---

name: eccv2022-rife description: "Operate ECCV2022-RIFE video frame interpolation, benchmark evaluation, and training workflows from a source checkout." disable-model-invocation: true metadata: disco-role: operating license: MIT

ECCV2022-RIFE repo skill

Use this skill when the task names ECCV2022-RIFE, RIFE, Real-Time Intermediate Flow Estimation, video frame interpolation, slow-motion generation, image-pair interpolation, RIFE benchmarks, or RIFE training/reproduction from this source checkout.

The repository is a source-script project rather than an installable Python distribution. Future agents should reason from the checkout scripts and bundled skill references, not from a package console entry point.

First checks

1. Check the source state against references/repo-provenance.md when exact behavior matters or the checkout looks newer than the skill.

2. Confirm dependencies. Base inference needs PyTorch, TorchVision, NumPy, OpenCV, scikit-video, MoviePy, tqdm, and an ffmpeg executable for video/audio work. Training also needs TensorBoard; HD benchmarks need scikit-image.

3. Confirm external assets. Pretrained checkpoints and benchmark/training datasets are not bundled with the source checkout or this skill.

4. Run a safe import/backend smoke before long work when the environment is uncertain:

   python scripts/smoke_model_api.py --repo-root <checkout> --device auto --size 32
   

   This smoke uses random weights. It proves source imports, torch backend, and Model.inference shape behavior only; it does not prove interpolation quality or official benchmark metrics.

Route map

User intent	Read
Interpolate two images, use --ratio, make 2X/4X/16X frames, process a video or numbered PNG directory, tune --scale/--fps/--fp16, understand output paths or audio transfer	sub-skills/interpolation/SKILL.md
Select or validate UCF101, Vimeo90K, MiddleBury, ATD12K, HD, HD 4X, or testtime.py evaluations; classify safe native verification cases; interpret PSNR/SSIM/IE	sub-skills/evaluation/SKILL.md
Prepare Vimeo triplet data, plan train.py distributed CUDA/NCCL launch, check TensorBoard/checkpoints, reason about world size, batch size, OOM, or long-running training	sub-skills/training/SKILL.md
Inspect model.RIFE.Model, checkpoint loading, tensor shapes, device selection, or VimeoDataset signatures	references/model-api.md
Diagnose install/import, checkpoint, CUDA/CPU, dataset, ffmpeg/audio, or output side-effect issues shared across workflows	references/troubleshooting.md

Operating boundaries

• Do not download checkpoints or datasets, run full benchmarks, launch training, or start long video inference without explicit user approval and a time/storage/GPU budget.
• Do not treat CPU importability as proof of CUDA-only training or HD benchmark behavior. train.py, benchmark/HD.py, and benchmark/HD_multi_4X.py require CUDA paths.
• Do not claim README-reported paper or official metrics are reproduced unless the matching external data, checkpoints, backend, and commands were actually run in the current session.
• Do not promise verified HD model variants from the default checkout. The inference scripts try HD import paths before falling back to model.RIFE, but the active model.RIFE_HD* paths are absent in this source snapshot.
• Prefer bundled validators/builders before mutating work:
  • sub-skills/interpolation/scripts/interpolation_command_builder.py
  • sub-skills/evaluation/scripts/check_benchmark_layout.py
  • sub-skills/training/scripts/check_vimeo_triplet_layout.py

Common source commands

These command shapes are documented in the sub-skill references; validate assets first.

python inference_img.py --img img0.png img1.png --exp 4 --model train_log
python inference_img.py --img img0.png img1.png --ratio 0.25 --model train_log
python inference_video.py --video input.mp4 --exp 1 --scale 0.5 --model train_log
python inference_video.py --img frames --exp 2 --png --model train_log
python benchmark/testtime.py
python -m torch.distributed.launch --nproc_per_node=4 train.py --world_size=4

Self-contained skill assets

• references/repo-provenance.md records the source commit, dirty-state caveat, evidence paths, and refresh triggers.
• references/repo-routing-metadata.json provides structured router metadata for managed repo-skill import tooling.
• references/model-api.md records verified signatures and tensor/device contracts.
• references/troubleshooting.md covers cross-cutting operational failures.
• scripts/smoke_model_api.py is the shared safe API/backend smoke helper.