CMU 10-799 Diffusion & Flow Matching
Spring 2026, Mini 3, Carnegie Mellon University | Tue & Thu 5:00 PM - 6:20 PM, POS 147
Instructor
Course Advisors
Announcements
| Nov 16, 2025 | Thanks to everyone who signed up or wants to join the class! We’re thrilled by your enthusiasm and a waitlist of 100+ students is truly something we never anticipated! We’re currently working on scaling up the class to accomodate more people. Please stay on the waitlist while we figure this out! |
|---|---|
| Oct 29, 2025 | Welcome to Spring 2026 10-799: Diffusion Models & Flow Matching (a.k.a Dungeons & Diffusion, because our classroom will be on the first floor of Posner :))! Check out the schedule and syllabus pages for more details about the class! |
Course Description
Want to understand how Stable Diffusion, DALL-E, and Sora actually work – and how to build something even better? This course takes you from mathematical foundations to hands-on research frontiers in diffusion models and flow matching, the generative AI frameworks reshaping computer vision and beyond.
In this class, you will explore topics from foundational probabilistic modeling through modern advances: denoising diffusion models, score-based SDEs, flow matching, fast sampling algorithms, controllable generation, flow maps & distillation methods, and discrete variants.
Choose your path to level up – fidelity (photorealistic quality), controllability (precise user control), or speed (real-time generation) – and build from scratch towards a complete working system through cumulative homework. You’ll strengthen both your theoretical understanding and practical implementation skills by the end of this course.
This class has no exams and is ChatGPT friendly! You are free to use resources like pre-trained models, open-sourced GitHub repositories and AGI-powered coding assistants for your assignments!
Course Information
Units: 6 units (half-semester course, Mini 3)
Time & Location: Tuesdays & Thursdays 5:00 PM - 6:20 PM, POS 147
Course Duration: Jan 13th - Feb 26th, 2026
Target Audience: Anyone who wants to learn about diffusion/flow matching (as long as they have met the class prerequisites)! If you are an MLD PhD or master’s student, this class can also be counted as a 6-credit elective!
Prerequisites
It’ll be helpful if you’ve taken the following CMU courses or have a similar background — that way, you’ll be ready to dive right in! (P.S. You don’t have to have taken exactly these courses, as long as you have the relevant knowledge you should be fine. :))
- 15112 - Fundamentals of Programming and Computer Science
- 21259 - Calculus in Three Dimensions
- 21341 - Linear Algebra
- 21260 - Differential Equations
- Probability: One of 15359, 21325, 36219, 36217, 15259, 36218, or 36225
- Machine Learing: Python coding and at least one machine learning/deep learning course