Tim Dockhorn
Tim Dockhorn
Research Scientist at Stability AI
Verified email at - Homepage
Cited by
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Align your latents: High-resolution video synthesis with latent diffusion models
A Blattmann, R Rombach, H Ling, T Dockhorn, SW Kim, S Fidler, K Kreis
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Sdxl: Improving latent diffusion models for high-resolution image synthesis
D Podell, Z English, K Lacey, A Blattmann, T Dockhorn, J Müller, J Penna, ...
arXiv preprint arXiv:2307.01952, 2023
Score-based generative modeling with critically-damped langevin diffusion
T Dockhorn, A Vahdat, K Kreis
arXiv preprint arXiv:2112.07068, 2021
A discussion on solving partial differential equations using neural networks
T Dockhorn
arXiv preprint arXiv:1904.07200, 2019
Genie: Higher-order denoising diffusion solvers
T Dockhorn, A Vahdat, K Kreis
Advances in Neural Information Processing Systems 35, 30150-30166, 2022
Differentially private diffusion models
T Dockhorn, T Cao, A Vahdat, K Kreis
arXiv preprint arXiv:2210.09929, 2022
Stable video diffusion: Scaling latent video diffusion models to large datasets
A Blattmann, T Dockhorn, S Kulal, D Mendelevitch, M Kilian, D Lorenz, ...
arXiv preprint arXiv:2311.15127, 2023
Latent Space Diffusion Models of Cryo-EM Structures
K Kreis, T Dockhorn, Z Li, E Zhong
arXiv preprint arXiv:2211.14169, 2022
Demystifying and generalizing binaryconnect
T Dockhorn, Y Yu, E Sari, M Zolnouri, V Partovi Nia
Advances in Neural Information Processing Systems 34, 13202-13216, 2021
Density deconvolution with normalizing flows
T Dockhorn, JA Ritchie, Y Yu, I Murray
arXiv preprint arXiv:2006.09396, 2020
Synthesizing content using diffusion models in content generation systems and applications
KJ Kreis, T Dockhorn, A Vahdat
US Patent App. 18/319,986, 2023
Diffusion-based generative modeling for synthetic data generation systems and applications
K Kreis, T Dockhorn, A Vahdat
US Patent App. 17/959,915, 2023
Generative Modeling with Neural Ordinary Differential Equations
T Dockhorn
University of Waterloo, 2019
Distilling the Knowledge in Diffusion Models
T Dockhorn, R Rombach, A Blatmann, Y Yu
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