Convolutional Variational Autoencoder (VAE)

This repository contains an implementation of a Convolutional Variational Autoencoder (VAE) using PyTorch. The project trains the VAE on the CelebA dataset and generates face images.

Features

• Convolution Variational Autoencoder implementation in PyTorch
• Trained on CelebA dataset
• Customizable hyperparameters for flexibility
• Progress tracking with tqdm
• Possible to specify parameter "attribute" in order to generate images with one of the 40 specified attributes form the dataset.

Installation

1. Clone the repository:

   git clone https://github.com/santysangro/Convolutional-Variational-Autoencoder.git
   cd Convolutional-Variational-Autoencoder  
   pip install -r requirements.txt 
   python scripts/train.py #To train the model
   python scripts/inference.py --num-images 5 --attribute Similing #To generate images
   
   

Dataset

Download the CelebA dataset from this link and place it in the dataset/ directory.