docs: capture the Qwen-improvement research (ship vs improve)

Cited deep-research report (22 sources, 3-vote adversarial verification, 5 refuted)
behind the "ship qwen as-is or improve first?" decision. Verdict: shippable now as
an opt-in text lane; strongest improvement lead is adding a Qwen-Image ControlNet
(InstantX / DiffSynth, Apache-2.0, diffusers QwenImageControlNetPipeline) for face/
skin structure; Z-Image-Turbo (6B, Apache-2.0) is the best cheaper text-preserving
substitute. No improvement has measured face-fidelity at our scrub floors yet --
validate with scripts/fidelity_metrics.py first. Linked from known-limitations.

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

docs/known-limitations.md

@@ -146,4 +146,6 @@ The scrub still comes from the img2img `strength` (same lever as SDXL); the call
 
 **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.
 
+**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).
+
 **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.