Google llc (20240296596). PERSONALIZED TEXT-TO-IMAGE DIFFUSION MODEL simplified abstract
Contents
PERSONALIZED TEXT-TO-IMAGE DIFFUSION MODEL
Organization Name
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 20240296596 titled 'PERSONALIZED TEXT-TO-IMAGE DIFFUSION MODEL
Simplified Explanation: The patent application describes methods, systems, and apparatus for training a text-to-image model to generate images based on text inputs, depicting variable instances of an object class without a unique identifier, and same subject instances of the object class with a unique identifier.
Key Features and Innovation:
- Training a text-to-image model to generate images of variable instances of an object class based on text inputs.
- Generating images of same subject instances of the object class when a unique identifier is provided in the text input.
Potential Applications: This technology can be applied in various fields such as computer vision, artificial intelligence, image generation, and content creation.
Problems Solved: The technology addresses the challenge of generating diverse images of object classes without unique identifiers and ensuring consistency in depicting the same subject instances with unique identifiers.
Benefits:
- Enhanced image generation capabilities based on text inputs.
- Improved accuracy in depicting object classes with and without unique identifiers.
- Streamlined content creation processes for generating images.
Commercial Applications: Potential commercial applications include automated image generation tools for e-commerce websites, virtual reality content creation platforms, and digital marketing agencies.
Prior Art: Researchers can explore prior art related to text-to-image models, image generation algorithms, and object class recognition systems to understand the existing technology landscape.
Frequently Updated Research: Stay updated on advancements in text-to-image models, image generation techniques, and object class recognition algorithms to leverage the latest innovations in the field.
Questions about Text-to-Image Model Technology: 1. How does this technology improve the efficiency of generating images based on text inputs? 2. What are the key factors influencing the accuracy of depicting object classes with and without unique identifiers in the generated images?
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.
