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Spiking neural networks (SNNs) have tremendous potential for energy-efficientneuromorphic chips due to their binary and event-driven architecture. SNNs havebeen primarily used in classification tasks, but limited exploration on imagegeneration tasks. To fill the gap, we propose a Spiking-Diffusion model, whichis based on the vector quantized discrete diffusion model. First, we develop avector quantized variational autoencoder with SNNs (VQ-SVAE) to learn adiscrete latent space for images. In VQ-SVAE, image features are encoded usingboth the spike firing rate and postsynaptic potential, and an adaptive spikegenerator is designed to restore embedding features in the form of spiketrains. Next, we perform absorbing state diffusion in the discrete latent spaceand construct a spiking diffusion image decoder (SDID) with SNNs to denoise theimage. Our work is the first to build the diffusion model entirely from SNNlayers. Experimental results on MNIST, FMNIST, KMNIST, Letters, and Cifar10demonstrate that Spiking-Diffusion outperforms the existing SNN-basedgeneration model. We achieve FIDs of 37.50, 91.98, 59.23, 67.41, and 120.5 onthe above datasets respectively, with reductions of 58.60\%, 18.75\%, 64.51\%,29.75\%, and 44.88\% in FIDs compared with the state-of-art work. Our code willbe available at \url{https://github.com/Arktis2022/Spiking-Diffusion}.

Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks