Master AI Image Generation using Stable Diffusion_1

Master AI Image Generation using Stable Diffusion, available at $79.99, has an average rating of 4.45, with 53 lectures, based on 324 reviews, and has 2935 subscribers.

You will learn about Understand the basic of Stable Diffusion to create new images Learn how to use Stable Diffusion parameters to get different results Create images using other models provided by the Open Source community Learn about Prompt Engineering to choose the best keywords to generate the best images How to use negative prompts to indicate what should not appear in the images Use fine-tuning to create your custom model to generate your own images Send initial images to condition image generation Use inpainting to edit images, remove unwanted elements or swap objects This course is ideal for individuals who are People who want to learn how to create images using Artificial Intelligence or People who want to create their own avatars or Beginners in Computer Vision or Undergraduate and graduate students who are taking courses on Computer Vision, Artificial Intelligence, Digital Image Processing or Computer Graphics It is particularly useful for People who want to learn how to create images using Artificial Intelligence or People who want to create their own avatars or Beginners in Computer Vision or Undergraduate and graduate students who are taking courses on Computer Vision, Artificial Intelligence, Digital Image Processing or Computer Graphics.

Enroll now: Master AI Image Generation using Stable Diffusion

Summary

Title: Master AI Image Generation using Stable Diffusion

Price: $79.99

Average Rating: 4.45

Number of Lectures: 53

Number of Published Lectures: 53

Number of Curriculum Items: 53

Number of Published Curriculum Objects: 53

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the basic of Stable Diffusion to create new images

Learn how to use Stable Diffusion parameters to get different results

Create images using other models provided by the Open Source community

Learn about Prompt Engineering to choose the best keywords to generate the best images

How to use negative prompts to indicate what should not appear in the images

Use fine-tuning to create your custom model to generate your own images

Send initial images to condition image generation

Use inpainting to edit images, remove unwanted elements or swap objects

Who Should Attend

People who want to learn how to create images using Artificial Intelligence

People who want to create their own avatars

Beginners in Computer Vision

Undergraduate and graduate students who are taking courses on Computer Vision, Artificial Intelligence, Digital Image Processing or Computer Graphics

Target Audiences

People who want to learn how to create images using Artificial Intelligence

People who want to create their own avatars

Beginners in Computer Vision

Undergraduate and graduate students who are taking courses on Computer Vision, Artificial Intelligence, Digital Image Processing or Computer Graphics

The generation of images using Artificial Intelligence is an area that is gaining a lot of attention, both from technology professionals and people from other areas who want to create their own custom images. The tools used for this purpose are based on advanced and modern techniques from machine learning and computer vision, which can contribute to the creation of new compositions with high graphic quality. It is possible to create new images just by sending a textual description: you ask the AI (artificial intelligence) to create an image exactly as you want! For example, you can send the text “a cat reading a book in space” and the AI will create an image according to that description! This technique has been gaining a lot of attention in recent years and it tends to growth in the next few years.

There are several available tools for this purpose and one of the most used is Stable Diffusion developed by StabilityAI. It is Open Source, has great usability, speed, and is capable of generating high quality images. As it is open source, developers have created many extensions that are capable of generating an infinite variety of images in the most different styles.

In this course you will learn everything you need to know to create new images using Stable Diffusion and Python programming language. See below what you will learn in this course that is divided into six parts:

Part 1: Stable Diffusion basics: Intuition on how the technology works and how to create the first images. You will also learn about the main parameters to get different results, as well as how to create images with different styles

Part 2: Prompt Engineering: You will learn how to send the proper texts so the AI understands exactly what you want to generate

Part 3: Training a custom model: How about putting your own photos in the most different environments? In this section you will learn how to use your own images and generate your avatars

Part 4: Image to image: In addition to creating images by sending texts, it is also possible to send images as a starting point for the AI to generate the images

Part 5: Inpainting – exchaning classes: You will learn how to edit images to remove objects or swap them. For example: remove the dog and replace it with a cat

Part 6: ControlNet: In this section you will implement digital image processing techniques (edge and pose detection) to improve the results

All implementations will be done step by step in Google Colab online with GPU, so you don’t need a powerful computer to get amazing results in a matter of seconds! More than 50 lessons and more than 6 hours of videos!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Course materials

Chapter 2: Stable Diffusion basics

Lecture 1: Stable Diffusion – intuition 1

Lecture 2: Stable Diffusion – intuition 2

Lecture 3: Stable Diffusion – intuition 3

Lecture 4: Stable Diffusion – intuition 4

Lecture 5: Stable Diffusion – limitations of use

Lecture 6: Note about the implementation

Lecture 7: Installing the libraries

Lecture 8: Prompts – intuition

Lecture 9: Generating the first image

Lecture 10: Generating multiple images

Lecture 11: Parameters – seed

Lecture 12: Parameters – inference step

Lecture 13: Parameters – guidance scale

Lecture 14: Negative prompts – intuition

Lecture 15: Negative prompts – implementation

Lecture 16: Other models – intuition

Lecture 17: Other models – implementation

Lecture 18: Specific styles

Lecture 19: Changing the scheduler

Chapter 3: Prompt engineering

Lecture 1: Preparing the environment

Lecture 2: Subject/object, action/location, and type

Lecture 3: Style, colors, and artist

Lecture 4: Resolution, site, and other attributes

Lecture 5: Negative prompts

Lecture 6: Stable Diffusition v2

Lecture 7: Generating arts and photographs

Lecture 8: Generating landscapes and 3D images

Lecture 9: Generating drawings and architectures

Lecture 10: Custom models

Chapter 4: Custom training

Lecture 1: Fine-tuning with Dreambooth – intuition

Lecture 2: Preparing the environment

Lecture 3: Training 1

Lecture 4: Training 2

Lecture 5: Generating the images

Lecture 6: Improving the results

Chapter 5: Image to image

Lecture 1: Preparing the environment

Lecture 2: Generating the image

Lecture 3: Strength parameter

Lecture 4: Other image styles

Lecture 5: Other models

Lecture 6: Adding elements

Chapter 6: Inpainting – exchanging classes

Lecture 1: Preparing the enviroment

Lecture 2: Exchanging classes 1

Lecture 3: Exchanging classes 2

Chapter 7: ControlNet

Lecture 1: Preparing the enviroment

Lecture 2: Generating images using edges 1

Lecture 3: Generating images using edges 2

Lecture 4: Generating images using poses 1

Lecture 5: Generating images using poses 2

Chapter 8: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor

Gabriel Alves
Developer

AI Expert Academy
Instructor

Rating Distribution

1 stars: 6 votes

2 stars: 13 votes

3 stars: 39 votes

4 stars: 86 votes

5 stars: 180 votes

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