Pytorch-diffusion - A basic PyTorch implementation of 'Denoising Diffusion Probabilistic Models'

Last update: Jan 7, 2023

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Overview

PyTorch implementation of 'Denoising Diffusion Probabilistic Models'

This repository contains my attempt at reimplementing the main algorithm and model presenting in Denoising Diffusion Probabilistic Models, the recent paper by Ho et al., 2020. A nice summary of the paper by the authors is available here.

This implementation uses pytorch lightning to limit the boilerplate as much as possible. Due to time and computational constraints, I only experimented with 32x32 image datasets, but it should scale up to larger datasets like LSUN and CelebA as demonstrated in the original paper. This implementation was done for my own self-education, and hopefully it can help others learn as well.

Use the provided entry.ipynb notebook to train model and sample generated images.

Supports MNIST, Fashion-MNIST and CIFAR datasets.

Requirements

PyTorch
PyTorch-Lightning
Torchvision
imageio (for gif generation)

Generated Images

MNIST

Fashion-MNIST

CIFAR

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Comments

Wondering about attention

First of all - Thanks for the great implementation, which is very readable and simple to use and reach quick results with.

I noticed you're using the self attention in a different manner than the one written by Ho et al - they used the self attention in the 16x16 resolution layers in the network (both for 32x32 and 256x256 inputs) while you used it in the 8x8 resolution layers in the U-Net. Did you just happen to use it there / was there some guiding logic behind it?

opened by yanivnik 1

Owner

Arthur Juliani

GitHub

Pytorch-diffusion - A basic PyTorch implementation of 'Denoising Diffusion Probabilistic Models'

Related tags

Overview

PyTorch implementation of 'Denoising Diffusion Probabilistic Models'

Requirements

Generated Images

MNIST

Fashion-MNIST

CIFAR

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