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video-restoration【开发与工程】KAIR VRT and RVRT video restoration testing and training guidance for video super-resolution, deblurring, denoising, frame interpolation, and space-time SR.
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Use this sub-skill when the task is about KAIR VRT or RVRT video workflows: video super-resolution, video deblurring, video denoising, video frame interpolation, space-time video SR, VRT/RVRT task IDs, video tiling/OOM, VRT or RVRT training configs, or RVRT custom CUDA extension failures.
Before giving commands, assume the user is in their own KAIR checkout and has
installed PyTorch plus KAIR's requirement.txt dependencies. Treat VRT/RVRT
full inference and training as CUDA-first workflows; RVRT's guided deformable
attention path requires a working CUDA-capable PyTorch build, nvcc, and
ninja for the custom extension.
references/vrt-rvrt-task-reference.md for the VRT 001-009 and RVRT
001-006 task namespaces, checkpoint names, default test folders, and
training config paths.references/video-restoration-workflows.md for quick testing, custom-folder
commands, training command patterns, data layout expectations, tile semantics,
auto-download caveats, and metric/output behavior.references/troubleshooting.md when CUDA extension builds, missing weights,
missing data, Vimeo preparation, DDP checkpoint/static-graph resumes,
num_workers, or OOM are involved.scripts/build_video_restoration_command.py to print safe KAIR VRT/RVRT test
commands without importing KAIR, downloading files, or launching inference.../data-preparation/SKILL.md.../image-testing/SKILL.md.../image-training/SKILL.md.Do not ask future agents to inspect the original KAIR source or docs for these workflows. The operational task tables, command patterns, caveats, and failure modes are distilled into this sub-skill's bundled references.