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This repo implements Denoising Diffusion Probabilistic Models (DDPM) in Pytorch

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Denoising Diffusion Probabilistic Models Implementation in Pytorch

This repository implements DDPM with training and sampling methods of DDPM and unet architecture mimicking the stable diffusion unet used in diffusers library from huggingface from scratch.

DDPM Explanation Videos

DDPM Math Video DDPM Implementation Video

Sample Output by trained DDPM on Mnist

Data preparation

For setting up the mnist dataset:

Follow - https://github.com/explainingai-code/Pytorch-VAE#data-preparation

Training on your own images

For this one would need to make the following changes

Quickstart

  • Create a new conda environment with python 3.8 then run below commands
  • git clone https://github.com/explainingai-code/DDPM-Pytorch.git
  • cd DDPM-Pytorch
  • pip install -r requirements.txt
  • For training/sampling use the below commands passing the desired configuration file as the config argument in case you want to play with it.
  • python -m tools.train_ddpm for training ddpm
  • python -m tools.sample_ddpm for generating images

Configuration

  • config/default.yaml - Allows you to play with different components of ddpm

Output

Outputs will be saved according to the configuration present in yaml files.

For every run a folder of task_name key in config will be created

During training of DDPM the following output will be saved

  • Latest Model checkpoint in task_name directory

During sampling the following output will be saved

  • Sampled image grid for all timesteps in task_name/samples/*.png

Citations

@misc{ho2020denoising,
      title={Denoising Diffusion Probabilistic Models}, 
      author={Jonathan Ho and Ajay Jain and Pieter Abbeel},
      year={2020},
      eprint={2006.11239},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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