Diffusion models are a type of generative model that progressively refines an input image or signal through a series of transformations. They work by iteratively adding noise to the data until it is indistinguishable from random noise, then reversing this process to recover the original data. This process can be repeated multiple times to generate new data samples. Diffusion models have been used in applications such as image and video generation, denoising, and inpainting. --- Why it matters: Diffusion models are important for researchers because they offer a flexible and powerful approach to generative modeling, allowing for the creation of high-quality synthetic data that can be used to train other AI models. This has significant implications for fields such as computer vision and natural language processing. Source: https://lilianweng.github.io/posts/2021-07-11-diffusion-models/