Introduction to Diffusion Models

Introduction to Diffusion Models, available at $34.99, has an average rating of 4.41, with 56 lectures, based on 142 reviews, and has 1041 subscribers.

You will learn about How Diffusion Models work Implementation of Diffusion Models from scratch using PyTorch In depth understanding of inpainting with Diffusion Models Deep analysis of Stable Diffusion: opening the black box Making great animations with Diffusion Models Review of impactful research papers This course is ideal for individuals who are To engineers and programmers or To students and researchers or To entrepreneurs, CEOs and CTOs or Machine Learning enthusiast It is particularly useful for To engineers and programmers or To students and researchers or To entrepreneurs, CEOs and CTOs or Machine Learning enthusiast.

Enroll now: Introduction to Diffusion Models

Summary

Title: Introduction to Diffusion Models

Price: $34.99

Average Rating: 4.41

Number of Lectures: 56

Number of Published Lectures: 56

Number of Curriculum Items: 56

Number of Published Curriculum Objects: 56

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

How Diffusion Models work
Implementation of Diffusion Models from scratch using PyTorch
In depth understanding of inpainting with Diffusion Models
Deep analysis of Stable Diffusion: opening the black box
Making great animations with Diffusion Models
Review of impactful research papers

Who Should Attend

To engineers and programmers
To students and researchers
To entrepreneurs, CEOs and CTOs
Machine Learning enthusiast

Target Audiences

To engineers and programmers
To students and researchers
To entrepreneurs, CEOs and CTOs
Machine Learning enthusiast

Welcome to this course on Diffusion Models!

This course delves into the fascinating world of diffusion models, starting from the initial research paper and advancing to cutting-edge applications such as image generation, inpainting, animations, and more. By combining a theoretical approach, and hands-on implementation using PyTorch, this course will equip you with the knowledge and expertise needed to excel in this exciting field of Generative AI.

Why choose this Diffusion Models Course?

From Theory to Practice: This course begins by dissecting the initial research paper on diffusion models, explaining the concepts and techniques from scratch. Once you have gained a deep understanding of the underlying principles, we will reproduce results from the initial diffusion model paper, from scratch, using PyTorch.
Advanced Image Generation: Building upon the foundational knowledge, we will dive into advanced techniques for image generation using diffusion models.
Inpainting and DALL-E-like Applications: Discover how diffusion models can be used for inpainting, enabling you to fill in missing or damaged parts of images with stunning accuracy. After this session, you will have a deep understanding of how inpainting works with models such as Stable Diffusion or DALL-E, and you will have the knowledge needed to modify it to your needs.
Animation Mastery: Unleash your creativity and learn how to create captivating animations using diffusion models.
Dive into Stable Diffusion: Gain an in-depth understanding of Stable Diffusion and its inner workings by reviewing and analyzing the source code. This will empower you to utilize Stable Diffusion effectively in your own industrial and research projects, beyond just using the API.
Stay Informed with Impactful Research: Stay up to date with the latest advancements in diffusion models by reviewing impactful research papers. Gain insights into the cutting-edge techniques and applications driving the field forward, and expand your knowledge to stay ahead of the curve. Register now to access our comprehensive online course on Diffusion Models and learn how this technology can enhance your projects. Don’t miss this opportunity to learn about the latest advances in Generative AI with Diffusion Models!

Register now to access our comprehensive online course on Diffusion Models and learn how this technology can enhance your projects.

Don’t miss this opportunity to learn about the latest advances in Generative AI with Diffusion Models!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Initial paper on Diffusion Models

Lecture 1: Forward / Diffusion process

Lecture 2: Forward / Diffusion process: implementation

Lecture 3: Diffusion process: tricks

Lecture 4: Diffusion process: incorporation of the tricks in the implementation

Lecture 5: Diffusion process: visualization

Lecture 6: Reverse process

Lecture 7: Reverse process: implementation

Lecture 8: Architecture of the model

Lecture 9: Reverse process: sampling

Lecture 10: Reverse process: visualization

Lecture 11: Training equations – part 1

Lecture 12: Training equations – part 2

Lecture 13: Training equations : implementation – part 1

Lecture 14: Training equations : implementation – part 2

Lecture 15: Implementation of the training loop

Lecture 16: Training on GPU

Lecture 17: Correct typo

Lecture 18: Reproduction of a Figure from the paper: Analysis of the results

Chapter 3: Denoising Diffusion Probabilistic Models

Lecture 1: Review of the paper

Lecture 2: Time embedding

Lecture 3: Pseudocode

Lecture 4: U-Net Implementation : time embedding

Lecture 5: U-Net Implementation : downsampling

Lecture 6: U-Net Implementation : upsampling

Lecture 7: U-Net Implementation : ResNet – part1

Lecture 8: U-Net Implementation : ResNet – part2

Lecture 9: U-Net Implementation : ResNet – part3

Lecture 10: U-Net Implementation : Attention Mechanism – part1

Lecture 11: U-Net Implementation : Attention Mechanism – part2

Lecture 12: Finishing the U-Net Implementation – part1

Lecture 13: Finishing the U-Net Implementation – part2

Lecture 14: Finishing the U-Net Implementation – part3

Lecture 15: Finishing the U-Net Implementation – part4

Lecture 16: Finishing the U-Net Implementation – part5

Lecture 17: Denoising Diffusion Probabilistic Models: implementation

Lecture 18: Denoising Diffusion Probabilistic Models: training

Lecture 19: Denoising Diffusion Probabilistic Models: sampling

Lecture 20: Denoising Diffusion Probabilistic Models: utils

Lecture 21: Denoising Diffusion Probabilistic Models: training loop

Lecture 22: Denoising Diffusion Probabilistic Models: visualization

Lecture 23: Denoising Diffusion Probabilistic Models: training on GPU

Lecture 24: Analysis of the results

Chapter 4: Inpainting

Lecture 1: Inpainting with Diffusion Models: explanation

Lecture 2: Inpainting with Diffusion Models: implementation

Lecture 3: Inpainting with Diffusion Models: bug correction

Chapter 5: Animating Diffusion Models

Lecture 1: Animations – part1

Lecture 2: Animations – part2

Lecture 3: Animations – part3

Chapter 6: Stable Diffusion

Lecture 1: Stable Diffusion Paper

Lecture 2: Stable Diffusion: Hugging Face API – part1

Lecture 3: Stable Diffusion: Hugging Face API – part2

Lecture 4: Stable Diffusion: Hugging Face API – seeding and reproducibility

Lecture 5: Stable Diffusion: review of the code – part1

Lecture 6: Stable Diffusion: review of the code – part2

Lecture 7: Stable Diffusion: review of the code – part3

Instructors

Maxime Vandegar
Ingénieur de recherche

Rating Distribution

1 stars: 0 votes
2 stars: 8 votes
3 stars: 16 votes
4 stars: 39 votes
5 stars: 79 votes

Frequently Asked Questions

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