ComfyUI-RMBG

A ComfyUI custom node designed for advanced image background removal and object, face, clothes, and fashion segmentation, utilizing multiple models including RMBG-2.0, INSPYRENET, BEN, BEN2, BiRefNet-HR, SAM, and GroundingDINO.

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News & Updates

• 2025/03/21: Update ComfyUI-RMBG to v2.1.1 ( update.md )

  • Enhanced compatibility with Transformers

• 2025/03/19: Update ComfyUI-RMBG to v2.1.0 ( update.md )

  • Integrated internationalization (i18n) support for multiple languages.
  • Improved user interface for dynamic language switching.
  • Enhanced accessibility for non-English speaking users with fully translatable features.

• 2025/03/13: Update ComfyUI-RMBG to v2.0.0 ( update.md )

  • Added Image and Mask Tools improved functionality.
  • Enhanced code structure and documentation for better usability.
  • Introduced a new category path: 🧪AILab/🛠️UTIL/🖼️IMAGE.

• 2025/02/24: Update ComfyUI-RMBG to v1.9.3 Clean up the code and fix the issue ( update.md )

• 2025/02/21: Update ComfyUI-RMBG to v1.9.2 with Fast Foreground Color Estimation ( update.md )

  • Added new foreground refinement feature for better transparency handling
  • Improved edge quality and detail preservation
  • Enhanced memory optimization

• 2025/02/20: Update ComfyUI-RMBG to v1.9.1 ( update.md )

  • Changed repository for model management to the new repository and Reorganized models files structure for better maintainability.

• 2025/02/19: Update ComfyUI-RMBG to v1.9.0 with BiRefNet model improvements ( update.md )

  • Enhanced BiRefNet model performance and stability
  • Improved memory management for large images

• 2025/02/07: Update ComfyUI-RMBG to v1.8.0 with new BiRefNet-HR model ( update.md )

  • Added a new custom node for BiRefNet-HR model.
  • Support high resolution image processing (up to 2048x2048)

• 2025/02/04: Update ComfyUI-RMBG to v1.7.0 with new BEN2 model ( update.md )

  • Added a new custom node for BEN2 model.

• 2025/01/22: Update ComfyUI-RMBG to v1.6.0 with new Face Segment custom node ( update.md )

  • Added a new custom node for face parsing and segmentation
  • Support for 19 facial feature categories (Skin, Nose, Eyes, Eyebrows, etc.)
  • Precise facial feature extraction and segmentation
  • Multiple feature selection for combined segmentation
  • Same parameter controls as other RMBG nodes

• 2025/01/05: Update ComfyUI-RMBG to v1.5.0 with new Fashion and accessories Segment custom node ( update.md )

  • Added a new custom node for fashion segmentation.

• 2025/01/02: Update ComfyUI-RMBG to v1.4.0 with new Clothes Segment node ( update.md )

  • Added intelligent clothes segmentation with 18 different categories
  • Support multiple item selection and combined segmentation
  • Same parameter controls as other RMBG nodes

• 2024/12/29: Update ComfyUI-RMBG to v1.3.2 with background handling ( update.md )

  • Enhanced background handling to support RGBA output when "Alpha" is selected.
  • Ensured RGB output for all other background color selections.

• 2024/12/25: Update ComfyUI-RMBG to v1.3.1 with bug fixes ( update.md )

  • Fixed an issue with mask processing when the model returns a list of masks.
  • Improved handling of image formats to prevent processing errors.

• 2024/12/23: Update ComfyUI-RMBG to v1.3.0 with new Segment node ( update.md )

  • Added text-prompted object segmentation
  • Support both tag-style ("cat, dog") and natural language ("a person wearing red jacket") prompts
  • Multiple models: SAM (vit_h/l/b) and GroundingDINO (SwinT/B) (as always model file will be downloaded automatically when first time using the specific model)
  • This update requires install requirements.txt

• 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

• Background Removal (RMBG Node)

  • Multiple models: RMBG-2.0, INSPYRENET, BEN, BEN2
  • Various background options
  • Batch processing support

• Object Segmentation (Segment Node)

  • Text-prompted object detection
  • Support both tag-style and natural language inputs
  • High-precision segmentation with SAM
  • Flexible parameter controls

Installation

Method 1. install on ComfyUI-Manager, search Comfyui-RMBG and install

install requirment.txt in the ComfyUI-RMBG folder

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

Method 2. Clone this repository to your ComfyUI custom_nodes folder:

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

install requirment.txt in the ComfyUI-RMBG folder

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

Method 3: Install via Comfy CLI

Ensure pip install comfy-cli is installed. Installing ComfyUI comfy install (if you don't have ComfyUI Installed) install the ComfyUI-RMBG, use the following command:

comfy node install ComfyUI-RMBG

install requirment.txt in the ComfyUI-RMBG folder

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

4. 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/RMBG/INSPYRENET folder.
• Manually download the BEN model by visiting the link, then download the files and place them in the /ComfyUI/models/RMBG/BEN folder.
• Manually download the BEN2 model by visiting the link, then download the files and place them in the /ComfyUI/models/RMBG/BEN2 folder.
• Manually download the BiRefNet-HR by visiting the link, then download the files and place them in the /ComfyUI/models/RMBG/BiRefNet-HR folder.
• Manually download the SAM models by visiting the link, then download the files and place them in the /ComfyUI/models/SAM folder.
• Manually download the GroundingDINO models by visiting the link, then download the files and place them in the /ComfyUI/models/grounding-dino folder.
• Manually download the Clothes Segment model by visiting the link, then download the files and place them in the /ComfyUI/models/RMBG/segformer_clothes folder.
• Manually download the Fashion Segment model by visiting the link, then download the files and place them in the /ComfyUI/models/RMBG/segformer_fashion folder.
• Manually download BiRefNet models by visiting the link, then download the files and place them in the /ComfyUI/models/RMBG/BiRefNet folder.

Usage

RMBG Node

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.
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
Refine Foreground	Use Fast Foreground Color Estimation to optimize transparent background	Enable for better edge quality and transparency handling
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

Segment Node

1. Load Segment (RMBG) node from the 🧪AILab/🧽RMBG category
2. Connect an image to the input
3. Enter text prompt (tag-style or natural language)
4. Select SAM and GroundingDINO models
5. Adjust parameters as needed:
   • Threshold: 0.25-0.35 for broad detection, 0.45-0.55 for precision
   • Mask blur and offset for edge refinement
   • Background color options

About Models

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

BEN2

BEN2 is a more advanced version of BEN, offering:

• Improved accuracy and speed
• Better handling of complex scenes
• Support for more image types
• Suitable for batch processing

BIREFNET MODELS

BIREFNET is a powerful model for image segmentation, offering:

• BiRefNet-general purpose model (balanced performance)
• BiRefNet_512x512 model (optimized for 512x512 resolution)
• BiRefNet-portrait model (optimized for portrait/human matting)
• BiRefNet-matting model (general purpose matting)
• BiRefNet-HR model (high resolution up to 2560x2560)
• BiRefNet-HR-matting model (high resolution matting)
• BiRefNet_lite model (lightweight version for faster processing)
• BiRefNet_lite-2K model (lightweight version for 2K resolution)

SAM

SAM is a powerful model for object detection and segmentation, 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

GroundingDINO

GroundingDINO is a model for text-prompted object detection and segmentation, 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

BiRefNet Models

• BiRefNet-general purpose model (balanced performance)
• BiRefNet_512x512 model (optimized for 512x512 resolution)
• BiRefNet-portrait model (optimized for portrait/human matting)
• BiRefNet-matting model (general purpose matting)
• BiRefNet-HR model (high resolution up to 2560x2560)
• BiRefNet-HR-matting model (high resolution matting)
• BiRefNet_lite model (lightweight version for faster processing)
• BiRefNet_lite-2K model (lightweight version for 2K resolution)

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
  • opencv-python>=4.7.0

Credits

• RMBG-2.0: https://huggingface.co/briaai/RMBG-2.0

• INSPYRENET: https://github.com/plemeri/InSPyReNet

• BEN: https://huggingface.co/PramaLLC/BEN

• BEN2: https://huggingface.co/PramaLLC/BEN2

• BiRefNet: https://huggingface.co/ZhengPeng7

• SAM: https://huggingface.co/facebook/sam-vit-base

• GroundingDINO: https://github.com/IDEA-Research/GroundingDINO

• Clothes Segment: https://huggingface.co/mattmdjaga/segformer_b2_clothes

• Created by: AILab

License

GPL-3.0 License