[AI/ML] Diffusion Models — A Beginner’s Guide to Math Behind Stable Diffusion and Dall-e!

Diffusion Models are revolutionizing generative modeling in computer vision, especially through tools like DALL-E and Stable Diffusion. These models add and remove noise to and from images across multiple steps, enhancing image generation quality. Key mathematical perspectives include Markov Chains and Langevin Dynamics. The architecture commonly involves U-Net and various conditioning methods, such as classifier-guided and classifier-free guidance. Enhancements to these models, like the use of ControlNet and improvements in sampling techniques, make them more efficient and versatile for generating high-quality images.