Drop dead detect_background_color; sync docs with landed optimizations

- chroma.py: remove detect_background_color — orphaned since auto-detection
  goes through estimate_background_model -> _background_border_cluster;
  nothing in the repo calls it.
- settings.py: fix stale "Segmentation always stays fp32" comment
  (--precision now fans out to the BiRefNet models too).
- README: add a measured CPU performance section (9700X reference numbers,
  memory ceiling explanation, Zen 2 fallback guidance).
- docs/hair_gap_artifacts.md: record that the cross-check cost note is
  obsolete — reuse_as_seg returns the net model count to 2, bf16 and
  chunked attention absorb the rest.
- docs/green_screen_matting_workflow.md: add the 2026-07 additions to the
  architecture-evolution note.

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

README.md

@@ -114,6 +114,26 @@ D:\MiniConda\envs\lightML\python.exe -m bgfilter.cli `
   --debug-dir Outputs\debug
 ```
 
+## Performance (CPU reference numbers)
+
+Measured on a Ryzen 9700X (Zen 5, native bf16), 32 GB RAM, default settings
+(cross-check on, reused as seg; chunked global attention on):
+
+| input | precision | warm / image | peak memory |
+|---|---|---|---|
+| 1024x1536 | fp32 | ~45 s | — |
+| 1024x1536 | bf16 | ~33 s | — |
+| 2048x2048 | bf16 | ~53 s | 11.4 GB (batch), 8.1 GB (single image) |
+
+The memory ceiling is the cross-check BiRefNet_HR forward (fixed
+`input_size` 2048 regardless of the image size) — genuine live activations
+of a full-resolution dense prediction net. Before chunked attention and the
+mimalloc fix, the same 2048x2048 bf16 run peaked at 25.2 GB; a fully
+un-optimized fp32 run would need an estimated 55-60 GB (ViTMatte's un-chunked
+N^2 attention alone ~39 GB). CPUs without native bf16 (e.g. Zen 2) auto-fall
+back to fp32 — on such machines prefer `--no-cross-check` if memory or
+latency is tight.
+
 ## Chroma-alpha debug mode
 
 `--matting-method chroma` skips ViTMatte and uses chroma confidence directly as the