Step-by-Step Diffusion: An Elementary Tutorial
Preetum Nakkiran, Arwen Bradley, Hattie Zhou, Madhu Advani
TL;DR
This paper provides an accessible, simplified tutorial on diffusion models and flow matching, designed for learners new to the field, balancing clarity with mathematical accuracy.
Contribution
It offers an elementary, heuristic-focused introduction to diffusion models and flow matching, making complex concepts approachable for beginners.
Findings
Simplified explanations facilitate learning for newcomers.
Heuristic methods retain core algorithmic principles.
Accessible tutorial bridges gap between theory and practice.
Abstract
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.
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Generative Adversarial Networks and Image Synthesis · Model Reduction and Neural Networks
MethodsDiffusion