ComfyUI Rife TensorRT ⚡

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This project provides a TensorRT implementation of RIFE for ultra fast frame interpolation inside ComfyUI

This project is licensed under CC BY-NC-SA, everyone is FREE to access, use, modify and redistribute with the same license.

If you like the project, please give me a star! ⭐

⏱️ Performance

Note: The following results were benchmarked on FP16 engines inside ComfyUI, using 2000 frames consisting of 2 alternating similar frames, averaged 2-3 times

| Device | Rife Engine | Resolution| Multiplier | FPS | | :----: | :-: | :-: | :-: | :-: | | H100 | rife49_ensemble_True_scale_1_sim | 512 x 512 | 2 | 45 | | H100 | rife49_ensemble_True_scale_1_sim | 512 x 512 | 4 | 57 | | H100 | rife49_ensemble_True_scale_1_sim | 1280 x 1280 | 2 | 21 |

🚀 Installation

Navigate to the ComfyUI /custom_nodes directory

git clone https://github.com/yuvraj108c/ComfyUI-Rife-Tensorrt
cd ./ComfyUI-Rife-Tensorrt
pip install -r requirements.txt

🛠️ Building Tensorrt Engine

Download one of the following onnx models:
Edit onnx/trt paths inside export_trt.py and build tensorrt engine by running:
- python export_trt.py
Place the exported engine inside ComfyUI /models/tensorrt/rife directory

☀️ Usage

Insert node by Right Click -> tensorrt -> Rife Tensorrt
Image resolutions between 256x256 and 3840x3840 will work with the tensorrt engines

🤖 Environment tested

Ubuntu 22.04 LTS, Cuda 12.4, Tensorrt 10.4.0, Python 3.10, RTX 3070 GPU
Windows (Not tested, but should work)

👏 Credits

https://github.com/styler00dollar/VSGAN-tensorrt-docker
https://github.com/Fannovel16/ComfyUI-Frame-Interpolation
https://github.com/hzwer/ECCV2022-RIFE

License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

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