212 lines
7.6 KiB
Markdown
212 lines
7.6 KiB
Markdown
<div align="center">
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<img src="https://raw.githubusercontent.com/huchukato/ComfyUI-RIFE-TensorRT-Auto/master/banner.png" alt="Auto TensorRT RIFE Banner" width="100%" />
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# ComfyUI Rife TensorRT ⚡
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[](https://www.python.org/downloads/)
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[](https://developer.nvidia.com/cuda-13-0-2-download-archive)
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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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[](README.it.md)
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[](https://buymeacoffee.com/huchukato)
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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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cd ComfyUI/custom_nodes
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git clone https://github.com/huchukato/ComfyUI-RIFE-TensorRT-Auto.git
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```
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### 🎯 Fully Automatic Installation (Recommended)
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**This node features fully automatic CUDA detection and TensorRT installation!**
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When ComfyUI loads the node for the first time, it will:
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1. **Auto-detect your CUDA version** (12 or 13)
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2. **Install the appropriate TensorRT packages** automatically
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3. **Configure everything** for seamless operation
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**No manual steps required!** Just clone the repo and restart ComfyUI.
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### 📦 Manual Installation Options
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If you prefer manual installation or encounter issues:
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**Auto-install scripts:**
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```bash
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# Linux/macOS
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./install.sh
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# Windows
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install.bat
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# Python (cross-platform)
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python install.py
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```
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**Manual requirements files:**
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```bash
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# For CUDA 13 (RTX 50 series)
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pip install -r requirements.txt
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# For CUDA 12 (RTX 30/40 series) - LEGACY METHOD
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pip install -r requirements_cu12.txt
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```
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> **💡 Note:** The `requirements_cu12.txt` is provided as a legacy fallback method. The automatic installation is strongly recommended as it handles CUDA detection and package installation seamlessly.
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### 📦 CUDA Toolkit Required
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The node automatically detects your CUDA installation via `CUDA_PATH` or `CUDA_HOME` environment variables.
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If CUDA is not detected, download from: https://developer.nvidia.com/cuda-13-0-2-download-archive
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### 🎯 Resolution Limits
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**Important**: The TensorRT engine supports different resolution ranges based on the selected profile:
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- **small profile**: 384-1080px
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- **medium profile**: 672-1312px
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- **large profile**: 720-1920px (perfect for 1440x960 and higher resolutions)
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For images larger than your selected profile's maximum, resize them before using the RIFE node or select a higher profile.
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### 🎯 Resolution Profiles
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The node supports resolution profiles to optimize VRAM usage:
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- **small**: 384-1080px (recommended for most video generation)
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- **medium**: 672-1312px (for higher resolution videos)
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- **large**: 720-1920px (for 4K and high-resolution content)
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- **custom**: Connect a "RIFE Custom Resolution Config" node for manual control
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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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- Select resolution profile (small, medium, or custom)
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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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## 🔧 Troubleshooting
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### CUDA Initialization Errors
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If you encounter `CUDA initialization failure with error: 35` or similar TensorRT errors:
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1. **Check NVIDIA Drivers:**
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```bash
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nvidia-smi
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```
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Ensure your drivers support your CUDA version
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2. **Restart System:**
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Sometimes CUDA state gets corrupted - a system restart can fix this
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3. **Verify CUDA Installation:**
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```bash
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nvcc --version
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```
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Ensure CUDA toolkit is properly installed
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4. **Check PyTorch CUDA:**
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```python
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import torch
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print(f"CUDA available: {torch.cuda.is_available()}")
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print(f"CUDA version: {torch.version.cuda}")
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```
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5. **Reinstall TensorRT:**
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```bash
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python install.py
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```
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The node now includes automatic CUDA diagnostics that will help identify the specific issue.
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### Memory Issues
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For large resolution engines, ensure you have sufficient VRAM:
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- **small profile**: ~2GB VRAM minimum
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- **medium profile**: ~4GB VRAM minimum
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- **large profile**: ~6GB VRAM minimum
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### Installation Issues
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If auto-installation fails, try manual installation:
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```bash
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# CUDA 13
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pip install -r requirements.txt
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# CUDA 12
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pip install -r requirements_cu12.txt
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```
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## 🤖 Environment tested
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- Windows 11, CUDA 13.0, TensorRT 10.15.1.29, Python 3.12, RTX 5090
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- WSL Ubuntu 24.04.03 LTS, CUDA 12.9, TensorRT 10.13.3.9, Python 3.12.11, RTX 5080
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## 🚨 Updates
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### February 2026
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- **Fixed Dependencies**: Updated TensorRT to 10.15.1.29 to resolve installation conflicts
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- **RTX 5090 Support**: Tested and confirmed compatibility with RTX 5090
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- **Resolution Documentation**: Added clear guidance on resolution limits and preprocessing
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### January 2026
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- **CUDA 13 Default**: Updated to CUDA 13.0 and TensorRT 10.14.1.48
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- **Auto CUDA Detection**: Automatically finds CUDA toolkit and DLL paths
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- **Resolution Profiles**: Added small/medium/custom profiles to reduce VRAM usage
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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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