ComfyUI Rife TensorRT ⚡

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! ⭐

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⏱️ 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

cd ComfyUI/custom_nodes
git clone https://github.com/huchukato/ComfyUI-RIFE-TensorRT-Auto.git

🎯 Fully Automatic Installation (Recommended)

This node features fully automatic CUDA detection and TensorRT installation!

When ComfyUI loads the node for the first time, it will:

1. Auto-detect your CUDA version (12 or 13)
2. Install the appropriate TensorRT packages automatically
3. Configure everything for seamless operation

No manual steps required! Just clone the repo and restart ComfyUI.

📦 Manual Installation Options

If you prefer manual installation or encounter issues:

Auto-install scripts:

# Linux/macOS
./install.sh

# Windows  
install.bat

# Python (cross-platform)
python install.py

Manual requirements files:

# For CUDA 13 (RTX 50 series)
pip install -r requirements.txt

# For CUDA 12 (RTX 30/40 series) - LEGACY METHOD
pip install -r requirements_cu12.txt

💡 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.

📦 CUDA Toolkit Required

The node automatically detects your CUDA installation via CUDA_PATH or CUDA_HOME environment variables.

If CUDA is not detected, download from: https://developer.nvidia.com/cuda-13-0-2-download-archive

🎯 Resolution Limits

Important: The TensorRT engine supports different resolution ranges based on the selected profile:

• small profile: 384-1080px
• medium profile: 672-1312px
• large profile: 720-1920px (perfect for 1440x960 and higher resolutions)

For images larger than your selected profile's maximum, resize them before using the RIFE node or select a higher profile.

🎯 Resolution Profiles

The node supports resolution profiles to optimize VRAM usage:

• small: 384-1080px (recommended for most video generation)
• medium: 672-1312px (for higher resolution videos)
• large: 720-1920px (for 4K and high-resolution content)
• custom: Connect a "RIFE Custom Resolution Config" node for manual control

The following RIFE models are supported and will be automatically downloaded and built:

• rife49_ensemble_True_scale_1_sim (default) - Latest and most accurate
• rife48_ensemble_True_scale_1_sim - Good balance of speed and quality
• rife47_ensemble_True_scale_1_sim - Fastest option

Models are automatically downloaded from HuggingFace and TensorRT engines are built on first use.

☀️ Usage

1. Load Model: Insert Right Click -> Add Node -> tensorrt -> Load Rife Tensorrt Model

   • Choose your preferred RIFE model (rife47, rife48, or rife49)
   • Select precision (fp16 recommended for speed, fp32 for maximum accuracy)
   • Select resolution profile (small, medium, or custom)
   • The model will be automatically downloaded and TensorRT engine built on first use

2. Process Frames: Insert Right Click -> Add Node -> tensorrt -> Rife Tensorrt

   • Connect the loaded model from step 1
   • Input your video frames
   • Configure interpolation settings (multiplier, CUDA graph, etc.)

🔧 Troubleshooting

CUDA Initialization Errors

If you encounter CUDA initialization failure with error: 35 or similar TensorRT errors:

1. Check NVIDIA Drivers:

   nvidia-smi
   

   Ensure your drivers support your CUDA version

2. Restart System: Sometimes CUDA state gets corrupted - a system restart can fix this

3. Verify CUDA Installation:

   nvcc --version
   

   Ensure CUDA toolkit is properly installed

4. Check PyTorch CUDA:

   import torch
   print(f"CUDA available: {torch.cuda.is_available()}")
   print(f"CUDA version: {torch.version.cuda}")
   

5. Reinstall TensorRT:

   python install.py
   

The node now includes automatic CUDA diagnostics that will help identify the specific issue.

Memory Issues

For large resolution engines, ensure you have sufficient VRAM:

• small profile: ~2GB VRAM minimum
• medium profile: ~4GB VRAM minimum
• large profile: ~6GB VRAM minimum

Installation Issues

If auto-installation fails, try manual installation:

# CUDA 13
pip install -r requirements.txt

# CUDA 12
pip install -r requirements_cu12.txt

🤖 Environment tested

• Windows 11, CUDA 13.0, TensorRT 10.15.1.29, Python 3.12, RTX 5090
• WSL Ubuntu 24.04.03 LTS, CUDA 12.9, TensorRT 10.13.3.9, Python 3.12.11, RTX 5080

🚨 Updates

February 2026

• Fixed Dependencies: Updated TensorRT to 10.15.1.29 to resolve installation conflicts
• RTX 5090 Support: Tested and confirmed compatibility with RTX 5090
• Resolution Documentation: Added clear guidance on resolution limits and preprocessing

January 2026

• CUDA 13 Default: Updated to CUDA 13.0 and TensorRT 10.14.1.48
• Auto CUDA Detection: Automatically finds CUDA toolkit and DLL paths
• Resolution Profiles: Added small/medium/custom profiles to reduce VRAM usage

December 2025

• Automatic Model Management: No more manual downloads! Models are automatically downloaded from HuggingFace and TensorRT engines are built on demand
• Improved Workflow: New two-node system with Load Rife Tensorrt Model + Rife Tensorrt for better organization
• Updated Dependencies: TensorRT updated to 10.13.3.9 for better performance and compatibility

👏 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)