ProGAN Implementation

This repository contains the implementation of Progressive Growing of GANs. This type of GAN is designed to generate high-quality images.

Getting Started

Follow these steps to use this implementation:

Ensure you have Python installed on your system. This code is compatible with Python 3.9 and newer versions.

For training and testing the ProGAN model, you'll need a dataset. I used CelebA dataset which you download using the following link:

After downloading, place the dataset in an appropriate directory within the your project structure, such as "./data".

Clone the repository to your local computer:

git clone https://github.com/dykyivladk1/ProGAN.git

Install the required dependencies. It's recommended to create and use a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

Training model

To train a model for custom dataset, you can use the following command:
```
python scripts/trainer.py --train_dir <train_path>
```
Visualisations

You can use Netron app for opening the .onnx files stored in visualisations folder. I used them for understanding the model structure.
Note

If you want to see my documentation for this model you can visit the following link on Notion:

Documentation