Normalizing Flow with Diffusion Prior Model (NFDPM)

Welcome to the repository for my Master's thesis project at Technical University of Munich (TUM), titled "Diffusion Models as Latent Priors in Generative Models ". This project aims to explore the use of Normalizing Flows and Diffusion Probabilistic Models for efficient and accurate data sampling, which is a crucial task in many machine learning applications. The Normalizing Flow with Diffusion Prior Model (NFDPM) combines the strengths of both models and offers promising results for high-dimensional data synthesis. This repository contains the implementation of the NFDPM along with the experimental results and evaluations on various benchmark datasets. Access my Master's thesis here. Feedback is always welcome!

How to run

Create a virtual environment and install the requirements:

conda create -n env_diff_prior python=3.8
conda activate env_diff_prior

conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
pip install -r requirements.txt

Download the datasets by running ./data/get_datasets.sh bash script.

Precompute statistics for FID and KID evaluation:

python metrics/precompute_stats.py --action=precompute --data_root=<path-to-datasets-folder>

Experiments

Run a baseline Normalizing Flow experiment on MNIST

python run_baseline_experiment.py \
    hydra.job.chdir=True \
    phase=train \
    seed=42 \
    experiment_name=1_MNIST_base_42_L3_K4 \
    data.name=MNIST \
    data.root=<path-to-datasets-folder> \
    data.batch_size=64 \
    data.num_workers=0 \
    data.img_size=32 \
    data.transformations='[]' \
    model.architecture.L=3 \
    model.architecture.K=4 \
    model.training.epochs=1000 \
    model.training.print_freq=20 \
    model.training.save_checkpoint_freq=500 \
    model.training.n_bits=5 \
    model.training.temperature=0.7 \
    model.optimizer.type=adam \
    model.optimizer.lr=1e-4 \
    model.logging.log_gen_images_per_iter=20

Run a baseline Normalizing Flow experiment on CIFAR-10

python run_baseline_experiment.py \
    hydra.job.chdir=True \
    data.name=cifar10 \
    data.root=<path-to-datasets-folder> \
    experiment_name=2_CIFAR10_base_42_L3_K16 \
    seed=42 \
    phase=train \
    data.batch_size=128 \
    data.num_workers=0 \
    data.img_size=32 \
    data.transformations='["RandomHorizontalFlip"]' \
    model.architecture.L=3 \
    model.architecture.K=16 \
    model.training.epochs=1000 \
    model.training.print_freq=20 \
    model.training.save_checkpoint_freq=500 \
    model.training.n_bits=5 \
    model.training.temperature=0.7 \
    model.optimizer.type=adam \
    model.optimizer.lr=1e-4 \
    model.logging.log_gen_images_per_iter=10 \
    model.evaluation.metrics.FID.mode='["legacy_tensorflow"]' \
    model.evaluation.metrics.FID.model_name='["inception_v3"]'

Run a baseline Normalizing Flow experiment on ImageNet32

python run_baseline_experiment.py \
    hydra.job.chdir=True \
    data.name=imagenet32 \
    data.root=<path-to-datasets-folder> \
    experiment_name=3_imagenet32_base_42_L3_K16 \
    seed=42 \
    phase=train \
    data.batch_size=128 \
    data.num_workers=0 \
    data.img_size=32 \
    data.transformations='[]' \
    model.architecture.L=3 \
    model.architecture.K=16 \
    model.training.epochs=40 \
    model.training.print_freq=20 \
    model.training.save_checkpoint_freq=40 \
    model.training.n_bits=5 \
    model.training.temperature=0.7 \
    model.optimizer.type=adam \
    model.optimizer.lr=1e-4 \
    model.logging.log_gen_images_per_iter=400 \
    model.evaluation.metrics.FID.mode='["legacy_tensorflow","clean"]' \
    model.evaluation.metrics.FID.model_name='["inception_v3","inception_v3"]' \
    model.evaluation.metrics.KID.mode='["legacy_tensorflow","clean"]' \
    model.evaluation.metrics.KID.model_name='["inception_v3","inception_v3"]'

Run a baseline Normalizing Flow experiment on CelebA

python run_baseline_experiment.py \
    hydra.job.chdir=True \
    data.name=celeba \
    data.root=<path-to-datasets-folder> \
    experiment_name=4_celeba_base_42_L5_K16 \
    seed=42 \
    phase=train \
    data.batch_size=8 \
    data.num_workers=0 \
    data.img_size=128 \
    data.transformations='[]' \
    model.architecture.L=5 \
    model.architecture.K=16 \
    model.training.epochs=10 \
    model.training.print_freq=20 \
    model.training.save_checkpoint_freq=10 \
    model.training.n_bits=5 \
    model.training.temperature=0.7 \
    model.optimizer.type=adam \
    model.optimizer.lr=1e-4 \
    model.logging.log_gen_images_per_iter=1000 \
    model.evaluation.metrics.FID.mode='["legacy_tensorflow","clean","legacy_tensorflow","clean"]' \
    model.evaluation.metrics.FID.model_name='["inception_v3","inception_v3","clip_vit_b_32","clip_vit_b_32"]' \
    model.evaluation.metrics.KID.mode='["legacy_tensorflow","clean","legacy_tensorflow","clean"]' \
    model.evaluation.metrics.KID.model_name='["inception_v3","inception_v3","clip_vit_b_32","clip_vit_b_32"]'

Run a Diffusion prior experiment on MNIST

python run_diffusion_prior_experiment.py \
    hydra.job.chdir=True \
    data.name=MNIST \
    data.root=<path-to-datasets-folder> \
    experiment_name=5_MNIST_diff_42_L3_K4_frozen_identity \
    seed=42 \
    phase=train \
    data.batch_size=128 \
    data.num_workers=0 \
    data.img_size=32 \
    data.transformations='[]' \
    model.normalizing_flow.init_nf.mode=pretrain \
    model.normalizing_flow.init_nf.pretrain.dir=1_MNIST_base_42_L3_K4 \
    model.normalizing_flow.init_nf.pretrain.epoch=1000 \
    model.normalizing_flow.freeze=True \
    model.normalizing_flow.lr=1e-7 \
    model.normalizing_flow.latent_formater=IdentityFormater \
    model.unet.dim=64 \
    model.unet.dim_mults='[1, 2]' \
    model.unet.resnet_block_groups=8 \
    model.unet.learned_sinusoidal_cond=False \
    model.unet.random_fourier_features=False \
    model.unet.learned_sinusoidal_dim=16 \
    model.diffusion.timesteps=1000 \
    model.diffusion.sampling_timesteps=1000 \
    model.diffusion.loss_type=l2 \
    model.diffusion.beta_schedule=cosine \
    model.diffusion.ddim_sampling_eta=1.0 \
    model.training.epochs=300 \
    model.training.print_freq=40 \
    model.training.save_checkpoint_freq=100 \
    model.training.n_bits=5 \
    model.training.temperature=1.0 \
    model.logging.log_gen_images_per_iter=10 \
    model.logging.log_param_distribution=False \
    model.optimizer.lr=1e-5

Run a Diffusion prior experiment on CIFAR-10

python run_diffusion_prior_experiment.py \
    hydra.job.chdir=True \
    data.name=cifar10 \
    data.root=<path-to-datasets-folder> \
    experiment_name=6_CIFAR10_diff_42_L3_K16_frozen_identity \
    seed=42 \
    phase=train \
    data.batch_size=128 \
    data.num_workers=0 \
    data.img_size=32 \
    data.transformations='["RandomHorizontalFlip"]' \
    model.normalizing_flow.init_nf.mode=pretrain \
    model.normalizing_flow.init_nf.pretrain.dir=2_CIFAR10_base_42_L3_K16 \
    model.normalizing_flow.init_nf.pretrain.epoch=1000 \
    model.normalizing_flow.freeze=True \
    model.normalizing_flow.lr=1e-4 \
    model.normalizing_flow.latent_formater=IdentityFormater \
    model.unet.dim=64 \
    model.unet.dim_mults='[1, 2]' \
    model.unet.resnet_block_groups=8 \
    model.unet.learned_sinusoidal_cond=False \
    model.unet.random_fourier_features=False \
    model.unet.learned_sinusoidal_dim=16 \
    model.diffusion.timesteps=1000 \
    model.diffusion.sampling_timesteps=1000 \
    model.diffusion.loss_type=l2 \
    model.diffusion.beta_schedule=cosine \
    model.diffusion.ddim_sampling_eta=1.0 \
    model.training.epochs=300 \
    model.training.print_freq=20 \
    model.training.save_checkpoint_freq=100 \
    model.training.n_bits=5 \
    model.training.temperature=1.0 \
    model.logging.log_param_distribution=False \
    model.optimizer.lr=1e-5 \
    model.logging.log_gen_images_per_iter=10 \
    model.evaluation.metrics.FID.mode='["legacy_tensorflow"]' \
    model.evaluation.metrics.FID.model_name='["inception_v3"]'

Run a Diffusion prior experiment on ImageNet32

python run_diffusion_prior_experiment.py \
    hydra.job.chdir=True \
    data.name=imagenet32 \
    data.root=<path-to-datasets-folder> \
    experiment_name=7_imagenet32_diff_42_L3_K16_frozen_identity \
    seed=42 \
    phase=train \
    data.batch_size=64 \
    data.num_workers=0 \
    data.img_size=32 \
    data.transformations='[]' \
    model.normalizing_flow.init_nf.mode=pretrain \
    model.normalizing_flow.init_nf.pretrain.dir=3_imagenet32_base_42_L3_K16 \
    model.normalizing_flow.init_nf.pretrain.epoch=40 \
    model.normalizing_flow.freeze=True \
    model.normalizing_flow.lr=1e-4 \
    model.normalizing_flow.latent_formater=IdentityFormater \
    model.unet.dim=64 \
    model.unet.dim_mults='[1, 2]' \
    model.unet.resnet_block_groups=16 \
    model.unet.learned_sinusoidal_cond=False \
    model.unet.random_fourier_features=False \
    model.unet.learned_sinusoidal_dim=16 \
    model.diffusion.timesteps=1000 \
    model.diffusion.sampling_timesteps=1000 \
    model.diffusion.loss_type=l2 \
    model.diffusion.beta_schedule=cosine \
    model.diffusion.ddim_sampling_eta=1.0 \
    model.training.epochs=30 \
    model.training.print_freq=20 \
    model.training.save_checkpoint_freq=30 \
    model.training.n_bits=5 \
    model.training.temperature=1.0 \
    model.logging.log_param_distribution=False \
    model.optimizer.lr=1e-5 \
    model.logging.log_gen_images_per_iter=800 \
    model.evaluation.metrics.FID.mode='["legacy_tensorflow","clean"]' \
    model.evaluation.metrics.FID.model_name='["inception_v3","inception_v3"]' \
    model.evaluation.metrics.KID.mode='["legacy_tensorflow","clean"]' \
    model.evaluation.metrics.KID.model_name='["inception_v3","inception_v3"]'

Run a Diffusion Prior experiment on CelebA

python run_diffusion_prior_experiment.py \
    hydra.job.chdir=True \
    data.name=celeba \
    data.root=<path-to-datasets-folder> \
    experiment_name=8_celeba_diff_42_L5_K16_frozen_identity \
    seed=42 \
    phase=train \
    data.batch_size=8 \
    data.num_workers=0 \
    data.img_size=128 \
    data.transformations='[]' \
    model.normalizing_flow.init_nf.mode=pretrain \
    model.normalizing_flow.init_nf.pretrain.dir=4_celeba_base_42_L5_K16 \
    model.normalizing_flow.init_nf.pretrain.epoch=10 \
    model.normalizing_flow.freeze=True \
    model.normalizing_flow.lr=1e-4 \
    model.normalizing_flow.latent_formater=IdentityFormater \
    model.unet.dim=64 \
    model.unet.dim_mults='[1, 2, 2]' \
    model.unet.resnet_block_groups=16 \
    model.unet.learned_sinusoidal_cond=False \
    model.unet.random_fourier_features=False \
    model.unet.learned_sinusoidal_dim=16 \
    model.diffusion.timesteps=1000 \
    model.diffusion.sampling_timesteps=1000 \
    model.diffusion.loss_type=l2 \
    model.diffusion.beta_schedule=cosine \
    model.diffusion.ddim_sampling_eta=1.0 \
    model.training.epochs=10 \
    model.training.print_freq=80 \
    model.training.save_checkpoint_freq=10 \
    model.training.n_bits=5 \
    model.training.temperature=1.0 \
    model.logging.log_param_distribution=False \
    model.optimizer.lr=1e-5 \
    model.logging.log_gen_images_per_iter=250 \
    model.evaluation.metrics.FID.mode='["legacy_tensorflow","clean","legacy_tensorflow","clean"]' \
    model.evaluation.metrics.FID.model_name='["inception_v3","inception_v3","clip_vit_b_32","clip_vit_b_32"]' \
    model.evaluation.metrics.KID.mode='["legacy_tensorflow","clean","legacy_tensorflow","clean"]' \
    model.evaluation.metrics.KID.model_name='["inception_v3","inception_v3","clip_vit_b_32","clip_vit_b_32"]'

Monitoring

Launch a dashboard to monitor experiments:

cd ./outputs/nf_experiments/aim
aim up

Useful resources

I am deeply grateful for the following open-source repositories which have been instrumental in facilitating my research:

License

Licensed under an MIT license.

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
configs		configs
data		data
diffusion_prior		diffusion_prior
media		media
metrics		metrics
normalizing_flow		normalizing_flow
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt
run_baseline_experiment.py		run_baseline_experiment.py
run_diffusion_prior_experiment.py		run_diffusion_prior_experiment.py
utils.py		utils.py

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