Add bf16 precision option for ViTMatte (--precision, default fp32)

bf16 halves the matting model's activation memory -- its full-resolution
attention is the pipeline's memory peak -- with visually identical alpha
(measured: 0 px alpha deviation > 0.25 on samples; cross-check veto
behaviour unchanged, region IoU 0.984).

Guarded by a hardware check so it never lands on a slow emulation path:
CUDA requires torch.cuda.is_bf16_supported(); CPU requires the same
oneDNN native-bf16 gate PyTorch uses for matmul routing (AVX512-BF16/
AMX). Without support it warns and falls back to fp32 -- on a Zen2 EPYC
the fallback kernels measured 17-370x slower than fp32, so silent bf16
there would be a performance landmine.

Segmentation stays fp32: torchvision deform_conv2d (used by BiRefNet)
has no bf16 CPU kernel, and the segmenter is not the memory peak.

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

bgfilter/settings.py

@@ -119,6 +119,12 @@ class ModelSettings:
     device: str = "cpu"
     matting_method: str = "vitmatte"
     fallback_to_chroma_alpha: bool = False
+    # ViTMatte compute precision: "fp32" or "bf16". bf16 halves the matting
+    # model's activation memory (its full-resolution attention is the pipeline's
+    # memory peak) with visually identical alpha, but needs bf16-capable
+    # hardware (any modern GPU, or a CPU with AVX512-BF16/AMX) — otherwise it
+    # falls back to fp32 with a warning. Segmentation always stays fp32.
+    precision: str = "fp32"
 
 
 @dataclass(frozen=True)