mlsanityData to 14 Oct 2026

SDiT: Spiking Diffusion Model with Transformer

arXiv 2402.11588 ↗First posted 18 Feb 2024cs.CV cs.AIcited by 4

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 32 of its 38 references that are in the map. First posted on arXiv 18 Feb 2024.

Builds on

32 of 38 references.

Closest ancestors · 10

Same direction, newest first

  1. 2023Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks20 Aug
  2. 2023Spiking Denoising Diffusion Probabilistic Models29 Jun
  3. 2022Scalable Diffusion Models with Transformers19 Dec
  4. 2022All are Worth Words: A ViT Backbone for Diffusion Models25 Sep
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  6. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  7. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  8. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  9. 2020Denoising Diffusion Implicit Models6 Oct
  10. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2016Layer Normalizationcited by 5,669
  5. 2017Neural Discrete Representation Learningcited by 4,712
  6. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  7. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  8. 2013Auto-Encoding Variational Bayescited by 4,053
  9. 2016Improved Techniques for Training GANscited by 2,970
  10. 2023RWKV: Reinventing RNNs for the Transformer Eracited by 827
  11. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  12. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
10 more
  1. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 278
  2. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 271
  3. 2023SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligencecited by 169
  4. 2021An Attention Free Transformercited by 112
  5. 2023SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networkscited by 88
  6. 2021Advancing Spiking Neural Networks towards Deep Residual Learningcited by 79
  7. 2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 77
  8. 2021Fully Spiking Variational Autoencodercited by 22
  9. 2021Spiking-GAN: A Spiking Generative Adversarial Network Using Time-To-First-Spike Codingcited by 8
  10. 2023Spiking Generative Adversarial Network with Attention Scoring Decodingcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directionscited by 7
  2. 2024A Survey of RWKVcited by 6
  3. 2025Biologically Inspired Spiking Diffusion Model with Adaptive Lateral Selection Mechanismcited by 0
  4. 2026When Geometry Aligns: Dihedral Hidden-State Transformations in UNet, ViT, and DiT Architecturescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Spiking Diffusion Models
  2. 2023Fully Spiking Denoising Diffusion Implicit Models
  3. 2024Brain-inspired Action Generation with Spiking Transformer Diffusion Policy Model
  4. 2025CSDformer: A Conversion Method for Fully Spike-Driven Transformer
  5. 2023When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks
  6. 2025STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking
  7. 2022A Faster Approach to Spiking Deep Convolutional Neural Networks
  8. 2025Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control