Master Generative AI: Unleash Your Creative Potential with AI Art
What you will learn:
- Generative AI fundamentals
- Diffusion model implementation
- AI image and video generation
- Prompt engineering mastery
- Stable Diffusion and Automatic1111 expertise
- DreamBooth and ControlNet techniques
- Video-to-animation conversion
- CNC machining integration with AI art
- Ethical considerations in AI art
- Advanced techniques using Diffusers library
Description
Dive into the exciting world of generative AI and learn to create breathtaking AI art! This comprehensive course empowers you to generate stunning images and videos using cutting-edge techniques like Stable Diffusion and Automatic1111. You'll master prompt engineering, explore diffusion models, and understand the underlying principles of AI image generation. We'll cover everything from building your own unconditional and conditional diffusion models to fine-tuning Stable Diffusion with DreamBooth and ControlNet. This course isn't just about theory; you'll build practical projects, including converting videos into mesmerizing animations and even bringing your AI creations to life through CNC machining. Whether you're a complete beginner or an experienced AI enthusiast, you'll unlock new levels of creative expression and technical expertise. Discover how to leverage AI's power to generate realistic images for diverse applications, master the art of prompt engineering, and address the ethical considerations surrounding AI-generated content. Unleash your creativity and transform your ideas into reality. Enroll now and embark on your journey to mastering generative AI artistry.
Curriculum
Introduction to Generative AI
This foundational section lays the groundwork for understanding generative AI. Lectures cover core concepts like stable diffusion, Gaussian distribution, Markov chains, and the mechanics of forward and reverse diffusion processes. You'll delve into the intricacies of training neural networks, exploring techniques like reparameterization and variance scheduling (linear vs. cosine-based). We'll cover U-networks and positional embeddings, culminating in an understanding of unconditional and conditional diffusion models. This section also provides a solid foundation on where to find coding resources and what to expect throughout the entire course.
Building Unconditional Diffusion Models
This section provides hands-on experience in building and training unconditional diffusion models from scratch. You'll learn to leverage pre-trained models from Hugging Face Hub, implement inpainting techniques using the Repaint algorithm, and explore methods for optimizing the speed of your diffusion models. Comparisons between DDIM and DDPM algorithms will be covered, along with examples for handling out-of-domain images and practical applications of style transfer.
Mastering Stable Diffusion
Here, you'll dive deep into Stable Diffusion, learning to implement it from scratch in Python (parts 1 & 2). You'll gain skills in fine-tuning your models using DreamBooth, understanding the role of mixed precision, and utilizing ControlNet for enhanced control. The section also covers inpainting techniques within the Stable Diffusion framework. This section aims to give the student a hands-on approach in utilizing Stable Diffusion in different application scenarios.
Video to AI Animation with Stable Diffusion
This innovative section teaches you to transform videos into captivating animations using Stable Diffusion. You'll learn optical flow techniques (FlowNet, GMFlow), video-to-frame conversion, latent extraction, and the generation of new keyframes using ControlNet. Key concepts like cross-frame attention, texture consistency, shape-aware and pixel-aware modules will be explored through hands-on coding exercises, enabling you to create seamless and visually stunning animations from your video content.
EMO: Emote Portrait Alive
Explore the EMO model for animating portraits with emotion using audio input. Lectures cover the model's architecture, including the backbone neural network, audio injection into the U-network, and the roles of ReferenceNet, temporal modules, the speed layer, and face locator. This section will teach you the state of the art in portrait animation with audio synchronization.
Portrait Image Animation
This section provides a practical application of portrait animation, showing you how to bring still images to life.
Advanced Diffusers Techniques
This section covers advanced topics within the Diffusers library. You'll learn to create pipeline objects, understand how these pipelines function, utilize various diffusion models for different tasks, select optimal schedulers, and master the use of LoRA models, including AnimateDiff and AudioDML pipelines. The usage of depthmap controlnet will also be explored.
Conquering Automatic1111
This section is dedicated to mastering Automatic1111, a powerful tool for Stable Diffusion. You'll learn installation on Windows, adding models and control nets, using txt2img, img2img, inpainting and outpainting. You will also learn about LoRA models, training methods, prompt engineering (including regional prompters and dynamic prompts), ControlNet applications (open pose, upscaling, multi-ControlNets), advanced techniques for generating perfect fingers, and utilizing Photopea editor within Automatic1111. The section culminates in learning video-to-animation conversion techniques.
CNC and AI Art: Real-World Applications
Bridge the gap between digital and physical art. Learn about CNC machines and how to engrave your AI-generated images onto physical objects, demonstrated with a practical example of engraving on a notebook.
AI in Architecture and Tabular Data
This section explores emerging applications of AI. Learn to transform sketches into architectural designs using AI tools and explore the use of diffusion models with tabular data, covering data loading, model training, and application techniques.
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