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 models, SAM, and GroundingDINO.

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News & Updates

• 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

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/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 :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

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

<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

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)

</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

• 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