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Deep Learning & Machine Learning

[AI Practice] Understanding Diffusion Models through Prompt-to-prompt Paper Implementation

This course conducts hands-on practice related to Diffusion models among generative artificial intelligence models. By reading and implementing the prompt-to-prompt paper, which is a representative Diffusion model application paper, we expect to cultivate the ability to understand the latest artificial intelligence papers.

7 learners are taking this course

  • dongdong1
논문
논문구현
컴퓨터비전
Stable Diffusion
Python
Deep Learning(DL)
PyTorch
AI
Generative AI

What you will learn!

  • Understanding Diffusion Model Concepts

  • Understanding the Prompt-to-prompt Paper: A Representative Diffusion Model Application

  • Implementing the Prompt-to-prompt Paper Using PyTorch

  • Solutions for Overcoming Obstacles When Reading and Implementing AI Papers

Can I reproduce the results of famous Diffusion papers too? 📖

Through hands-on implementation of the Prompt-to-prompt paper, naturally understand the details of Diffusion models, and
gain the paper implementation skills that have become essential for pursuing an AI career!


Have you experienced the following difficulties while studying Diffusion models?

  • The latest model code is not publicly available or is written in a way that makes it difficult to understand.

  • There's an abundance of theoretical information, but when it comes to actually starting to implement a specific model, it feels overwhelming.


From the perspective of an AI major, I've included all the know-how for overcoming the difficulties mentioned above. In this course, you'll understand the essential concepts of Diffusion models and reproduce results by implementing code from key papers together.

Below is an example of implementation results from the Prompt-to-prompt paper.

You'll learn this content📚

Overview of Diffusion Model Concepts

Before reading the paper, we quickly review the prerequisite knowledge about Diffusion models from a practical perspective. We also discuss the Diffusion model architecture that you need to know from an implementation standpoint.


Understanding the Prompt-to-prompt Paper

We'll read through the Prompt-to-prompt paper, a representative Diffusion model application paper, and summarize the key contents together. We'll focus on the essential aspects to consider during implementation, and it includes overall tips on how to read AI research papers.


Implementing the Prompt-to-prompt paper

Based on the papers we read together, we will write code to reproduce the results of the papers. The lecture content consists of live coding and includes detailed explanations for implementing the paper's content. You can also learn troubleshooting methods for various problems that occur during implementation.


Expected Questions Q&A 💬

Q1. I just want to apply the latest AI models, but do I really need to find and read papers?
A. Finding and reading papers is the fastest way to accurately understand the application methods. Try to quickly grasp the latest AI trends through papers.


Q2. Don't you need a lot of prerequisite knowledge to understand research papers?

A. If you have basic prerequisite knowledge in the relevant field, you can understand unfamiliar concepts by looking them up as needed. Through this course, gain the core concepts and know-how needed to understand artificial intelligence papers.

Pre-enrollment Reference Information📜

Practice Environment

  • Programming Languages and Libraries: Python, PyTorch, Hugging Face

  • Development Environment Tools: Visual Studio Code, Anaconda, Jupyter Notebook

  • Execution Hardware Requirements: Nvidia GPU 12GB or higher / Apple Silicon 16GB or higher

Learning Materials

  • Lecture slides, papers, and practice code provided


Prerequisites

  • Understanding of the Python Language

  • Basic development experience using Visual Studio Code, Anaconda, and Jupyter Notebook

  • Basic understanding of linear algebra/artificial intelligence

Important Notes

  • This course is a hands-on project for understanding and implementing papers that apply Diffusion models.

  • This is not a course that covers all artificial intelligence theories from A to Z.

  • Covers the background knowledge needed to implement the paper's content from a practical perspective.

Recommended for
these people

Who is this course right for?

  • Everyone involved in projects implementing the content of the latest artificial intelligence papers

  • Those preparing for AI-related careers (AI engineers, AI graduate school, etc.)

  • Those preparing university graduation thesis/projects on the topic of artificial intelligence

Need to know before starting?

  • Understanding of the Python Language

  • Basic development experience using Visual Studio Code, Anaconda, and Jupyter Notebook

  • Basic understanding of linear algebra/artificial intelligence

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52 lectures ∙ (6hr 32min)

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