ComfyUI-RIFE-TensorRT-RTX

<div align="center">

ComfyUI Rife TensorRT ⚡

</div>

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

⚠️ CUDA Version Selection

This node defaults to CUDA 13 (RTX 50 series, driver 580+).

For CUDA 12 (RTX 30/40 series):

pip install -r requirements_cu12.txt

For CUDA 13 (Default):

pip install -r requirements.txt

📦 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 Profiles

The node supports resolution profiles to optimize VRAM usage:

• small: 480-896px (recommended for most video generation)
• medium: 720-1280px (for higher resolution videos)
• 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.)

🤖 Environment tested

• Windows 11, CUDA 13.0, TensorRT 10.14.1.48, Python 3.12, RTX 5070 Ti
• WSL Ubuntu 24.04.03 LTS, CUDA 12.9, TensorRT 10.13.3.9, Python 3.12.11, RTX 5080

🚨 Updates

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)