mlsanityData to 14 Oct 2026

Step-by-Step Diffusion: An Elementary Tutorial

arXiv 2406.08929 ↗First posted 13 Jun 2024cs.LG cs.AI cs.CVcited by 16

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

We present an accessible first course on diffusion models and flow matching for machine learning, aimed at a technical audience with no diffusion experience. We try to simplify the mathematical details as much as possible (sometimes heuristically), while retaining enough precision to derive correct algorithms.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. First posted on arXiv 13 Jun 2024.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 9; 2026: 5.

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  1. 2025An Introduction to Flow Matching and Diffusion Models
  2. 2025Shortcutting Pre-trained Flow Matching Diffusion Models is Almost Free Lunch
  3. 2024Multistep Distillation of Diffusion Models via Moment Matching
  4. 2026Rethinking the Diffusion Model from a Langevin Perspective
  5. 2025Diffusion Models: A Mathematical Introduction
  6. 2025On the Design of One-step Diffusion via Shortcutting Flow Paths
  7. 2025From Navigation to Refinement: Revealing the Two-Stage Nature of Flow-based Diffusion Models through Oracle Velocity
  8. 2022Diffusion Models: A Comprehensive Survey of Methods and Applications