86 lines
3.7 KiB
Markdown
86 lines
3.7 KiB
Markdown
<div align="center">
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# ComfyUI Rife TensorRT ⚡
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[](https://www.python.org/downloads/release/python-31211/)
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[](https://developer.nvidia.com/cuda-downloads)
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[](https://developer.nvidia.com/tensorrt)
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[](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
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</div>
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This project provides a [TensorRT](https://github.com/NVIDIA/TensorRT) implementation of [RIFE](https://github.com/hzwer/ECCV2022-RIFE) for ultra fast frame interpolation inside ComfyUI
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This project is licensed under [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/), everyone is FREE to access, use, modify and redistribute with the same license.
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If you like the project, please give me a star! ⭐
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---
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## ⏱️ Performance
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_Note: The following results were benchmarked on FP16 engines inside ComfyUI, using 2000 frames consisting of 2 alternating similar frames, averaged 2-3 times_
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| Device | Rife Engine | Resolution| Multiplier | FPS |
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| :----: | :-: | :-: | :-: | :-: |
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| H100 | rife49_ensemble_True_scale_1_sim | 512 x 512 | 2 | 45 |
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| H100 | rife49_ensemble_True_scale_1_sim | 512 x 512 | 4 | 57 |
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| H100 | rife49_ensemble_True_scale_1_sim | 1280 x 1280 | 2 | 21 |
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## 🚀 Installation
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Navigate to the ComfyUI `/custom_nodes` directory
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```bash
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git clone https://github.com/yuvraj108c/ComfyUI-Rife-Tensorrt
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cd ./ComfyUI-Rife-Tensorrt
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pip install -r requirements.txt
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```
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## 🛠️ Supported Models
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The following RIFE models are supported and will be automatically downloaded and built:
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- **rife49_ensemble_True_scale_1_sim** (default) - Latest and most accurate
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- **rife48_ensemble_True_scale_1_sim** - Good balance of speed and quality
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- **rife47_ensemble_True_scale_1_sim** - Fastest option
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Models are automatically downloaded from [HuggingFace](https://huggingface.co/yuvraj108c/rife-onnx) and TensorRT engines are built on first use.
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## ☀️ Usage
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1. **Load Model**: Insert `Right Click -> Add Node -> tensorrt -> Load Rife Tensorrt Model`
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- Choose your preferred RIFE model (rife47, rife48, or rife49)
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- Select precision (fp16 recommended for speed, fp32 for maximum accuracy)
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- The model will be automatically downloaded and TensorRT engine built on first use
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2. **Process Frames**: Insert `Right Click -> Add Node -> tensorrt -> Rife Tensorrt`
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- Connect the loaded model from step 1
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- Input your video frames
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- Configure interpolation settings (multiplier, CUDA graph, etc.)
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- Image resolutions between `256x256` and `3840x3840` are supported
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## 🤖 Environment tested
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- WSL Ubuntu 24.04.03 LTS, Cuda 12.9, Tensorrt 10.13.3.9, Python 3.12.11, RTX 5080 GPU
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- Windows (Not tested, but should work)
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## 🚨 Updates
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### December 2025
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- **Automatic Model Management**: No more manual downloads! Models are automatically downloaded from HuggingFace and TensorRT engines are built on demand
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- **Improved Workflow**: New two-node system with `Load Rife Tensorrt Model` + `Rife Tensorrt` for better organization
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- **Updated Dependencies**: TensorRT updated to 10.13.3.9 for better performance and compatibility
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## 👏 Credits
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- https://github.com/styler00dollar/VSGAN-tensorrt-docker
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- https://github.com/Fannovel16/ComfyUI-Frame-Interpolation
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- https://github.com/hzwer/ECCV2022-RIFE
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## License
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
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