Diffusion Models Explained: Build DDPM from Scratch

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Издатель Jun 4, 2026

Diffusion models are the backbone behind today’s most powerful generative AI systems, from image generation to video, audio, and scientific simulation.

In this video, we break down Denoising Diffusion Probabilistic Models, or DDPMs, from first principles. You will learn how diffusion models work by gradually adding Gaussian noise to data, then training a neural network to reverse that process step by step.

We cover the core architecture behind DDPMs, including forward diffusion, reverse denoising, variance schedules, timestep embeddings, U-Net-based noise prediction, and the training objective that makes diffusion models so effective.

You will also see how DDPMs compare to GANs and VAEs, why they became the foundation of modern generative AI, and how production techniques like faster samplers and distillation reduce inference cost.

This is a practical, technical guide for AI engineers, ML researchers, and developers who want to understand how diffusion models actually work under the hood.

Topics covered:
↳ What diffusion models are
↳ How DDPM works
↳ Forward noise process
↳ Reverse denoising process
↳ Gaussian noise and variance schedules
↳ Timestep embeddings
↳ DDPM loss function
↳ Diffusion vs GANs vs VAEs
↳ Faster sampling and production optimization
↳ Why diffusion powers modern generative AI

Watch next if you want to understand Stable Diffusion, image generation, video generation, and modern generative AI architecture.

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