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Unleash your Creativity with Stable Diffusion AI
Rating: 4.1 out of 5(16 ratings)
2,071 students

Unleash your Creativity with Stable Diffusion AI

Free & comprehensive course to learn Stable Diffusion covering Automatic1111 UI, API, ControlNet & lot more
Created byTechLatest Net
Last updated 8/2023
English

What you'll learn

  • Understand the basic concepts behind Stable Diffusion, including diffusion models, latent vector spaces, and text-to-image generation.
  • Learn how to set up and run Stable Diffusion in AWS, GCP & Azure cloud
  • Gain experience generating images and editing existing images using Stable Diffusion and Automatic1111 user interface.
  • How to use Stable Diffusion programmatically via API(Application Programming Interface)
  • Overview of ControlNet & Dreambooth with Hands on Examples
  • How to extend Stable Diffusion Functionality Using Automatic1111 Plugins
  • How to use custom models for image generation

Course content

1 section • 13 lectures • 1h 55m total length

  • Introduction6:12

    01-Course+Introduction+of+Learn+Stable+Diffusion+using+Automatic1111+web+interface

  • 02 Introduction to Generative AI & Stable Diffusion7:22

    An Overview of Generative AI & Stable Diffusion

  • 03 Setup of Stable Diffusion with Automatic1111 web interface on AWS11:24

    This lecture covers step by step guide on how to setup Stable Diffusion with Automatic1111 on AWS Cloud(Amazon Web Services)

  • 04 Setup of Stable Diffusion with Automatic1111 web interface on GCP10:11

    This lecture covers step by step guide on how to setup Stable Diffusion with Automatic1111 on GCP Cloud(Google Cloud Platform)

  • 05 Setup of Stable Diffusion with Automatic1111 web interface on Azure11:53

    This lecture covers step by step guide on how to setup Stable Diffusion with Automatic1111 on Azure Cloud(Microsoft Azure)

  • 06-01- Introduction to Automatic1111 features Part 111:09

    Part 1/3 : Overview of Automatic1111 web interface covering high level overview and various features.

  • 06-02- Introduction to Automatic1111 features Part 211:35

    Part 2/3 of Automatic1111 web interface covering its features in details.

  • 06-03- Introduction to Automatic1111 features Part 37:13

    Part 3/3 of the Automatic1111 web interface covering its features in details.

  • 07 Enabling Stable Diffusion API and How to run it A Step-by-Step Guide7:40

    In this lecture, we provide an overview of enabling the Stable Diffusion API and how to run it.

  • 08 Introduction to ControlNet A Step-by-Step Guide8:11

    In this lecture, we explained the ControlNet Features, a powerful extension of Stable Diffusion, and Provided a Step by Step Guide for using ControlNet on Automatic 1111 Stable Diffusion Interface.

  • 09 Introduction to Dreambooth A Step-by-Step Guide7:02

    In this lecture, we explained the Dreambooth Features, a powerful extension of Stable Diffusion, and Provided a Step by Step Guide for using Dreambooth on Automatic 1111 Stable Diffusion Interface.

  • 10 Intro to Automatic1111 & Stable Diffusion functionality using Extensions5:58

    In this lecture, we have provided insights into how to extend the functionality of Automatic1111 and Stable Diffusion using extensions, By extending the capabilities of these tools, users can harness the extension ecosystem and extend the Stable Diffusion capabilities.

  • 11 Introduction to Stable Diffusion models in Automatic11119:31

    This lecture serves as useful for those seeking to go beyond the AI capabilities provided by the Stable Diffusion foundation model by utilizing custom-trained models on top of the base model.

    Learn about what a custom model is and how pre-trained custom Stable Diffusion models can be used to further enhance image generation by skewing it towards certain attributes on which the custom model is trained. The type of images a model can generate depends on the data used to train the foundation and custom models.

    The lecture aims to introduce different models, explain how to install and use them, and also provide instructions on how to merge different custom models together.

Requirements

  • Basic computer vision knowledge. Concepts like images, pixels, channels, kernels, and filters. This will help understand how Stable Diffusion works.
  • Curiosity and interest in generative AI and text-to-image models. A desire to learn how to use these powerful new tools to create novel images.

Description

  • This course will teach you how to use the powerful Stable Diffusion AI image generator to unleash your creativity and supercharge your productivity as an artist, designer, creative professional & app developer.

  • You'll learn how to generate stunning digital art from simple text prompts and customize the model to align with your creative vision. The course covers the techniques behind text-to-image synthesis like diffusion models, latent spaces, and attention mechanisms.


  • Deployment & setup of Stable Diffusion on AWS, GCP & Azure Cloud platforms.

  • Through hands-on exercises, you'll gain practical skills for harnessing Stable Diffusion to generate concept art, illustrations, variations, and novel ideas that can transform your work.

  • The course also covers best practices for prompt engineering to exploit the different features of Stable Diffusion.


  • The course also covers topics like API(Application Programming Interface) for App developers to create custom apps using Stable Diffusion.


  • How to further customize your generated images by using ControlNet, Dreambooth & Custom models.


  • Learn how to extend Stable Diffusion & Automatic1111 Functionality using plugins.


  • By the end of the course, you will be able to use Stable Diffusion as a creative tool that amplifies your imagination and helps you produce more compelling images, graphics, and visual content for your projects.

Who this course is for:

  • Artists and creative professionals interested in learning how to use AI image generation tools in their work. Stable diffusion can help generate novel concepts, variations, and ideas.
  • Developers and machine learning engineers wanting to learn how to build and customize stable diffusion models. The course would cover the underlying architecture and techniques.
  • Researchers studying generative models, text-to-image synthesis, and diffusion models. The course content could provide insights and inspiration for new research directions.
  • Students in computer science, machine learning, or artificial intelligence programs. A stable diffusion course could help teach important concepts like generative modelling, latent spaces, and diffusion processes through a practical application.
  • Anyone curious about the capabilities of modern generative AI and wanting to learn how to harness this technology in an ethical and responsible manner. The course would likely cover best practices, limitations, and societal considerations.

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