Comfy.ICU
ExploreDocsPricing
Support

ComfyUI Extension: ComfyUI-RMBG

Authored by 1038lab

Created

Updated

178 stars

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 segmentation, utilizing multiple models including RMBG-2.0, INSPYRENET, BEN, SAM, and GroundingDINO.

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

    News & Updates

    • 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
      • 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 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.

    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

    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
    </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
    • 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: 1038 Lab

    License

    GPL-3.0 License