52 citations · 73 across the 5 of their papers we have counts for
10 papers · 1 filter
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…
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
Patrick Esser, Robin Rombach, Andreas Blattmann +1
Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis. Nevertheless, the…
Stochastic Image-to-Video Synthesis using cINNs
Michael Dorkenwald, Timo Milbich, Andreas Blattmann +3
Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a futur…
High-Resolution Complex Scene Synthesis with Transformers
Manuel Jahn, Robin Rombach, Björn Ommer
The use of coarse-grained layouts for controllable synthesis of complex scene images via deep generative models has recently gained popularity. However, results of current approach…
Geometry-Free View Synthesis: Transformers and no 3D Priors
Robin Rombach, Patrick Esser, Björn Ommer
Is a geometric model required to synthesize novel views from a single image? Being bound to local convolutions, CNNs need explicit 3D biases to model geometric transformations. In…
A Note on Data Biases in Generative Models
Patrick Esser, Robin Rombach, Björn Ommer
It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter a…