How Diffusion Models Generate Images & Videos | Inference Engineering — Chapter 2 Part 2

How does AI turn pure random noise into realistic images and videos? In Part 2 of Chapter 2 — Models from Inference Engineering by Philip Kiely, we go deep into the foundations behind modern generative AI and understand what happens under the hood when AI generates images and videos. We cover: VAE — Variational Autoencoders and latent space GAN — Generator vs Discriminator Diffusion Models and iterative denoising KL Divergence JS Divergence Wasserstein Distance How noise is transformed into realistic images Image generation model architecture How image generation extends to video generation The mathematical intuition behind generative models The engineering considerations behind image and video generation The goal is to understand why these models work, how they differ, and how modern generative AI systems actually generate images and videos, rather than treating them as black boxes. First Principles Engineering Think Deeper. Build Better. Engineer from First Principles. #inference #generativeai #vae #gan #diffusion #imagegenerationai #imagegeneration #videogenai #kldivergence #jsdivergence #deeplearning #maths #aiengineering #aiengineer #firstprinciplesengineering