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Table of contents
  1. Environment setup
  2. Dataset preparation
  3. Training
  4. Evaluation
  5. Contacts

Official code for ECCV 2024 paper “A high-quality robust diffusion framework for corrupted dataset”

Abstract: Developing image-generative models, which are robust to outliers in the training process, has recently drawn attention from the research community. Due to the ease of integrating unbalanced optimal transport (UOT) into adversarial frameworks, existing works focus mainly on developing robust frameworks for generative adversarial model (GAN). Meanwhile, diffusion models have recently dominated GAN in various tasks and datasets. However, according to our knowledge, none of them are robust to corrupted datasets. Motivated by DDGAN, our work introduces the first robust-to-outlier diffusion. We suggest replacing the UOT-based generative model for GAN in DDGAN to learn the backward diffusion process. Additionally, we demonstrate that the Lipschitz property of divergence in our framework contributes to more stable training convergence. Remarkably, our method not only exhibits robustness to corrupted datasets but also achieves superior performance on clean datasets.

TLDR: This work introduces the first robust-to-outlier diffusion and suggests replacing the UOT-based generative model for GAN in DDGAN to learn the backward diffusion process, and demonstrates that the Lipschitz property of divergence in the framework contributes to more stable training convergence.

Details of algorithms, experimental results, and configurations can be found in our following paper:

@inproceedings{dao2025high,
  title={A High-Quality Robust Diffusion Framework for Corrupted Dataset},
  author={Dao, Quan and Ta, Binh and Pham, Tung and Tran, Anh},
  booktitle={European Conference on Computer Vision},
  pages={107--123},
  year={2025},
  organization={Springer}
}

Please CITE our paper whenever this repository is used to help produce published results or incorporated into other software.

Environment setup

First, install Pytorch v1.12.1:

pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116

Then, install other modules using:

pip install -r requirements.txt

Set up Datasets

We use these datasets to create corrupted datasets:

  • CIFAR-10
  • STL-10
  • LSUN Church Outdoor 256
  • CelebA HQ 256
  • MNIST
  • FashionMNIST

For MNIST, FashionMNIST, CIFAR-10, and STL-10, they will be automatically downloaded in the first time execution.

For CelebA HQ 256 and LSUN, please check out here for dataset preparation.

Once a dataset is downloaded, please put it in data/ directory as follows:

data/
├── cifar-10-batches-py
├── STL-10
├── celeba-lmdb
├── LSUN
├── mnist 
└── fashion_mnist

Each corrupted dataset consists of two components: clean data and outliers. In our paper, we use CIFAR-10, STL-10, or CelebA HQ 256 as the clean data; and any other datasets as the outliers.

Training

We use the following commands to train our proposed model:

CIFAR-10 perturbed by MNIST (3%)

python3 train.py --dataset cifar10 --batch_size 256 --num_channels_dae 128 --num_epoch 1800 --r1_gamma 0.02 --lr_d 1.25e-4 --lr_g 1.6e-4 --lazy_reg 15 --ch_mult 1 2 2 2 --version bs256 --perturb_dataset mnist --perturb_percent 3

STL-10

python3 train.py --dataset stl10 --image_size 64 --num_channels_dae 128 --ch_mult 1 2 2 2 --batch_size 72 --num_epoch 1800 --r1_gamma 0.02 --lr_d 1.25e-4 --lr_g 1.6e-4 --lazy_reg 15 --version bs64 --tau 1e-4

CelebA HQ 256

CUDA_VISIBLE_DEVICES=0,1 python3 train.py --image_size 256 --dataset celeba_hq --num_timesteps 2 --batch_size 12 --r1_gamma 2.0 --lazy_reg 10 --num_process_per_node 2 --ch_mult 1 1 2 2 4 4 --version bs12 --tau 1e-7 --schedule 800

CelebA-64 perturbed by FashionMNIST (5%)

python3 train.py --dataset celeba_hq --image_size 64 --num_channels_dae 96 --num_timesteps 2 --batch_size 72 --num_epoch 800 --r1_gamma 0.02 --lr_d 1e-4 --lr_g 1.6e-4 --lazy_reg 15 --ch_mult 1 2 2 2 4 --perturb_dataset fashion_mnist --perturb_percent 5 --tau 0.0003

CelebA-64 perturbed by LSUN Church (5%)

python3 train.py --dataset celeba_hq --image_size 64 --num_channels_dae 96 --num_timesteps 2 --batch_size 72 --num_epoch 800 --r1_gamma 0.02 --lr_d 1e-4 --lr_g 1.6e-4 --lazy_reg 15 --ch_mult 1 2 2 2 4 --perturb_dataset lsun --perturb_percent 5 --tau 0.0003

Meaning of hyperparameters:

--dataset: Name of the clean dataset

--batch_size: Batch size at each training iteration

--image_size: Size of images

--num_channels_dae: Number of initial channels in denosing model

--ch_mult: Channel multiplier per scale

--num_epoch #: Number of epochs for training

--r1_gamma #: Coefficient for R1 regularization

--lr_d #: Learning rate for potential (discriminator) network

--lr_g #: Learning rate for generator network

--lazy_reg #: Number of training iterations for each regularization

--num_process_per_node #: Number of GPUs

--version: Training version (name of experiment)

--tau #: Proportion of the cost c in UOT

--schedule #: Number of beginning epochs for cosine scheduler

--perturb_dataset: Name of the outlier dataset

--perturb_percent: Percentage of perturbed training samples

--num_timesteps: Number of timesteps to generate samples

Note: Remove --perturb_dataset and --perturb_percent for a clean training dataset. Hyperparameters with # are only needed in training phase.

Evaluation

After training, samples can be generated by calling test.py.

You can also download pretrained checkpoints from this link. Each checkpoint should be placed in the following directory:

saved_info/rduot/<dataset>_<perturb_percent>p_<perturb_dataset>/<version>/

Remove _<perturb_percent>p_<perturb_dataset> if training dataset does not contain any outliers. For example, directories containing checkpoints for these experiments are:

CIFAR-10 perturbed by MNIST (3%):

saved_info/rduot/cifar10_3p_mnist/bs256/

STL-10 (without perturbed dataset):

saved_info/rduot/stl10/bs72/

We use --epoch_start (first epoch), --epoch_end (last epoch), --epoch_jump (number of epochs before the next evaluation) to specify the checkpoint saved at a particular epoch; and --compute_fid to calculate FID score. All other hyperparameters have the same meaning as in the training phase.

We use the following commands to evaluate the models:

CIFAR-10 perturbed by MNIST (3%)

python3 test.py --dataset cifar10 --ch_mult 1 2 2 2 --num_timesteps 4 --num_channels_dae 128 --version bs256 --compute_fid --epoch_start 1200 --epoch_end 1800 --epoch_jump 25 --perturb_dataset mnist --perturb_percent 3

STL-10

python3 test.py --dataset stl10 --image_size 64 --ch_mult 1 2 2 2 --num_timesteps 4 --num_channels_dae 128 --version bs72 --compute_fid --epoch_start 1200 --epoch_end 1800 --epoch_jump 25

CelebA HQ 256

python3 test.py --dataset celeba_hq --image_size 256 --ch_mult 1 1 2 2 4 4 --num_timesteps 2 --version bs12 --compute_fid --epoch_start 500 --epoch_end 800 --epoch_jump 25

CelebA-64 perturbed by FashionMNIST (5%)

python3 test.py --dataset celeba_hq --image_size 64 --num_channels_dae 96 --ch_mult 1 2 2 2 4 --num_timesteps 2 --version bs12 --compute_fid --epoch_start 500 --epoch_end 800 --epoch_jump 25 --perturb_dataset fashion_mnist --perturb_percent 5

CelebA-64 perturbed by LSUN Church (5%)

python3 test.py --dataset celeba_hq --image_size 64 --num_channels_dae 96 --ch_mult 1 2 2 2 4 --num_timesteps 2 --version bs12 --compute_fid --epoch_start 500 --epoch_end 800 --epoch_jump 25 --perturb_dataset lsun --perturb_percent 5

We use the PyTorch implementation to compute the FID scores, and in particular, codes for computing the FID are adapted from FastDPM.

For Improved Precision and Recall, follow the instructions here.

Contacts

If you have any problems, please open an issue in this repository or send an email to [email protected] or [email protected].