Diffusion Demystified: From Noise to Image with Flow Matching

A clear, step-by-step look at how diffusion models generate images. We start with Gaussian forward diffusion, cover reverse processes like DDPM and DDIM, and explain the broader flow-matching framework that enables flexible, efficient sampling. We discuss practical challenges—samplers, speed, and generalization—and what the latest research says about turning noise into coherent, high-quality images.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

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