mlsanityData to 7 Oct 2026

Interpretable ODE-style Generative Diffusion Model via Force Field Construction

arXiv 2303.08063 ↗First posted 14 Mar 2023cs.CVcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

For a considerable time, researchers have focused on developing a method that establishes a deep connection between the generative diffusion model and mathematical physics. Despite previous efforts, progress has been limited to the pursuit of a single specialized method. In order to advance the interpretability of diffusion models and explore new research directions, it is essential to establish a unified ODE-style generative diffusion model. Such a model should draw inspiration from physical models and possess a clear geometric meaning. This paper aims to identify various physical models that are suitable for constructing ODE-style generative diffusion models accurately from a mathematical perspective. We then summarize these models into a unified method. Additionally, we perform a case study where we use the theoretical model identified by our method to develop a range of new diffusion model methods, and conduct experiments. Our experiments on CIFAR-10 demonstrate the effectiveness of our approach. We have constructed a computational framework that attains highly proficient results with regards to image generation speed, alongside an additional model that demonstrates exceptional performance in both Inception score and FID score. These results underscore the significance of our method in advancing the field of diffusion models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 25 of its 33 references that are in the map. First posted on arXiv 14 Mar 2023.

Builds on

25 of 33 references.

Closest ancestors · 6

Same direction, newest first

  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applications2 Sep
  2. 2021Score-based Generative Modeling in Latent Space10 Jun
  3. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  5. 2020Denoising Diffusion Implicit Models6 Oct
  6. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2022Flow Matching for Generative Modelingcited by 5,426
  4. 2013Auto-Encoding Variational Bayescited by 3,980
  5. 2016Improved Techniques for Training GANscited by 2,938
  6. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  7. 2017Improved Training of Wasserstein GANscited by 2,223
  8. 2015Variational Inference with Normalizing Flowscited by 2,161
  9. 2016Density estimation using Real NVPcited by 2,029
  10. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  11. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  12. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
7 more
  1. 2014Semi-Supervised Learning with Deep Generative Modelscited by 494
  2. 2019Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Designcited by 258
  3. 2018Do CIFAR-10 Classifiers Generalize to CIFAR-10?cited by 197
  4. 2022Poisson Flow Generative Modelscited by 95
  5. 2021Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learningcited by 30
  6. 2017Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fieldscited by 20
  7. 2021Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamicscited by 19

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Close in wording, no citation either way

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  3. 2024GUD: Generation with Unified Diffusion
  4. 2024Tutorial on Diffusion Models for Imaging and Vision
  5. 2023Generative Modeling with Phase Stochastic Bridges
  6. 2023State of the Art on Diffusion Models for Visual Computing
  7. 2025Geometric Regularity in Deterministic Sampling Dynamics of Diffusion-based Generative Models
  8. 2026A Tutorial on Diffusion Theory: From Differential Equations to Diffusion Models