What are diffusion models in machine learning?

Diffusion models are a type of generative model in machine learning that create data, such as images, by simulating a process where noise is gradually removed from a random signal. These models learn to reverse a diffusion process, transforming noisy data into structured outputs that resemble real examples from the training set. They have gained popularity for producing high-quality, realistic images and other media. Diffusion models are used in various applications, including image synthesis, inpainting, and audio generation.