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Introduction to Diffusion Models
In this diffusion models course, you will explore their workings and architecture and learn to create images from noise using neural networks and pretrained models with practical implementations.
4.6
11 Lessons
2h
Updated 3 weeks ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of the fundamental concepts and principles underlying diffusion models
- Working knowledge of theoretical concepts associated with diffusion models
- Hands-on experience in leveraging state-of-the-art diffusion models available to perform various tasks using the Diffusers library
- Working knowledge of how diffusion models work, their needs, and benefits
- Hands-on experience in image generation, neural network training, and sampling
Learning Roadmap
2.
Getting Started
Getting Started
Look at the fundamentals, tasks, challenges, and applications of diffusion models.
3.
Image Creation from Scratch
Image Creation from Scratch
3 Lessons
3 Lessons
Work your way through U-Net setup, model training, and sampling for image creation.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
In this course, you’ll gain practical insight into the theoretical concepts associated with diffusion models and hands-on expertise in creating images from noise and training neural networks for effective image sampling.
You’ll start with an introduction to generative models, focusing on what is a diffusion model, specifically focusing on how diffusion models fall under this category. You’ll dive deep into how diffusion models work, exploring their workings, architecture, and the theoretical foundations supporting them. Subsequently, various diffusion model tasks will be introduced, and you will implement them using the Diffusers library, which provides cutting-edge pretrained diffusion models. You’ll learn how to set up and train a neural network model and sample images.
After completing this course, you’ll understand diffusion models clearly, generate images from noise, navigate the complexities of diffusion models, and harness the full potential of generative models in diverse applications.
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