Implements .dev/docs/keep-fix5-implement1.txt against the 9e6c50f tree:
Phase 1 - the paste pipeline: the face upscaler joins the batched core
(the _batched_paste/_batched_paste_multiface aborts are deleted), mode
is forced chunked with a bg model, face_upscale_model threads through
the paste chunk bodies (parse masks stay on the 512 faces, the ROI
grid_sample warps the upscaled faces), upscale_faces_batch_gpu (B=1,
FP16 autocast on CUDA), and _tiled_scale_and_resize_batch replaces
_tiled_scale_batch + the round-12 bg grouping (B=1 FP16 loop, one
bicubic-antialias resize straight to the paste canvas; the no-model
branch keeps _resize_bg_frames). bg_chunk_size and its pins are removed
as dead machinery. parse_soft_masks and _run_upscaler run under
autocast(enabled=(device.type == 'cuda')).
Phase 2 - facelib: RetinaFace caches priors by (h, w, device) and
detect_faces filters by confidence and runs torchvision.ops.nms on the
device before the single CPU transfer (output format preserved);
read_image gains check_gray=True, only the three sequence call sites
pass False.
Phase 3 - archs: SDPA in CrossAttention._attention, AttnBlock and both
gmflow attention forms; get_flow pinned to float32 with autocast
disabled; pos_emb cast to query_emb.dtype; keep_net under autocast at
both sequence call sites.
Phase 4 - the vendored basicsr pruning: data/, losses/, models/,
train.py, test.py, 23 unused archs, 7 unused utils deleted (78 files);
DCNv2Pack removed from arch_util; the three __init__.py rewrites;
enumeration manifests regenerated. metrics/ and ops/ stay on disk
byte-identical, off the import path. 4.2 skipped (explicitly optional).
Autocast is CUDA-only everywhere, so the CPU gate keeps exercising the
FP32 paths; no model is loaded. Gate: 556 passed, 0 failed (embedded
python; 557 -> 556 = the dead bg_chunk_size pins out, new pins in).
GPU throughput and the FP16/SDPA numerics stay owner-measured.
WildAi
aba4cfa739