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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.

Our tips

For 4K videos, add the --scale=0.5 option to reduce the optical flow model's processing resolution and avoid memory errors.
If disordered patterns appear in the interpolated video, try --scale=2.0 to refine the optical flow computation.
To create a slow-motion effect without changing the audio, use the --fps=60 option; note that the audio track will be removed from the output file.
For animation scenes, prefer the v4.7 to v4.10 models available in the Practical-RIFE repository, which are specifically optimized for this type of content.
The 4.22.lite model is recommended for post-processing videos generated by diffusion models, according to the August 2024 release notes.

Watch out

Installation requires Git, Python 3, and manual dependency management via pip, making it inaccessible without basic technical knowledge.
macOS support is not official: a community pull request exists but has not been merged into the main repository.

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

In the same category

Whole category Video →