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
-
2025/02/21: 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
- Load
RMBG (Remove Background)
node from the 🧪AILab/🧽RMBG
category
- Connect an image to the input
- Select a model from the dropdown menu
- select the parameters as needed (optional)
- 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
- Load
Segment (RMBG)
node from the 🧪AILab/🧽RMBG
category
- Connect an image to the input
- Enter text prompt (tag-style or natural language)
- Select SAM and GroundingDINO models
- 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