TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation

This software project accompanies the research paper TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation

Citation:

@article{berthelot2023tract,
  title={TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation},
  author={Berthelot, David and Autef, Arnaud and Lin, Jierui and Yap, Dian Ang and Zhai, Shuangfei and Hu, Siyuan and Zheng, Daniel and Talbott, Walter and Gu, Eric},
  journal={arXiv preprint arXiv:2303.04248},
  year={2023}
}

Setup

Git clone with --recurse-submodules to initialize EDM submodule properly.

Setup environment variables:

export ML_DATA=~/Data/DDPM-Images
export PYTHONPATH=$PYTHONPATH:.

Then run

sudo apt install python3.8-dev python3.8-venv python3-dev -y

Set up a virtualenv

python3.8 -m venv ~/tract_venv
source ~/tract_venv/bin/activate

or via pyenv

pyenv install 3.8.0
pyenv virtualenv 3.8.0 tract_venv
pyenv local tract_venv

then upgrade pip

pip install --upgrade pip

Install pip pkgs

pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html

Setup data

Please follow README to setup datasets.

Set up teacher models

Please follow README to setup teacher checkpoints.

Set up EDM

For running with NVIDIA's Elucidated model (EDM), ensure the edm/ submodule has been initialized properly.

Real activation stats for FID

Please follow the Real activation statistics section in README in order to compute and save the real activation statistics to be used in FID evaluation.

Training

The below commands will reproduce results from our paper when run on a cluster of 8 NVIDIA A100 or V100 GPUs.

Example: Run TC distillation on Cifar10 using distillation time schedule: 1024, 32, 1.

python tc_distill.py --dataset=cifar10 --time_schedule=1024,32,1 --fid_len=50000 --report_fid_len=8M --report_img_len=8M --train_len=96M

Example: Run TC distillation on Cifar10 using EDM teacher

python tc_distill_edm.py --dataset=cifar10 --time_schedule=40,1 --fid_len=50000 --report_fid_len=8M --report_img_len=8M --train_len=96M --batch=512

Getting help

python tc_distill.py --help

Tensorboard outputs are generated in a dir like e/DATASET/MODEL/EXP_NAME/tb/. For example, you can start tensorboard to view metrics like

tensorboard --logdir e/cifar10/EluDDIM05TCMultiStepx0\(EluUNet\)/[email protected]_batch@[email protected][email protected][email protected]_lr_warmup@None_res@[email protected]_time_schedule@40,1_timesteps@40/tb/ --bind_all

Evaluation

Please follow README to run FID evaluation.