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August 11, 2024

BiRefNet State Of The Art Newest Very Best Background Batch Remover APP

BiRefNet is the very best state of the art newest published background removal open source model. It is way better than BRIA RMBG V1.4 that we knew as best.

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Official repo : https://github.com/ZhengPeng7/BiRefNet

Download APP and installers from : https://www.patreon.com/posts/109913645

I have developed a very advanced Gradio APP for this with full proper file saving and batch processing. Also my version removes BG and saves as transparent background.

The APP uses huge VRAM for high resolution images. However it is still working uber fast even though using shared VRAM. So make sure that you have high RAM or set virtual RAM.

Click here to see how to set virtual RAM on Windows.

On Massed Compute A6000 GPU (31 cents per hour) you can very fast remove even very high res images backgrounds.

Currently we have 1 click installers for RunPod, Massed Compute, Kaggle and Windows.

Windows Requirements

  • Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git

  • If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial

  • https://youtu.be/-NjNy7afOQ0

How To Use On Windows

  • Just extract files into like c:/BiRefNet_v1

  • Double click Windows_Install.bat file and it will generate a isolated virtual environment and install requirements

  • It will automatically download models into your Hugging Face cache (best model under 1 GB)

  • Then start and use the Gradio APP with Windows_Start_App.bat

Cloud How To Use

  • Massed Compute, RunPod has instructions txt files. Follow them

  • Kaggle has all the instructions 1 by 1

  • On Kaggle set resolution 1024x1024 or you will get out of memory error

 

 

Comments (4)

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Philip Heggie · August 13, 2024

It's great Portrait weight works best for my difficult images

S
SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts · August 13, 2024

great

G
GenerativeJoy · August 12, 2024

How much Vram does it require?

S
SECourses: FLUX, Tutorials, Guides, Resources, Training, Scripts · August 13, 2024

it totally depends on resolution of the image. just make sure you have high RAM or virtual RAM and it works great even when using shared VRAM