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# ComfyUI RIFE TensorRT-RTX
<div align="center">
[![python](https://img.shields.io/badge/python-3.13.9-green)](https://www.python.org/downloads/release/python-31313/)
[![cuda](https://img.shields.io/badge/cuda-13.0-green)](https://developer.nvidia.com/cuda-downloads)
[![trt](https://img.shields.io/badge/TRT_RTX-1.4.0.76-green)](https://developer.nvidia.com/tensorrt)
# ComfyUI Rife TensorRT ⚡
[![python](https://img.shields.io/badge/python-3.12-green)](https://www.python.org/downloads/)
[![cuda](https://img.shields.io/badge/cuda-13.0-green)](https://developer.nvidia.com/cuda-13-0-2-download-archive)
[![trt](https://img.shields.io/badge/TRT-10.14.1.48-green)](https://developer.nvidia.com/tensorrt)
[![by-nc-sa/4.0](https://img.shields.io/badge/license-CC--BY--NC--SA--4.0-lightgrey)](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
## Overview
This project is a fork of the original [ComfyUI_RIFE_TensorRT_Auto](https://github.com/silveroxides/ComfyUI_RIFE_TensorRT_Auto) by [silveroxides](https://github.com/silveroxides).
This project uses TensorRT-RTX to do RIFE interpolation
## Features
- **NVIDIA TensorRT Acceleration**: Leverages Tensor Cores for up to 30x faster frame interpolation compared to standard local methods.
- **Dynamic Shape Support**: Configure minimum, optimal, and maximum resolutions to handle varying input sizes without rebuilding the engine for every image.
- **Automatic Engine Building**: Automatically compiles specialized `.trt` files from your `.onnx` models if a matching engine isn't found.
- **Persistent Timing Cache**: Saves hardware-specific optimization data to a local cache to speed up subsequent engine builds.
- **Memory Efficient**: Optimized for VRAM management with automatic garbage collection and cache clearing after inference.
![node](https://github.com/user-attachments/assets/5fd6d529-300c-42a5-b9cf-46e031f0bcb5)
## Examples
</div>
![Simple Example](assets/example.png)
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
## Installation
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.
### ComfyUI Manager
If you like the project, please give me a star! ⭐
This project has not been submitted to the ComfyUI Manager registry yet. But you can still install it this way:
---
1. Open ComfyUI Manager.
2. Click the `Custom Nodes Manager` to open the custom nodes manager page.
3. On the bottom right corner, click the `Install via Git URL` button.
4. Enter the URL of this repository: `https://github.com/ThreadsOfFate/ComfyUI-RIFE-TensorRT-RTX`
5. Click "Confirm".
6. Restart ComfyUI.
## ⏱️ Performance
### Manual
_Note: The following results were benchmarked on FP16 engines inside ComfyUI, using 2000 frames consisting of 2 alternating similar frames, averaged 2-3 times_
1. On the github page, click on the green `<> Code` button and then "Download ZIP".
2. Extract the root folder within the downloaded ZIP file to your ComfyUI `custom_nodes` directory.
3. Using the same python environment that runs ComfyUI, install the required dependencies: `python -m pip install -r custom_nodes/ComfyUI-Upscaler-TensorRT-Advanced/requirements.txt`.
4. Restart ComfyUI.
| 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
### Requirements
Navigate to the ComfyUI `/custom_nodes` directory
```bash
git clone https://github.com/yuvraj108c/ComfyUI-Rife-Tensorrt
cd ./ComfyUI-Rife-Tensorrt
pip install -r requirements.txt
```
### ⚠️ CUDA Version Selection
### CUDA Toolkit Required
Please download NVIDIA CUDA Toolkit [https://developer.nvidia.com/cuda/toolkit](https://developer.nvidia.com/cuda/toolkit)
This node defaults to **CUDA 13** (RTX 50 series, driver 580+).
###Nodes Included
🟦 RIFE RTX Engine Builder (RIFERTXEngineLoader)
Loads a TensorRT-RTX engine. If the engine file for your specific GPU and resolution settings does not exist, it will automatically build one from the source ONNX model.
🟦 RIFE RTX Frame Interpolation (RIFERTXFrameInterpolation)
The core execution node. It takes an image and a loaded engine to perform the upscale. It supports an optional resize input for custom final dimensions.
🟦 RIFE RTX Engine Config (RIFERTXEngineConfig)
Allows you to define the resolution bounds (min/opt/max) for the engine.
Min/Max: The range of resolutions the engine can handle.
Opt: The resolution the engine is most optimized for.
GPU: NVIDIA RTX Series (TensorRT-RTX requires Tensor Cores).
Software: NVIDIA Drivers and a compatible version of TensorRT (handled via requirements.txt).
**For CUDA 12 (RTX 30/40 series):**
```bash
pip install -r requirements_cu12.txt
```
###Troubleshooting
First Build Time: The first time you load a model with new shape settings, it may take several minutes to compile the engine. Check the console for progress via the TQDM bar.
VRAM Errors: If the build fails, try reducing the width_max or height_max in the Dynamic Shape Config.
**For CUDA 13 (Default):**
```bash
pip install -r requirements.txt
```
### 📦 CUDA Toolkit Required
## 🛠️ Supported Models
The node automatically detects your CUDA installation via `CUDA_PATH` or `CUDA_HOME` environment variables.
rife47_ensemble_True_scale_1_sim
rife48_ensemble_True_scale_1_sim
rife49_ensemble_True_scale_1_sim
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](https://huggingface.co/yuvraj108c/rife-onnx) 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)](https://creativecommons.org/licenses/by-nc-sa/4.0/)