Practical-RIFE

Practical-RIFE generates intermediate video frames to increase frame rates. Built on RIFE and SAFA, this Python project is aimed at engineers and developers incorporating interpolation into video processing workflows, including animation and some diffusion-generated footage.

• Frame rate conversion: Process video files or PNG frame sequences, multiply the input frame rate, or choose a target output frame rate.
• Animation and generated video: The model selection includes work focused on anime scenes and models suited to post-processing some videos produced by diffusion models.
• Lower computation options: Lighter model variants reduce computational cost, and reduced-scale processing supports high-resolution footage such as 4K.
• Visual comparison: Produce a side-by-side view of the original and interpolated video to inspect the result.

Processing runs locally with downloaded trained models. Both the project and those models use the MIT license. It's a code-oriented tool; the maintainers recommend packaged applications for general users. The available model-training material is for reference rather than a fully organized training workflow.