# glow-pytorch

**Repository Path**: mirrors_rosinality/glow-pytorch

## Basic Information

- **Project Name**: glow-pytorch
- **Description**: PyTorch implementation of Glow
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2022-01-07
- **Last Updated**: 2026-02-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# glow-pytorch
PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions (https://arxiv.org/abs/1807.03039)

Usage:

> python train.py PATH

as trainer uses ImageFolder of torchvision, input directory should be structured like this even when there are only 1 classes. (Currently this implementation does not incorporate class classification loss.)

> PATH/class1 <br/>
> PATH/class2 <br/>
> ...

## Notes

![Sample](sample.png)

I have trained model on vanilla celebA dataset. Seems like works well. I found that learning rate (I have used 1e-4 without scheduling), learnt prior, number of bits (in this cases, 5), and using sigmoid function at the affine coupling layer instead of exponential function is beneficial to training a model.

In my cases, LU decomposed invertible convolution was much faster than plain version. So I made it default to use LU decomposed version.

![Progression of samples](progression.gif)

Progression of samples during training. Sampled once per 100 iterations during training.