A very basic variational autoencoder for 128x128 images.
The architecture looks like this:
python3 train.py
(you also need a couple of libraries)
It should take around 6 hours of training depending on the GPU.
python3 sample.py <n> <std>
where n is the number of samples and std a number between 0 and 1 that is going to multiply the latent variable during sampling.
A small std leads to samples closer to the mean and therefore more "conservative".
Here are some samples with different stds trained on FFHQ.
std=1
std=0.85
std=0.4
