Merge master: matte-confidence gate + cross-model hair-gap veto

Brings in the two algorithm optimisations from master:
- Gate chroma suppression by the matte's own confidence (639fdf0)
- Add cross-model veto of hair-gap background residue (adbbb9d)

Server-edition integration on top of the plain merge:
- PipelineManager caches the cross-check model (BiRefNet_HR-matting) and
  injects it into MattingPipeline, so HTTP requests never reload it.
- MattingResult carries cross_check_alpha; debug output matches master.
- /healthz reports crossCheck; README lists models/BiRefNet_HR-matting
  in the local-weights bundle.

Verified: CLI --no-cross-check baseline and full cross-check run both
pass on samples/TestImage3.png; cross_check_alpha.png written to debug.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

README.md

@@ -9,6 +9,7 @@ RGB input
   -> chroma bg-confidence (keyed to the auto-detected or given colour)
   -> [optional] semantic segmentation mask
   -> trimap -> ViTMatte -> alpha cleanup
+  -> cross-model veto (second matting opinion on bg-hued residue)
   -> pymatting foreground -> despill -> RGBA PNG -> QA previews
 ```
 
@@ -28,6 +29,15 @@ Both share pymatting foreground estimation and a colour de-spill. There is **no*
 green-contamination rescue / recolour layer — with clean source images it is
 unnecessary, so it was removed.
 
+Both also run a **cross-model veto** by default: a second, trimap-free matting
+model (`ZhengPeng7/BiRefNet_HR-matting`) may only *lower* alpha, only on
+background-hued bright pixels the primary result is confident about — this clears
+colour-drifted background residue trapped between hair strands that the chroma
+key, the segmenter and ViTMatte all read as foreground. Costs one extra model
+download (~0.9 GB) and one inference pass per image; disable with
+`--no-cross-check` (see the `cross_check` config section, and
+`docs/hair_gap_artifacts.md` for the analysis behind it).
+
 The segmentation trimap defaults to `directional` mode (chroma + seg + a hue-direction
 split: it keeps a background-coloured garment such as a white shirt while dropping a
 background-hued residual such as blue trapped between hair strands). Switch with
@@ -207,7 +217,8 @@ D:\MiniConda\envs\lightML\python.exe scripts\smoke_samples.py `
   (as the service does in production).
 - Bundling weights in the project — instead of the HF cache, drop each model into a
   plain folder named after the repo basename under `models/`:
-  `models/vitmatte-base-composition-1k`, `models/BiRefNet`, `models/anime-seg`. The loader
+  `models/vitmatte-base-composition-1k`, `models/BiRefNet`, `models/anime-seg`,
+  `models/BiRefNet_HR-matting` (cross-check second opinion, default on). The loader
   prefers a matching local folder and falls back to the HF repo id / cache when absent, so
   it is opt-in. Populate them with e.g. `hf download ZhengPeng7/BiRefNet --local-dir
   models/BiRefNet`. Override the base directory with `BGFILTER_WEIGHTS_DIR`. `models/` is