123 lines
6.1 KiB
Markdown
123 lines
6.1 KiB
Markdown
# ComfyUI-RMBG
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A ComfyUI node for removing image backgrounds with multiple models: RMBG-2.0, INSPYRENET, and BEN.
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$${\color{red}If\ this\ custom\ node\ helps\ you\ or\ you\ like\ my\ work,\ please\ give\ me⭐on\ this\ repo!}$$
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$${\color{red}It's\ a\ greatest\ encouragement\ for\ my\ efforts!}$$
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## News
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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) )
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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) )
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## Features
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## Installation
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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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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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3. 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/briaai/RMBG-2.0/tree/main), 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/INSPYRENET` folder.
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- Manually download the BEN model by visiting the [link](https://huggingface.co/PramaLLC/BEN), then download the files and place them in the `/ComfyUI/models/BEN` folder.
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## Usage
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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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| **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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<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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</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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## 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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- Created by: [1038 Lab](https://github.com/1038lab)
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## License
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MIT License
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