Practical-RIFE

Practical-RIFE is an open-source video frame interpolation project that improves the usability and visual quality of the RIFE deep learning architecture. It generates intermediate frames between existing video frames to produce smoother motion and higher frame rates. The project provides multiple pretrained models optimized for different content types, including animation and AI-generated video. Users can increase frame rates by two or four times through Python inference scripts. Configurable scaling options help process high-resolution footage while managing GPU memory requirements. Additional scripts support image sequence processing, comparison previews, and experimental video enhancement using SAFA. Lightweight model variants reduce computational demands. It is primarily intended for developers and engineers integrating frame interpolation into video processing workflows.