What does a Diffusion Model do?

A Diffusion Model is a technique primarily used in generative artificial intelligence to create images from noise. This process involves starting with a random signal or noise and then incrementally refining it through a series of steps dictated by the model. Each step corresponds to a gradual transformation aimed at converting the noise into a coherent image that reflects the underlying data distribution the model has been trained on.

This model works by first learning from a dataset of images, capturing complex patterns and features. When generating images, it essentially reverses a diffusion process, where the model starts from noise and gradually conditions it to result in clear images. The concept is rooted in probabilistic modeling, allowing the model to create diverse and high-quality images that can range significantly in style and content.

While generating textual data and analyzing datasets or classifying text are important tasks in natural language processing and data analysis, they do not align with the primary function of Diffusion Models, which is focused on image generation.