ComfyUI-RMBG

A ComfyUI custom node designed for advanced image background removal utilizing multiple models, including RMBG-2.0, INSPYRENET, and BEN.

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

News & Updates

• 2024/12/12: Update Comfyui-RMBG ComfyUI Custom Node to v1.2.2 ( update.md )

• 2024/12/02: Update Comfyui-RMBG ComfyUI Custom Node to v1.2.1 ( update.md )

• 2024/11/29: Update Comfyui-RMBG ComfyUI Custom Node to v1.2.0 ( update.md )

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

Features

Installation

1. install on ComfyUI-Manager, search Comfyui-RMBG and install. Install the dependencies requirements.txt file within the ComfyUI-RMBG folder.

   ./ComfyUI/python_embeded/python -m pip install -r requirements.txt
   

2. Clone this repository to your ComfyUI custom_nodes folder:

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

3. Manually download the models:

• The model will be automatically downloaded to ComfyUI/models/RMBG/ 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.
• Manually download the INSPYRENET models by visiting the link, then download the files and place them in the /ComfyUI/models/INSPYRENET folder.
• Manually download the BEN model by visiting the link, then download the files and place them in the /ComfyUI/models/BEN folder.

Usage

Optional Settings :bulb: Tips

| Optional Settings | :memo: Description | :bulb: 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. | | Background | Choose output background color | Alpha (transparent background) Black, White, Green, Blue, Red | | Invert Output | Flip mask and image output | Invert both image and mask output | | 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. Select a model from the dropdown menu
4. select the parameters as needed (optional)
5. Get two outputs:
   • IMAGE: Processed image with transparent, black, white, green, blue, or red 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)
• background: Choose output background color
• invert_output: Flip mask and image output
• optimize: Toggle model optimization

<details> <summary><h2>About Models</h2></summary>

RMBG-2.0

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

• 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 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

INSPYRENET

INSPYRENET is specialized in human portrait segmentation, offering:

• Fast processing speed
• Good edge detection capability
• Ideal for portrait photos and human subjects

BEN

BEN is robust on various image types, offering:

• Good balance between speed and accuracy
• Effective on both simple and complex scenes
• Suitable for batch processing

</details>

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
  • huggingface-hub>=0.19.0
  • tqdm>=4.65.0
  • transformers>=4.35.0
  • transparent-background>=1.2.4

Credits

• RMBG-2.0: https://huggingface.co/briaai/RMBG-2.0
• INSPYRENET: https://github.com/plemeri/InSPyReNet
• BEN: https://huggingface.co/PramaLLC/BEN
• Created by: 1038 Lab

License

GPL-3.0 License