ComfyUI_BiRefNet_ll

ComfyUI_BiRefNet_ll
★ 278

ComfyUI集成BiRefNet集成模型自动下载背景抠图
为ComfyUI提供BiRefNet新旧版本兼容支持，含AutoDownloadBiRefNetModel、LoadRembgByBiRefNetModel和RembgByBiRefNet节点，自动下载并用BiRefNet执行rembg抠图。
💡 在ComfyUI流水线中自动下载并用BiRefNet进行抠图处理。
🍴 30 Forks💻 Python🔄 2025-06-01
📦
网盘下载
复制链接后前往夸克网盘下载
https://pan.quark.cn/s/2df45d172dc1
📦 requirements.txt
numpy
opencv-python
timm
save api extended
save api extended
📄 README

中文文档

Support the use of new and old versions of BiRefNet models

Preview

Install

  • Manual
    •     cd custom_nodes
    git clone https://github.com/lldacing/ComfyUI_BiRefNet_ll.git
    cd ComfyUI_BiRefNet_ll
    pip install -r requirements.txt
    # restart ComfyUI
  • Via ComfyUI Manager

    • Models

    The available newest models are:

  • General: A pre-trained model for general use cases.
  • General-HR: A pre-trained model for general use cases which shows great performance on higher resolution images (2048×2048).
  • General-Lite: A light pre-trained model for general use cases.
  • General-Lite-2K: A light pre-trained model for general use cases in high resolution (2560×1440).
  • General-dynamic: A pre-trained model for dynamic resolution, trained with images in range from 256×256 to 2304×2304.
  • General-reso_512: A pre-trained model for faster and more accurate lower resolution, trained with images in 512×512.
  • General-legacy: A pre-trained model for general use trained on DIS5K-TR,DIS-TEs, DUTS-TR_TE,HRSOD-TR_TE,UHRSD-TR_TE, HRS10K-TR_TE (w/o portrait seg data).
  • Portrait: A pre-trained model for human portraits.
  • Matting: A pre-trained model for general trimap-free matting use.
  • Matting-HR: A pre-trained model for general matting use which shows great matting performance on higher resolution images (2048×2048).
  • Matting-Lite: A light pre-trained model for general trimap-free matting use.
  • DIS: A pre-trained model for dichotomous image segmentation (DIS).
  • HRSOD: A pre-trained model for high-resolution salient object detection (HRSOD).
  • COD: A pre-trained model for concealed object detection (COD).
  • DIS-TR_TEs: A pre-trained model with massive dataset.

    • Model files go here (when use AutoDownloadBiRefNetModel automatically downloaded if the folder is not present during first run): ${comfyui_rootpath}/models/BiRefNet.

    If necessary, they can be downloaded from:

  • General ➔ model.safetensors must be renamed General.safetensors
  • General-HR ➔ model.safetensors must be renamed General-HR.safetensors
  • General-Lite ➔ model.safetensors must be renamed General-Lite.safetensors
  • General-Lite-2K ➔ model.safetensors must be renamed General-Lite-2K.safetensors
  • General-dynamic ➔ model.safetensors must be renamed General-dynamic.safetensors
  • General-legacy ➔ model.safetensors must be renamed General-legacy.safetensors
  • General-reso_512 ➔ model.safetensors must be renamed General-reso_512.safetensors
  • Portrait ➔ model.safetensors must be renamed Portrait.safetensors
  • Matting ➔ model.safetensors must be renamed Matting.safetensors
  • Matting-HR ➔ model.safetensors must be renamed Matting-HR.safetensors
  • Matting-Lite ➔ model.safetensors must be renamed Matting-Lite.safetensors
  • DIS ➔ model.safetensors must be renamed DIS.safetensors
  • HRSOD ➔ model.safetensors must be renamed HRSOD.safetensors
  • COD ➔ model.safetensors must be renamed COD.safetensors
  • DIS-TR_TEs ➔ model.safetensors must be renamed DIS-TR_TEs.safetensors

    • Some models on GitHub:

    BiRefNet Releases

    Old models:

  • BiRefNet-DIS_ep580.pth
  • BiRefNet-ep480.pth

    • Weight Models (Optional)

  • swin_large_patch4_window12_384_22kto1k.pth(not General-Lite, General-Lite-2K and Matting-Lite model)
  • swin_tiny_patch4_window7_224_22kto1k_finetune.pth(just General-Lite, General-Lite-2K and Matting-Lite model)

    • Nodes

  • AutoDownloadBiRefNetModel
  • Automatically download the model into ${comfyui_rootpath}/models/BiRefNet, do not support weight model
  • LoadRembgByBiRefNetModel
  • Can select model from ${comfyui_rootpath}/models/BiRefNet or the path of birefnet configured in the extra YAML file
  • You can download latest models from BiRefNet Releases or old models BiRefNet-DIS_ep580.pth and BiRefNet-ep480.pth
  • When param use_weight is True, need download weight model swin_large_patch4_window12_384_22kto1k.pth

    • model General-Lite, General-Lite-2K and Matting-Lite must use weight model swin_tiny_patch4_window7_224_22kto1k_finetune.pth

  • RembgByBiRefNet
  • Output transparent foreground image and mask
  • RembgByBiRefNetAdvanced
  • Output foreground image and mask, provide some fine-tuning parameters
  • GetMaskByBiRefNet
  • Only output mask
  • BlurFusionForegroundEstimation
  • Use the fast-foreground-estimation method to estimate the foreground image

    • Thanks

    ZhengPeng7/BiRefNet

    dimitribarbot/sd-webui-birefnet