mlsanityData to 7 Oct 2026

Diffusion Explorer: Interactive Exploration of Diffusion Models

arXiv 2507.01178 ↗First posted 1 Jul 2025cs.LGcited by 1

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Diffusion models have been central to the development of recent image, video, and even text generation systems. They posses striking geometric properties that can be faithfully portrayed in low-dimensional settings. However, existing resources for explaining diffusion either require an advanced theoretical foundation or focus on their neural network architectures rather than their rich geometric properties. We introduce Diffusion Explorer, an interactive tool to explain the geometric properties of diffusion models. Users can train 2D diffusion models in the browser and observe the temporal dynamics of their sampling process. Diffusion Explorer leverages interactive animation, which has been shown to be a powerful tool for making engaging visualizations of dynamic systems, making it well suited to explaining diffusion models which represent stochastic processes that evolve over time. Diffusion Explorer is open source and a live demo is available at alechelbling.com/Diffusion-Explorer.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 3 of its 8 references that are in the map. First posted on arXiv 1 Jul 2025.

Builds on

3 of 8 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 2

  1. 2023Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusioncited by 17
  2. 2023ManimML: Communicating Machine Learning Architectures with Animationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2024Explaining generative diffusion models via visual analysis for interpretable decision-making process
  2. 2024Interactive Visual Learning for Stable Diffusion
  3. 2022Diffusion Models: A Comprehensive Survey of Methods and Applications
  4. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
  5. 2023State of the Art on Diffusion Models for Visual Computing
  6. 2024Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices
  7. 2024Generative Diffusion Modeling: A Practical Handbook
  8. 2022Let us Build Bridges: Understanding and Extending Diffusion Generative Models