We begin with an introduction to diffusion models, followed by a comprehensive study of their generalization: when and why they can learn low-dimensional target structures, how sample complexity scales with intrinsic data dimension, and how they transition from memorization to generalization. We also introduce a probability flow-based metric to quantify generalization and highlight several intriguing behaviors observed during training.