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}