What you'll learn
learning about diffusion models
practical applications of AI-generated images
students will have the knowledge and skills to build their own machine that can generate realistic images
How to generate your own art using AI
Requirements
Basic understanding of machine learning concepts
Programming skills
Description
Welcome to this in-depth and comprehensive course where you will explore the fascinating world of artificial intelligence and learn how to generate realistic images using cutting-edge techniques. With the rapid development of deep learning and neural networks, the potential of AI-generated images is enormous. In this course, you will learn how to build your own machine that can generate images that look strikingly real.You will start with an introduction to diffusion models, which are a powerful class of models that can be used for image generation. You will explore how they work, their underlying principles, and how to use them in different tasks like inpainting and image-to-image generation. You will also delve into the techniques that are used to train these models and how they can be optimized to produce the best possible results.Throughout the course, you will gain hands-on experience with practical applications of AI-generated images. You will learn how to use diffusion models to create stunning, high-quality images for a variety of applications. You will also gain an understanding of the ethical implications of AI-generated images and how to navigate these issues in your work.By the end of the course, you will have the knowledge and skills to build your own machine that can generate realistic images using AI. You will have a deep understanding of the underlying principles of diffusion models and how to apply them to create images that are not only realistic but also aesthetically pleasing. Whether you are a beginner or an experienced AI developer, this course will equip you with the tools you need to take your work to the next level.Note // this course is not complete yet we will update it frequently and add more and more lectures
Overview
Section 1: Introduction about stable diffusion
Lecture 1 what is stable diffusion ?
Lecture 2 what is gaussian distribution?
Lecture 3 How does stable diffusion model work?
Lecture 4 What is Markove chain ?
Lecture 5 What is Forward diffusion ?
Lecture 6 What is Reparameterization Trick ?
Lecture 7 What is variance schedule ?
Lecture 8 Linear variance schedule Vs cosine-based variance schedule
Lecture 9 What is Reverse diffusion ?
Lecture 10 How can we train our network part1 ?
Lecture 11 How can we train our network part2 ?
Lecture 12 How can we train our network part3 ?
Lecture 13 How can we train our network part4 ?
Lecture 14 Stable Diffusion Inference
Lecture 15 What is U network ?
Lecture 16 How to create Unconditional diffusion model ?
Lecture 17 What is positional embedding ?
Lecture 18 How to create Conditional diffusion model ?
Section 2: Unconditional diffusion model
Lecture 19 How to build and train unconditional diffusion model from scratch ?
Lecture 20 How to use any pretrained diffusion model from Hugging Face Hub ?
Lecture 21 How can we do Inpaint using our diffusion model?
students and professionals


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