A custom node set for Video Frame Interpolation in ComfyUI. UPDATE Memory management is improved. Now this extension takes less RAM and VRAM than before.
UPDATE 2 VFI nodes now accept scheduling multipiler values
- KSampler Gradually Adding More Denoise (efficient)
- GMFSS Fortuna VFI
- IFRNet VFI
- IFUnet VFI
- M2M VFI
- RIFE VFI (4.0 - 4.9) (Note that option
fast_modewon't do anything from v4.5+ ascontextnetis removed) - FILM VFI
- Sepconv VFI
- AMT VFI
- Make Interpolation State List
- STMFNet VFI (requires at least 4 frames, can only do 2x interpolation for now)
- FLAVR VFI (same conditions as STMFNet)
Incompatibile issue with it is now fixed
Following this guide to install this extension
https://github.com/ltdrdata/ComfyUI-Manager#how-to-use
Run install.bat
For Window users, if you are having trouble with cupy, please run install.bat instead of install-cupy.py or python install.py.
Open your shell app and start venv if it is used for ComfyUI. Run:
python install.py
If you don't have a NVidia card, you can try taichi ops backend powered by Taichi Lang
On Windows, you can install it by running install.bat or pip install taichi on Linux
Then change value of ops_backend from cupy to taichi in config.yaml
If NotImplementedError appears, a VFI node in the workflow isn't supported by taichi
All VFI nodes can be accessed in category ComfyUI-Frame-Interpolation/VFI if the installation is successful and require a IMAGE containing frames (at least 2, or at least 4 for STMF-Net/FLAVR).
Regarding STMFNet and FLAVR, if you only have two or three frames, you should use: Load Images -> Other VFI node (FILM is recommended in this case) with multiplier=4 -> STMFNet VFI/FLAVR VFI
clear_cache_after_n_frames is used to avoid out-of-memory. Decreasing it makes the chance lower but also increases processing time.
It is recommended to use LoadImages (LoadImagesFromDirectory) from ComfyUI-Advanced-ControlNet and ComfyUI-VideoHelperSuite along side with this extension.
Workflow metadata isn't embeded
Download these two images anime0.png and anime1.png and put them into a folder like E:\test in this image.
It's used in AnimationDiff (can load workflow metadata)
Big thanks for styler00dollar for making VSGAN-tensorrt-docker. About 99% the code of this repo comes from it.
Citation for each VFI node:
The All-In-One GMFSS: Dedicated for Anime Video Frame Interpolation
https://github.com/98mxr/GMFSS_Fortuna
@InProceedings{Kong_2022_CVPR,
author = {Kong, Lingtong and Jiang, Boyuan and Luo, Donghao and Chu, Wenqing and Huang, Xiaoming and Tai, Ying and Wang, Chengjie and Yang, Jie},
title = {IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}RIFE with IFUNet, FusionNet and RefineNet
https://github.com/98mxr/IFUNet
@InProceedings{hu2022m2m,
title={Many-to-many Splatting for Efficient Video Frame Interpolation},
author={Hu, Ping and Niklaus, Simon and Sclaroff, Stan and Saenko, Kate},
journal={CVPR},
year={2022}
}@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}
}Frame interpolation in PyTorch
@inproceedings{reda2022film,
title = {FILM: Frame Interpolation for Large Motion},
author = {Fitsum Reda and Janne Kontkanen and Eric Tabellion and Deqing Sun and Caroline Pantofaru and Brian Curless},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2022}
}@misc{film-tf,
title = {Tensorflow 2 Implementation of "FILM: Frame Interpolation for Large Motion"},
author = {Fitsum Reda and Janne Kontkanen and Eric Tabellion and Deqing Sun and Caroline Pantofaru and Brian Curless},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/google-research/frame-interpolation}}
}[1] @inproceedings{Niklaus_WACV_2021,
author = {Simon Niklaus and Long Mai and Oliver Wang},
title = {Revisiting Adaptive Convolutions for Video Frame Interpolation},
booktitle = {IEEE Winter Conference on Applications of Computer Vision},
year = {2021}
}[2] @inproceedings{Niklaus_ICCV_2017,
author = {Simon Niklaus and Long Mai and Feng Liu},
title = {Video Frame Interpolation via Adaptive Separable Convolution},
booktitle = {IEEE International Conference on Computer Vision},
year = {2017}
}[3] @inproceedings{Niklaus_CVPR_2017,
author = {Simon Niklaus and Long Mai and Feng Liu},
title = {Video Frame Interpolation via Adaptive Convolution},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2017}
}@inproceedings{licvpr23amt,
title={AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation},
author={Li, Zhen and Zhu, Zuo-Liang and Han, Ling-Hao and Hou, Qibin and Guo, Chun-Le and Cheng, Ming-Ming},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}@InProceedings{Danier_2022_CVPR,
author = {Danier, Duolikun and Zhang, Fan and Bull, David},
title = {ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {3521-3531}
}@article{kalluri2021flavr,
title={FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation},
author={Kalluri, Tarun and Pathak, Deepak and Chandraker, Manmohan and Tran, Du},
booktitle={arxiv},
year={2021}
}