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ComfyDeploy: How ComfyUI-Pixelate works in ComfyUI?

What is ComfyUI-Pixelate?

[a/sd-webui-pixelart](https://github.com/mrreplicart/sd-webui-pixelart) are referenced by many webui users, this node is mean to use it in ComfyUI.

How to install it in ComfyDeploy?

Head over to the machine page

  1. Click on the "Create a new machine" button
  2. Select the Edit build steps
  3. Add a new step -> Custom Node
  4. Search for ComfyUI-Pixelate and select it
  5. Close the build step dialig and then click on the "Save" button to rebuild the machine

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

Credits