ComfyUI-RMBG

A ComfyUI node for removing image backgrounds using RMBG-2.0.

{\color{red}If\ this\ custom\ node\ helps\ you\ or\ you\ like\ my\ work,\ please\ give\ me⭐on\ this\ repo!} {\color{red}It's\ a\ greatest\ encouragement\ for\ my\ efforts!}

News

• 2024/11/21: Update Comfyui-RMBG ComfyUI Custom Node to v1.1.0 ( update.md )

Features

RMBG-2.0 is built on the innovative BiRefNet (Bilateral Reference Network) architecture, offering:

• High accuracy in complex environments
• Precise edge detection and preservation
• Excellent handling of fine details
• Support for multiple objects in a single image
• Output Comparison
• Output with background
• Batch output for video

Installation

1. Clone this repository to your ComfyUI custom_nodes folder:

cd ComfyUI/custom_nodes
git clone https://github.com/1038lab/ComfyUI-RMBG

2. RMBG Model Download:

• The model will be automatically downloaded to ComfyUI/models/RMBG/RMBG-2.0 when first time using the custom node.
• Manually download the RMBG-2.0 model by visiting this link, then download the files and place them in the /ComfyUI/models/RMBG/RMBG-2.0 folder.

Usage

Optional Settings 💡 Tips

Optional Settings	📝 Description	💡 Tips
Sensitivity	Adjusts the strength of mask detection. Higher values result in stricter detection.	Default value is 0.5. Adjust based on image complexity; more complex images may require higher sensitivity.
Processing Resolution	Controls the processing resolution of the input image, affecting detail and memory usage.	Choose a value between 256 and 2048, with a default of 1024. Higher resolutions provide better detail but increase memory consumption.
Mask Blur	Controls the amount of blur applied to the mask edges, reducing jaggedness.	Default value is 0. Try setting it between 1 and 5 for smoother edge effects.
Mask Offset	Allows for expanding or shrinking the mask boundary. Positive values expand the boundary, while negative values shrink it.	Default value is 0. Adjust based on the specific image, typically fine-tuning between -10 and 10.
Performance Optimization	Properly setting options can enhance performance when processing multiple images.	If memory allows, consider increasing process_res and mask_blur values for better results, but be mindful of memory usage.

Basic Usage

1. Load RMBG (Remove Background) node from the 🧪AILab/🧽RMBG category
2. Connect an image to the input
3. Get two outputs:
   • IMAGE: Processed image with transparent background
   • MASK: Binary mask of the foreground

Parameters

• sensitivity: Controls the background removal sensitivity (0.0-1.0)
• process_res: Processing resolution (512-2048, step 128)
• mask_blur: Blur amount for the mask (0-64)
• mask_offset: Adjust mask edges (-20 to 20)

About RMBG-2.0

RMBG-2.0 is developed by BRIA AI and uses the BiRefNet architecture which includes:

• Localization Module (LM): Generates semantic maps for primary image areas
• Restoration Module (RM): Performs precise boundary restoration using:
  • Original Reference: Provides general background context
  • Gradient Reference: Focuses on edges and fine details

The model is trained on a diverse dataset of over 15,000 high-quality images, ensuring:

• Balanced representation across different image types
• High accuracy in various scenarios
• Robust performance with complex backgrounds

Requirements

• ComfyUI
• Python 3.10+
• Required packages (automatically installed):
  • torch>=2.0.0
  • torchvision>=0.15.0
  • Pillow>=9.0.0
  • numpy>=1.22.0
  • transformers>=4.30.0
  • safetensors>=0.3.0

Credits

• RMBG-2.0: https://huggingface.co/briaai/RMBG-2.0
• Created by: 1038 Lab

License

MIT License