| # Fast-DDPM | |
| Official PyTorch implementation of: | |
| [Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation](https://ieeexplore.ieee.org/abstract/document/10979336) (JBHI 2025) | |
| 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. | |
| <p align="center"> | |
| <img src="Overview.png" alt="DDPM vs. Fast-DDPM" width="750"> | |
| </p> | |
| 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. | |
| * Pretrained model weights are also available on [Hugging Face](https://huggingface.co/SebastianJiang/FastDDPM). | |
| * 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: | |
| ```bash | |
| ├── 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](https://github.com/ermongroup/ddim). | |
| ## Citations | |
| If you use our code or dataset, please cite our paper as below: | |
| ```bibtex | |
| @article{jiang2025fast, | |
| title={Fast-DDPM: Fast denoising diffusion probabilistic models for medical image-to-image generation}, | |
| author={Jiang, Hongxu and Imran, Muhammad and Zhang, Teng and Zhou, Yuyin and Liang, Muxuan and Gong, Kuang and Shao, Wei}, | |
| journal={IEEE Journal of Biomedical and Health Informatics}, | |
| year={2025}, | |
| publisher={IEEE} | |
| } | |
| ``` | |