emad2001/back-up-fast-ddpm

Fast-DDPM

Official PyTorch implementation of:

Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation (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-hwGmTqerx/view?usp=drivelink.

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.
• —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{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}
}