ComfyUI-RMBG

A sophisticated ComfyUI custom node engineered for advanced image background removal and precise segmentation of objects, faces, clothing, and fashion elements. This tool leverages a diverse array of models, including RMBG-2.0, INSPYRENET, BEN, BEN2, BiRefNet, Lucida, SDMatte models, SAM, SAM2 and GroundingDINO, while also incorporating a new feature for real-time background replacement and enhanced edge detection for improved accuracy.

News & Updates

• 2026/07/21: Update ComfyUI-RMBG to v3.1.0 ( update.md )

  • Added Lucida model to BiRefNet node — BiRefNet fine-tune for transparent objects, camouflage, text/logos, glow/VFX, and illustrations)
  • Added sensitivity parameter to BiRefNet node

• 2026/01/01: Update ComfyUI-RMBG to v3.0.0 ( update.md )

• 2025/12/09: Update ComfyUI-RMBG to v2.9.6 ( update.md ) 

• 2025/11/25: Update ComfyUI-RMBG to v2.9.5 SAM3 Segmentaion bug fixed( update.md ) 

• 2025/11/24: Update ComfyUI-RMBG to v2.9.4 SAM3 Segmentaion ( update.md )

• 2025/10/05: Update ComfyUI-RMBG to v2.9.3 ( update.md )  

• 2025/09/30: Update ComfyUI-RMBG to v2.9.2 ( update.md )

• Add new BiRefNet_toonOut Model

• Updated Imagestitch

• 2025/09/12: Update ComfyUI-RMBG to v2.9.1 ( update.md )

• 2025/08/18: Update ComfyUI-RMBG to v2.9.0 ( update.md )

  • Added SDMatte Matting node

• 2025/08/11: Update ComfyUI-RMBG to v2.8.0 ( update.md )

  • Added SAM2Segment node for text-prompted segmentation with the latest Facebook Research SAM2 technology.
  • Enhanced color widget support across all nodes

• 2025/08/06: Update ComfyUI-RMBG to v2.7.1 ( update.md )

  • Enhanced LoadImage into three distinct nodes to meet different needs, all supporting direct image loading from local paths or URLs
  • Completely redesigned ImageStitch node compatible with ComfyUI's native functionality
  • Fixed background color handling issues reported by users

• 2025/07/15: Update ComfyUI-RMBG to v2.6.0 ( update.md )

• Added Kontext Refence latent Mask node, Which uses a reference latent and mask for precise region conditioning.

• 2025/07/11: Update ComfyUI-RMBG to v2.5.2 ( update.md )

• 2025/07/07: Update ComfyUI-RMBG to v2.5.1 ( update.md )

• 2025/07/01: Update ComfyUI-RMBG to v2.5.0 ( update.md )

• Added MaskOverlay, ObjectRemover, ImageMaskResize new nodes.

• Added 2 BiRefNet models: BiRefNet_lite-matting and BiRefNet_dynamic

• Added batch image support for Segment_v1 and Segment_V2 nodes

• 2025/06/01: Update ComfyUI-RMBG to v2.4.0 ( update.md )

  • Added CropObject, ImageCompare, ColorInput nodes and new Segment V2 (see update.md for details)

• 2025/05/15: Update ComfyUI-RMBG to v2.3.2 ( update.md )

• 2025/05/02: Update ComfyUI-RMBG to v2.3.1 ( update.md )

• 2025/05/01: Update ComfyUI-RMBG to v2.3.0 ( update.md )

  • Added new nodes: IC-LoRA Concat, Image Crop
  • Added resizing options for Load Image: Longest Side, Shortest Side, Width, and Height, enhancing flexibility.

• 2025/04/05: Update ComfyUI-RMBG to v2.2.1 ( update.md )

• 2025/04/05: Update ComfyUI-RMBG to v2.2.0 ( update.md )

  • Added new nodes: Image Combiner, Image Stitch, Image/Mask Converter, Mask Enhancer, Mask Combiner, and Mask Extractor
  • Fixed compatibility issues with transformers v4.49+
  • Fixed i18n translation errors
  • Added mask image output to segment nodes

• 2025/03/21: Update ComfyUI-RMBG to v2.1.1 ( update.md )

  • Enhanced compatibility with Transformers

• 2025/03/19: Update ComfyUI-RMBG to v2.1.0 ( update.md )

  • Integrated internationalization (i18n) support for multiple languages.
  • Improved user interface for dynamic language switching.
  • Enhanced accessibility for non-English speaking users with fully translatable features.

https://github.com/user-attachments/assets/7faa00d3-bbe2-42b8-95ed-2c830a1ff04f

• 2025/03/13: Update ComfyUI-RMBG to v2.0.0 ( update.md )

  • Added Image and Mask Tools improved functionality.
  • Enhanced code structure and documentation for better usability.
  • Introduced a new category path: 🧪AILab/🛠️UTIL/🖼️IMAGE.

• 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

• SAM2 Segmentation

  • Text-prompted segmentation with the latest SAM2 models (Tiny/Small/Base+/Large)
  • Automatic model download on first use, with manual download option

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

[!NOTE] Windows desktop app: if the app crashes after install, set PYTHONUTF8=1 before installing requirements, then retry.

[!NOTE] YOLO nodes require the optional ultralytics package. Install it only if you need YOLO to avoid dependency conflicts: ./ComfyUI/python_embeded/python -m pip install ultralytics --no-deps.

[!TIP] Note: If your environment cannot install dependencies with the system Python, you can use ComfyUI's embedded Python instead. Example (embedded Python): ./ComfyUI/python_embeded/python.exe -m pip install --no-user --no-cache-dir -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

Method 3: Install via Comfy CLI

Ensure pip install comfy-cli is installed. Installing ComfyUI comfy install (if you don't have ComfyUI Installed) install the ComfyUI-RMBG, use the following command:

comfy node install ComfyUI-RMBG

install requirment.txt in the ComfyUI-RMBG folder

./ComfyUI/python_embeded/python -m pip install -r requirements.txt

4. 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 SAM2 models by visiting the link, then download the files (e.g., sam2.1_hiera_tiny.safetensors, sam2.1_hiera_small.safetensors, sam2.1_hiera_base_plus.safetensors, sam2.1_hiera_large.safetensors) and place them in the /ComfyUI/models/sam2 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.
• Manually download SDMatte safetensors models by visiting the link, then download the files and place them in the /ComfyUI/models/RMBG/SDMatte folder.

Usage

RMBG Node

Optional Settings :bulb: Tips

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

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

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)

Lucida

Lucida is a BiRefNet fine-tune by egeorcun (MIT License), optimized for cases where general-purpose background removers fall apart:

• Semi-transparent objects (glass, reflections)
• Camouflaged subjects
• Text, logos, and typography with soft shadows
• Glow/VFX effects
• Illustrations and line art
• Print designs (stickers, tees)
• Original model: egeorcun/lucida

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

SAM2

SAM2 is the latest segmentation model family designed for efficient, high-quality text-prompted segmentation:

• Multiple sizes: Tiny, Small, Base+, Large
• Optimized inference with strong accuracy
• Automatic download on first use; manual placement supported in ComfyUI/models/sam2

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)
• Lucida model (BiRefNet fine-tune for transparent objects, camouflage, text/logos, glow/VFX, illustrations)

Requirements

• ComfyUI
• Python 3.10+
• Required packages (automatically installed):
  • huggingface-hub>=0.19.0
  • transparent-background>=1.1.2
  • segment-anything>=1.0
  • groundingdino-py>=0.4.0
  • opencv-python>=4.7.0
  • onnxruntime>=1.15.0
  • onnxruntime-gpu>=1.15.0
  • protobuf>=3.20.2,<6.0.0
  • hydra-core>=1.3.0
  • omegaconf>=2.3.0
  • iopath>=0.1.9

SDMatte models (manual download)

• Auto-download on first run to models/RMBG/SDMatte/
• If network restricted, place weights manually:
  • models/RMBG/SDMatte/SDMatte.safetensors (standard) or SDMatte_plus.safetensors (plus)
  • Components (config files) are auto-downloaded; if needed, mirror the structure from the Hugging Face repo to models/RMBG/SDMatte/ (scheduler/, text_encoder/, tokenizer/, unet/, vae/)

Troubleshooting (short)

• 401 error when initializing GroundingDINO / missing models/sam2:
  • Delete %USERPROFILE%\.cache\huggingface\token (and %USERPROFILE%\.huggingface\token if present)
  • Ensure no HF_TOKEN/HUGGINGFACE_TOKEN env vars are set
  • Re-run; public repos download anonymously (no login required)
• Preview shows "Required input is missing: images":
  • Ensure image outputs are connected and upstream nodes ran successfully

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

• Lucida: https://huggingface.co/egeorcun/lucida

• SAM: https://huggingface.co/facebook/sam-vit-base

• GroundingDINO: https://github.com/IDEA-Research/GroundingDINO

• Clothes Segment: https://huggingface.co/mattmdjaga/segformer_b2_clothes

• SDMatte: https://github.com/vivoCameraResearch/SDMatte

• Created by: AILab

Star History

If this custom node helps you or you like my work, please give me ⭐ on this repo! It's a great encouragement for my efforts!

License

GPL-3.0 License