Generative AI - Create an impressive AI Art

Generative AI : Create an impressive AI Art, available at $19.99, has an average rating of 4.3, with 91 lectures, based on 14 reviews, and has 586 subscribers.

You will learn about 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 learning about diffusers package learning about Automatic1111 and how to use it How to understand and implement research papers How to build a system to convert your video into animation How to use diffusers library How to convert your audio to video using AI This course is ideal for individuals who are students or professionals It is particularly useful for students or professionals.

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Summary

Title: Generative AI : Create an impressive AI Art

Price: $19.99

Average Rating: 4.3

Number of Lectures: 91

Number of Published Lectures: 90

Number of Curriculum Items: 91

Number of Published Curriculum Objects: 90

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will 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

learning about diffusers package

learning about Automatic1111 and how to use it

How to understand and implement research papers

How to build a system to convert your video into animation

How to use diffusers library

How to convert your audio to video using AI

Who Should Attend

students

professionals

Target Audiences

students

professionals

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: What you will learn in this course ?

Lecture 2: Where to find the codes ?

Lecture 3: what is stable diffusion ?

Lecture 4: what is gaussian distribution?

Lecture 5: How does stable diffusion model work?

Lecture 6: What is Markove chain ?

Lecture 7: What is Forward diffusion ?

Lecture 8: What is Reparameterization Trick ?

Lecture 9: What is variance schedule ?

Lecture 10: Linear variance schedule Vs cosine-based variance schedule

Lecture 11: What is Reverse diffusion ?

Lecture 12: How can we train our network part1 ?

Lecture 13: How can we train our network part2 ?

Lecture 14: How can we train our network part3 ?

Lecture 15: How can we train our network part4 ?

Lecture 16: Stable Diffusion Inference

Lecture 17: What is U network ?

Lecture 18: How to create Unconditional diffusion model ?

Lecture 19: What is positional embedding ?

Lecture 20: How to create Conditional diffusion model ?

Chapter 2: Unconditional diffusion model

Lecture 1: How to build and train unconditional diffusion model from scratch ?

Lecture 2: How to use any pretrained diffusion model from Hugging Face Hub ?

Lecture 3: How can we do Inpaint using our diffusion model?

Lecture 4: How to implement Repaint algorithm? python code

Lecture 5: How to speed up your diffusion model ?

Lecture 6: DDIM vs DDPM python code

Lecture 7: DDIM and out of domain images

Lecture 8: DDIM and out of domain images python code

Lecture 9: What is style transfer ?

Lecture 10: style transfer python code

Chapter 3: Stable diffusion model

Lecture 1: What is stable diffusion ?

Lecture 2: stable diffusion part 1 python code

Lecture 3: stable diffusion part 2 python code

Lecture 4: Fine-tuning your stable diffusion model using DreamBooth notebook part1

Lecture 5: What is Mixed Precision ?

Lecture 6: Fine-tuning your stable diffusion model using DreamBooth notebook part2

Lecture 7: What is ControlNet ?

Lecture 8: ControlNet with stable diffusion model using diffusers

Lecture 9: Inpainting with stable diffusion model

Chapter 4: Video to AI Animation with stable diffusion from scratch [New]

Lecture 1: Optical flow using FlowNet

Lecture 2: Optical flow using GMFlow

Lecture 3: Optical flow using GMFlow ?python code

Lecture 4: How to convert your video into frames ?python code

Lecture 5: How to extract the latents ? python code

Lecture 6: How to find the canny inputs and the latents for each key-frame ? python code

Lecture 7: How to generate new key-frames using sd model with control net ? python code

Lecture 8: What is cross frame-attention ?

Lecture 9: How to do cross-frame attention part1 ? python code

Lecture 10: How to do cross-frame attention part2 ? python code

Lecture 11: How to make your generated key-frames sharing the same textures ?

Lecture 12: Shape-aware module python code

Lecture 13: Pixel-Aware module

Lecture 14: Pixel-Aware module python code

Lecture 15: How to do keyframes to animation?

Chapter 5: EMO: Emote Portrait Alive [New]

Lecture 1: EMO introduction

Lecture 2: EMO introduction part2

Lecture 3: backbone NN

Lecture 4: How to inject an audio file in the U network ?

Lecture 5: What are ReferenceNet.& Temporal Modules ?

Lecture 6: What is Speed layer and Face locator ?

Chapter 6: Portrait Image Animation

Lecture 1: How the model works ?

Lecture 2: lets try it !!!

Chapter 7: Recap [New]

Lecture 1: what is diffusers library ?

Lecture 2: how to create a pipeline object and how to use it ?

Lecture 3: how these pipelines work ?

Lecture 4: How to use the same diffusion model to do different tasks ?

Lecture 5: How to select the best scheduler ?

Lecture 6: How to use lora models ?

Lecture 7: How to use lora models ? part2

Lecture 8: How to use AnimateDiff pipeline?

Lecture 9: How to use AudioDML pipeline?

Lecture 10: How to use depthmap controlnet with your sd model ?

Chapter 8: AUTOMATIC1111

Lecture 1: How to install AUTOMATIC1111 on Windows ?

Lecture 2: How to add your stable diffusion models & control nets in AUTOMATIC1111 ?

Lecture 3: txt2img in AUTOMATIC1111

Lecture 4: What are LoRA models ?

Lecture 5: How to train stable difusion models using LoRA method?

Lecture 6: img2img + inpainting + outpainting

Lecture 7: sketch + inpaint sketch

Lecture 8: How to create good prompt ?

Lecture 9: What is Regional Prompter ?

Lecture 10: Dynamic prompts

Lecture 11: open pose Controlnet

Lecture 12: How to upscale your image using controlnet ?

Lecture 13: How to use multi controlNets ?

Lecture 14: How to do perfect fingers ?

Lecture 15: How to use photopea editor in AUTOMATIC1111 ?

Lecture 16: control lighting with controlnet

Lecture 17: How to do zoom in and zoom out in automatic 1111 ?

Lecture 18: How can you convert your video into animation ??

Instructors

Riad Almadani
Machine Learning Engineer

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 0 votes

4 stars: 1 votes

5 stars: 11 votes

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