Deep Generative Models, Stable Diffusion, and the Revolution in Visual Synthesis
We had the pleasure of having Professor Björn Ommer as a guest lecturer in my course SSY340, Deep machine learning at Chalmers University of Technology.
Chapters:
0:00 Introduction
8:10 Overview of generative models
15:00 Diffusion models
19:37 Stable diffusion
26:10 Retrieval-Augmented Diffusion Models
Abstract:
Recently, deep generative modeling has become the most prominent paradigm for learning powerful representations of our (visual) world and for generating novel samples thereof. Consequently, they have already become the main building block for numerous algorithms and practical applications. This talk will contrast the most commonly used generative models to date with a particular focus on denoising diffusion probabilistic models, the core of the currently leading approaches to visual synthesis. Despite their enormous potential, these models come with their own specific limitations. We will then discuss a solution, latent diffusion models a.k.a. "Stable Diffusion", that significantly improves the efficiency of diffusion models. Now billions of training samples can be summarized in compact representations of just a few gigabyte so that the approach runs on even consumer GPUs. Time permitting, the talk will conclude with an outlook on current extensions and future work.
https://ommer-lab.com/research/latent...
https://compvis.github.io/taming-tran...
Short Bio:
Björn Ommer is a full professor at the University of Munich where he is heading the Machine Vision and Learning Group. Before he was a full professor in the department of mathematics and computer science at Heidelberg University. He received his diploma in computer science from University of Bonn and his PhD from ETH Zurich. Thereafter, he was a postdoc in the vision group of Jitendra Malik at UC Berkeley.
Björn serves as an associate editor for IEEE T-PAMI and previously for Pattern Recognition Letters. His research interests include semantic scene understanding, visual synthesis and retrieval, self-supervised metric and representation learning, and explainable AI. Moreover, he is applying this basic research in interdisciplinary projects within the digital humanities and the life sciences.
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