Introduction To Diffusion Models 2023

Diffusion Models from scratch using PyToch | In depth breaking down of Stable Diffusion and DALL-E

What you'll 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

Requirements

Basic programming knowledge

Basic Machine Learning knowledge

Description

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!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Initial paper on Diffusion Models

Lecture 2 Forward / Diffusion process

Lecture 3 Forward / Diffusion process: implementation

Lecture 4 Diffusion process: tricks

Lecture 5 Diffusion process: incorporation of the tricks in the implementation

Lecture 6 Diffusion process: visualization

Lecture 7 Reverse process

Lecture 8 Reverse process: implementation

Lecture 9 Architecture of the model

Lecture 10 Reverse process: sampling

Lecture 11 Reverse process: visualization

Lecture 12 Training equations - part 1

Lecture 13 Training equations - part 2

Lecture 14 Training equations : implementation - part 1

Lecture 15 Training equations : implementation - part 2

Lecture 16 Implementation of the training loop

Lecture 17 Training on GPU

Lecture 18 Correct typo

Lecture 19 Reproduction of a Figure from the paper: Analysis of the results

Section 3: Denoising Diffusion Probabilistic Models

Lecture 20 Review of the paper

Lecture 21 Time embedding

Lecture 22 Pseudocode

Lecture 23 U-Net Implementation : time embedding

Lecture 24 U-Net Implementation : downsampling

Lecture 25 U-Net Implementation : upsampling

Lecture 26 U-Net Implementation : ResNet - part1

Lecture 27 U-Net Implementation : ResNet - part2

Lecture 28 U-Net Implementation : ResNet - part3

Lecture 29 U-Net Implementation : Attention Mechanism - part1

Lecture 30 U-Net Implementation : Attention Mechanism - part2

Lecture 31 Finishing the U-Net Implementation - part1

Lecture 32 Finishing the U-Net Implementation - part2

Lecture 33 Finishing the U-Net Implementation - part3

Lecture 34 Finishing the U-Net Implementation - part4

Lecture 35 Finishing the U-Net Implementation - part5

Lecture 36 Denoising Diffusion Probabilistic Models: implementation

Lecture 37 Denoising Diffusion Probabilistic Models: training

Lecture 38 Denoising Diffusion Probabilistic Models: sampling

Lecture 39 Denoising Diffusion Probabilistic Models: utils

Lecture 40 Denoising Diffusion Probabilistic Models: training loop

Lecture 41 Denoising Diffusion Probabilistic Models: visualization

Lecture 42 Denoising Diffusion Probabilistic Models: training on GPU

Lecture 43 Analysis of the results

Section 4: Inpainting

Lecture 44 Inpainting with Diffusion Models: explanation

Lecture 45 Inpainting with Diffusion Models: implementation

Section 5: Animating Diffusion Models

Lecture 46 Animations - part1

Lecture 47 Animations - part2

Lecture 48 Animations - part3

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