diffusion-models

This repo provides a clean implementation of various types of diffusion models. Many of these are experimental research prototypes and represent work in progress.

Structure

.
├── data                  # Utilities to load datasets
└── diffusion             # Implements various types of diffusion processes
  ├── gaussian.py         # Classical Gaussian diffusion
  ├── infomax.py          # Auxiliary-variable and information maximizing models (controllable diffusion models with small disentangled latents; experimental & in progress)
  └── learned.py          # Diffusion models where the noising process is learned (experimental & in progress)
├── misc                  # Miscellaneous utilities, like evaluation
└── models                # Implementations of denoising models
  └── unet                # Various Unet-type architectures
    ├── standard.py       # A standard Unet
    └── auxiliary.py      # Unets that take auxiliary inputs (e.g., auxiliary latents) in various ways (e.g., via concatenation, cross-attention, etc.)
  └── modules             # Modules needed by the denoising models (attention, resnets, etc.)
├── trainer               # Module that performs diffusion model training
└── README.md

Environment

This code was tested in a conda environment created using:

conda create --name name python=3.7 pytorch=1.12.1 torchvision=0.13.1 cudatoolkit=11.3 -c pytorch

You can also use the provided package list to recreate the full environment (note that the requirements file hasn't been cleaned up and has some unused pacakges).

conda create --name <env> --file conda-requirements.txt
conda activate <env>

To use this package, simply clone the git repo:

git https://github.com/kuleshov-group/diffusion-models.git;
cd diffusion-models
python run.py --help # make sure the code is in your PYTHONPATH

Training the models

Training runs can be launched via the run.py script.

usage: run.py train [-h] [--model {gaussian,infomax,learned}]
                    [--dataset {fashion-mnist,mnist}]
                    [--checkpoint CHECKPOINT] [-e EPOCHS]
                    [--batch-size BATCH_SIZE] [--learning-rate LEARNING_RATE]
                    [--optimizer {adam}] [--folder FOLDER]

optional arguments:
  -h, --help            show this help message and exit
  --model {gaussian,infomax,learned}
                        type of ddpm model to run
  --dataset {fashion-mnist,mnist}
                        training dataset
  --checkpoint CHECKPOINT
                        path to training checkpoint
  -e EPOCHS, --epochs EPOCHS
                        number of epochs to train
  --batch-size BATCH_SIZE
                        training batch size
  --learning-rate LEARNING_RATE
                        learning rate
  --optimizer {adam}    optimization algorithm
  --folder FOLDER       folder where logs will be stored

For example, this command trains an standard gaussian diffusion model for 50 epochs:

python run.py train --model gaussian --dataset fashion-mnist --folder gaussian-run --epochs 50

This command trains an auxiliary-variable diffusion model:

python run.py train --model infomax --folder infomax-run --epochs 50

Evaluation

The launcher script can also be used to trigger model evaluation.

usage: run.py eval [-h] [--model {gaussian,infomax,learned}]
                   [--dataset {fashion-mnist,mnist}] --checkpoint CHECKPOINT
                   [--deterministic] [--sample SAMPLE]
                   [--interpolate INTERPOLATE] [--latents LATENTS]
                   [--folder FOLDER] [--name NAME]

optional arguments:
  -h, --help            show this help message and exit
  --model {gaussian,infomax,learned}
                        type of ddpm model to run
  --dataset {fashion-mnist,mnist}
                        training dataset
  --checkpoint CHECKPOINT
                        path to training checkpoint
  --deterministic       run in deterministic mode
  --sample SAMPLE       how many samples to draw
  --interpolate INTERPOLATE
                        how many samples to interpolate
  --latents LATENTS     how many points to visualize in latent space
  --folder FOLDER       folder where output will be stored
  --name NAME           name of the files that will be saved

For example, this command loads the weights of an infomax model from an existing checkpoint, generates a figure with 100 samples from the model, and creates another figure with 128 test set points in a 2d latent space, colored according to their class label.

python run.py eval --model infomax --folder infomax-run --sample 100 --latents 128 --checkpoint infomax-run/model-49.pth

Acknowledgements

• Jonathan Ho's orginal OpenAI codebase
• Phil Wang's (lucidrains) codebase
• The Annotated Diffusion Model
• The Latent Diffusion codebase