ComfyUI RIFE TensorRT-RTX

Overview

This project is a fork of the original ComfyUI_RIFE_TensorRT_Auto by 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.

Examples

Installation

ComfyUI Manager

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.

Manual

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.

Requirements

pip install -r requirements.txt

CUDA Toolkit Required

Please download NVIDIA CUDA Toolkit https://developer.nvidia.com/cuda/toolkit

###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).

###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.

🛠️ Supported Models

rife47_ensemble_True_scale_1_sim rife48_ensemble_True_scale_1_sim rife49_ensemble_True_scale_1_sim