ComfyUI Rife TensorRT ⚡

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

Last tested: 08 June 2026 (ComfyUI v0.23.0 | Torch 2.12.0 | Python 3.12.3 | L40S | CUDA 13.0 | Ubuntu 24.04)

⭐ Support

If you like my projects and wish to see updates and new features, please consider supporting me. It helps a lot!

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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

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

🛠️ Supported Models

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)
   • 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, etc.)
   • Image resolutions between 256x256 and 3840x3840 are supported

🚨 Updates

08 June 2026

• Automatic Model Management: No more manual downloads! Models are automatically downloaded from HuggingFace and TensorRT engines are built on demand. PR#14 by @reaperhammer
• Improved Workflow + Codebase: New two-node system with Load Rife Tensorrt Model + Rife Tensorrt for better organization
• Remove cuda-python: No more cuda installation issues on windows

👏 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)