jenissimo/unfake.js

unfake.js

unfake.js is a versatile JavaScript library designed to bridge the gap between AI-generated images and clean, usable assets. It offers two primary modes of operation: a powerful Pixel Art Processor and a sophisticated Image Vectorizer. The library is bundled with a feature-rich browser-based tool for easy, interactive use.

Deep-Dive Articles

Want to learn more about how it works under the hood? Check out these in-depth articles on the pixel art cleanup pipeline:

• 🇺🇸 English: How to Tame Your AI Pixel Art (dev.to)
• 🇷🇺 Russian: Как приручить AI-пиксель-арт (Habr)

Integrations & Online Demo

• Try it online: unfaker on itch.io — browser-based demo, no install required.
• ComfyUI Node: ComfyUI-Unfake-Pixels — use unfake.js directly in your ComfyUI workflows.
• Python Port: unfake.py — A blazing fast Python implementation with Rust-accelerated quantization, offering a 10-20% speedup over the JS version.

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Features

1. Pixel Art Processor

This mode is tailored for cleaning up pixel art, especially pieces generated by AI that often suffer from inconsistent pixel sizes, color bleeding, and other artifacts.

• Intelligent Scale Detection: Automatically detects the "true" pixel size of an upscaled pixel art image using runs-based or edge-aware algorithms. It can also accept a manual scale factor.
• Content-Aware Downscaling: Provides multiple high-quality downscaling methods (dominant, median, content-adaptive, etc.) to reduce the image to its native 1x scale without introducing new colors or blurriness.
• Grid Snapping: Automatically crops the image to align it perfectly with the detected pixel grid, ensuring clean edges.
• Color Quantization: Reduces the color palette to a specified maximum number of colors using imagequant (libimagequant) in the WASM core, with support for fixed custom palettes.
• Artifact Cleanup: Includes optional post-processing steps like morphological cleanup (to fill holes and remove noise) and jaggy cleanup.
• Alpha Channel Processing: Supports alpha binarization to create sharp, clean transparency.

2. Image Vectorizer

This mode converts raster images (PNG, JPG) into clean, scalable SVG files. It's an intelligent wrapper around imagetracer.js with extensive pre- and post-processing capabilities powered by the unfake-core WASM filters.

• Noise Reduction Pre-Processing: Applies filters like Bilateral or Median blur before vectorization to reduce noise and create smoother outlines.
• Smart Color Quantization: Reduces and simplifies the color palette before tracing for cleaner vector shapes. It can even auto-detect the optimal number of colors.
• Background Handling: Intelligently handles transparent images by adding a temporary background during processing to prevent edge artifacts, which is then removed from the final SVG.
• Post-Quantization Smoothing: Applies a gentle blur after color reduction to soften jagged edges before the final trace.
• Fine-Tuned Tracing: Exposes detailed imagetracer.js options for fine-grained control over the final vector output.
• Palette Extraction: Extracts the final color palette from the generated SVG.

Browser Tool

The project includes a browser-based tool (/browser-tool) that provides a user-friendly interface for all of the library's features.

• Dual Modes: Easily switch between Pixel Art and Vectorization modes.
• Live Settings Control: Use the Tweakpane panel to adjust all processing parameters in real-time.
• Drag-and-Drop & Paste: Upload images by dragging them onto the window, selecting a file, or pasting directly from the clipboard.
• Before/After View: Instantly see the original and processed images side-by-side.
• Magnifier Tool: A built-in magnifying glass to inspect pixel-level details on both the original and result images.
• Palette Editor: View the result's color palette and interactively replace colors.
• Download & Copy: Download the final asset (.png for pixel art, .svg for vector) or copy it directly to the clipboard.

How to Use

Running the Browser Tool Locally

⚠️ You need a local server!

> Modern browsers do not allow ES module imports (importmap, type="module") from local files (file://). To use the browser tool, you must run it via a local http(s) server.

Quick ways to start a server:

• Python 3.x (from the project root):

  python -m http.server 8080
  # or
  python3 -m http.server 8080
  

Then open: http://localhost:8080/browser-tool/

• Node.js (http-server):

  npx http-server -p 8080
  

Then open: http://localhost:8080/browser-tool/

• VSCode Live Server:

Click "Go Live" at the bottom of VSCode and select the browser-tool folder.

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1. Start a local server using one of the methods above. 2. Open http://localhost:8080/browser-tool/ in your browser (or the port you chose). 3. Upload an image, tweak the settings, enjoy!

Using the Library (unfake.js) in Your Own Project

The library is written as ES modules. You can import and use its core functions processImage and vectorizeImage.

1. Pixel Processing Example:

import unfake from './lib/index.js';
const fileInput = document.getElementById('my-file-input');
const file = fileInput.files[0];
const options = {
    file: file,
    maxColors: 32,
    detectMethod: 'auto', // 'auto', 'runs', 'edge'
    downscaleMethod: 'dominant',
    snapGrid: true,
    cleanup: { 
        morph: true, 
        jaggy: true 
    }
};
try {
    const { png, imageData, palette, manifest } = await unfake.processImage(options);
    
    // png is a Uint8Array of the final PNG file
    const blob = new Blob([png], { type: 'image/png' });
    const url = URL.createObjectURL(blob);
    
    // Now you can use the URL for an  tag or download link
    document.getElementById('result-image').src = url;
} catch (error) {
    console.error("Pixel processing failed:", error);
}

2. Vectorization Example:

import unfake from './lib/index.js';
const fileInput = document.getElementById('my-file-input');
const file = fileInput.files[0];
const options = {
    file: file,
    preProcess: {
        enabled: true,
        filter: 'bilateral',
        value: 15
    },
    quantize: {
        enabled: true,
        maxColors: 'auto' // or a number like 16
    },
    // imagetracer.js options
    ltres: 1,
    qtres: 1,
};
try {
    const { svg, palette, manifest } = await unfake.vectorizeImage(options);
    
    // svg is a string containing the SVG markup
    document.getElementById('result-container').innerHTML = svg;
} catch (error) {
    console.error("Vectorization failed:", error);
}

Architecture

The image-processing core lives in unfake-core, a headless Rust crate compiled to WebAssembly for the browser tool and to a native binary for the CLI (crates/; unfake detect / snap / downscale / quantize, with fixed palettes, a reserved chroma color, and a JSON pipeline manifest). It owns scale detection, grid snapping, downscaling (dominant / median / mode / mean / qvote), morphological cleanup, color quantization (imagequant), content-adaptive downscaling, the vector pre/post filters, and PNG encoding — no OpenCV. The JavaScript layer in browser-tool/ is a thin interface: file I/O, canvas, the Tweakpane UI, and calls into the WASM core.

bun run core:wasm   # build the WASM core into browser-tool/vendor/unfake-core/
bun run core:test   # cargo test
bun run core:eval   # CLI scale-detection eval on the bghira corpus

Core Dependencies

• unfake-core (Rust → WebAssembly / native): The headless image-processing core (see Architecture). Uses imagequant for quantization and the png crate for encoding.
• ImageTracer.js: The engine for the vectorization (raster → SVG) process.
• Tweakpane: The interactive settings UI in the browser tool.

License

This project is licensed under the MIT License. See the LICENSE file for details.