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ComfyUI-SmartImageTools

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Last updated
2025-05-03

A suite of intelligent image processing tools designed for ComfyUI, focusing on sophisticated techniques for image manipulation. This collection enhances the capabilities of ComfyUI by providing advanced functionalities for color palette management and dithering.

  • Offers a SmartImagePaletteConvert node that transforms images into indexed color palettes, utilizing K-means clustering for optimal color selection.
  • Supports customizable dithering effects and the option to use a reference image for palette extraction, allowing for greater creative control.
  • Maintains transparency during the conversion process, ensuring that the integrity of the original image is preserved.

Context

This repository consists of various image processing tools tailored for use with ComfyUI. Its primary aim is to facilitate advanced image manipulation through features that enhance color management and visual quality.

Key Features & Benefits

The standout feature is the SmartImagePaletteConvert node, which enables users to convert images to indexed color palettes efficiently. By employing K-means clustering in the LAB color space, users can generate a tailored color palette, making it easier to achieve specific aesthetic goals.

Advanced Functionalities

The tool allows users to specify the number of colors in the palette, ranging from 2 to 256, and includes an adjustable dithering option using the Floyd-Steinberg algorithm. This capability provides users with the flexibility to fine-tune the visual output, making it suitable for various artistic styles.

Practical Benefits

Integrating this tool into a ComfyUI workflow significantly enhances control over color representation and image quality. By simplifying the process of creating indexed color images, users can achieve more efficient and effective results in their projects.

Credits/Acknowledgments

This project is licensed under the MIT License and includes contributions from various authors. The repository relies on established libraries such as scikit-learn, scikit-image, numpy, and Pillow to function effectively.