remove-ai-watermarks

mirror of https://github.com/wiltodelta/remove-ai-watermarks.git synced 2026-07-12 02:56:32 +02:00

Files

T

Victor Kuznetsov 27a921b54f docs(visible): record the 0.12.1-vs-0.14 head-to-head + backend quality

Full-dataset validation of reverse-alpha (v0.12.1) vs the current localize->fill:
doubao/jimeng identical (100% coverage + clearance across all backends); gemini
strict coverage a few points below reverse-alpha (the FP tightening), every
missed mark recovered under assume_ai, clearance ~98% both, no outside-box
damage. Clearance is fill-independent (cv2/MI-GAN/LaMa all strip the mark shape);
the difference is visual fill quality on textured/structured backgrounds -- LaMa
best, MI-GAN can ghost/hallucinate, cv2 smears -- which motivates auto = LaMa >
MI-GAN > cv2. Added to module-internals, known-limitations, and the CLAUDE.md
compact list.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

2026-07-09 19:18:25 +03:00

controlnet-removal-pipeline-research.md

docs(restore): document that restore methods REGENERATE, not preserve

2026-06-08 21:08:11 -07:00

known-limitations.md

docs(visible): record the 0.12.1-vs-0.14 head-to-head + backend quality

2026-07-09 19:18:25 +03:00

module-internals.md

docs(visible): record the 0.12.1-vs-0.14 head-to-head + backend quality

2026-07-09 19:18:25 +03:00

qwen-improvement-research.md

fix(qwen): native-geometry img2img + pipeline-aware strength; record dropped auto/mixed/Z-Image leads