公开(公告)号：US20250045892A1

公开(公告)日：2025-02-06

申请号：US18593742

申请日：2024-03-01

Applicant: NVIDIA Corporation

Inventor： Morteza Mardani ,  Jiaming Song ,  Jan Kautz ,  Arash Vahdat

IPC: G06T7/00 ,  G06T3/40 ,  G06T5/70 ,  G06T5/73 ,  G06T5/77

Abstract: Diffusion models are machine learning algorithms that are uniquely trained to generate high-quality data from an input lower-quality data. For example, they can be trained in the image domain, for example, to perform specific image restoration tasks, such as inpainting (e.g. completing an incomplete image), deblurring (e.g. removing blurring from an image), and super-resolution (e.g. increasing a resolution of an image), or they can be trained to perform image rendering tasks, including 2D-to-3D image generation tasks. However, current approaches to training diffusion models only allow the models to be optimized for a specific task such that they will not achieve high-quality results when used for other tasks. The present disclosure provides a diffusion model that uses variational inferencing to approximate a distribution of data, which allows the diffusion model to universally solve different tasks without having to be re-trained specifically for each task.

公开(公告)号：US20240253217A1

公开(公告)日：2024-08-01

申请号：US18538248

申请日：2023-12-13

Applicant: NVIDIA Corporation

Inventor： Arash Vahdat ,  Hongxu Yin ,  Jan Kautz ,  Jiaming Song ,  Ming-Yu Liu ,  Morteza Mardani ,  Qinsheng Zhang

IPC: B25J9/16

CPC classification number: B25J9/163 ,  B25J9/1664 ,  B25J9/1697

Abstract: Apparatuses, systems, and techniques to calculate a combined loss value based on applying one or more loss functions to the plurality of samples generated by a diffusion model to update the samples to determine a synthesized motions of one or more objects.