Build Diffusion Models from Scratch with PyTorch

Ever wondered how AI creates stunning images from simple text prompts? Diffusion models are the core technology, and this course shows you exactly how they work from the ground up, using PyTorch. You will move beyond just using APIs to gain a fundamental understanding of the denoising process that powers modern image generation. By the end of the course, you'll have the skills to implement, train, and experiment with your own diffusion models, giving you a solid foundation in this exciting field of generative AI. What you'll learn: - Understand the core theory of Denoising Diffusion Probabilistic Models (DDPMs). - Build a complete diffusion model from the ground up using PyTorch and the U-Net architecture. - Practice training your models on image datasets to generate entirely new images. - Learn techniques for conditioning your models to allow for text-guided image creation. - Apply fundamental prompt engineering principles to guide and control your model's output. - Explore practical applications like image inpainting to fill in missing parts of a picture. The course begins with the essential mathematical concepts and progresses to hands-on coding exercises where you'll implement and train models piece by piece. You'll read through explanations and apply your knowledge through practical examples. This course is designed for learners with some Python and deep learning familiarity but who are new to diffusion models. No prior experience with generative AI is required to get started. Begin building your generative AI skills today.