18569844. PERSONALIZED TEXT-TO-IMAGE DIFFUSION MODEL simplified abstract (GOOGLE LLC)

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PERSONALIZED TEXT-TO-IMAGE DIFFUSION MODEL

Organization Name

GOOGLE LLC

Inventor(s)

Kfir Aberman of San Mateo CA (US)

Nataniel Ruiz Gutierrez of Brookline MA (US)

Michael Rubinstein of Natick MA (US)

Yuanzhen Li of Newton Centre CA (US)

Yael Pritch Knaan of Tel Aviv (IL)

Varun Jampani of Rockland MA (US)

PERSONALIZED TEXT-TO-IMAGE DIFFUSION MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 18569844 titled 'PERSONALIZED TEXT-TO-IMAGE DIFFUSION MODEL

The patent application describes methods, systems, and apparatus for training a text-to-image model to generate images based on text inputs.

  • The model can generate images of variable instances of an object class without a unique identifier.
  • It can also generate images of the same subject instance of the object class when a unique identifier is provided as the text input.

Potential Applications:

  • This technology can be used in e-commerce for generating product images based on textual descriptions.
  • It can be applied in virtual reality and gaming for creating visual representations of objects described in text.

Problems Solved:

  • Addresses the challenge of generating accurate images from textual descriptions without unique identifiers.
  • Provides a solution for creating consistent visual representations of objects with unique identifiers.

Benefits:

  • Streamlines the process of generating images from text inputs.
  • Improves the accuracy and consistency of image generation in various applications.

Commercial Applications:

  • Title: "Enhanced Image Generation Technology for E-commerce and Virtual Reality"
  • This technology can be utilized by e-commerce platforms to enhance product visualization and customer experience.
  • It can also benefit virtual reality developers in creating realistic virtual environments.

Questions about the Technology: 1. How does this technology improve the efficiency of image generation from text inputs? 2. What are the potential limitations of using this text-to-image model in real-world applications?

Frequently Updated Research: There may be ongoing research in the field of computer vision and natural language processing to further enhance the capabilities of text-to-image models.


Original Abstract Submitted

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text-to-image model so that the text-to-image model generates images that each depict a variable instance of an object class when the object class without the unique identifier is provided as a text input, and that generates images that each depict a same subject instance of the object class when the unique identifier is provided as the text input.

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