Master AI Image Generation using Stable Diffusion
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:
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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
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Part 2: Prompt Engineering: You will learn how to send the proper texts so the AI understands exactly what you want to generate
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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
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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
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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
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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
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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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