The main takeaway from this exploration is that whenever you see a random process converting one distribution to another, SDEs should come to mind as a powerful mathematical framework. The three sampling methods we studied (Langevin dynamics, DDPM, SMLD) are all discrete approximations of continuous SDEs, differing mainly in their drift and diffusion terms. Understanding them through the SDE lens helps unify these approaches and provides a foundation for developing new sampling techniques.