no code implementations • 18 Mar 2024 • Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, Robin Rombach
Distillation methods, like the recently introduced adversarial diffusion distillation (ADD) aim to shift the model from many-shot to single-step inference, albeit at the cost of expensive and difficult optimization due to its reliance on a fixed pretrained DINOv2 discriminator.
1 code implementation • 5 Mar 2024 • Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, Robin Rombach
Rectified flow is a recent generative model formulation that connects data and noise in a straight line.
4 code implementations • 28 Nov 2023 • Axel Sauer, Dominik Lorenz, Andreas Blattmann, Robin Rombach
We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1-4 steps while maintaining high image quality.
1 code implementation • 23 Jan 2023 • Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, Timo Aila
Text-to-image synthesis has recently seen significant progress thanks to large pretrained language models, large-scale training data, and the introduction of scalable model families such as diffusion and autoregressive models.
Ranked #19 on Text-to-Image Generation on MS COCO
1 code implementation • 15 Jun 2022 • Katja Schwarz, Axel Sauer, Michael Niemeyer, Yiyi Liao, Andreas Geiger
State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields.
2 code implementations • 1 Feb 2022 • Axel Sauer, Katja Schwarz, Andreas Geiger
StyleGAN in particular sets new standards for generative modeling regarding image quality and controllability.
Ranked #1 on Image Generation on CIFAR-10 (NFE metric)
3 code implementations • NeurIPS 2021 • Axel Sauer, Kashyap Chitta, Jens Müller, Andreas Geiger
Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train.
Ranked #1 on Image Generation on Stanford Cars
1 code implementation • ICLR 2021 • Axel Sauer, Andreas Geiger
Prior works on image classification show that instead of learning a connection to object shape, deep classifiers tend to exploit spurious correlations with low-level texture or the background for solving the classification task.
1 code implementation • 25 Oct 2020 • Nirnai Rao, Elie Aljalbout, Axel Sauer, Sami Haddadin
Additionally, techniques from supervised learning are often used by default but influence the algorithms in a reinforcement learning setting in different and not well-understood ways.
2 code implementations • 21 Jul 2019 • Axel Sauer, Elie Aljalbout, Sami Haddadin
The framework leverages the idea of obtaining additional object templates during the tracking process.
Ranked #3 on Visual Object Tracking on VOT2017/18
1 code implementation • 18 Jun 2018 • Axel Sauer, Nikolay Savinov, Andreas Geiger
Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs.