Diffusion Models

Diffusion models are a class of generative algorithms that create data, such as images, by gradually transforming noise into a coherent output. They do this through a process that simulates adding small amounts of randomness (noise) to data and then learning how to reverse this process, removing the noise step-by-step. Think of it like starting with static and carefully refining it until a clear picture emerges. This approach allows models to generate highly detailed and diverse results, making them powerful tools for creating realistic images, audio, and more.