Hugo-Dz/spritefusion-pixel-snapper

A tool to snap pixels to a perfect grid. Designed to fix messy and inconsistent pixel art generated by AI.

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Hugo-Dz/spritefusion-pixel-snapper has 3k+ stars and 200+ forks on GitHub, is written in Rust, and was last pushed on Jul 16, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 9 minutes ago
Added to GitGenius on September 21st, 2026
Created on November 24th, 2025
Open Issues & Pull Requests: 0 (+0)
GitHub issues: Enabled
Number of forks: 205
Total Stargazers: 3,286 (+0)
Total Subscribers: 9 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 20.8 hours
Mean response time: 33.8 hours
90th percentile: 3.6 days
Tracked items: 7

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Open issues: 1
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 1 days
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Stale 90+ days: 0

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Detailed Description

Spritefusion Pixel Snapper is an image processing tool that snaps pixels to a perfect grid, designed to fix messy and inconsistent pixel art generated by AI.

The tool addresses the problem of imperfect pixel alignment in AI-generated artwork. When AI generates pixel art, the results often contain irregular pixel placement and inconsistent grid alignment that makes the art unsuitable for use in games or other applications requiring clean, grid-based sprites. The tool solves this by analyzing the input image and snapping each pixel to the nearest grid position, creating uniform alignment across the entire artwork.

Developers working with AI-generated pixel art who need production-ready sprites should consider this tool. It is particularly suited for game development workflows where consistent pixel-perfect alignment is essential. The tool is built in Rust, which provides performance benefits for image processing operations on potentially large batches of artwork.

The project shows active development with regular commits addressing bug fixes and feature improvements. Work has focused on refining the core snapping algorithm and expanding the tool's capability to handle various pixel art styles and grid configurations. The codebase demonstrates attention to code quality through iterative refinement of the image processing pipeline.