The paste stage spent its host time on three full-canvas passes per frame. The face warp now runs on the device with cv2's own fixed-point convention (the matrix inversion of warpAffine, the 1/1024 pre-scale, the 1/32 tap grid, the (+512)>>10 rounding), the mask warp reuses the one batched grid sliced to the face ROI, and the blend and the quantizing tail run inside that ROI. The frame-global maximum branch is kept: a near-black frame falls back to the full-canvas path, which stays bit-exact by construction.
Measured over 48 frames at 720p on CUDA: the paste call's process CPU time falls 20% (38.74 -> 30.92 ms/frame), the host-device transfers shrink 6.66x (the blended canvas download by 85%), and the result is bit-identical to the full-canvas path on the smooth and the noise corpus, at interior and edge-touching ROIs.
process_image_sequence converts the batch once, detects per frame while capturing the frames the detector saw (the read_image products, including the min(h,w)<512 upscale), estimates each affine once with the vendored call, crops with cv2.warpAffine (decision D2: the uint8-bound warp stays bit-exact; the batched grid_sample helper stays parked and pinned), normalizes the whole crop batch on the device, keeps the KEEP chunk contract byte-identical, converts the restored faces with one batched tensor2img, pastes through the batched paste with the vendored fallback, and stacks the output in one transfer. The paste canvas is the detection frame, so small frames keep the base's upscaled canvas (the round-1 QA defect F1, fixed and pinned). A failed affine degrades to the neutral crop with a warning instead of crashing like the base. Progress still reports num_frames * 4 in the four families. Pins: the read-once/affine-once/chunk-contract AST guards, the progress contract, and the new end-to-end base-vs-shipped suite (tests/test_gpu_sequence_e2e.py) with the real vendored helper - measured drift 0/255 on the vendored branch, 1/255 on the batched branch.
WildAi