Hilbert Diffusion Model (HDM)

This is an official repository for Hilbert Diffusion Model (HDM) based on the paper Score-based Generative Modeling through Stochastic Evolution Equations in Hilbert Spaces, Lim et al., NeurIPS (Spotlight), 2023.

Code Usage

Dependencies

Run the following command to install the necessary packages.

# Python 3.8.16
pip install -r requirements.txt

Training

Configuration

• dataset: cifar10, afhq, ffhq, lsun, quadratic, melbourne, gridwatch
• architecture: unet, fno

Single GPU training Run the following code with hdm_{dataset}_{architecture}.yml.

python main.py --config {config_name}.yml --exp {folder_name}

Multiple GPU training Run the following code with hdm_{dataset}_{architecture}.yml.

torchrun --nproc_per_node=NUMBER_OF_GPUS main.py --config {config_name}.yml --exp {folder_name} --distributed

main.py requires the following arguments for training a model.

main.py 
--config: Configuration file stored in "./configs"
--exp: The folder name to store checkpoints, tensorboard logger. (recommend to use "./outs/MODALITY_DATASET")
--distributed: whether to train a model using multiple GPUs. (default is set to False)
--resume: Whether to resume training from the last checkpoint. (No resume in the 1D dataset)

Sampling

Using trained checkpoints, the sampling command requires the following arguments.

main.py 
--config: Configuration file stored in "./configs"
--exp: The folder name to store checkpoints, tensorboard logger. (recommend to use "./outs/MODALITY_DATASET")
--distributed: whether to train a model using multiple GPUs. (default is set to False)
--nfe: Number of function evaluations. (default is set to be 1,000)
--fid: Whether to calculate FID scores. (default is set to be False)
--prior: whether to use HDM prior or IHDM prior. (default is set to be 'hdm')
--sample_type: Whether to comduct Imputation or Super-resolution sampling for 2D image experiments. (default is set to be 'sde')
--degraded_type: Whether to use Gaussian blur or pixelate to generate degraded images for Super-resolution task. (default is set to be 'blur')

Multi-GPU

Our code supports both single and multi-GPU training codes. We recommended using TorchElastic to run multi-GPU training. For example, using a single node with multiple GPUs, you can try the following command.

torchrun --standalone --nnodes=NUMBER_OF_NODE --nproc_per_node=NUMBER_OF_GPUS main.py {--args} --distributed

Citation

If you use this code in your research, please cite our paper.

@inproceedings{
lim2023scorebased,
title={Score-based Generative Modeling through Stochastic Evolution Equations},
author={Lim, Sungbin and Yoon, Eunbi and Byun, Taehyun and Kang, Taewon and Kim, Seungwoo and Lee, Kyungjae and Choi, Sungjoon},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=GrElRvXnEj}
}

Contact

• Sungbin Lim, sungbin@korea.ac.kr