miyang-ai/Mi-Ripple

MIYANG diagnosis-guided restoration for digital ripple artifacts in iteratively edited AI images

Generative mediaPython

1.1 Steady Magnitude out of 10. Star growth looks organic. Data as of September 18, 2026.

About the project

Reference implementation of Mi-Ripple, a diagnosis-guided pipeline for restoring AI images that picked up grid-like and scale-like artifacts during iterative editing. It separates periodic lattice artifacts, granular noise in flat regions, and content-entangled artifacts, and requires human review in ambiguous cases.

Useful for

Run mi-ripple on an image to get a diagnosis JSON, masks, and a final picture
Inspect the generated heat maps and comparison boards before accepting output
Trigger optional regeneration with --allow-regen and a MIYANG API key after manual approval

README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.

Why it’s trending

5 stars so far today, about 6 expected by the end of the day.
The repository is 8 days old and already has 83 stars.

Stars per day

Bars are daily stars, the line is the usual pace. Red marks spike days.

Numbers

Total stars: 83
Stars in a day: 6
Forks: 6
Issues and pull requests: 1
Watchers: 0
Language: Python
License: MIT
Created: September 10, 2026
Last push: September 10, 2026

Star trust

Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.

These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.

Spotted in

September 16, 2026Top new repositories this week: #152
September 15, 2026Top new repositories this week: #169

README badge

Paste it into your README — the badge shows the current magnitude and links to this page.

[![Seismograph](https://gitnova.dev/badge/miyang-ai/Mi-Ripple.svg?lang=en)](https://gitnova.dev/en/r/miyang-ai/Mi-Ripple)

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