Master Stable Diffusion, Generative AI, and AI Image & Video Generation from the ground up with one of the most comprehensive hands-on courses available. Whether you want to understand how diffusion models work internally or build real AI-powered applications with Python, this course will take you from the mathematical foundations to advanced real-world implementations.

Unlike courses that only demonstrate AI tools, this course explains the technology behind them. You will first understand the theory of diffusion models, including Gaussian Distribution, Markov Chains, Forward and Reverse Diffusion, DDPM, DDIM, U-Net architecture, Positional Embeddings, and the complete Stable Diffusion pipeline. Then, you will apply this knowledge by building and using modern AI image generation systems with Python.

Throughout the course, you will implement real projects using Stable Diffusion, ControlNet, DreamBooth, LoRA, Hugging Face Diffusers, AnimateDiff, and AUTOMATIC1111. You will learn how to generate high-quality AI images, perform image-to-image generation, inpainting, outpainting, style transfer, portrait animation, AI video generation, and fine-tune your own diffusion models.

This course combines deep theoretical explanations with practical coding sessions. Every major concept is accompanied by Python implementations so that you understand not only how to use these technologies but also why they work.

What you'll learn

1. Understand Diffusion Models from first principles

2. Build diffusion models with Python

3. Master Stable Diffusion architecture

4. Learn DDPM and DDIM algorithms

5. Train and fine-tune DreamBooth models

6. Fine-tune models using LoRA

7. Generate realistic AI images

8. Create AI videos and image animations

9. Master ControlNet for advanced image generation

10. Perform Inpainting and Outpainting

11. Work with Hugging Face Diffusers

12. Build Stable Diffusion applications with Python

13. Understand U-Net architecture and Positional Embeddings

14. Learn image-to-image generation techniques

15. Apply diffusion models to real-world AI projects