Diffusion Models: How They Work

Diffusion models are a cutting-edge approach to AI-generated images, producing visuals by gradually refining random noise into a structured composition. Unlike GANs, which generate images directly, diffusion models operate in a stepwise manner, reversing the process of noise addition.

The model is trained by progressively corrupting an image with Gaussian noise and then learning to denoise it. This iterative reconstruction allows AI to create high-resolution images with fine details, outperforming GANs in certain applications like text-to-image synthesis.

Stable Diffusion, developed by Stability AI, utilizes a latent diffusion approach where images are processed in a compressed representation space, significantly reducing computational requirements while maintaining high quality.