Imaging Seminar: Generative AI, Stable Diffusion, and the Revolution in Visual Synthesis Abstract
Event details
| Date | 16.03.2023 |
| Hour | 17:00 › 18:00 |
| Speaker | Prof. Björn Ommer, Ludwig Maximilian University of Munich |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
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, this has 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 consumer hardware. Making high-quality visual synthesis accessible to everyone has revolutionized the way we create visual content and spurred research and the development of numerous novel applications.
We will then discuss recent extensions that cast an interesting perspective on future generative modelling. Time permitting, the talk will also cover applications of generative AI in the life sciences and beyond.
Biography:
Björn Ommer is a full professor at the University of Munich where he is heading the Computer Vision and Learning Group. Before he was a full professor in the department of mathematics and computer science at Heidelberg University and a co-director of the IWR and the HCI. 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. His research interests include semantic scene understanding and retrieval, generative AI and visual synthesis, 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. His group has published a series of generative approaches, including “VQGAN” and “Stable Diffusion”, which are now democratizing the creation of visual content and have already opened up a breadth of new directions in research, industry, the media, and beyond.
The talk is followed by an aperitif.
Registration appreciated
More info here
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, this has 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 consumer hardware. Making high-quality visual synthesis accessible to everyone has revolutionized the way we create visual content and spurred research and the development of numerous novel applications.
We will then discuss recent extensions that cast an interesting perspective on future generative modelling. Time permitting, the talk will also cover applications of generative AI in the life sciences and beyond.
Biography:
Björn Ommer is a full professor at the University of Munich where he is heading the Computer Vision and Learning Group. Before he was a full professor in the department of mathematics and computer science at Heidelberg University and a co-director of the IWR and the HCI. 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. His research interests include semantic scene understanding and retrieval, generative AI and visual synthesis, 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. His group has published a series of generative approaches, including “VQGAN” and “Stable Diffusion”, which are now democratizing the creation of visual content and have already opened up a breadth of new directions in research, industry, the media, and beyond.
The talk is followed by an aperitif.
Registration appreciated
More info here
Practical information
- General public
- Free
Organizer
- EPFL Center for Imaging