mlsanityData to 7 Oct 2026

Spiking Diffusion Models

arXiv 2408.16467 ↗First posted 29 Aug 2024cs.NE cs.CVcited by 3

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Recent years have witnessed Spiking Neural Networks (SNNs) gaining attention for their ultra-low energy consumption and high biological plausibility compared with traditional Artificial Neural Networks (ANNs). Despite their distinguished properties, the application of SNNs in the computationally intensive field of image generation is still under exploration. In this paper, we propose the Spiking Diffusion Models (SDMs), an innovative family of SNN-based generative models that excel in producing high-quality samples with significantly reduced energy consumption. In particular, we propose a Temporal-wise Spiking Mechanism (TSM) that allows SNNs to capture more temporal features from a bio-plasticity perspective. In addition, we propose a threshold-guided strategy that can further improve the performances by up to 16.7% without any additional training. We also make the first attempt to use the ANN-SNN approach for SNN-based generation tasks. Extensive experimental results reveal that our approach not only exhibits comparable performance to its ANN counterpart with few spiking time steps, but also outperforms previous SNN-based generative models by a large margin. Moreover, we also demonstrate the high-quality generation ability of SDM on large-scale datasets, e.g., LSUN bedroom. This development marks a pivotal advancement in the capabilities of SNN-based generation, paving the way for future research avenues to realize low-energy and low-latency generative applications. Our code is available at https://github.com/AndyCao1125/SDM.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 58 of its 84 references that are in the map. First posted on arXiv 29 Aug 2024.

Builds on

58 of 84 references.

Closest ancestors · 17

Same direction, newest first

  1. 2023Fully Spiking Denoising Diffusion Implicit Models4 Dec
  2. 2023Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks20 Aug
  3. 2023Spiking Denoising Diffusion Probabilistic Models29 Jun
  4. 2023One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale12 Mar
  5. 2022Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models28 Nov
  6. 2022Classifier-Free Diffusion Guidance26 Jul
  7. 2022EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations14 Jul
  8. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps2 Jun
  9. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  10. 2022Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models17 Jan
  11. 2021Variational Diffusion Models1 Jul
  12. 2021Cascaded Diffusion Models for High Fidelity Image Generation30 May
5 more
  1. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  2. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  4. 2020Denoising Diffusion Implicit Models6 Oct
  5. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 41

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  6. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  7. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  8. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  9. 2016Improved Techniques for Training GANscited by 2,938
  10. 2022Video Diffusion Modelscited by 2,280
  11. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
  12. 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,207
29 more
  1. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  2. 2021Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speechcited by 445
  3. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 427
  4. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  5. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  6. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  7. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 268
  8. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 263
  9. 2018Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithmcited by 233
  10. 2022Versatile Diffusion: Text, Images and Variations All in One Diffusion Modelcited by 199
  11. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
  12. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 174
  13. 2021Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networkscited by 134
  14. 2019Enabling Spike-based Backpropagation for Training Deep Neural Network Architecturescited by 119
  15. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 116
  16. 2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 111
  17. 2022Attention Spiking Neural Networkscited by 103
  18. 2021Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networkscited by 74
  19. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 72
  20. 2015Spiking Deep Networks with LIF Neuronscited by 63
  21. 2021PokeBNN: A Binary Pursuit of Lightweight Accuracycited by 48
  22. 2023Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANNcited by 42
  23. 2021Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium Statecited by 39
  24. 2021Fully Spiking Variational Autoencodercited by 22
  25. 2021Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneckcited by 15
  26. 2021Spiking-GAN: A Spiking Generative Adversarial Network Using Time-To-First-Spike Codingcited by 8
  27. 2021Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learningcited by 8
  28. 2023Spiking Generative Adversarial Network with Attention Scoring Decodingcited by 7
  29. 2021Encoding Event-Based Data With a Hybrid SNN Guided Variational Auto-encoder in Neuromorphic Hardwarecited by 5

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and O(T) Complexitycited by 4
  2. 2025Block Flow: Learning Straight Flow on Data Blockscited by 1
  3. 2025ES-Parkour: Advanced Robot Parkour with Bio-inspired Event Camera and Spiking Neural Networkcited by 1

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