Papers › Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks
Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks
Mingxuan Liu, Jie Gan, Rui Wen, Tao Li, Yongli Chen, Hong Chen
Spiking neural networks (SNNs) have tremendous potential for energy-efficient neuromorphic chips due to their binary and event-driven architecture. SNNs have been primarily used in classification tasks, but limited exploration on image generation tasks. To fill the gap, we propose a Spiking-Diffusion model, which is based on the vector quantized discrete diffusion model. First, we develop a vector quantized variational autoencoder with SNNs (VQ-SVAE) to learn a discrete latent space for images. In VQ-SVAE, image features are encoded using both the spike firing rate and postsynaptic potential, and an adaptive spike generator is designed to restore embedding features in the form of spike trains. Next, we perform absorbing state diffusion in the discrete latent space and construct a spiking diffusion image decoder (SDID) with SNNs to denoise the image. Our work is the first to build the diffusion model entirely from SNN layers. Experimental results on MNIST, FMNIST, KMNIST, Letters, and Cifar10 demonstrate that Spiking-Diffusion outperforms the existing SNN-based generation model. We achieve FIDs of 37.50, 91.98, 59.23, 67.41, and 120.5 on the 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 will be available at \url{https://github.com/Arktis2022/Spiking-Diffusion}.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | EMNIST-Letters | Spiking-Diffusion | FID | 67.41 | #1 of 1 | Archive leaderboard | report |
| Image Generation | Fashion-MNIST | Spiking-Diffusion | FID | 91.98 | #7 of 7 | Archive leaderboard | report |
| Image Generation | KMNIST | Spiking-Diffusion | FID | 59.23 | #1 of 1 | Archive leaderboard | report |
| Image Generation | MNIST | Spiking-Diffusion | FID | 27.61 | #12 of 15 | Archive leaderboard | report |
| Image Generation | MNIST | Spiking-Diffusion | Precision | 0.83 | #12 of 15 | Archive leaderboard | report |
| Image Generation | MNIST | Spiking-Diffusion | Recall | 0.65 | #12 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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