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README.md

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-# ComfyUI-RIFE-TensorRT-RTX
-ComfyUI custom node that performs RIFE Interpolation using Tensorrt-RTX
+# ComfyUI RIFE TensorRT-RTX
+
+[![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)
+[![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.
+
+
+## Examples
+
+![Simple Example](assets/example.png)
+
+## 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](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