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


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