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 Core Idea: Noise and Denoise
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- Deep Unsupervised Learning using Nonequilibrium Thermodynamics, Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Stefano Ermon, 2015 Proceedings of the 32nd International Conference on Machine Learning (ICML), Vol. 37 (PMLR (Proceedings of Machine Learning Research)) DOI: 10.55989/ah-3 - This is the original paper that introduced the concept of diffusion probabilistic models for generative tasks, laying the theoretical groundwork for subsequent advancements.
- Denoising Diffusion Probabilistic Models, Jonathan Ho, Ajay Jain, and Pieter Abbeel, 2020 Advances in Neural Information Processing Systems 33 (NeurIPS) DOI: 10.48550/arXiv.2006.11239 - This paper refined and popularized the Denoising Diffusion Probabilistic Model (DDPM) framework, providing a simpler training objective and demonstrating impressive results in image generation, making diffusion models widely adopted.
- Score-Based Generative Modeling through Stochastic Differential Equations, Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole, 2020 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2011.13456 - This work unified Denoising Diffusion Probabilistic Models and score-based generative models under a continuous-time stochastic differential equation framework, significantly advancing the theoretical understanding and practical applications of these models.