89 citations · 370 across the 13 of their papers we have counts for
13 papers
FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
Black Forest Labs, Stephen Batifol, Andreas Blattmann +18
We present evaluation results for FLUX.1 Kontext, a generative flow matching model that unifies image generation and editing. The model generates novel output views by incorporatin…
Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation
Axel Sauer, Frederic Boesel, Tim Dockhorn +3
Diffusion models are the main driver of progress in image and video synthesis, but suffer from slow inference speed. Distillation methods, like the recently introduced adversarial…
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann +14
Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perc…
Adversarial Diffusion Distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann +1
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 ma…
StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
Axel Sauer, Tero Karras, Samuli Laine +2
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 familie…
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
Katja Schwarz, Axel Sauer, Michael Niemeyer +2
State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields. While demonstrating impressive results, querying an MLP for every samp…
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
Axel Sauer, Katja Schwarz, Andreas Geiger
Computer graphics has experienced a recent surge of data-centric approaches for photorealistic and controllable content creation. StyleGAN in particular sets new standards for gene…
Projected GANs Converge Faster
Axel Sauer, Kashyap Chitta, Jens Müller +1
Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. They need careful regularization, vast amounts of compute, and expensive hyper-para…
Zoomorphic Gestures for Communicating Cobot States
Vanessa Sauer, Axel Sauer, Alexander Mertens
Communicating the robot state is vital to creating an efficient and trustworthy collaboration between humans and collaborative robots (cobots). Standard approaches for Robot-to-hum…
Counterfactual Generative Networks
Axel Sauer, Andreas Geiger
Neural networks are prone to learning shortcuts -- they often model simple correlations, ignoring more complex ones that potentially generalize better. Prior works on image classif…
How to Make Deep RL Work in Practice
Nirnai Rao, Elie Aljalbout, Axel Sauer +1
In recent years, challenging control problems became solvable with deep reinforcement learning (RL). To be able to use RL for large-scale real-world applications, a certain degree…
Tracking Holistic Object Representations
Axel Sauer, Elie Aljalbout, Sami Haddadin
Recent advances in visual tracking are based on siamese feature extractors and template matching. For this category of trackers, latest research focuses on better feature embedding…
Conditional Affordance Learning for Driving in Urban Environments
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 approach…