350 lines
20 KiB
Markdown
350 lines
20 KiB
Markdown
# ComfyUI-RMBG
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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-HR, SAM, and GroundingDINO.
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## News & Updates
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- **2025/07/11**: Update ComfyUI-RMBG to **v2.5.2** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v252-20250711) )
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- **2025/07/07**: Update ComfyUI-RMBG to **v2.5.1** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v251-20250707) )
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- **2025/07/01**: Update ComfyUI-RMBG to **v2.5.0** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v250-20250701) )
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- Added `MaskOverlay`, `ObjectRemover`, `ImageMaskResize` new nodes.
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- Added 2 BiRefNet models: `BiRefNet_lite-matting` and `BiRefNet_dynamic`
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- Added batch image support for `Segment_v1` and `Segment_V2` nodes
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- **2025/06/01**: Update ComfyUI-RMBG to **v2.4.0** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v240-20250601) )
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- Added `CropObject`, `ImageCompare`, `ColorInput` nodes and new Segment V2 (see update.md for details)
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- **2025/05/15**: Update ComfyUI-RMBG to **v2.3.2** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v232-20250515) )
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- **2025/05/02**: Update ComfyUI-RMBG to **v2.3.1** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v231-20250502) )
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- **2025/05/01**: Update ComfyUI-RMBG to **v2.3.0** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v230-20250501) )
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- Added new nodes: IC-LoRA Concat, Image Crop
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- Added resizing options for Load Image: Longest Side, Shortest Side, Width, and Height, enhancing flexibility.
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- **2025/04/05**: Update ComfyUI-RMBG to **v2.2.1** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v221-20250405) )
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- **2025/04/05**: Update ComfyUI-RMBG to **v2.2.0** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v220-20250405) )
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- Added new nodes: Image Combiner, Image Stitch, Image/Mask Converter, Mask Enhancer, Mask Combiner, and Mask Extractor
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- Fixed compatibility issues with transformers v4.49+
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- Fixed i18n translation errors
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- Added mask image output to segment nodes
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- **2025/03/21**: Update ComfyUI-RMBG to **v2.1.1** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v211-20250321) )
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- Enhanced compatibility with Transformers
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- **2025/03/19**: Update ComfyUI-RMBG to **v2.1.0** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v210-20250319) )
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- Integrated internationalization (i18n) support for multiple languages.
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- Improved user interface for dynamic language switching.
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- Enhanced accessibility for non-English speaking users with fully translatable features.
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https://github.com/user-attachments/assets/7faa00d3-bbe2-42b8-95ed-2c830a1ff04f
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- **2025/03/13**: Update ComfyUI-RMBG to **v2.0.0** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v200-20250313) )
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- Added Image and Mask Tools improved functionality.
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- Enhanced code structure and documentation for better usability.
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- Introduced a new category path: `🧪AILab/🛠️UTIL/🖼️IMAGE`.
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- **2025/02/24**: Update ComfyUI-RMBG to **v1.9.3** Clean up the code and fix the issue ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v193-20250224) )
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- **2025/02/21**: Update ComfyUI-RMBG to **v1.9.2** with Fast Foreground Color Estimation ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v192-20250221) )
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- Added new foreground refinement feature for better transparency handling
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- Improved edge quality and detail preservation
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- Enhanced memory optimization
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- **2025/02/20**: Update ComfyUI-RMBG to **v1.9.1** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v191-20250220) )
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- Changed repository for model management to the new repository and Reorganized models files structure for better maintainability.
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- **2025/02/19**: Update ComfyUI-RMBG to **v1.9.0** with BiRefNet model improvements ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v190-20250219) )
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- Enhanced BiRefNet model performance and stability
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- Improved memory management for large images
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- **2025/02/07**: Update ComfyUI-RMBG to **v1.8.0** with new BiRefNet-HR model ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v180-20250207) )
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- Added a new custom node for BiRefNet-HR model.
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- Support high resolution image processing (up to 2048x2048)
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- **2025/02/04**: Update ComfyUI-RMBG to **v1.7.0** with new BEN2 model ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v170-20250204) )
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- Added a new custom node for BEN2 model.
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- **2025/01/22**: Update ComfyUI-RMBG to **v1.6.0** with new Face Segment custom node ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v160-20250122) )
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- Added a new custom node for face parsing and segmentation
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- Support for 19 facial feature categories (Skin, Nose, Eyes, Eyebrows, etc.)
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- Precise facial feature extraction and segmentation
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- Multiple feature selection for combined segmentation
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- Same parameter controls as other RMBG nodes
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- **2025/01/05**: Update ComfyUI-RMBG to **v1.5.0** with new Fashion and accessories Segment custom node ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v150-20250105) )
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- Added a new custom node for fashion segmentation.
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- **2025/01/02**: Update ComfyUI-RMBG to **v1.4.0** with new Clothes Segment node ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v140-20250102) )
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- Added intelligent clothes segmentation with 18 different categories
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- Support multiple item selection and combined segmentation
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- Same parameter controls as other RMBG nodes
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- **2024/12/29**: Update ComfyUI-RMBG to **v1.3.2** with background handling ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v132-20241229) )
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- Enhanced background handling to support RGBA output when "Alpha" is selected.
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- Ensured RGB output for all other background color selections.
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- **2024/12/25**: Update ComfyUI-RMBG to **v1.3.1** with bug fixes ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v131-20241225) )
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- Fixed an issue with mask processing when the model returns a list of masks.
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- Improved handling of image formats to prevent processing errors.
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- **2024/12/23**: Update ComfyUI-RMBG to **v1.3.0** with new Segment node ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v140-20241222) )
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- Added text-prompted object segmentation
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- Support both tag-style ("cat, dog") and natural language ("a person wearing red jacket") prompts
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- 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)
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- This update requires install requirements.txt
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- **2024/12/12**: Update Comfyui-RMBG ComfyUI Custom Node to **v1.2.2** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v122-20241212) )
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- **2024/12/02**: Update Comfyui-RMBG ComfyUI Custom Node to **v1.2.1** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.mdv121-20241202) )
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- **2024/11/29**: Update Comfyui-RMBG ComfyUI Custom Node to **v1.2.0** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v120-20241129) )
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- **2024/11/21**: Update Comfyui-RMBG ComfyUI Custom Node to **v1.1.0** ( [update.md](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v110-20241121) )
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## Features
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- Background Removal (RMBG Node)
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- Multiple models: RMBG-2.0, INSPYRENET, BEN, BEN2
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- Various background options
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- Batch processing support
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- Object Segmentation (Segment Node)
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- Text-prompted object detection
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- Support both tag-style and natural language inputs
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- High-precision segmentation with SAM
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- Flexible parameter controls
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## Installation
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### Method 1. install on ComfyUI-Manager, search `Comfyui-RMBG` and install
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install requirment.txt in the ComfyUI-RMBG folder
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```bash
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./ComfyUI/python_embeded/python -m pip install -r requirements.txt
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```
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### Method 2. Clone this repository to your ComfyUI custom_nodes folder:
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```bash
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cd ComfyUI/custom_nodes
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git clone https://github.com/1038lab/ComfyUI-RMBG
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```
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install requirment.txt in the ComfyUI-RMBG folder
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```bash
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./ComfyUI/python_embeded/python -m pip install -r requirements.txt
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```
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### Method 3: Install via Comfy CLI
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Ensure `pip install comfy-cli` is installed.
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Installing ComfyUI `comfy install` (if you don't have ComfyUI Installed)
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install the ComfyUI-RMBG, use the following command:
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```bash
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comfy node install ComfyUI-RMBG
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```
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install requirment.txt in the ComfyUI-RMBG folder
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```bash
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./ComfyUI/python_embeded/python -m pip install -r requirements.txt
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```
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### 4. Manually download the models:
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- The model will be automatically downloaded to `ComfyUI/models/RMBG/` when first time using the custom node.
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- Manually download the RMBG-2.0 model by visiting this [link](https://huggingface.co/1038lab/RMBG-2.0), then download the files and place them in the `/ComfyUI/models/RMBG/RMBG-2.0` folder.
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- Manually download the INSPYRENET models by visiting the [link](https://huggingface.co/1038lab/inspyrenet), then download the files and place them in the `/ComfyUI/models/RMBG/INSPYRENET` folder.
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- Manually download the BEN model by visiting the [link](https://huggingface.co/1038lab/BEN), then download the files and place them in the `/ComfyUI/models/RMBG/BEN` folder.
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- Manually download the BEN2 model by visiting the [link](https://huggingface.co/1038lab/BEN2), then download the files and place them in the `/ComfyUI/models/RMBG/BEN2` folder.
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- Manually download the BiRefNet-HR by visiting the [link](https://huggingface.co/1038lab/BiRefNet_HR), then download the files and place them in the `/ComfyUI/models/RMBG/BiRefNet-HR` folder.
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- Manually download the SAM models by visiting the [link](https://huggingface.co/1038lab/sam), then download the files and place them in the `/ComfyUI/models/SAM` folder.
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- Manually download the GroundingDINO models by visiting the [link](https://huggingface.co/1038lab/GroundingDINO), then download the files and place them in the `/ComfyUI/models/grounding-dino` folder.
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- Manually download the Clothes Segment model by visiting the [link](https://huggingface.co/1038lab/segformer_clothes), then download the files and place them in the `/ComfyUI/models/RMBG/segformer_clothes` folder.
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- Manually download the Fashion Segment model by visiting the [link](https://huggingface.co/1038lab/segformer_fashion), then download the files and place them in the `/ComfyUI/models/RMBG/segformer_fashion` folder.
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- Manually download BiRefNet models by visiting the [link](https://huggingface.co/1038lab/BiRefNet), then download the files and place them in the `/ComfyUI/models/RMBG/BiRefNet` folder.
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## Usage
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### RMBG Node
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### Optional Settings :bulb: Tips
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| Optional Settings | :memo: Description | :bulb: Tips |
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|----------------------|-----------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|
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| **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. |
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| **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. |
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| **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. |
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| **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. |
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| **Background** | Choose output background color | Alpha (transparent background) Black, White, Green, Blue, Red |
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| **Invert Output** | Flip mask and image output | Invert both image and mask output |
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| **Refine Foreground** | Use Fast Foreground Color Estimation to optimize transparent background | Enable for better edge quality and transparency handling |
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| **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. |
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### Basic Usage
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1. Load `RMBG (Remove Background)` node from the `🧪AILab/🧽RMBG` category
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2. Connect an image to the input
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3. Select a model from the dropdown menu
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4. select the parameters as needed (optional)
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3. Get two outputs:
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- IMAGE: Processed image with transparent, black, white, green, blue, or red background
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- MASK: Binary mask of the foreground
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### Parameters
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- `sensitivity`: Controls the background removal sensitivity (0.0-1.0)
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- `process_res`: Processing resolution (512-2048, step 128)
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- `mask_blur`: Blur amount for the mask (0-64)
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- `mask_offset`: Adjust mask edges (-20 to 20)
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- `background`: Choose output background color
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- `invert_output`: Flip mask and image output
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- `optimize`: Toggle model optimization
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### Segment Node
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1. Load `Segment (RMBG)` node from the `🧪AILab/🧽RMBG` category
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2. Connect an image to the input
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3. Enter text prompt (tag-style or natural language)
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4. Select SAM and GroundingDINO models
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5. Adjust parameters as needed:
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- Threshold: 0.25-0.35 for broad detection, 0.45-0.55 for precision
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- Mask blur and offset for edge refinement
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- Background color options
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<details>
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<summary><h2>About Models</h2></summary>
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## RMBG-2.0
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RMBG-2.0 is is developed by BRIA AI and uses the BiRefNet architecture which includes:
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- High accuracy in complex environments
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- Precise edge detection and preservation
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- Excellent handling of fine details
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- Support for multiple objects in a single image
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- Output Comparison
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- Output with background
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- Batch output for video
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The model is trained on a diverse dataset of over 15,000 high-quality images, ensuring:
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- Balanced representation across different image types
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- High accuracy in various scenarios
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- Robust performance with complex backgrounds
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## INSPYRENET
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INSPYRENET is specialized in human portrait segmentation, offering:
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- Fast processing speed
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- Good edge detection capability
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- Ideal for portrait photos and human subjects
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## BEN
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BEN is robust on various image types, offering:
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- Good balance between speed and accuracy
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- Effective on both simple and complex scenes
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- Suitable for batch processing
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## BEN2
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BEN2 is a more advanced version of BEN, offering:
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- Improved accuracy and speed
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- Better handling of complex scenes
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- Support for more image types
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- Suitable for batch processing
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## BIREFNET MODELS
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BIREFNET is a powerful model for image segmentation, offering:
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- BiRefNet-general purpose model (balanced performance)
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- BiRefNet_512x512 model (optimized for 512x512 resolution)
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- BiRefNet-portrait model (optimized for portrait/human matting)
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- BiRefNet-matting model (general purpose matting)
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- BiRefNet-HR model (high resolution up to 2560x2560)
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- BiRefNet-HR-matting model (high resolution matting)
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- BiRefNet_lite model (lightweight version for faster processing)
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- BiRefNet_lite-2K model (lightweight version for 2K resolution)
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## SAM
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SAM is a powerful model for object detection and segmentation, offering:
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- High accuracy in complex environments
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- Precise edge detection and preservation
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- Excellent handling of fine details
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- Support for multiple objects in a single image
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- Output Comparison
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- Output with background
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- Batch output for video
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## GroundingDINO
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GroundingDINO is a model for text-prompted object detection and segmentation, offering:
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- High accuracy in complex environments
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- Precise edge detection and preservation
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- Excellent handling of fine details
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- Support for multiple objects in a single image
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- Output Comparison
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- Output with background
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- Batch output for video
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## BiRefNet Models
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- BiRefNet-general purpose model (balanced performance)
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- BiRefNet_512x512 model (optimized for 512x512 resolution)
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- BiRefNet-portrait model (optimized for portrait/human matting)
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- BiRefNet-matting model (general purpose matting)
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- BiRefNet-HR model (high resolution up to 2560x2560)
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- BiRefNet-HR-matting model (high resolution matting)
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- BiRefNet_lite model (lightweight version for faster processing)
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- BiRefNet_lite-2K model (lightweight version for 2K resolution)
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</details>
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## Requirements
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- ComfyUI
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- Python 3.10+
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- Required packages (automatically installed):
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- torch>=2.0.0
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- torchvision>=0.15.0
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- Pillow>=9.0.0
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- numpy>=1.22.0
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- huggingface-hub>=0.19.0
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- tqdm>=4.65.0
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- transformers>=4.35.0
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- transparent-background>=1.2.4
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- opencv-python>=4.7.0
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## Credits
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- RMBG-2.0: https://huggingface.co/briaai/RMBG-2.0
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- INSPYRENET: https://github.com/plemeri/InSPyReNet
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- BEN: https://huggingface.co/PramaLLC/BEN
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- BEN2: https://huggingface.co/PramaLLC/BEN2
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- BiRefNet: https://huggingface.co/ZhengPeng7
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- SAM: https://huggingface.co/facebook/sam-vit-base
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- GroundingDINO: https://github.com/IDEA-Research/GroundingDINO
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- Clothes Segment: https://huggingface.co/mattmdjaga/segformer_b2_clothes
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- Created by: [AILab](https://github.com/1038lab)
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## Star History
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<a href="https://www.star-history.com/#1038lab/comfyui-rmbg&Date">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=1038lab/comfyui-rmbg&type=Date&theme=dark" />
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<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=1038lab/comfyui-rmbg&type=Date" />
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<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=1038lab/comfyui-rmbg&type=Date" />
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</picture>
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</a>
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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!
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## License
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GPL-3.0 License
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