This intermediate-level course delves into the field of generative models, particularly focusing on diffusion models for image generation. Through an exploration of cutting-edge techniques, you'll uncover how diffusion models work, from the fundamental concepts to the intricate architectures powering text-to-image generation. As you deepen your understanding, you'll also evaluate training strategies and discover how to fine-tune these models for maximum effectiveness.
How to Build a Diffusion Model - An Introduction
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Fundamentals of diffusion models, evaluate training strategies, analyze text-to-image workflows, and build innovative models.
Skills you'll gain
Details to know
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Recently updated!
May 2025
Assessments
7 assignments
Taught in English
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There are 3 modules in this course
In this module, you'll dive into the intricate world of diffusion models, beginning with the fundamentals of deep learning and generative models. You'll also develop a solid understanding of the principles that underpin these advanced technologies and receive a detailed overview of the diffusion process, their types, and their applications. By the end of this module, you'll gain thorough knowledge of diffusion models and how to leverage them effectively.
What's included
7 videos4 readings3 assignments2 discussion prompts
In this module, you'll delve into the process of building diffusion models, starting from the basics to advanced techniques. You'll begin by understanding the critical steps involved in constructing a diffusion model, including the architecture design required for setting up successful models. You'll also gain insight into the mechanics of the forward and reverse pass processes, which are essential for the model's ability to refine noise into detailed data representations. Additionally, you'll explore different loss functions and strategies for training and optimizing these models for better performance.
What's included
29 videos5 readings4 assignments5 ungraded labs
In this module, you'll explore the comprehensive process of building a text-to-image diffusion model, starting from data preparation to model evaluation. You'll begin by learning how to prepare and preprocess datasets specifically tailored for text-to-image tasks, ensuring the data is optimized for high-quality model training. The module will guide you through the steps of constructing the model architecture, followed by detailed training techniques to enhance model performance. Additionally, you'll learn how to evaluate the effectiveness of your model through various metrics and real-world applications. To reinforce learning and ensure a thorough understanding, the module also includes hands-on labs related to each topic.
What's included
1 video3 readings1 peer review
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