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.

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News & Updates

2025/01/05: Update ComfyUI-RMBG to v1.5.0 with new FashionSegment custom node ( update.md )
- Introduced 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
- 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 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

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
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

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

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

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

README.md

ComfyUI-RMBG