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Prerequisites

Before installing Real-ESRGAN, ensure you have the following requirements:

Python

Python >= 3.7

PyTorch

PyTorch >= 1.7
We recommend using Anaconda or Miniconda to manage your Python environment.

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Installation Steps

1

Clone the Repository

First, clone the Real-ESRGAN repository from GitHub:
git clone https://github.com/xinntao/Real-ESRGAN.git
cd Real-ESRGAN
2

Install PyTorch

Install PyTorch (version >= 1.7) according to your system configuration. Visit pytorch.org for installation instructions specific to your CUDA version.Example for CUDA 11.8:
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
Example for CPU only:
pip install torch torchvision
3

Install Dependencies

Install the required Python packages:
# Install basicsr - https://github.com/xinntao/BasicSR
# We use BasicSR for both training and inference
pip install basicsr

# facexlib and gfpgan are for face enhancement
pip install facexlib
pip install gfpgan

# Install other dependencies
pip install -r requirements.txt

basicsr>=1.4.2
facexlib>=0.2.5
gfpgan>=1.3.5
numpy
opencv-python
Pillow
torch>=1.7
torchvision
tqdm
4

Install Real-ESRGAN

Install the Real-ESRGAN package in development mode:
python setup.py develop
Using develop mode allows you to modify the source code and have changes take effect immediately without reinstalling.
5

Verify Installation

Verify that Real-ESRGAN is installed correctly:
python -c "import realesrgan; print('Real-ESRGAN installed successfully!')"

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Alternative: Install via pip

For a simpler installation without cloning the repository, you can install Real-ESRGAN directly from PyPI: 
pip install realesrgan
The pip package may not include the latest features and models. For development or access to all models, we recommend installing from the source repository.

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Package Dependencies

Real-ESRGAN requires the following key packages:
BasicSR is an open-source image and video restoration toolbox used for both training and inference in Real-ESRGAN.
A collection of face-related functions used for face detection and analysis.
GFPGAN (Generative Facial Prior GAN) is used for optional face enhancement in Real-ESRGAN.
OpenCV is used for image I/O operations and basic image processing.
PyTorch is the deep learning framework powering Real-ESRGAN’s neural networks.

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GPU Support

While Real-ESRGAN can run on CPU, using a GPU is highly recommended for practical performance. Image upscaling is significantly faster with CUDA-enabled GPUs.
To check if PyTorch can access your GPU: 
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}'); print(f'GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else "None"}')"

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Portable Executables (Alternative)

If you prefer not to set up a Python environment, portable executable files are available for Windows, Linux, and macOS: These executables use the NCNN framework and work on Intel/AMD/Nvidia GPUs without requiring Python or CUDA.
The portable executables may not support all features available in the Python implementation (such as --outscale and --face_enhance).

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Troubleshooting

Make sure you’ve installed BasicSR:
pip install basicsr
Try using the --tile option with a smaller tile size (e.g., --tile 400) to reduce memory usage during inference.
Ensure you’ve installed the correct PyTorch version for your CUDA version. Visit pytorch.org to find the right installation command.

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Next Steps

Quick Start Guide

Learn how to upscale your first image with Real-ESRGAN

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