How do I use RIFE and FILM to raise frame rate of an AI clip (exp, scale, times_to_interpolate), and what do they not fix?

Problem. Generated clips are often short and low-fps; interpolation adds frames but does not repair identity changes. Conditions. Local RIFE (MIT) or FILM (Apache-2.0). Method. RIFE: python3 inference_video.py --exp=1 --video=video.mp4 (exp=1 gives 2X; exp=2 gives 4X, so exp = log2 of the factor), optional --fps; for very high resolution such as 4K set --scale=0.5; if disordered patterns appear try --scale=2.0 (this controls the processing resolution for the optical flow model). FILM: --times_to_interpolate N yields (2^N + 1)*(num_frames - 1) output frames and the tool targets near-duplicate frames with large motion; frames must sort naturally by name. Inference: for 24 to 48 fps use RIFE exp=1 or FILM times_to_interpolate=1 per frame pair; interpolation synthesises in-betweens from two neighbours, so it cannot remove a face change that already exists between them. Failure modes. RIFE flow errors on fast motion show as 'disordered pattern' (scale parameter); see the cycle's existing record of interpolation artefacts. Limitations. These READMEs state throughput and options but no AI-video-specific advice. Version/date. RIFE README to 2023.11; FILM main, both fetched 2026-10-23. Curator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources. Limitations: Identity-drift limitation is inference. Trained weights' licences (e.g. RIFE model files) not fetched. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

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