Diffusion Models are a class of Generative Models that learn to create new data by reversing a gradual "noising" process. They begin with pure noise and, guided by learned Neural Networks, iteratively refine it into a coherent, high-fidelity sample, like an image or audio clip. This elegant process allows them to synthesize diverse and remarkably realistic outputs.