Patents by Inventor Tim Salimans

Tim Salimans has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11908180
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium. In one aspect, a method includes receiving a text prompt describing a scene; processing the text prompt using a text encoder neural network to generate a contextual embedding of the text prompt; and processing the contextual embedding using a sequence of generative neural networks to generate a final video depicting the scene.
    Type: Grant
    Filed: March 24, 2023
    Date of Patent: February 20, 2024
    Assignee: Google LLC
    Inventors: Jonathan Ho, William Chan, Chitwan Saharia, Jay Ha Whang, Tim Salimans
  • Publication number: 20230385990
    Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.
    Type: Application
    Filed: July 27, 2023
    Publication date: November 30, 2023
    Inventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
  • Patent number: 11769228
    Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.
    Type: Grant
    Filed: August 2, 2021
    Date of Patent: September 26, 2023
    Assignee: Google LLC
    Inventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
  • Patent number: 11756166
    Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.
    Type: Grant
    Filed: January 17, 2023
    Date of Patent: September 12, 2023
    Assignee: Google LLC
    Inventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
  • Publication number: 20230267315
    Abstract: A computer-implemented method for use of a diffusion model having improved accuracy comprises obtaining input data, the input data comprising one or more channels; providing the input data to a machine-learned diffusion model, the machine-learned diffusion model comprising: a noising model comprising a plurality of noising stages, the noising model configured to introduce noise to receive the input data and produce intermediate data in response to receipt of the input data; and a denoising model configured to reconstruct output data from the intermediate data; and receiving, by the computing system, the output data from the machine-learned diffusion model. The diffusion model can include a learned noise schedule. Additionally and/or alternatively, input to the denoising model can include a set of Fourier features. Additionally and/or alternatively, the diffusion model can be trained based at least in part on a continuous-time loss for an evidence lower bound.
    Type: Application
    Filed: June 13, 2022
    Publication date: August 24, 2023
    Inventors: Diederik Pieter Kingma, Tim Salimans
  • Publication number: 20230153959
    Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.
    Type: Application
    Filed: January 17, 2023
    Publication date: May 18, 2023
    Inventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
  • Publication number: 20230103638
    Abstract: A method includes receiving training data comprising a plurality of pairs of images. Each pair comprises a noisy image and a denoised version of the noisy image. The method also includes training a multi-task diffusion model to perform a plurality of image-to-image translation tasks, wherein the training comprises iteratively generating a forward diffusion process by predicting, at each iteration in a sequence of iterations and based on a current noisy estimate of the denoised version of the noisy image, noise data for a next noisy estimate of the denoised version of the noisy image, updating, at each iteration, the current noisy estimate to the next noisy estimate by combining the current noisy estimate with the predicted noise data, and determining a reverse diffusion process by inverting the forward diffusion process to predict the denoised version of the noisy image. The method additionally includes providing the trained diffusion model.
    Type: Application
    Filed: October 5, 2022
    Publication date: April 6, 2023
    Inventors: Chitwan Saharia, Mohammad Norouzi, William Chan, Huiwen Chang, David James Fleet, Christopher Albert Lee, Jonathan Ho, Tim Salimans
  • Publication number: 20230067841
    Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.
    Type: Application
    Filed: August 2, 2021
    Publication date: March 2, 2023
    Inventors: Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David Fleet, Mohammad Norouzi
  • Publication number: 20210383790
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generative neural network to convert conditioning text inputs to audio outputs using energy scores.
    Type: Application
    Filed: June 4, 2021
    Publication date: December 9, 2021
    Inventors: Tim Salimans, Alexey Alexeevich Gritsenko