Demystifying Variational Diffusion Models

• URL: http://arxiv.org/abs/2401.06281v2
• Date: Thu, 22 May 2025 11:45:40 GMT
• Title: Demystifying Variational Diffusion Models
• Authors: Fabio De Sousa Ribeiro, Ben Glocker,
• Abstract summary: Most existing work on diffusion models focuses on either applications or theoretical contributions.<n>We revisit predecessors to diffusion models like hierarchical latent variable models and synthesize a holistic perspective.<n>The resulting narrative is easier to follow as it imposes fewer prerequisites on the average reader.
• Score: 19.977841588918373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the growing interest in diffusion models, gaining a deep understanding of the model class remains an elusive endeavour, particularly for the uninitiated in non-equilibrium statistical physics. Thanks to the rapid rate of progress in the field, most existing work on diffusion models focuses on either applications or theoretical contributions. Unfortunately, the theoretical material is often inaccessible to practitioners and new researchers, leading to a risk of superficial understanding in ongoing research. Given that diffusion models are now an indispensable tool, a clear and consolidating perspective on the model class is needed to properly contextualize recent advances in generative modelling and lower the barrier to entry for new researchers. To that end, we revisit predecessors to diffusion models like hierarchical latent variable models and synthesize a holistic perspective using only directed graphical modelling and variational inference principles. The resulting narrative is easier to follow as it imposes fewer prerequisites on the average reader relative to the view from non-equilibrium thermodynamics or stochastic differential equations.

Related papers

• A Convergence Theory for Diffusion Language Models: An Information-Theoretic Perspective [8.15094483029656]
  Diffusion models enable parallel token sampling, leading to faster generation and eliminating left-to-right generation constraints.<n>We develop convergence guarantees for diffusion language models from an information-theoretic perspective.<n>These results offer novel theoretical insights into the practical effectiveness of diffusion language models.
  arXiv  Detail & Related papers  (2025-05-27T16:24:20Z)
• Generative Diffusion Modeling: A Practical Handbook [25.81859481634996]
  diffusion probabilistic models, score-based generative models, consistency models, rectified flow, and related methods. Content encompasses the fundamentals of diffusion models, the pre-training process, and various post-training methods. Designed as a practical guide, it emphasizes clarity and usability over theoretical depth.
  arXiv  Detail & Related papers  (2024-12-22T21:02:36Z)
• Alignment of Diffusion Models: Fundamentals, Challenges, and Future [28.64041196069495]
  Diffusion models have emerged as the leading paradigm in generative modeling, excelling in various applications. Despite their success, these models often misalign with human intentions, generating outputs that may not match text prompts or possess desired properties. Inspired by the success of alignment in tuning large language models, recent studies have investigated aligning diffusion models with human expectations and preferences.
  arXiv  Detail & Related papers  (2024-09-11T13:21:32Z)
• Diffusion Models in Low-Level Vision: A Survey [82.77962165415153]
  diffusion model-based solutions have emerged as widely acclaimed for their ability to produce samples of superior quality and diversity. We present three generic diffusion modeling frameworks and explore their correlations with other deep generative models. We summarize extended diffusion models applied in other tasks, including medical, remote sensing, and video scenarios.
  arXiv  Detail & Related papers  (2024-06-17T01:49:27Z)
• An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization [59.63880337156392]
  Diffusion models have achieved tremendous success in computer vision, audio, reinforcement learning, and computational biology. Despite the significant empirical success, theory of diffusion models is very limited. This paper provides a well-rounded theoretical exposure for stimulating forward-looking theories and methods of diffusion models.
  arXiv  Detail & Related papers  (2024-04-11T14:07:25Z)
• Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory [87.00653989457834]
  Conditional diffusion models serve as the foundation of modern image synthesis and find extensive application in fields like computational biology and reinforcement learning. Despite the empirical success, theory of conditional diffusion models is largely missing. This paper bridges the gap by presenting a sharp statistical theory of distribution estimation using conditional diffusion models.
  arXiv  Detail & Related papers  (2024-03-18T17:08:24Z)
• Latent Diffusion Counterfactual Explanations [28.574246724214962]
  We introduce Latent Diffusion Counterfactual Explanations (LDCE) LDCE harnesses the capabilities of recent class- or text-conditional foundation latent diffusion models to expedite counterfactual generation. We show how LDCE can provide insights into model errors, enhancing our understanding of black-box model behavior.
  arXiv  Detail & Related papers  (2023-10-10T14:42:34Z)
• On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization [68.13034137660334]
  We propose theoretical connections between three recent consistency'' notions designed to enhance diffusion models for distinct objectives. Our insights offer the potential for a more comprehensive and encompassing framework for consistency-type models.
  arXiv  Detail & Related papers  (2023-06-01T05:57:40Z)
• Diffusion Models for Time Series Applications: A Survey [23.003273147019446]
  Diffusion models are used in image, video, and text synthesis nowadays. We focus on diffusion-based methods for time series forecasting, imputation, and generation. We conclude the common limitation of diffusion-based methods and highlight potential future research directions.
  arXiv  Detail & Related papers  (2023-05-01T02:06:46Z)
• Diffusion Models in Vision: A Survey [80.82832715884597]
  A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens.
  arXiv  Detail & Related papers  (2022-09-10T22:00:30Z)
• A Survey on Generative Diffusion Model [75.93774014861978]
  Diffusion models are an emerging class of deep generative models. They have certain limitations, including a time-consuming iterative generation process and confinement to high-dimensional Euclidean space. This survey presents a plethora of advanced techniques aimed at enhancing diffusion models.
  arXiv  Detail & Related papers  (2022-09-06T16:56:21Z)
• Diffusion Models: A Comprehensive Survey of Methods and Applications [10.557289965753437]
  Diffusion models are a class of deep generative models that have shown impressive results on various tasks with dense theoretical founding. Recent studies have shown great enthusiasm on improving the performance of diffusion model.
  arXiv  Detail & Related papers  (2022-09-02T02:59:10Z)
• How Much is Enough? A Study on Diffusion Times in Score-based Generative Models [76.76860707897413]
  Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution. We show how an auxiliary model can be used to bridge the gap between the ideal and the simulated forward dynamics, followed by a standard reverse diffusion process.
  arXiv  Detail & Related papers  (2022-06-10T15:09:46Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.