Introduction to Diffusion Models

About this course

Welcome to this course on Diffusion Models! This course delves into the fascinating world of diffusion models, starting from the initial research paper and advancing to cutting-edge applications such as image generation, inpainting, animations, and more. By combining a theoretical approach, and hands-on implementation using PyTorch, this course will equip you with the knowledge and expertise needed to excel in this exciting field of Generative AI. Why choose this Diffusion Models Course?

From Theory to Practice: This course begins by dissecting the initial research paper on diffusion models, explaining the concepts and techniques from scratch. Once you have gained a deep understanding of the underlying principles, we will reproduce results from the initial diffusion model paper, from scratch, using PyTorch. Advanced Image Generation: Building upon the foundational knowledge, we will dive into advanced techniques for image generation using diffusion models. Inpainting and DALL-E-like Applications: Discover how diffusion models can be used for inpainting, enabling you to fill in missing or damaged parts of images with stunning accuracy. After this session, you will have a deep understanding of how inpainting works with models such as Stable Diffusion or DALL-E, and you will have the knowledge needed to modify it to your needs. Animation Mastery: Unleash your creativity and learn how to create captivating animations using diffusion models. Dive into Stable Diffusion: Gain an in-depth understanding of Stable Diffusion and its inner workings by reviewing and analyzing the source code. This will empower you to utilize Stable Diffusion effectively in your own industrial and research projects, beyond just using the API. Stay Informed with Impactful Research: Stay up to date with the latest advancements in diffusion models by reviewing impactful research papers.

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