Cut pipeline cost: seg-reuse, unified bf16 knob, eager mimalloc purge

Three optimizations from profiling the cross-check-dominated pipeline
(9700X CPU, all pilot-validated on TestImage3/FixImage1):

- Reuse the cross-check HR-matting@2048 forward as the segmentation mask
  (cross_check.reuse_as_seg, default ON; --no-cross-check-as-seg to opt
  out). Skips the BiRefNet@1024 load+forward entirely: ~66s -> ~45s,
  one less 0.9GB model. Trimap 99.8% identical, no structural change.

- --precision bf16 now fans out to all three models: ViTMatte keeps its
  weight cast; both BiRefNets run their forward under autocast with a
  dispatcher-level AutocastCPU fp32 shim for torchvision::deform_conv2d
  (no bf16 CPU kernel, no autocast wrapper upstream). Shared hardware
  gate in bgfilter/precision.py falls back to fp32 off native-bf16
  hardware. TestImage3: 51.9s -> 33.1s; alpha diff max 0.15, none >0.25.

- MIMALLOC_PURGE_DELAY=0 (bgfilter/__init__.py, before torch loads):
  Windows torch's bundled mimalloc lazily retains ~10GB of freed
  BiRefNet activations, stacking under ViTMatte's attention peak.
  2048x2048 bf16: peak 25.2 -> 21.4GB and slightly faster (73 -> 63s).

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

bgfilter/settings.py

@@ -74,6 +74,15 @@ class CrossCheckSettings:
     enabled: bool = True
     model_name: str = "ZhengPeng7/BiRefNet_HR-matting"
     input_size: int = 2048
+    # Reuse the cross-check forward as the segmentation mask: one HR-matting
+    # inference serves both trimap topology and the second opinion, skipping the
+    # primary seg model entirely (saves its load + forward). Pilot-validated on
+    # TestImage3/FixImage1: trimap 99.8% identical, no structural change. Only
+    # takes effect when both cross_check and segmentation are enabled.
+    reuse_as_seg: bool = True
+    # Compute precision of the cross-check forward: "fp32" or "bf16" (autocast;
+    # needs bf16-capable hardware, else falls back to fp32 with a warning).
+    precision: str = "fp32"
     proj_min: float = 3.0       # bg-hue projection above which a pixel is suspect
     l_min: float = 45.0         # Lab lightness below which a pixel is exempt (dark hair)
     feather_sigma: float = 2.0  # Gaussian feather of the zone boundary, in px
@@ -134,6 +143,10 @@ class SegmentationSettings:
     model_name: str = "ZhengPeng7/BiRefNet"
     device: str = "cpu"
     input_size: int = 1024
+    # Compute precision of the BiRefNet forward: "fp32" or "bf16" (autocast; needs
+    # bf16-capable hardware, else falls back to fp32 with a warning). The ONNX
+    # anime-seg backend ignores this.
+    precision: str = "fp32"
 
 
 @dataclass(frozen=True)