Merge origin/master: bf16 precision, chunked attention, seg-reuse, mimalloc purge

Adopts master as the standard for all overlapping work. Master's landed
optimizations supersede server-edition's own fp16 experiment:

- Unified --precision fp32|bf16 knob (bgfilter/precision.py) driving all
  three models: ViTMatte weight cast + both BiRefNets via autocast, with a
  hardware gate (falls back to fp32 off native-bf16 CPUs) and an
  AutocastCPU fp32 shim for torchvision deform_conv2d.
- Query-chunked ViTMatte global attention (bgfilter/attn_chunk.py), exact
  and bitwise-identical, caps the N^2 spike (~19 -> ~4 GB at 2048).
- Cross-check HR-matting forward reused as the seg mask (birefnet backend
  only), skipping the primary seg model; MIMALLOC_PURGE_DELAY=0.
- inference_mode and the detect_background_color removal converge with
  server-edition's earlier equivalents.

Conflict resolution (favoring master, preserving server-only features):
- vitmatte_infer/segmentation: dropped server's device-derived fp16 for
  master's precision path, kept resolve_model_source (local weights).
- service.py: cross-check SegmentationSettings now passes precision so the
  HTTP service honors bf16 like the CLI's _second_opinion does.

Verified on CPU: default fp32 pipeline loads 2 models (seg-reuse active)
and bf16 path runs (autocast + deform_conv2d shim) — both exit 0.

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

docs/green_screen_matting_workflow.md

@@ -11,6 +11,7 @@
 - **两条管线**（`segmentation.enabled` 切换）：无分割模型的 Chroma + ViTMatte；或单个语义分割模型（默认 birefnet，可切 anime-seg，用 `--seg-backend`）定 trimap 拓扑，ViTMatte 细化。
 - **背景色泛化**：`screen_color` 先验（如 `#CFEFFF`）；`null` 时自动从图像边框探测平背景色（探测不到干净平背景会报错），不再写死绿幕。
 - **已移除**：染绿修复（recolor）、发丝补救（dropout-fill）、BiRefNet+anime-seg 结合方案——它们只是给“生图即染色/断裂”的坏素材擦屁股；源头改用干净背景生图后不再需要。
+- **新增（2026-07）**：交叉模型否决（`cross_check`，BiRefNet_HR-matting 第二意见 min 融合，清发丝缝隙残蓝，见 `docs/hair_gap_artifacts.md`）；其前向默认兼任 seg mask（`reuse_as_seg`，净模型数回到 2）；`--precision bf16` 一个旋钮加速全部三类模型；ViTMatte 全局 attention 默认按 query 分块（输出逐位不变，2048² 内存峰值 25→11 GB）。
 
 下文其余章节描述绿幕基线流程，仍然有效。
 

docs/hair_gap_artifacts.md

@@ -89,6 +89,14 @@ min 融合;B2 的意见锐化(remap 0.5→0.95)被否——数字更低(0.673 vs
 **已落地(2026-07-04,默认开启)。** 新增 `cross_check` 设置块 +
 `--cross-check/--no-cross-check`;pipeline 在 clean_alpha 后插入
 `cross_check_alpha`(alpha_post.py),第二意见加载复用 BiRefNetSegmenter。
+
+**成本已消化(2026-07-06)。** 当年"+1 个模型 +1 次推理"的账已改写:
+`cross_check.reuse_as_seg`(默认开)让 HR-matting 前向兼任 seg mask,
+**净模型数回到 2 个**(trimap 99.8% 不变,先导 Outputs/segreuse/);
+`--precision bf16` 经 autocast + deform_conv2d shim 覆盖 BiRefNet
+(35s→26s @2048);ViTMatte 全局 attention 分块(逐位等价)把内存峰值
+从 25.2GB 压到 11.4GB(2048×2048 bf16,9700X)。CPU 上开 cross-check
+的常规图约 33s/张(bf16)。
 验证:开启时 TestImage3 输出与评审原型逐字节一致;`--no-cross-check` 与
 旧 baseline 逐字节一致;FixImage1(粉底)脸部无损、发缘粉残留被清,
 唯一副作用是羽化权重外溢到暗豁免区边缘(弓弦零星像素最多 -0.5,中位