Steering Dynamical Regimes of Diffusion Models by Breaking Detailed Balance
On the map: Diffusion models → Diffusion foundations and sampling
Abstract (arXiv)
We show that deliberately breaking detailed balance in generative diffusion processes can accelerate the reverse process without changing the stationary distribution. Considering the Ornstein--Uhlenbeck process, we decompose the dynamics into a symmetric component and a non-reversible anti-symmetric component that generates rotational probability currents. We then construct an exponentially optimal non-reversible perturbation that improves the long-time relaxation rate while preserving the stationary target. We analyze how such non-reversible control reshapes the macroscopic dynamical regimes of the phase transitions recently identified in generative diffusion models. We derive a general criterion for the speciation time and show that suitable non-reversible perturbations can accelerate speciation. In contrast, the collapse transition is governed by a trace-controlled phase-space contraction mechanism that is fixed by the symmetric component, and the corresponding collapse time remains unchanged under anti-symmetric perturbations. Numerical experiments on Gaussian mixture models support these findings.
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 23 of its 46 references that are in the map. First posted on arXiv 17 Feb 2026.
Builds on
23 of 46 references.
Closest ancestors · 18
Same direction, newest first
- 2026Dynamical Regimes of Multimodal Diffusion Models4 Feb
- 2025Memorization to Generalization: Emergence of Diffusion Models from Associative Memory27 May
- 2025Why Diffusion Models Don't Memorize: The Role of Implicit Dynamical Regularization in Training23 May
- 2024Random Walks with Tweedie: A Unified View of Score-Based Diffusion Models27 Nov
- 2024A solvable generative model with a linear, one-step denoiser26 Nov
- 2024Probing the Latent Hierarchical Structure of Data via Diffusion Models17 Oct
- 2024Losing dimensions: Geometric memorization in generative diffusion11 Oct
- 2024Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity24 May
- 2024Nonequilbrium physics of generative diffusion models20 May
- 2024Dynamical Regimes of Diffusion Models28 Feb
- 2024A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data26 Feb
- 2023The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability26 Oct
6 more
- 2023Generalization in diffusion models arises from geometry-adaptive harmonic representations4 Oct
- 2023In search of dispersed memories: Generative diffusion models are associative memory networks29 Sep
- 2023Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models28 Sep
- 2023Spontaneous Symmetry Breaking in Generative Diffusion Models31 May
- 2023A Complete Recipe for Diffusion Generative Models3 Mar
- 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
Other references · 5
- 2025Mean Flows for One-step Generative Modelingcited by 524
- 2023Sampling, Diffusions, and Stochastic Localizationcited by 38
- 2024Geometric Dynamics of Signal Propagation Predict Trainability of Transformerscited by 24
- 2024Cascade of phase transitions in the training of Energy-based modelscited by 12
- 2024Harmonic Path Integral Diffusioncited by 11
Built on by
Citing papers per year: 2026: 3.
3 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Generative Modeling of Stochastic Dynamics for Long-Time Evolution
- 2025A PDE Perspective on Generative Diffusion Models
- 2026A Theory of Speciation in Generative Diffusion Models on Compact Riemannian Manifolds
- 2025The Information Dynamics of Generative Diffusion
- 2024Non-equilibrium active noise enhances generative memory in diffusion models
- 2023Non-Denoising Forward-Time Diffusions
- 2024Demystifying Variational Diffusion Models
- 2025Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction