  Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented
success in computer vision. However, they remain underutilized in medical
imaging, a field crucial for disease diagnosis and treatment planning. This is
primarily due to the high computational cost associated with (1) the use of
large number of time steps (e.g., 1,000) in diffusion processes and (2) the
increased dimensionality of medical images, which are often 3D or 4D. Training
a diffusion model on medical images typically takes days to weeks, while
sampling each image volume takes minutes to hours. To address this challenge,
we introduce Fast-DDPM, a simple yet effective approach capable of improving
training speed, sampling speed, and generation quality simultaneously. Unlike
DDPM, which trains the image denoiser across 1,000 time steps, Fast-DDPM trains
and samples using only 10 time steps. The key to our method lies in aligning
the training and sampling procedures to optimize time-step utilization.
Specifically, we introduced two efficient noise schedulers with 10 time steps:
one with uniform time step sampling and another with non-uniform sampling. We
evaluated Fast-DDPM across three medical image-to-image generation tasks:
multi-image super-resolution, image denoising, and image-to-image translation.
Fast-DDPM outperformed DDPM and current state-of-the-art methods based on
convolutional networks and generative adversarial networks in all tasks.
Additionally, Fast-DDPM reduced the training time to 0.2x and the sampling time
to 0.01x compared to DDPM. Our code is publicly available at:
https://github.com/mirthAI/Fast-DDPM.
