  Spiking neural networks (SNNs) have low power consumption and
bio-interpretable characteristics, and are considered to have tremendous
potential for energy-efficient computing. However, the exploration of SNNs on
image generation tasks remains very limited, and a unified and effective
structure for SNN-based generative models has yet to be proposed. In this
paper, we explore a novel diffusion model architecture within spiking neural
networks. We utilize transformer to replace the commonly used U-net structure
in mainstream diffusion models. It can generate higher quality images with
relatively lower computational cost and shorter sampling time. It aims to
provide an empirical baseline for research of generative models based on SNNs.
Experiments on MNIST, Fashion-MNIST, and CIFAR-10 datasets demonstrate that our
work is highly competitive compared to existing SNN generative models.
