What is Diffusion Model in AI? Explained in Everyday Language for AI Beginners

Good Morning, Good Evening, and Good Night.

In our past series, we’ve delved deeply into language models, conversational AI, and their mechanisms. Today, we’ll explore a critical and highly relevant term in today’s AI landscape: the Diffusion Model.

What is Diffusion Model?

Diffusion models in AI, including their significant use in computer vision tasks like image generation and super-resolution, were introduced in 2015. These models learn complex probability distributions of datasets by modeling how data points diffuse through latent space. (BTW — Think of latent space in AI as a magical closet where complex information, like photos or conversations, is stored in a simpler form. This closet helps AI understand patterns and create new things based on those patterns, making tasks like recognizing faces or generating new images easier.)

So now, back to it’s definition: imagine you’re mixing various paint colors gradually to create a beautiful painting. A diffusion model in AI works somewhat similarly.

It starts with a random noise (like a blank canvas) and gradually refines this noise through a series of steps (mixing paints) until it transforms into a detailed image or piece of text. Initially…