Generative Adversarial Networks and Diffusion Models

From scratch implementation of GANs and Diffusion deep learning models using PyTorch.

This project implements the ground breaking papers on Generative Adversarial Networks and Diffusion Networks.

Generative Adversarial Networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144.

Diffusion Networks

Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015, June). Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning (pp. 2256-2265). PMLR.

Installation

OS X & Linux:

pip install -r requirements

Unit tests

Unit tests check the backbones and building blocks of the neural networks.

python test.py

Usage example

These are the arguments taken by the main.py script.

-h, --help            show this help message and exit
-epoch EPOCH          Number of epochs.
-timesteps TIMESTEPS  Number of timesteps.
-batch_size BATCH_SIZE
Batch size.
-lr LR                Learning Rate.
-latent_vs LATENT_VS  Latent vector size.
-model MODEL          Choose model to train.
-dataset DATASET      Choose dataset.

To train the model you can run.

python main.py

About the Author

Carlos Gustavo Salas Flores – LinkedIn – yuseicarlos2560@gmail.com

Distributed under the MIT license. See LICENSE.txt for more information.

https://github.com/cs582