mlsanityData to 7 Oct 2026

Understanding Diffusion Models via Code Execution

arXiv 2512.07201 ↗First posted 8 Dec 2025cs.CV cs.LGcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Diffusion models have achieved remarkable performance in generative modeling, yet their theoretical foundations are often intricate, and the gap between mathematical formulations in papers and practical open-source implementations can be difficult to bridge. Existing tutorials primarily focus on deriving equations, offering limited guidance on how diffusion models actually operate in code. To address this, we present a concise implementation of approximately 300 lines that explains diffusion models from a code-execution perspective. Our minimal example preserves the essential components -- including forward diffusion, reverse sampling, the noise-prediction network, and the training loop -- while removing unnecessary engineering details. This technical report aims to provide researchers with a clear, implementation-first understanding of how diffusion models work in practice and how code and theory correspond. Our code and pre-trained models are available at: https://github.com/disanda/GM/tree/main/DDPM-DDIM-ClassifierFree.

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 10 of its 15 references that are in the map. First posted on arXiv 8 Dec 2025.

Builds on

10 of 15 references.

Closest ancestors · 3

Same direction, newest first

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  2. 2020Denoising Diffusion Probabilistic Models19 Jun
  3. 2020Improved Techniques for Training Score-Based Generative Models16 Jun

Other references · 7

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2014Conditional Generative Adversarial Netscited by 2,152
  4. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  5. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
  6. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  7. 2021Improving generative adversarial network inversion via fine-tuning GAN encoderscited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026A Tutorial on Diffusion Theory: From Differential Equations to Diffusion Models
  2. 2024Generative Diffusion Modeling: A Practical Handbook
  3. 2023Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians
  4. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
  5. 2024Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices
  6. 2025Diffusion Models: A Mathematical Introduction
  7. 2023The Emergence of Reproducibility and Generalizability in Diffusion Models
  8. 2024Evaluating the design space of diffusion-based generative models