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>

README.md

@@ -89,9 +89,11 @@ D:\MiniConda\envs\lightML\python.exe -m bgfilter.cli `
 No `--config` is needed — the built-in defaults are identical to
 `configs/default.yaml`. Pass `--config configs\default.yaml` only after you edit that
 file to tune the detailed parameters. Runtime choices stay on the command line:
-`--device` (default **CPU**; use `--device cuda` for GPU), `--seg-backend` (default
-`birefnet`; `anime-seg` for anime characters), `--screen-color` (default: auto-detect
-the flat background), and `--trimap-mode`.
+`--device` (default **CPU**; use `--device cuda` for GPU), `--precision` (default
+`fp32`; `bf16` halves the matting model's activation memory with visually identical
+alpha — needs bf16-capable hardware, falls back to fp32 elsewhere), `--seg-backend`
+(default `birefnet`; `anime-seg` for anime characters), `--screen-color` (default:
+auto-detect the flat background), and `--trimap-mode`.
 
 ## Batch