Awesome

Fast-DDPM

Official PyTorch implementation of:

Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation

We propose Fast-DDPM, a simple yet effective approach that improves training speed, sampling speed, and generation quality of diffusion models simultaneously. Fast-DDPM trains and samples using only 10 time steps, reducing the training time to 0.2x and the sampling time to 0.01x compared to DDPM.

The code is only for research purposes. If you have any questions regarding how to use this code, feel free to contact Hongxu Jiang (hongxu.jiang@medicine.ufl.edu).

Requirements

• Python==3.10.6
• torch==1.12.1
• torchvision==0.15.2
• numpy
• opencv-python
• tqdm
• tensorboard
• tensorboardX
• scikit-image
• medpy
• pillow
• scipy
• pip install -r requirements.txt

Publicly available Dataset

• Prostate-MRI-US-Biopsy dataset
• LDCT-and-Projection-data dataset
• BraTS 2018 dataset
• The processed dataset can be accessed here: https://drive.google.com/file/d/1kF0g8fMR5XPQ2FTbutfTQ-hwG_mTqerx/view?usp=drive_link.

Usage

1. Git clone or download the codes.

2. Pretrained model weights

• We provide pretrained model weights for all three tasks, where you can access them here: https://drive.google.com/file/d/1ndS-eLegqwCOUoLT1B-HQiqRQqZUMKVF/view?usp=sharing.
• As shown in ablation study, the defaulted 10 time steps may not be optimal for every task, you're more welcome to train Fast-DDPM model on your dataset using different settings.

3. Prepare data

• Please download our processed dataset or download from the official websites.
• After downloading, extract the file and put it into folder "data/". The directory structure should be as follows:

├── configs
│
├── data
│	├── LD_FD_CT_train
│	├── LD_FD_CT_test
│	├── PMUB-train
│	├── PMUB-test
│	├── Brats_train
│	└── Brats_test
│
├── datasets
│
├── functions
│
├── models
│
└── runners

4. Training/Sampling a Fast-DDPM model

• Please make sure that the hyperparameters such as scheduler type and timesteps are consistent between training and sampling.
• The total number of time steps is defaulted as 1000 in the paper, so the number of involved time steps for Fast-DDPM should be less than 1000 as an integer.

python fast_ddpm_main.py --config {DATASET}.yml --dataset {DATASET_NAME} --exp {PROJECT_PATH} --doc {MODEL_NAME} --scheduler_type {SAMPLING STRATEGY} --timesteps {STEPS}

python fast_ddpm_main.py --config {DATASET}.yml --dataset {DATASET_NAME} --exp {PROJECT_PATH} --doc {MODEL_NAME} --sample --fid --scheduler_type {SAMPLING STRATEGY} --timesteps {STEPS}

where

• DATASET_NAME should be selected among LDFDCT for image denoising task, BRATS for image-to-image translation task and PMUB for multi image super-resolution task.
• SAMPLING STRATEGY controls the scheduler sampling strategy proposed in the paper (either uniform or non-uniform).
• STEPS controls how many timesteps used in the training and inference process. It should be an integer less than 1000 for Fast-DDPM, which is 10 by default.

5. Training/Sampling a DDPM model

• Please make sure that the hyperparameters such as scheduler type and timesteps are consistent between training and sampling.
• The total number of time steps is defaulted as 1000 in the paper, so the number of time steps for DDPM is defaulted as 1000.

python ddpm_main.py --config {DATASET}.yml --dataset {DATASET_NAME} --exp {PROJECT_PATH} --doc {MODEL_NAME} --timesteps {STEPS}

python ddpm_main.py --config {DATASET}.yml --dataset {DATASET_NAME} --exp {PROJECT_PATH} --doc {MODEL_NAME} --sample --fid --timesteps {STEPS}

where

• DATASET_NAME should be selected among LDFDCT for image denoising task, BRATS for image-to-image translation task and PMUB for multi image super-resolution task.
• STEPS controls how many timesteps used in the training and inference process. It should be 1000 in the setting of this paper.

References

• The code is mainly adapted from DDIM.

Citations

If you use our code or dataset, please cite our paper as below:

@article{jiang2024fast,
  title={Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation},
  author={Jiang, Hongxu and Imran, Muhammad and Ma, Linhai and Zhang, Teng and Zhou, Yuyin and Liang, Muxuan and Gong, Kuang and Shao, Wei},
  journal={arXiv preprint arXiv:2405.14802},
  year={2024}
}