Practical-RIFE

Practical-RIFE 
V4.0 Anime Demo Video | 迭代经验 | 迭代QA | Colab

This project is based on RIFE and SAFA. We aim to enhance their practicality for users by incorporating various features and designing new models. Since improving the PSNR index is not consistent with subjective perception. This project is intended for engineers and developers. For general users, we recommend the following software:

SVFI (中文) | RIFE-App | FlowFrames

Thanks to SVFI team to support model testing on Animation. 

VapourSynth-RIFE | RIFE-ncnn-vulkan | VapourSynth-RIFE-ncnn-Vulkan | vs-mlrt | Drop frame fixer and FPS converter

Frame Interpolation
2024.08 - We find that 4.24+ is quite suitable for post-processing of some diffusion model generated videos.

Trained Model
The content of these links is under the same MIT license as this project. lite means using similar training framework, but lower computational cost model.
Currently, it is recommended to choose 4.25 by default for most scenes.

4.26 - 2024.09.21 | Google Drive 百度网盘 || 4.25.lite - 2024.10.20 

4.25 - 2024.09.19 | Google Drive 百度网盘 | I am trying using more flow blocks, so the scale_list will change accordingly. It seems that the anime scenes have been significantly improved.

4.22 - 2024.08.08 | Google Drive  百度网盘 || 4.22.lite || 4.21 - 2024.08.04 | Google Drive 百度网盘

4.20 - 2024.07.24 | Google Drive 百度网盘
|| 4.18 - 2024.07.03 | Google Drive 百度网盘

4.17 - 2024.05.24 | Google Drive 百度网盘 : Add gram loss from FILM || 4.17.lite

4.15 - 2024.03.11 | Google Drive 百度网盘 || 4.15.lite || 4.14 - 2024.01.08 | Google Drive 百度网盘 || 4.14.lite

v4.9.2 - 2023.11.01 | Google Drive 百度网盘 || v4.3 - 2022.8.17 | Google Drive 百度网盘 

v3.8 - 2021.6.17 | Google Drive 百度网盘 || v3.1 - 2021.5.17 | Google Drive 百度网盘 

More Older Version

Installation
python 

We are developing a practical model of SAFA. Welcome to check its demo (BiliBili) and provide advice.

v0.5 - 2023.12.26 | Google Drive

python3 inference_video_enhance.py --video=demo.mp4

Citation

@inproceedings{huang2022rife,
  title={Real-Time Intermediate Flow Estimation for Video Frame Interpolation},
  author={Huang, Zhewei and Zhang, Tianyuan and Heng, Wen and Shi, Boxin and Zhou, Shuchang},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2022}
}

@inproceedings{huang2024safa,
  title={Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution},
  author={Huang, Zhewei and Huang, Ailin and Hu, Xiaotao and Hu, Chen and Xu, Jun and Zhou, Shuchang},
  booktitle={Winter Conference on Applications of Computer Vision (WACV)},
  year={2024}
}

Reference

Optical Flow:
ARFlow  pytorch-liteflownet  RAFT  pytorch-PWCNet

Video Interpolation: 
DVF  TOflow  SepConv  DAIN  CAIN  MEMC-Net   SoftSplat  BMBC  EDSC  EQVI RIFE