How Diffusion Models Work

What You'll Learn

• Understand diffusion models in use today.
• Build your own diffusion model, and learn to train it.
• Implement algorithms to speed up sampling 10x.

About This Course

This course provides an in-depth exploration of diffusion-based generative AI models, guiding you through the creation of a diffusion model from scratch. You will work hands-on to explore the following:

• Explore diffusion-based generative AI and create your own diffusion model.
• Gain familiarity with the diffusion process and underlying models.
• Work through labs on sampling, model training, noise prediction, and personalized image generation.

By the end, you’ll have a foundational model to expand upon in your own diffusion model applications.

Course Outline

• Introduction
  Overview of diffusion models and their applications in generative AI.
• Intuition
  Building a foundational understanding of diffusion processes and how they apply to generative models.
• Sampling
  Hands-on examples with code to explore sampling techniques and optimize the sampling process.
• Neural Network
  Building a neural network to predict noise, integral to the diffusion model process.
• Training
  Techniques and code examples for training diffusion models from scratch.
• Controlling
  Adding control and customization to generated outputs for more tailored applications.
• Speeding Up
  Implementing techniques to speed up sampling by 10x, optimizing the model’s performance.
• Summary
  Recap of key concepts and next steps for further exploration in diffusion models.

Who Should Join?

This course is designed for intermediate-level learners. A foundational understanding of Python, TensorFlow, or PyTorch will help you gain the most from this content.