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

- watermark_remover: _build_qwen_kwargs now passes explicit height/width (via
  _qwen_target_size, floored to /16). Without it QwenImageImg2ImgPipeline defaults to
  1024x1024 and silently squishes non-square inputs, distorting the scene and garbling text.
- watermark_profiles: resolve_strength gains a `pipeline` arg + a Qwen strength ladder
  (_QWEN_VENDOR_STRENGTH, Gemini 0.25), so `--pipeline qwen` gets its certified floor
  automatically; retires the manual "pass --strength 0.25 for Gemini on qwen" workaround.
- fidelity_metrics: replace per-face nearest matching (collided on multi-face images when a
  variant dropped a face, corrupting the identity metric) with a collision-free one-to-one
  assignment (assign_faces_one_to_one). lapvar/LPIPS were always bbox-anchored and immune.
  Regression-guarded by tests/test_fidelity_matching.py.
- docs: record the measured outcomes of the qwen-improvement arc. The Qwen ControlNet
  face-fix is CLOSED (no permissive Qwen detail/tile ControlNet exists; canny carries edges,
  not skin grain). The `--pipeline auto` router + faces+text mixed dual-pass were prototyped
  and DROPPED (controlnet wins faces AND display text: abba CER 0.114 vs qwen 0.379).
  Z-Image-Turbo was tried and dropped (same regeneration limits). qwen stays a manual opt-in;
  controlnet is the default for everything.

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

docs/known-limitations.md

@@ -144,8 +144,8 @@ The scrub still comes from the img2img `strength` (same lever as SDXL); the call
 - **Text:** Qwen wins on substantial Latin/mixed-script text -- 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**. On a SHORT CJK sign (gemini_1, cnet 0.15 / Qwen 0.25) it is a TIE (0.037 vs 0.037 -- both near-perfect; the earlier Qwen 0.000 was at the higher 0.30, not the certified floor).
 - **Faces:** controlnet wins -- gemini_3, 18 faces (cnet 0.15 / Qwen 0.25): ArcFace identity 0.546 vs 0.382, Laplacian-variance retention 0.62 vs 0.40, face LPIPS 0.09 vs 0.17 (Qwen smooths faces MORE; the gap narrows vs Qwen 0.30 but controlnet still wins clearly).
 
-**Conclusion: Qwen is the better TEXT-preserving remover (substantial Latin/mixed text), NOT a universal fidelity win — controlnet's canny edge map holds face skin detail better, so the path is a content-routed lane (text→qwen, faces→controlnet), not a blanket migration.** Caveat: `resolve_strength` is shared and pipeline-independent, so the Gemini default (0.15) UNDER-scrubs Gemini on `qwen` (floor 0.25) — pass `--strength 0.25` for Gemini on `qwen` until a Qwen ladder is wired. Flat-graphic content was not in the sample.
+**Conclusion: Qwen wins TEXT only for clean body text on a plain background with NO faces; controlnet wins faces AND display/decorative text in a scene. So `qwen` is a MANUAL `--pipeline qwen` opt-in, not a routed lane.** A content `--pipeline auto` router + a faces+text mixed dual-pass were prototyped and DROPPED (2026-06-20): on the canonical faces+text case (the abba poster, faces + display text) controlnet won EVERY metric incl. text (CER 0.114 vs qwen 0.379), so grafting qwen text only hurts; and "text→qwen" is undecidable cheaply (body-vs-display text is what matters). Caveat: `resolve_strength(..., pipeline="qwen")` carries the Qwen ladder (`_QWEN_VENDOR_STRENGTH`, Gemini 0.25), so `--pipeline qwen` gets the 0.25 Gemini floor automatically — the old manual `--strength 0.25` workaround is retired. `_build_qwen_kwargs` now passes an explicit height/width (qwen squished non-square inputs to 1024² without it). Flat-graphic content was not in the sample.
 
-**Improving Qwen (ship vs improve):** the cited research on fixing the face-smoothing while keeping the text win (Qwen-Image ControlNet for structure conditioning, Qwen-Image-Edit, Z-Image-Turbo as a cheaper text-preserving substitute, non-regenerative detail restoration) lives in `docs/qwen-improvement-research.md` -- read it before extending the `qwen` pipeline. Verdict: shippable now as an opt-in text lane; the strongest improvement lead is adding a Qwen-Image ControlNet, but no improvement has measured face-fidelity at our floors yet (validate with `scripts/fidelity_metrics.py` first).
+**Improving Qwen (ship vs improve):** the cited research lives in `docs/qwen-improvement-research.md` -- read it before extending the `qwen` pipeline. Verdict: shippable as an opt-in text lane. **The "add a Qwen-Image ControlNet to fix face smoothing" lead was built, measured, and CLOSED (2026-06-20):** a DiffSynth-Studio Qwen + Apache-2.0 blockwise-canny ControlNet at the Gemini floor 0.25 did NOT restore face skin texture (face Laplacian-variance retention flat 0.40 -> 0.40, 13/16 faces within +-0.02; the SDXL+canny target 0.62 was not approached), because canny carries edges not skin grain and Qwen's higher Gemini floor (0.25 vs SDXL+canny 0.15) forces more smoothing -- and a deep-research sweep confirmed NO permissively-licensed Qwen tile/detail/realism/skin ControlNet exists anywhere (every Qwen conditioning is geometry). So **faces stay on SDXL+controlnet; Qwen is the text lane, not a face fix.** The strongest remaining lead is **Z-Image-Turbo** (6B, Apache-2.0, `ZImageImg2ImgPipeline`, scrub mechanism preserved) -- its own SynthID floor and face/text fidelity are UNMEASURED; that is the next experiment. Non-regenerative high-frequency detail re-injection is NOT safe by assumption (the "clean-output high frequencies do not carry the watermark" claim was refuted) -- it must be oracle-gated. Always validate any improvement at the certified floors with `scripts/fidelity_metrics.py` first.
 
 **Seed as a quality lever (measured, openai_1 at 0.10, seeds 0-4):** the seed barely moves whole-image fidelity (img LPIPS 0.062-0.065, SSIM 0.855-0.857, PSNR 28.5-28.7 — flat) but does shift TEXT legibility (OCR CER 0.241-0.290, ~17% spread) -- the seed changes WHICH details get regenerated, not the overall level. So a per-image best-of-N-seed selection is a WEAK, text-only lever (pick the lowest-CER seed that still scrubs; fidelity selection needs no oracle). Not worth the N× cost for general use -- pin one decent seed in prod; reserve best-of-N for text-heavy premium cases.