Diffusion Models Study Plan
A comprehensive 8-week curriculum covering diffusion models from mathematical foundations and DDPM to Stable Diffusion, conditional generation, and deployment.
Recommended Study Path
Prerequisites
Foundations Study Plan
- Python Foundations
- NumPy & Linear Algebra
- Probability & Calculus
Complete the Foundations study plan first →
Foundations
Weeks 1-2
- Ch 1: Generative Models Overview
- Ch 2: Math Foundations
Probability, SDEs, variational inference
Core Theory
Weeks 3-6
- Ch 3-4: Forward & Reverse Process
- Ch 5-6: Sampling & Stable Diffusion
DDPM, DDIM, latent diffusion, ControlNet
Applications
Weeks 7-8
- Ch 7: Conditional Generation
- Ch 8: Video, 3D & DiT
Text-to-image, flow matching, deployment
All Chapters
Introduction to Generative Models
Generative vs discriminative models, taxonomy of generative approaches, likelihood-based models, latent variables, evaluation metrics, and why diffusion models.
Mathematical Foundations
Gaussian distributions, Markov chains, KL divergence, variational inference, stochastic differential equations, and Langevin dynamics.
Forward Diffusion Process
Adding noise to data, noise schedules, the forward SDE, reparameterization trick, signal-to-noise ratio, and continuous vs discrete time.
Reverse Process & Denoising
Score functions, denoising score matching, the reverse SDE, DDPM training objective, parameterizing the noise predictor, and loss weighting.
Sampling & Acceleration
Ancestral sampling, DDIM deterministic sampling, DPM-Solver, progressive distillation, consistency models, and guidance during sampling.
Latent Diffusion & Stable Diffusion
Autoencoders for latent space, LDM architecture, cross-attention conditioning, text encoders, the Stable Diffusion pipeline, and ControlNet.
Conditional Generation
Classifier guidance, classifier-free guidance, text-to-image systems, image-to-image translation, inpainting, and super-resolution.
Advanced Topics & Applications
Flow matching, rectified flows, video diffusion, 3D generation, Diffusion Transformers (DiT), deployment optimization, and ethical considerations.
Practice Problem Sets
Sharpen your skills with coding challenges and system design problems.
Curriculum designed to take you from generative model fundamentals to production-ready diffusion systems.