ComfyUI Extension: Transparency Background Remover

Authored by Limbicnation

Created 2 months ago

Updated 2 months ago

5 stars

Automatic background removal and transparency generation for ComfyUI

Custom Nodes (0)

README

ComfyUI-TransparencyBackgroundRemover

Intelligent Background Removal Node for ComfyUI

ComfyUI-TransparencyBackgroundRemover is a powerful custom node that automatically removes backgrounds from images using advanced AI-powered detection algorithms. Designed for seamless integration with ComfyUI workflows, this node excels at preserving fine edges and details while generating high-quality transparency masks.

✨ Key Features

🎯 Automatic Background Detection - Intelligent color clustering and edge analysis
🖼️ Edge Preservation - Advanced edge refinement algorithms for crisp boundaries
🔄 Batch Processing Support - Process multiple images efficiently in a single operation
📐 Power-of-8 Scaling - Optimized scaling with NEAREST neighbor interpolation for pixel-perfect results
🎨 Multiple Output Formats - RGBA with embedded alpha or RGB with separate mask
🖥️ Dither Pattern Handling - Specialized processing for pixel art and dithered images
⚙️ Highly Customizable - Fine-tune parameters for different image types and requirements

ComfyUI-TransparencyBackgroundRemover

📦 Installation

Method 1: ComfyUI Manager (Recommended)

Open ComfyUI and navigate to Manager → Install via Git URL

Enter the repository URL:

https://github.com/Limbicnation/ComfyUI-TransparencyBackgroundRemover

Click Install and restart ComfyUI
The node will appear under image/processing category

Method 2: Manual Installation

Clone the repository to your ComfyUI custom nodes directory:

cd ComfyUI/custom_nodes/
git clone https://github.com/Limbicnation/ComfyUI-TransparencyBackgroundRemover.git

Install dependencies:

cd ComfyUI-TransparencyBackgroundRemover
pip install -r requirements.txt

Restart ComfyUI to load the new node

Required Dependencies

torch - PyTorch for tensor operations
numpy - Numerical computing
Pillow - Image processing
opencv-python - Computer vision operations
scikit-learn - Machine learning algorithms for clustering

🎛️ Node Parameters

Core Processing Parameters

| Parameter | Type | Range | Default | Description | |-----------|------|-------|---------|-------------| | tolerance | INT | 0-255 | 30 | Color similarity threshold for background detection. Lower values = more selective background detection | | edge_sensitivity | FLOAT | 0.0-1.0 | 0.8 | Edge detection sensitivity. Higher values = more edge detail preservation | | foreground_bias | FLOAT | 0.0-1.0 | 0.7 | Bias towards preserving foreground elements. Higher values = stronger foreground protection | | color_clusters | INT | 2-20 | 8 | Number of color clusters for background analysis. More clusters = finer color distinction | | binary_threshold | INT | 0-255 | 128 | Threshold for generating binary alpha masks. Higher values = more opaque areas |

Output & Scaling Options

| Parameter | Type | Options | Default | Description | |-----------|------|---------|---------|-------------| | output_size | DROPDOWN | ORIGINAL, 64x64, 96x96, 128x128, 256x256, 512x512, 768x768, 1024x1024, 1280x1280, 1536x1536, 1792x1792, 2048x2048 | ORIGINAL | Target output dimensions (power-of-8 for optimal scaling) | | scaling_method | DROPDOWN | NEAREST | NEAREST | Interpolation method. NEAREST preserves pixel-perfect detail for pixel art | | output_format | DROPDOWN | RGBA, RGB_WITH_MASK | RGBA | Output format: RGBA (transparency embedded) or RGB with separate mask |

Advanced Options

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | edge_refinement | BOOLEAN | True | Apply post-processing edge refinement for smoother boundaries | | dither_handling | BOOLEAN | True | Enable specialized processing for dithered patterns and pixel art | | batch_processing | BOOLEAN | True | Process all images in batch (True) or only first image (False) |

🚀 Usage Examples

Basic Background Removal

Load your image using any ComfyUI image loader node
Connect the image output to the image input of the TransparencyBackgroundRemover node
Adjust parameters based on your image type:
- Photographic images: Use default settings
- Pixel art: Enable dither_handling, set edge_sensitivity to 0.9+
- Complex backgrounds: Increase color_clusters to 12-16
Connect the outputs to preview or save nodes

Batch Processing Workflow

Load Images (Batch) → TransparencyBackgroundRemover → Save Images
                                  ↓
                            (Set batch_processing = True)

Pixel Art Optimization

Recommended settings for pixel art:

tolerance: 10-20
edge_sensitivity: 0.9-1.0
color_clusters: 4-8
dither_handling: True
scaling_method: NEAREST
output_size: Power-of-8 dimensions (256x256, 512x512, etc.)

High-Quality Photo Processing

Recommended settings for photographs:

tolerance: 25-40
edge_sensitivity: 0.7-0.8
foreground_bias: 0.8-0.9
color_clusters: 10-16
edge_refinement: True

📋 Workflow Integration

Example Workflow JSON

{
  "nodes": [
    {
      "type": "LoadImage",
      "pos": [100, 100]
    },
    {
      "type": "TransparencyBackgroundRemover",
      "pos": [400, 100],
      "inputs": {
        "tolerance": 30,
        "edge_sensitivity": 0.8,
        "output_format": "RGBA"
      }
    },
    {
      "type": "PreviewImage", 
      "pos": [700, 100]
    }
  ]
}

Node Connections

Input: Connect any IMAGE output to the image input
Outputs:
- image → Connect to preview, save, or further processing nodes
- mask → Use for compositing, masking, or additional processing

🔧 Technical Details

Supported Image Formats

Input: RGB/RGBA images as ComfyUI tensors
Output: RGBA images with transparency or RGB + separate mask
Batch Format: 4D tensors [batch, height, width, channels]

Performance Considerations

Memory Usage: ~2-4x input image size during processing
Batch Processing: Processes images sequentially with progress indicators
Minimum Size: 64x64 pixels required
Recommended: Use power-of-8 dimensions for optimal scaling performance

Algorithm Overview

Color Analysis: K-means clustering to identify dominant colors
Background Detection: Multi-criteria analysis including edge proximity and color distribution
Edge Refinement: Gradient-based boundary smoothing
Alpha Generation: Soft masking with configurable thresholds
Post-Processing: Optional dither handling and edge enhancement

🐛 Troubleshooting

Common Issues

"Input image must be at least 64x64 pixels"

Ensure your input images meet the minimum size requirement
Use an upscaling node if needed before processing

"Insufficient memory for processing"

Reduce batch size or process images individually
Set batch_processing to False for large images
Close other memory-intensive applications

Poor background detection

Increase tolerance for similar colors
Adjust color_clusters (more clusters for complex backgrounds)
Try different foreground_bias values

Jagged edges

Enable edge_refinement
Increase edge_sensitivity
For pixel art, ensure dither_handling is enabled

🤝 Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

Development Setup

git clone https://github.com/Limbicnation/ComfyUI-TransparencyBackgroundRemover.git
cd ComfyUI-TransparencyBackgroundRemover
pip install -r requirements.txt

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

ComfyUI team for the excellent framework
Community contributors and testers
Built with ❤️ for the AI art community

📞 Support

Issues: GitHub Issues
Documentation: Project Wiki
Discussions: GitHub Discussions