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.
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 & computingMore from Google in AI & software
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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.