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AppWatch outofficial publisher

RIFE

App : A product you use directly

AI-based video frame interpolation, running locally.

The engine behind smooth interpolation, integrated into Flowframes and Video2X.

Official site GitHub · 5,626 ★
Overall score
6.6 /10
Security: nothing to flag
Utility
8.0
Reliability
2.0
Security
No caveat on record
Ease
Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Videos shot at low frame rates or animation sequences often lack smoothness. RIFE (Real-Time Intermediate Flow Estimation) addresses this by automatically generating intermediate frames between two existing frames, increasing the frames per second of a video.

The engine relies on intermediate optical flow estimation: it analyzes motion between two consecutive frames and computes a plausible synthetic frame at any point in time between them. It supports arbitrary-timestep interpolation, making it possible to go from a 24 fps video to 48 or 96 fps with just a few commands.

RIFE is aimed at developers, video editors, and animation enthusiasts who want to smooth out their videos via command line or integrate the engine into their own tools. It is also used in post-production of videos generated by diffusion models.

However, it is not designed for non-technical users: there is no official graphical interface and installation requires Python, Git, and dependency management. General users will prefer third-party software that integrates it, such as Flowframes.

What it offers

  • ·AI-based video interpolation: generates intermediate frames to multiply the frames per second (x2, x4, x16, etc.).
  • ·Arbitrary-timestep interpolation: produces a synthetic frame at any point in time between two source frames.
  • ·Local processing: all computation runs on the user's machine, with no data sent to a server.
  • ·High-resolution video support: a scale parameter allows processing 4K videos by reducing the optical flow model's working resolution.
  • ·Still image interpolation: generates a sequence of intermediate frames between two PNG images, up to 16 interpolated frames.
  • ·Models optimized for animation: specific versions (v4.7 to v4.10) are available for animated scenes.
  • ·Integration into third-party software: the engine is used by Flowframes, SVFI, Waifu2x-Extension-GUI, Autodesk Flame, and others.
  • ·Docker execution: an official container allows running RIFE without manually configuring the Python environment.

In practice

Price
Free
Commercial use
Commercial use: yes
Available on
cli, windows
Licence
MIT
Runs locally
Yes, 4 GB VRAM recommended
FR interface
No

Security and data

Rating
Security: nothing to flag
Your data
Data stays local
Maintenance
Inactive for over six months

Links and repository checked on 25 October 2026 · added on 1 November 2020

How to install RIFE

1. Clone the repository: git clone git@github.com:megvii-research/ECCV2022-RIFE.git 2. Navigate to the folder: cd ECCV2022-RIFE 3. Install Python dependencies: pip3 install -r requirements.txt 4. Download the pre-trained HD models from the Google Drive link in the README and extract them into the train_log/ folder 5. Run a 2x interpolation: python3 inference_video.py --exp=1 --video=your_video.mp4 6. All processing runs locally: no data is sent to an external server.
git clone git@github.com:megvii-research/ECCV2022-RIFE.git && cd ECCV2022-RIFE && pip3 install -r requirements.txt

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