Diffusion models are a class of generative models that have gained significant attention in the field of artificial intelligence and machine learning. They are designed to create new data that resembles the data they were trained on, such as images, music, or text. The core concept of diffusion models is inspired by the physical phenomenon of diffusion, where particles spread from areas of high concentration to low concentration. These models utilize two main processes: a forward diffusion process that gradually adds noise to the data, and a reverse process that reconstructs the data from the noisy version, resulting in high-quality outputs.