Diffusion models are a class of generative models that have gained attention for their ability to generate high-quality data, particularly in image synthesis. They are inspired by the physical process of diffusion, where particles spread from areas of high concentration to low concentration. This concept is applied in a probabilistic framework to generate data by reversing a diffusion process.

The core mechanism of diffusion models involves a two-step process: a forward diffusion process that gradually adds noise to the data, and a reverse process that learns to denoise the data step by step. This approach allows the model to generate new samples by starting from random noise and iteratively refining it into coherent data, such as images or audio ¹⁵.

Diffusion models have shown advantages over traditional generative models like GANs and VAEs, particularly in terms of stability during training and the diversity of generated outputs. They have been successfully applied in various applications, including text-to-image generation and other creative tasks ⁴².