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ComfyUI Extension: ComfyUI-RMBG

Authored by 1038lab

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Updated

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A ComfyUI node for removing image backgrounds using RMBG-2.0

Custom Nodes (0)

    README

    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.

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

    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 ) RMBG_V1 9 2

      • 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 ) rmbg_v1 9 0

      • 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 ) RMBG-v1 8 0

      • 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 ) rmbg_v1 7 0

      • 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 ) RMBG_v1 6 0

      • 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 ) RMBGv_1 5 0

      • 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 ) rmbg_v1 4 0

      • 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 ) rmbg v1.3.0

      • 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 ) RMBG1 2 2

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

    • 2024/11/29: Update Comfyui-RMBG ComfyUI Custom Node to v1.2.0 ( update.md ) RMBGv1 2 0

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

    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

    RMBG Demo

    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

    RMBG

    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