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

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

• Put the image files in a folder created within the repo root (example: data/images/*.png ). The data folder should only have one directory 'images'
• Comment https://github.com/explainingai-code/DDPM-Pytorch/blob/main/dataset/mnist_dataset.py#L42 as this is only valid for mnist
• Update the expected number of channels here and image dimensions(assumed square images) here - https://github.com/explainingai-code/DDPM-Pytorch/blob/main/config/default.yaml#L10
• Change the config path here to point to 'data' directory('data' and not 'data/images') - https://github.com/explainingai-code/DDPM-Pytorch/blob/main/config/default.yaml#L2
• Right now the code has been written for picking up png files in mnist data directory format, so I assume there are subdirectories inside the directory mentioned in config and these sub-directories have .png files. This would work if you have .png files. If the images are of other formats or combination of different formats then one would have to change the load_images function correspondingly here - https://github.com/explainingai-code/DDPM-Pytorch/blob/main/dataset/mnist_dataset.py#L29C9-L29C9
• As of now code is written assuming square images, if thats not the case then just changing the dimensions to desired one during sampling should work - https://github.com/explainingai-code/DDPM-Pytorch/blob/main/tools/sample_ddpm.py#L20

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}
}