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Coursera

How to Build a Diffusion Model - An Introduction

Fractal Analytics via Coursera

Overview

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Explore the fascinating world of generative models with a deep focus on diffusion models for high-quality image generation. You’ll begin by mastering the core principles of diffusion and then advance to the architectures that power modern text-to-image systems. Learn how these models transform random noise into stunning visuals through forward and reverse processes, and discover optimization techniques using loss functions and training strategies. By the end of this course, you’ll be equipped to build your own diffusion models from scratch, fine-tune them for specific tasks, and evaluate their performance using real-world metrics. Whether you’re an ML engineer, data scientist, or AI enthusiast, this course will give you the practical skills to excel in one of the most transformative areas of generative AI.

Syllabus

  • Introduction to Diffusion Models
    • Explore the fundamentals of deep learning and generative models. Understand the diffusion process, its types, and applications in AI.
  • Building Diffusion Models from Scratch
    • Learn the architecture and mechanics of diffusion models. Dive into forward/reverse passes, loss functions, and training strategies.
  • Creating Text-to-Image Diffusion Models
    • Build end-to-end text-to-image systems. Cover data preparation, model construction, training, evaluation, and hands-on labs.

Taught by

Fractal Analytics Academy and Akshesh Shah

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