ComfyUI Extension: AI Pixel Art Enhancer for ComfyUI

Authored by HSDHCdev

Created

Updated

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Pixel art Enhancement Node for ComfyUI

Custom Nodes (0)

    README

    AI Pixel Art Enhancer for ComfyUI

    A powerful ComfyUI custom node that transforms AI-generated images into high-quality pixel art with advanced processing options and intelligent enhancement algorithms.

    <img width="256" height="128" alt="demo2" src="https://github.com/user-attachments/assets/1084e266-d493-4744-9f9d-c5c6b4840472" />

    Recommended LoRA:

    https://civitai.com/models/1631459/pixel-art-style-illustrious-by-skormino

    Features

    • Multiple Conversion Methods: Choose from 6 different pixel art conversion algorithms
    • AI-Enhanced Processing: Intelligent noise reduction, edge enhancement, and detail preservation
    • Advanced Color Management: Color quantization, similarity clustering, and brightness weighting
    • Post-Processing Effects: Dithering, anti-aliasing, and contrast/saturation adjustments
    • Comparison Output: Visual comparison grid showing original, pixel art, and final enhanced result
    • Flexible Grid Sizing: Support for various pixel art resolutions (8x8 to 256x256)
    • Scalable Output: Configurable output scaling (1x to 16x)

    Installation

    Method 1: ComfyUI Manager (Recommended)

    1. Install ComfyUI Manager if you haven't already
    2. Search for "AI Pixel Art Enhancer" in the manager
    3. Click Install and restart ComfyUI

    Method 2: Manual Installation

    1. Navigate to your ComfyUI custom nodes directory:

      cd ComfyUI/custom_nodes/

    2. Clone this repository:

      git clone https://github.com/yourusername/ai-pixel-art-enhancer.git

    3. Install required dependencies:

      cd ai-pixel-art-enhancer
pip install -r requirements.txt

    4. Restart ComfyUI

    Dependencies

    • torch
    • numpy
    • Pillow (PIL)
    • opencv-python
    • scikit-learn (optional, for advanced color quantization)

    Usage

    Basic Setup

    1. Add the "AI Pixel Art Enhancer" node to your ComfyUI workflow
    2. Connect an image input to the node
    3. Configure the desired settings
    4. The node outputs both the enhanced pixel art and a comparison grid

    Input Parameters

    Required Parameters

    • image: Input image tensor
    • grid_width (8-256): Width of the pixel art grid
    • grid_height (8-256): Height of the pixel art grid
    • conversion_method: Algorithm used for pixel conversion
    • color_similarity_threshold (5.0-100.0): Threshold for color clustering
    • output_scale (1-16): Final output scaling factor

    Optional Enhancement Parameters

    • enable_ai_enhancement (Boolean): Enable AI preprocessing and post-processing
    • noise_reduction (0.0-1.0): Strength of noise reduction filter
    • edge_enhancement (0.0-2.0): Edge enhancement intensity
    • color_quantization (4-256): Number of colors in final output
    • dithering_strength (0.0-1.0): Floyd-Steinberg dithering intensity
    • contrast_boost (0.5-2.0): Contrast adjustment multiplier
    • saturation_boost (0.0-2.0): Color saturation multiplier
    • preserve_details (Boolean): Enable detail preservation during processing
    • anti_aliasing (Boolean): Apply subtle anti-aliasing to final output

    Conversion Methods

    1. Most Frequent

    Analyzes each grid cell and selects the most common color using intelligent clustering based on the color similarity threshold.

    Best for: Images with distinct color regions, logos, simple illustrations

    2. Average

    Calculates the mathematical average of all colors in each grid cell.

    Best for: Smooth gradients, photographic content, general purpose conversion

    3. Neighbor Aware

    Considers neighboring pixels when determining the representative color for better context awareness.

    Best for: Complex scenes, maintaining spatial relationships

    4. Brightness Weighted Light

    Prioritizes lighter colors within each grid cell, weighted by luminance.

    Best for: High-key images, light backgrounds, preserving highlights

    5. Brightness Weighted Dark

    Emphasizes darker colors within each grid cell, weighted by luminance.

    Best for: Low-key images, dark themes, preserving shadows

    6. Edge Preserving

    Uses edge detection to maintain important structural details during conversion.

    Best for: Images with fine details, architectural content, complex patterns

    Advanced Features

    AI Enhancement Pipeline

    When enable_ai_enhancement is true, the node applies a sophisticated processing pipeline:

    1. Preprocessing:

      • Bilateral filtering for noise reduction
      • Canny edge detection and enhancement
      • Contrast and saturation adjustments

    2. Conversion: Selected algorithm with optimized parameters

    3. Post-processing:

      • Intelligent color quantization using K-means clustering
      • Floyd-Steinberg dithering
      • Subtle anti-aliasing (optional)

    Color Management

    • Similarity Clustering: Groups similar colors together based on Euclidean distance in RGB space
    • Brightness Weighting: Applies perceptual brightness weighting (0.299R + 0.587G + 0.114B)
    • Transparency Support: Properly handles RGBA images with transparent regions

    Output

    The node provides two outputs:

    1. Enhanced Image: The final pixel art result at the specified scale
    2. Comparison Grid: Side-by-side comparison of original, intermediate, and final images

    Tips and Best Practices

    Grid Size Selection

    • 8x8 to 16x16: Extreme pixelation, best for icons or very stylized art
    • 32x32 to 64x64: Classic pixel art resolution, good balance of detail and style
    • 128x128+: High-detail pixel art, maintains more original information

    Method Selection Guide

    • Portraits: Use "average" or "brightness_weighted_light"
    • Landscapes: Try "neighbor_aware" or "edge_preserving"
    • Graphics/UI: Use "most_frequent" with low similarity threshold
    • Artistic images: Experiment with "brightness_weighted_dark" and dithering

    Performance Optimization

    • Larger grid sizes process faster but produce less pixelated results
    • Disable AI enhancement for faster processing on simple images
    • Use color quantization values appropriate to your target (16-64 colors typical)

    Example Workflows

    Basic Pixel Art Conversion

    Load Image → AI Pixel Art Enhancer → Save Image
Settings: 32x32 grid, "most_frequent" method, 4x scale

    Enhanced Artistic Processing

    Load Image → AI Pixel Art Enhancer → Save Image
Settings: 64x64 grid, "edge_preserving" method, AI enhancement enabled,
          16 colors, 0.3 dithering, 6x scale

    Troubleshooting

    Common Issues

    • Memory errors with large images: Reduce grid size or disable AI enhancement
    • Colors look washed out: Increase contrast_boost and saturation_boost
    • Too much noise: Increase noise_reduction parameter
    • Loss of detail: Enable preserve_details and try "edge_preserving" method

    Performance Tips

    • Process images at reasonable resolutions (512-1024px recommended)
    • Use appropriate grid sizes for your target output
    • Disable anti_aliasing for pure pixel art aesthetic

    Contributing

    Contributions are welcome! Please feel free to submit pull requests or open issues for:

    • New conversion algorithms
    • Performance improvements
    • Bug fixes
    • Documentation improvements

    License

    This project is licensed under the Apache 2.0. - see the LICENSE file for details.

    Changelog

    v1.0.0

    • Initial release with 6 conversion methods

    Acknowledgments

    • Claude
    • https://github.com/nygaard91