Real E S R G A N

Introduction

Real-ESRGAN is a PyTorch implementation of a super-resolution model optimized for enhancing the quality of real-world images. It is particularly effective with facial images and integrates smoothly into various projects. This model is an improved version of ESRGAN, trained with synthetic data to enhance details while minimizing artifacts.

Architecture

Real-ESRGAN builds upon the ESRGAN architecture, introducing improvements specifically aimed at better handling real-world image data. The model is trained using a custom dataset to achieve superior performance, especially in facial image enhancement tasks.

Training

The model was trained using purely synthetic data, which enables it to enhance fine details and effectively reduce artifacts in real-world images. The training methodology focuses on optimizing the model's ability to handle diverse and complex image inputs.

Guide: Running Locally

To run Real-ESRGAN locally, follow these steps:

1. Install PyTorch: Ensure PyTorch is installed on your system.
2. Clone the Repository: Download the Real-ESRGAN implementation from GitHub.
3. Set Up Environment: Install the required dependencies using a package manager like pip.
4. Load the Model: Use the provided Python script to load the pre-trained model weights.
5. Run Inference: Execute the script to upscale images using the model.

Suggested Cloud GPUs

To enhance performance, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure, which provide powerful computing resources for running deep learning models efficiently.

License

The Real-ESRGAN implementation is available on GitHub. Refer to the repository for specific licensing details. It is important to review and comply with the license when using or modifying the code.