with the untouched source.

Inputs and settings

Provide image_url and choose model and operating_resolution. The 2304x2304 resolution requires General Use (Dynamic).

Use output_mask to include a mask; mask_only returns the mask instead of a cutout. Select output_format and review refine_foreground.

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