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Google patents on-device image creation using neural networks

Google describes a method for creating images directly on a device using a specific type of neural network called a generator. The system starts with a small, low-resolution version of an image and gradually increases its size through multiple steps to reach the final high-quality picture. This process is designed to happen quickly without sending data to a remote server.

Why it mattersThis approach could allow phones or laptops to create or edit images instantly and privately, without relying on internet connections or cloud servers.

In the filing’s own words

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating images. For example, the system can perform low-latency on-device image generation using a generator neural network.

What exactly they claim (claim 1)

1. A method performed by one or more computers, the method comprising: obtaining a conditioning input; and generating an output image having a target resolution conditioned on the conditioning input by processing the conditioning input through a generator neural network, comprising: initializing a latent representation of the output image to have an initial resolution; processing the latent representation through a sequence of convolutional layer blocks, wherein each convolutional layer block is configured to upsample the latent representation by performing operations comprising: receiving the latent representation; generating, from the latent representation, an upsampled latent representation having a corresponding resolution for the convolutional layer block by processing the latent representation through an upsampling layer block; and updating the latent representation by processing the upsampled latent representation through one or more styled convolutional layer blocks that are each conditioned on the conditioning input, wherein (i) the corresponding resolution for the last convolutional layer block in the sequence is lower than the target resolution and (ii) for each convolutional block after the first convolutional layer block in the sequence, the corresponding resolution for the convolutional layer block is higher than the corresponding resolution for the preceding convolutional layer block in the sequence; and processing the latent representation after being updated by the last convolutional layer block in the sequence using an output head, wherein the output head is configured to perform operations comprising: generating, from the latent representation, a final upsampled latent representation having the target resolution by processing the latent representation through an upsampling layer block; and applying a depthwise separable modulated convolution conditioned on the conditioning input to the final upsampled latent representation to generate an initial output image having the target resolution.

Facts

Company
Google (Google LLC)
Official title
ON-DEVICE IMAGE GENERATION WITH GENERATIVE ADVERSARIAL NETWORKS
Publication number
US20260268441A1
Application number
19491309
Filed
Jun 6, 2024
Published
Sep 10, 2026
Inventors
9
Claims
20

Classified under

G06T 3/4046 Physics & computingG06N 3/045 Physics & computingG06N 3/08 Physics & computingG06T 3/4053 Physics & computing

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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.