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Introduction to Diffusion Models for Generative AI

Chapter 1: Generative Modeling Fundamentals

Overview of Generative Models

Motivation for Diffusion Models

The Core Idea: Noise and Denoise

Probabilistic Framework Introduction

Quiz for Chapter 1

Chapter 2: The Forward Diffusion Process

Defining the Markov Chain

Gaussian Noise Schedule

Mathematical Formulation per Step

Sampling from Intermediate Steps

Properties of the Forward Process

Practice: Simulating Forward Diffusion

Quiz for Chapter 2

Chapter 3: The Reverse Diffusion Process

The Goal: Reversing the Markov Chain

Approximating the Reverse Transition

Parameterizing the Reverse Process with Neural Networks

Predicting the Noise Component

Mathematical Formulation of the Denoising Step

Quiz for Chapter 3

Chapter 4: Model Architecture and Training

The U-Net Architecture for Noise Prediction

Integrating Timestep Information

Defining the Training Objective

Simplified Training Loss Derivation

The Training Algorithm

Hands-on Practical: Setting up the U-Net

Quiz for Chapter 4

Chapter 5: Sampling and Generation Process

Generating Data from Noise

The DDPM Sampling Algorithm

Understanding Sampling Variance

Introduction to Faster Sampling: DDIM

The DDIM Sampling Algorithm

Trade-offs Between DDPM and DDIM

Practice: Implementing Sampling Loops

Quiz for Chapter 5

Chapter 6: Conditional Generation with Diffusion Models

Motivation for Conditional Generation

Classifier Guidance

Classifier-Free Guidance (CFG)

Implementing Classifier-Free Guidance

Text Conditioning Basics

Architecture Modifications for Conditioning

Hands-on Practical: Applying Guidance

Quiz for Chapter 6

Quiz
Beta

Chapter: The Forward Diffusion Process

Test your understanding and practice the concepts from this chapter

Quiz Instructions

This quiz contains 10 questions to help you practice.
You need to score at least 70% to pass.
Attempts: Unlimited.
Your highest score will be kept.
Please attempt this quiz without assistance; however, feel free to refer to the chapter notes or use a code interpreter if needed.
This assessment is for your learning and understanding, It is not formally accredited (yet).

Question Format

The questions are designed to be engaging, focusing on understanding, application, and interpretation rather than rote memorization. Expect scenario-based problems that test your ability to apply what you've learned.

Attempts

Best scores and quiz attempts will appear.

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