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URL: https://www.coursera.org/learn/build-diffusion-model-introduction

⇱ How to Build a Diffusion Model - An Introduction | Coursera

How to Build a Diffusion Model - An Introduction

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How to Build a Diffusion Model - An Introduction

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Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
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Learn at your own pace
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

  • Explain the core concepts of diffusion models and their role in the generative AI landscape.

  • Design diffusion models from scratch using effective training strategies.

  • Build text-to-image generation systems leveraging advanced techniques.

  • Evaluate model performance using real-world metrics.

Details to know

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Assessments

7 assignments

Taught in English

There are 3 modules in this course

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.

Explore the fundamentals of deep learning and generative models. Understand the diffusion process, its types, and applications in AI.

What's included

7 videos3 readings3 assignments

7 videos•Total 25 minutes
  • Course Introduction•3 minutes
  • Fundamentals of Deep Learning•5 minutes
  • Introduction to Generative Models•3 minutes
  • Overview of Diffusion Process•3 minutes
  • Why Diffusion Models?•4 minutes
  • Types of Diffusion Models•3 minutes
  • Applications and Use-cases•4 minutes
3 readings•Total 45 minutes
  • Course Syllabus•5 minutes
  • Diffusion Explainer•10 minutes
  • Recent Buzz and Advancements about Diffusion Models•30 minutes
3 assignments•Total 35 minutes
  • Diffusion 101: A Primer on Generative Models•15 minutes
  • Understanding Diffusion Models•10 minutes
  • Starting with Diffusion Models•10 minutes

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

29 videos•Total 138 minutes
  • Steps in Building a Diffusion Model•4 minutes
  • Architecture Design - Forward Pass Part-1•5 minutes
  • Architecture Design - Forward Pass Part-2•4 minutes
  • Architecture Design - Reverse Pass Part-1•4 minutes
  • Architecture Design - Reverse Pass Part-2•4 minutes
  • Tailoring the Architecture to Data Types•4 minutes
  • Handling Multimodal Architectures•4 minutes
  • Text Data Preparation•4 minutes
  • Image Data Preprocessing•5 minutes
  • Preparing the Dataset•3 minutes
  • Advance Techniques for Image Data Preparation•5 minutes
  • Data Integration and Fusion•5 minutes
  • Data Preprocessing•2 minutes
  • Training and Optimization•6 minutes
  • Building the Model•3 minutes
  • Learning Rate Scheduling•5 minutes
  • Regularization Techniques•5 minutes
  • Understanding Loss Functions•5 minutes
  • Optimization Algorithms•5 minutes
  • Gradient Clipping•5 minutes
  • Hyperparameter Tuning•6 minutes
  • Training the Model•5 minutes
  • Importance of Model Evaluation•6 minutes
  • Basic Evaluation Metrics for Diffusion Models•6 minutes
  • Qualitative Evaluation Metrics for Diffusion Models•6 minutes
  • Evaluating the Model•3 minutes
  • Bootstrapping Validation Technique•5 minutes
  • Validation using Ensemble Methods•5 minutes
  • Classical Validation Technique•6 minutes
5 readings•Total 65 minutes
  • Flow-based Architecture•10 minutes
  • Data Preparation•10 minutes
  • Data Splitting and Partitioning•15 minutes
  • Batch Training vs Online Training•15 minutes
  • Understanding Validation Strategies•15 minutes
4 assignments•Total 45 minutes
  • Transforming Data into Art•10 minutes
  • Mastering the Fundamentals•10 minutes
  • Performance Proofs: Validating Diffusion Models•15 minutes
  • Review and Refinement•10 minutes
5 ungraded labs•Total 225 minutes
  • How to Prepare Data Set•45 minutes
  • How to Handle Data Preprocessing•45 minutes
  • Building Your Diffusion Model•45 minutes
  • Training Your Model•45 minutes
  • Model Evaluation•45 minutes

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

1 video•Total 1 minute
  • Conclusion•1 minute
3 readings•Total 17 minutes
  • Ethical Considerations While Using Text-to-Image Diffusion Tools•10 minutes
  • Congratulations•5 minutes
  • Acknowledgments•2 minutes
1 peer review•Total 90 minutes
  • The Final Assignment: Your Model•90 minutes

Instructors

Fractal Analytics
23 Courses•136,400 learners

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Frequently asked questions

It’s a practical introduction to diffusion models—one of the most powerful techniques in generative AI for image creation.

You’ll be able to build, train, and evaluate diffusion models, including text-to-image systems.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,