Denoising Diffusion Models from Scratch
From Mathematical Foundations to Image Generation
Master diffusion models from mathematical foundations to image generation. Learn the theory, implement DDPM in PyTorch, and understand modern systems like Stable Diffusion.
Math Foundations— Probability, Gaussians, and variational inference.
Prerequisites
Essential mathematical background for understanding diffusion models
Introduction to Generative Models
Context and motivation for diffusion models
Theory— Forward and reverse diffusion processes.
The Forward Diffusion Process
Understanding how to systematically destroy data with noise
The Reverse Diffusion Process
Learning to denoise - the heart of diffusion models
Understanding the Loss Function
Deep dive into training objectives and their properties
Architecture— U-Net, DDPM implementation, and samplers.
U-Net Architecture for Diffusion
The neural network backbone of diffusion models
Building the Diffusion Model
Assembling all components into a working DDPM
Improved Sampling Methods
Faster and better generation algorithms
Conditioning— Guidance and text-to-image foundations.
Conditional Generation
Controlling what the model generates
Text-to-Image Foundations
Understanding the path to modern text-to-image models
Project— End-to-end image generation system.
Dataset Preparation
Setting up data for training a diffusion model
Training the Model
Complete training setup and execution
Generation and Evaluation
Using the trained model and measuring quality
Advanced— Latent diffusion and modern techniques.
Latent Diffusion Models
Understanding the architecture behind Stable Diffusion
Advanced Conditioning Techniques
ControlNet, IP-Adapter, and beyond
Score-Based and SDE Perspective
The unified mathematical framework
Production— Optimization and future directions.
Optimization and Deployment
Making models practical
The Future of Diffusion Models
Current research directions
Where the book lands in practice.
Generation and Evaluation
Using the trained model and measuring quality
Open chapter76 sections. Begin with one.
Chapter 0 — Prerequisites — is where every reader starts.