Glow is a normalizing flow model introduced by OpenAI that uses an invertible generative architecture.
Glow’s flow blocks consist of 3 components: act norm, 1x1 invertible convolutions and affine coupling layers.
This repository contains the complete workflow for training and testing Glow. All code was developed during the GenAI UCU course.
Here are presented:
- model implementation from scratch
- train script with hydra configs
- tensorboard logging
- DDP trainer
- tests with pytest
- CI using github actions