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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

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

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

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

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

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

Introduction to Diffusion Models for Generative AI

Prerequisites: Python & ML Concepts

Level: Intermediate

What You'll Learn

Diffusion Process Mechanics
Explain the mathematical formulation of the forward (noising) and reverse (denoising) processes.
Model Architecture
Understand the role and structure of the U-Net architecture in diffusion models.
Training and Loss
Describe the training objective and loss functions used for diffusion models.
Sampling Techniques
Implement sampling procedures like DDPM and understand faster alternatives like DDIM.
Conditional Generation
Apply basic techniques to guide the generation process using conditioning information.
Implementation Basics
Build and train a simple diffusion model using a deep learning framework.

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