Chunk ViTMatte's global attention to cap its N^2 memory spike

ViTMatte's VitDet backbone runs 4 global attention blocks that materialize
the full [heads x N x N] map: ~19 GB transient at 2048x2048 (16384 tokens),
the pipeline's memory peak. transformers has no SDPA path for this
architecture (the decomposed rel-pos bias is added to raw scores), so
compute the same attention in query-row chunks instead: the bias
factorizes over query rows, making the chunked form mathematically exact
-- output verified BITWISE-identical (unit: fp32/bf16 x 3 sizes; full
pipeline: TestImage3 fp32 and 2048x2048 bf16, all byte-equal).

model.attn_query_chunk (default 2048, 0 = stock one-shot) engages only on
blocks seeing more tokens than the chunk size, so window blocks keep the
original path. Measured @2048x2048 bf16 (9700X): ViTMatte spike
19.8 -> 4.0 GB for ~15% more ViTMatte time; whole pipeline peak
21.4 -> 11.4 GB, warm 51 -> 53 s.

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

configs/default.yaml

@@ -13,6 +13,10 @@ model:
   # AVX512-BF16 CPU), else falls back to fp32. --precision bf16 sets bf16 here
   # AND on the BiRefNet models below in one go.
   precision: fp32
+  # Query-chunked global attention (exact, bitwise-identical output): caps the
+  # VitDet global blocks' N^2 attention transient at O(N * chunk) — 19.8 -> 4.0
+  # GB at 2048x2048 for ~15% more ViTMatte time. 0 = stock one-shot attention.
+  attn_query_chunk: 2048
 
 segmentation:
   # enabled: true  = single-segmenter pipeline (one seg model drives the trimap)