ComfyUI Extension: Transparency Background Remover

Authored by Limbicnation

Created

Updated

6 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

    • 🆕 Content-Aware Edge Detection - Automatically adapts processing for Pixel Art or Photographic images to achieve the best results.
    • 🎯 Multi-Method Algorithm - Combines Roberts Cross, Sobel, and Canny edge detection for superior accuracy and detail preservation.
    • 🖼️ Advanced Edge Refinement - Specialized algorithms for crisp, pixel-perfect boundaries in pixel art and smooth, clean edges in photos.
    • 🔄 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 ComfyUI-TransparencyBackgroundRemover ComfyUI-TransparencyBackgroundRemover

    📦 Installation

    Method 1: ComfyUI Manager (Recommended)

    1. Open ComfyUI and navigate to Manager → Install via Git URL
    2. Enter the repository URL: 
      https://github.com/Limbicnation/ComfyUI-TransparencyBackgroundRemover
    3. Click Install and restart ComfyUI
    4. The node will appear under image/processing category

    Method 2: Manual Installation

    1. Clone the repository to your ComfyUI custom nodes directory:

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

    2. Install dependencies:

      cd ComfyUI-TransparencyBackgroundRemover
pip install -r requirements.txt

    3. 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 | Options | Default | Description | |-----------|------|---------|---------|-------------| | edge_detection_mode | DROPDOWN | AUTO, PIXEL_ART, PHOTOGRAPHIC | AUTO | Selects the edge detection pipeline. AUTO intelligently detects content type. | | 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

    1. Load your image using any ComfyUI image loader node
    2. Connect the image output to the image input of the TransparencyBackgroundRemover node
    3. Adjust parameters based on your image type:
      • For most images: Leave edge_detection_mode on AUTO.
      • For specific needs: Manually select PIXEL_ART or PHOTOGRAPHIC to override the automatic detection.
    4. 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:

    • edge_detection_mode: PIXEL_ART (or AUTO)
    • 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:

    • edge_detection_mode: PHOTOGRAPHIC (or AUTO)
    • 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": {
        "edge_detection_mode": "AUTO",
        "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

    1. Content-Aware Analysis: Detects if the image is pixel art or photographic to select the best pipeline.
    2. Multi-Method Edge Detection: Combines Roberts Cross, Sobel, and Canny algorithms for a robust edge map.
    3. Color Analysis: K-means clustering to identify dominant background colors.
    4. Edge Refinement: Applies specialized, content-aware filters to preserve sharp pixel art lines or create smooth photo edges.
    5. Alpha Generation: Creates a soft mask with configurable thresholds.
    6. Post-Processing: Optional dither handling and final enhancements.

    🐛 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

    • Try switching the edge_detection_mode between PIXEL_ART and PHOTOGRAPHIC.
    • Adjust tolerance for similar colors.
    • Modify color_clusters (more clusters for complex backgrounds).

    Jagged or blurry edges

    • Ensure edge_detection_mode is set correctly (PIXEL_ART for sharp edges, PHOTOGRAPHIC for smooth).
    • Enable edge_refinement.
    • Adjust edge_sensitivity.

    🤝 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