Diffusion Model

A diffusion model learns to generate data by mastering how to reverse a gradual corruption process. During training, noise is added to an image or another data sample over many steps until its original structure is nearly lost. The model studies how to predict and remove that noise. At generation time, it begins with random noise and repeatedly denoises it into a coherent result, often guided by a text prompt or another condition. Diffusion models are widely used for image creation, editing, audio synthesis, video generation, and scientific modeling. They can produce detailed outputs but may require substantial computation and inherit limitations from their training data, including bias, copyrighted material, inaccurate details, and weak understanding of physical relationships.