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 32×32 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)