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
The DDIM Sampling Algorithm
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- Denoising Diffusion Implicit Models, Jiaming Song, Chenlin Meng, Stefano Ermon, 2020 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2010.02502 - The original research paper introducing the DDIM algorithm, detailing its non-Markovian generative process, deterministic sampling, and ability to use subsequences for faster generation.
- Score-Based Generative Modeling through Stochastic Differential Equations, Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole, 2021 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2011.13456 - Provides a unifying theoretical framework for diffusion models, including DDPM and DDIM, by connecting them to SDEs and ODEs. This helps understand the theoretical underpinnings of DDIM's deterministic and faster sampling.
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