no code implementations • 28 Feb 2024 • Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van Den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
The field of deep generative modeling has grown rapidly and consistently over the years.
no code implementations • 11 Feb 2024 • Kushagra Pandey, Maja Rudolph, Stephan Mandt
We propose Splitting Integrators for fast stochastic sampling in pre-trained diffusion models in augmented spaces.
1 code implementation • 11 Oct 2023 • Kushagra Pandey, Maja Rudolph, Stephan Mandt
We propose two complementary frameworks for accelerating sample generation in pre-trained models: Conjugate Integrators and Splitting Integrators.
1 code implementation • ICCV 2023 • Kushagra Pandey, Stephan Mandt
Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks.
Ranked #17 on Image Generation on CIFAR-10
1 code implementation • 2 Jan 2022 • Kushagra Pandey, Avideep Mukherjee, Piyush Rai, Abhishek Kumar
Diffusion probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation.
Ranked #15 on Image Generation on CelebA 64x64