All-Addition Spiking Diffusion Models with Attention Enhancement

Diffusion models have achieved state-of-the-art (SoTA) performance in generative tasks through their iterative denoising mechanism, yet they remain computationally intensive and energy-prohibitive. Spiking Neural Networks (SNNs) represent a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs). Their binary spike encoding enables the conversion of multiplication operations into addition operations, which is a key attribute for reducing energy consumption. Integrating the strong generative capabilities of diffusion models with the energy efficiency of SNNs thus forms a highly promising research direction. In adherence to the design goal of all-additive computation, this paper proposes a novel spiking diffusion model with attention enhancement. Specifically, we adopt non-negative ternary spiking neurons (NNTSN) to construct the U-Net backbone, which helps mitigate information loss during feature processing. To align NNTSN with the requirement of using only additive operations, we further design two core components: a membrane potential attention mechanism and an all-additive shortcut module. Leveraging a reparameterization technique, the proposed diffusion model is developed to retain only additive operations during inference, ensuring strict compliance with energy-saving design principles. Extensive experiments on relevant benchmarks demonstrate that our method achieves SoTA performance in generative tasks. It also substantially outperforms other SNN-based generative models while using fewer time steps, which validates both its effectiveness and efficiency.