generative_minimal

a repository for minimal implementations of common generative models

each model class contains a train.py script to train the associated model

Models:

• Generative Adversarial Networks (GANs)
  • Generative Adversarial Network (GAN)
  • Deep Convolutional Generative Adversarial Network (DCGAN)
  • Wasserstein Generative Adversarial Network (WGAN)
• Variational Auto Encoder (VAEs)
  • Variation Auto Encoder (VAE)
  • Conditional Variational Auto Encoder (CVAE)
• Normalizing Flows
  • Normalizing Flow
• Diffusion Models
  • Denoising Diffusion Probabalistic Model (DDPM)
• Transformers
  • Encoder-only
  • Decoder-only
  • Encoder-Decoder

feel free to submit an issue or a pull request with additional models following the same format used here

Setup

• conda create -n generative -y python=3.11 && conda activate generative
• git clone [email protected]:KyleM73/generative_minimal.git
• cd generative_minimal
• pip install -e .
• cd generative_minimal
• python get_datasets.py
• python models/<model>/train.py

Datasets

• MNIST