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Diagram showing a cat image turning into noise in four steps in the forward process, then noise turning back into the cat image in the reverse process.

CourseRES.9-0101 lecture

Brain and Cognitive Sciences Computational Tutorial: Optimizing the Full Stack for Generative Image and Video Models

Massachusetts Institute of Technology

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About

This tutorial covers optimization strategies for diffusion models used in image and video generation; topics include architecture design, hardware efficiency, and inference techniques. Speaker: Sayak Paul, Hugging Face.

This tutorial is part of a seminar series led by graduate students and postdocs in the MIT Department of Brain and Cognitive Sciences (BCS) from 2015 to the present, featuring topics relevant to research on intelligence in neuroscience, cognitive science, and artificial intelligence. The tutorials are aimed at participants who have some computational background but are not experts on these topics.

This series was organized by Sol Markman and Ajani Stewart, graduate students in the Department of Brain and Cognitive Sciences.

Lectures

Fall 2025 Seminar: Optimizing the Full Stack for Generative Image and Video Models

  1. Optimizing the Full Stack for Generative Image and Video Models Video

Syllabus and materials

At MIT OpenCourseWare, as the course publishes them.

Credits and licence

Institution
Massachusetts Institute of Technology
Department
Brain and Cognitive Sciences
Term
Fall 2025
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering, Psychology and the brain
Source
MIT OpenCourseWare
Licence
CC BY 4.0, as MIT OpenCourseWare states it. MIT OpenCourseWare's terms
Attribution
Brain and Cognitive Sciences Computational Tutorial: Optimizing the Full Stack for Generative Image and Video Models. Fall 2025. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
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