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<p align="center">Score-Based Generative Modeling <br> with Critically-Damped Langevin Diffusion <br><br> ICLR 2022 (spotlight)</p>

Requirements

CLD-SGM is built in Python 3.8.0 using PyTorch 1.8.1 and CUDA 11.1. Please use the following command to install the requirements:

pip install --upgrade pip
pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html -f https://storage.googleapis.com/jax-releases/jax_releases.html

Optionally, you may also install NVIDIA Apex. The Adam optimizer from this library is faster than PyTorch's native Adam.

Preparations

CIFAR-10 does not require any data preparation as the data will be downloaded directly. To download CelebA-HQ-256 and prepare the dataset for training models, please run the following lines:

mkdir -p data/celeba/
wget -P data/celeba/ https://openaipublic.azureedge.net/glow-demo/data/celeba-tfr.tar
tar -xvf data/celeba/celeba-tfr.tar -C data/celeba/
python util/convert_tfrecord_to_lmdb.py --dataset=celeba --tfr_path=data/celeba/celeba-tfr --lmdb_path=data/celeba/celeba-lmdb --split=train
python util/convert_tfrecord_to_lmdb.py --dataset=celeba --tfr_path=data/celeba/celeba-tfr --lmdb_path=data/celeba/celeba-lmdb --split=validation

For multi-node training, the following environment variables need to be specified: $IP_ADDR is the IP address of the machine that will host the process with rank 0 during training (see here). $NODE_RANK is the index of each node among all the nodes.

Checkpoints

We provide pre-trained CLD-SGM checkpoints for CIFAR-10 and CelebA-HQ-256 here.

Training and evaluation

• Training our CIFAR-10 model on a single node with one GPU and batch size 64:

python main.py -cc configs/default_cifar10.txt -sc configs/specific_cifar10.txt --root $ROOT --mode train --workdir work_dir/cifar10 --n_gpus_per_node 1 --training_batch_size 64 --testing_batch_size 64 --sampling_batch_size 64

Hidden flags can be found in the config files: configs/default_cifar10.txt and configs/specific_cifar10.txt. The flag --sampling_batch_size indicates the batch size per GPU, whereas --training_batch_size and --eval_batch_size indicate the total batch size of all GPUs combined. The script will update a running checkpoint every --snapshot_freq iterations (saved, in this case, at work_dir/cifar10/checkpoints/checkpoint.pth), starting from --snapshot_threshold. In configs/specific_cifar10.txt, these values are set to 10000 and 1, respectively.

• Training our CIFAR-10 model on two nodes with 8 GPUs each and batch size 128:

mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_cifar10.txt -sc configs/specific_cifar10.txt --root $ROOT --mode train --workdir work_dir/cifar10 --n_gpus_per_node 8 --training_batch_size 8 --testing_batch_size 8 --sampling_batch_size 128 --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'

• To resume training, we simply change the mode from train to continue (two nodes of 8 GPUs):

mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_cifar10.txt -sc configs/specific_cifar10.txt --root $ROOT --mode continue --workdir work_dir/cifar10 --n_gpus_per_node 8 --training_batch_size 8 --testing_batch_size 8 --sampling_batch_size 128 --cont_nbr 1 --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'

Any file within work_dir/cifar10/checkpoints/ can be used to resume training by setting --checkpoint to the particular file name. If --checkpoint is unspecified, the script automatically uses the last snapshot checkpoint (checkpoint.pth) to continue training. The flag --cont_nbr makes sure that a new random seed is used for training continuation; for additional continuation runs --cont_nbr may be incremented by one.

• The following command can be used to evaluate the negative ELBO as well as the FID score (two nodes of 8 GPUs):

mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_cifar10.txt -sc configs/specific_cifar10.txt --root $ROOT --mode eval --workdir work_dir/cifar10 --n_gpus_per_node 8 --training_batch_size 8 --testing_batch_size 8 --sampling_batch_size 128 --eval_folder eval_elbo_and_fid --ckpt_file checkpoint_file --eval_likelihood --eval_fid --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'

Before running this you need to download the FID stats file from here and place it into $ROOT/assets/stats/).

To evaluate our provided CIFAR-10 model download the checkpoint here, create a directory work_dir/cifar10_pretrained_seed_0/checkpoints, place the checkpoint in it, and set --ckpt_file checkpoint_800000.pth as well as --workdir cifar10_pretrained.

• Training the CelebA-HQ-256 model from our paper (two nodes of 8 GPUs and batch size 64):

mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_celeba_paper.txt -sc configs/specific_celeba_paper.txt --root $ROOT --mode train --workdir work_dir/celeba_paper --n_gpus_per_node 8 --training_batch_size 4 --testing_batch_size 4 --sampling_batch_size 64 --data_location data/celeba/celeba-lmdb/ --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'

We found that training of the above model can potentially be unstable. Some modifications that we found post-publication lead to better numerical stability as well as improved performance:

mpirun --allow-run-as-root -np 2 -npernode 1 bash -c 'python main.py -cc configs/default_celeba_post_paper.txt -sc configs/specific_celeba_post_paper.txt --root $ROOT --mode train --workdir work_dir/celeba_post_paper --n_gpus_per_node 8 --training_batch_size 4 --testing_batch_size 4 --sampling_batch_size 64 --data_location data/celeba/celeba-lmdb/ --node_rank $NODE_RANK --n_nodes 2 --master_address $IP_ADDR'

In contrast to the model reported in our paper, we make use of a non-constant time reparameterization function β(t). For more details, please check the config files.

• Training on the multimodal Swiss Roll dataset using a single node with one GPU and batch size 512:

python main.py -cc configs/default_toy_data.txt --root $ROOT --mode train --workdir work_dir/multi_swiss_roll --n_gpus_per_node 1 --training_batch_size 512 --testing_batch_size 512 --sampling_batch_size 512 --dataset multimodal_swissroll

Additional toy datasets can be implemented in util/toy_data.py.

Monitoring the training process

We use Tensorboard to monitor the progress of training. For example, monitoring the CIFAR-10 process can be done as follows:

tensorboard --logdir work_dir/cifar10_seed_0/tensorboard

Demonstration

Load our pretrained checkpoints and play with sampling and likelihood computation:

Link	Description
	CIFAR-10
	CelebA-HQ-256

Citation

If you find the code useful for your research, please consider citing our ICLR paper:

@inproceedings{dockhorn2022score,
  title={Score-Based Generative Modeling with Critically-Damped Langevin Diffusion},
  author={Tim Dockhorn and Arash Vahdat and Karsten Kreis},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2022}
}

License

Copyright © 2022, NVIDIA Corporation. All rights reserved.

This work is made available under the NVIDIA Source Code License. Please see our main LICENSE file.

License Dependencies

For any code dependencies related to StyleGAN2, the license is the Nvidia Source Code License-NC by NVIDIA Corporation, see StyleGAN2 LICENSE.

This code it built on the excellent ScoreSDE codebase by Song et al., which can be found here. For any code dependencies related to ScoreSDE, the license is the Apache License 2.0, see ScoreSDE LICENSE.