This course explores the rapidly evolving field of generative models, with a focus on diffusion models for image generation. You’ll start with the foundational concepts and progress to advanced architectures that power text-to-image systems. Learn how diffusion models transform noise into coherent images through forward and reverse processes, and how to optimize them using various loss functions and training strategies.
How to Build a Diffusion Model - An Introduction
How to Build a Diffusion Model - An Introduction
Instructors: Fractal Analytics Academy
Access provided by Ladoke Akintola University of Technology
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Core concepts behind diffusion models and their role in generative AI
How to build and train diffusion models from scratch
Techniques for creating text-to-image generation systems
Evaluation metrics and real-world applications of diffusion models
Skills you'll gain
Details to know
Shareable certificate
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Assessments
7 assignments
Taught in English
Recently updated!
May 2025
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There are 3 modules in this course
Explore the fundamentals of deep learning and generative models. Understand the diffusion process, its types, and applications in AI.
What's included
7 videos4 readings3 assignments2 discussion prompts
Learn the architecture and mechanics of diffusion models. Dive into forward/reverse passes, loss functions, and training strategies.
What's included
29 videos5 readings4 assignments5 ungraded labs
Build end-to-end text-to-image systems. Cover data preparation, model construction, training, evaluation, and hands-on labs.
What's included
1 video3 readings1 peer review
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