ComfyUI Extension: ComfyUI-Pixelate

Authored by flycarl

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a/sd-webui-pixelart are referenced by many webui users, this node is mean to use it in ComfyUI.

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    README

    ComfyUIPixelate

    sd-webui-pixelart are referenced by many webui users, this node is mean to use it in ComfyUI.

    ComfyUIPixelate

    Features

    • Downscaling Options: Multiple high-quality scaling algorithms:

      • auto: Automatically selects the best method
      • nearest: Best for preserving exact colors
      • area: Optimal for general downscaling
      • linear: Smooth transitions but may blur
      • cubic: Sharper edges than linear
      • lanczos: High quality with edge preservation
    • Color Processing:

      • RGB color mode
      • Grayscale conversion
      • Binary (Black & White) conversion
    • Advanced Color Quantization:

      • Multiple palette generation methods:
        • auto: Smart method selection based on image size and color count
        • libimagequant: High-quality quantization
        • kmeans: GPU-accelerated when available (falls back to CPU)
        • mediancut: Fast with good quality
        • maxcoverage: Better color distribution
        • fastoctree: Fastest option for large images
        • median_cut: Custom implementation
    • Dithering Support:

      • Floyd-Steinberg dithering for smooth color transitions
      • Simple quantization for clean, sharp results
    • Custom Palette Support:

      • Use reference images to extract palettes
      • Control palette size (2-256 colors)

    Usage

    1. Add the "Pixelate" node to your ComfyUI workflow
    2. Connect an image input
    3. Configure parameters:
      • downscale_factor: How much to reduce the image (1-32)
      • scale_mode: Choose scaling algorithm
      • rescale_to_original: Option to restore original size
      • color_mode: RGB/Grayscale/BW
      • colors: Number of colors in output (2-256)
      • quantization_method: Palette generation method
      • dithering: None or Floyd-Steinberg
      • Optional: Connect a palette reference image

    Performance Considerations

    • The node automatically selects optimal methods based on image size:
      • Large images (>1M pixels) or many colors (>32): Uses fast octree
      • Medium images (>500K pixels): Uses libimagequant
      • Small images: Uses k-means clustering
    • GPU acceleration for k-means when available
    • Caching for color quantization to improve speed

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