How to Create Illustrations with Stable Diffusion Neural Network by Konstantin Klambotsky (Как создавать иллюстрации с помощью нейросети Stable Diffusion – Russian, Eng sub)

Become acquainted with the Stable Diffusion neural network and unlock its creative potential firsthand. With no prior graphic design, drawing, or programming skills required, you’ll delve into crafting unique illustrations of any complexity.

Upon completing this course, you’ll:

• Learn to craft accurate text queries for generating Stable Diffusion images (Text to Image).
• Master the installation of Stable Diffusion on your computer or in the Google Collab cloud service.
• Gain proficiency in fundamental neural network tools and their application.
• Discover how to locate and implement various models and styles to modify neural network output across different genres and aesthetics (photorealism, anime, art, cinematic, etc.).
• Acquire skills to generate images from sketches or transform existing images (Image to Image).

Stable Diffusion stands as a leading neural network in image generation, offering the capability to produce stunning visuals from text descriptions, enhance sketches into complete artworks, and creatively manipulate photos according to your vision.

This technology is freely accessible, allowing you to harness its power by installing Stable Diffusion on your computer.

This course caters to you if you:

• Have an interest in graphics and image creation.
• Felt your skills weren’t sufficient to pursue a professional career in design or illustration but aspire to augment your income.
• Seek to embrace neural networks as a novel tool for your work.
• Are keen on staying updated with the latest IT trends and mastering new technologies.

Course breakdown:

• Lesson 1: Introduction to Stable Diffusion
• Lesson 2: Generating images through “Text to Image”
• Lesson 3: Model search and selection
• Lesson 4: Understanding generation parameters
• Lesson 5: Character development
• Lesson 6: Image-to-image conversion
• Lesson 7: Replacement of image sections
• Bonus Lesson: Analysis of common mistakes