data/qwen_in/ground_truth.json is transcribed by vision (PaddleOCR mangled the
stylized Cyrillic), so the text metric scores variants against an accurate
reference instead of noisy OCR-vs-OCR. Re-measured text CER (controlnet vs qwen)
with this ground truth confirms qwen wins text across EN/RU/ZH: openai_1 0.385 vs
0.241, openai_2 0.341 vs 0.290, gemini_1 (ZH) 0.037 vs 0.000 (perfect Chinese even
at the higher 0.30 strength). Faces still favor controlnet. Refresh the numbers in
docs/known-limitations.md to this cleaner methodology.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- data/qwen_in/: a stable, committed set of 4 AI-generated images (OpenAI +
Google, carrying SynthID/C2PA -- same class as data/samples fixtures) used to
compare the controlnet/sdxl/qwen pipelines for fidelity. Two text-multi-script
(incl. RU/CJK), one EN poster, one face grid. README documents the set + the
ground-truth workflow. data/ is sdist-excluded so the wheel is unaffected.
- scripts/fidelity_metrics.py: switch text OCR from EasyOCR to PaddleOCR
(PP-OCRv6, higher accuracy esp. CJK, single multilingual stack); split into
`ocr` (seed a {basename: text} ground truth) and `compare` (--ground-truth for
a clean CER vs the hand-verified reference instead of noisy OCR-vs-OCR). Spatial
IoU-NMS keeps the best-scoring read per line so wrong-script models don't inject
garbage over Cyrillic/CJK.
- Oracle methodology: validate the OpenAI arm FIRST (openai.com/verify is more
accessible and the strongest Playwright/Chrome-MCP automation candidate; the
Gemini app is more manual). Recorded in CLAUDE.md + docs/synthid.md.
Ground-truth JSON (data/qwen_in/ground_truth.json) lands in a follow-up once
hand-verified.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Victor Kuznetsov