How diffusion models work - explanation and code!

Umar Jamil · Beginner ·🧬 Deep Learning ·2y ago

About this lesson

A gentle introduction to diffusion models without the math derivations, but rather, a focus on the concepts that define the diffusion models as described in the DDPM paper. Full code and PDF slides available at: https://github.com/hkproj/pytorch-ddpm Chapters 00:00 - Introduction 00:46 - Generative models 03:51 - Latent space 07:35 - Forward and reverse process 09:00 - Mathematical definitions 13:00 - Training loop 15:05 - Sampling loop 16:36 - U-Net 18:31 - Training code 19:28 - Sampling code 20:34 - Full code

Original Description

A gentle introduction to diffusion models without the math derivations, but rather, a focus on the concepts that define the diffusion models as described in the DDPM paper. Full code and PDF slides available at: https://github.com/hkproj/pytorch-ddpm Chapters 00:00 - Introduction 00:46 - Generative models 03:51 - Latent space 07:35 - Forward and reverse process 09:00 - Mathematical definitions 13:00 - Training loop 15:05 - Sampling loop 16:36 - U-Net 18:31 - Training code 19:28 - Sampling code 20:34 - Full code
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Chapters (11)

Introduction
0:46 Generative models
3:51 Latent space
7:35 Forward and reverse process
9:00 Mathematical definitions
13:00 Training loop
15:05 Sampling loop
16:36 U-Net
18:31 Training code
19:28 Sampling code
20:34 Full code
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