test(eval): vision-transcribed ground truth for qwen_in + clean text-CER numbers

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>

docs/known-limitations.md

@@ -140,4 +140,4 @@ The scrub still comes from the img2img `strength` (same lever as SDXL); the call
 
 **Prototype oracle floors (Modal A100-80GB, single seed, 2026-06-19 — PENDING seed-repeat cert):** on native-resolution OpenAI and Gemini cert inputs (both controls SynthID-POSITIVE), OpenAI cleared at strength **0.10** and Gemini at **0.30** (0.20 still detected).
 
-**Fidelity vs controlnet was MEASURED, not eyeballed (`scripts/fidelity_metrics.py`; an initial eyeball read was wrong and overturned by the metrics):** at EQUAL strength, Qwen beats controlnet on **text** (OpenAI typography 0.10: OCR CER 0.25 vs 0.37; higher SSIM/PSNR) but controlnet beats Qwen on **faces** (gemini_3, 18 faces, 0.15 each: Laplacian-variance retention 0.62 vs 0.41, face LPIPS 0.09 vs 0.13 — Qwen smooths faces MORE; ArcFace identity ~tied at 0.546/0.543). At each pipeline's OWN scrub floor the face gap widens (Qwen's Gemini floor 0.30 is 2x controlnet's 0.15). **Conclusion: Qwen is the better TEXT-preserving remover, NOT a universal fidelity win — controlnet's canny edge map holds face skin detail better.** Two caveats before relying on it: (1) near-floor scrub is SEED-NON-DETERMINISTIC (the general known-limitation above), so these single-seed floors are NOT certified — run a seed-repeat sweep before trusting them; (2) `resolve_strength` is shared and pipeline-independent, so the Gemini default (0.15, the certified controlnet floor) UNDER-scrubs Gemini on `qwen` (whose floor is ~0.30) — **pass an explicit `--strength` for Gemini content on `qwen`** until a Qwen-specific ladder is certified. Flat-graphic content was not in the prototype sample.
+**Fidelity vs controlnet was MEASURED, not eyeballed (`scripts/fidelity_metrics.py`, text scored against a vision-transcribed ground truth in `data/qwen_in/ground_truth.json` + PaddleOCR on the variants; an initial eyeball read was wrong and overturned by the metrics):** Qwen beats controlnet on **text** across EN/RU/ZH -- OCR CER, controlnet vs Qwen: openai_1 (EN+RU+ZH, both 0.10) 0.385 vs 0.241, openai_2 (EN, both 0.10) 0.341 vs 0.290, gemini_1 (ZH, controlnet 0.15 / Qwen 0.30) 0.037 vs **0.000** (Qwen renders the Chinese perfectly even at the higher strength). But controlnet beats Qwen on **faces** (gemini_3, 18 faces, 0.15 each: Laplacian-variance retention 0.62 vs 0.41, face LPIPS 0.09 vs 0.13 — Qwen smooths faces MORE; ArcFace identity ~tied at 0.546/0.543). At each pipeline's OWN scrub floor the face gap widens (Qwen's Gemini floor 0.30 is 2x controlnet's 0.15). **Conclusion: Qwen is the better TEXT-preserving remover, NOT a universal fidelity win — controlnet's canny edge map holds face skin detail better.** Two caveats before relying on it: (1) near-floor scrub is SEED-NON-DETERMINISTIC (the general known-limitation above), so these single-seed floors are NOT certified — run a seed-repeat sweep before trusting them; (2) `resolve_strength` is shared and pipeline-independent, so the Gemini default (0.15, the certified controlnet floor) UNDER-scrubs Gemini on `qwen` (whose floor is ~0.30) — **pass an explicit `--strength` for Gemini content on `qwen`** until a Qwen-specific ladder is certified. Flat-graphic content was not in the prototype sample.