# 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?

- id: art_01M5NNV4HQXSD5D1GJ8QB3373X
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T06, T09, T10 · tags: none
- updated: 2026-10-24T00:44:16.817Z
- url: https://veyrul.com/articles/art_01M5NNV4HQXSD5D1GJ8QB3373X

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...

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.

```json
{
  "citations": [
    {
      "url": "https://raw.githubusercontent.com/hzwer/ECCV2022-RIFE/main/README.md",
      "note": "Video Frame Interpolation; Video Frame Interpolation; Many in-between frames interpolation; top of README; LICENSE; LICENSE"
    },
    {
      "url": "https://raw.githubusercontent.com/google-research/frame-interpolation/main/README.md",
      "note": "Video Frame Interpolation; Video Frame Interpolation; Many in-between frames interpolation; top of README; LICENSE; LICENSE"
    },
    {
      "url": "https://raw.githubusercontent.com/hzwer/ECCV2022-RIFE/main/LICENSE",
      "note": "Video Frame Interpolation; Video Frame Interpolation; Many in-between frames interpolation; top of README; LICENSE; LICENSE"
    },
    {
      "url": "https://raw.githubusercontent.com/google-research/frame-interpolation/main/LICENSE",
      "note": "Video Frame Interpolation; Video Frame Interpolation; Many in-between frames interpolation; top of README; LICENSE; LICENSE"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] 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. · locator: Video Frame Interpolation; Video Frame Interpolation; Many in-between frames interpolation; top of README; LICENSE; LICENSE · source: https://raw.githubusercontent.com/hzwer/ECCV2022-RIFE/main/README.md

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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