Generative AI with Diffusion Models

Abstract

Thanks to improvements in computing power and scientific theory, generative AI is more accessible than ever before. Generative AI plays a significant role across industries due to its numerous applications, such as creative content generation, data augmentation, simulation and planning, anomaly detection, drug discovery, personalized recommendations, and more. In this workshop, attendees will take a deeper dive into denoising diffusion models, which are a popular choice for text-to-* pipelines such as text-to-images and text-to-videos. By participating in this is hands-on workshop, attendees will: - Build and train a U-Net model to generate images from pure noise - Improve the quality of generated images with the denoising diffusion process - Control the image output with context embeddings - Generate images from English text prompts using the Contrastive Language Image Pretraining (CLIP) neural network Pre-requisites: a basic understanding of deep learning concepts.