18624960. GENERATING IMAGES USING SEQUENCES OF GENERATIVE NEURAL NETWORKS simplified abstract (GOOGLE LLC)

GENERATING IMAGES USING SEQUENCES OF GENERATIVE NEURAL NETWORKS

Organization Name

GOOGLE LLC

Inventor(s)

Chitwan Saharia of Toronto (CA)

William Chan of Toronto (CA)

Mohammad Norouzi of Richmond Hill (CA)

Saurabh Saxena of Mississauga (CA)

Yi Li of Oakville (CA)

Jay Ha Whang of Austin TX (US)

David James Fleet of Toronto (CA)

Jonathan Ho of New York NY (US)

GENERATING IMAGES USING SEQUENCES OF GENERATIVE NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18624960 titled 'GENERATING IMAGES USING SEQUENCES OF GENERATIVE NEURAL NETWORKS

Simplified Explanation

The patent application describes a method for generating images based on a text prompt. The process involves using neural networks to convert the text into contextual embeddings, which are then used to create a final image depicting the scene described in the text.

• Receiving a text prompt in natural language
• Processing the text using a text encoder neural network
• Generating contextual embeddings of the text prompt
• Using generative neural networks to create an image based on the embeddings

Key Features and Innovation

• Utilizes neural networks to convert text into images
• Allows for the generation of images based on textual descriptions
• Combines text processing and image generation technologies

Potential Applications

• Content creation for storytelling or marketing purposes
• Automated image generation for e-commerce websites
• Assistive technology for visually impaired individuals

Problems Solved

• Streamlines the process of creating visual content from text
• Enables quick and efficient image generation based on textual input
• Bridges the gap between text and visual representation

Benefits

• Saves time and resources in creating visual content
• Enhances the accessibility of information through visual aids
• Offers a novel approach to image generation technology

Commercial Applications

"Text-to-Image Generation Technology: Revolutionizing Content Creation and Visual Communication"

Prior Art

There may be prior art related to text-to-image generation technologies, such as research papers, patents, or existing products in the field of artificial intelligence and image processing.

Frequently Updated Research

Stay updated on advancements in neural networks, natural language processing, and image generation technologies for potential improvements in text-to-image generation methods.

Questions about Text-to-Image Generation

How accurate is the image generation process based on text descriptions?

The accuracy of the image generation process can vary depending on the complexity of the text prompt and the capabilities of the neural networks used. Continuous advancements in AI technology aim to improve the accuracy and realism of generated images.

What are the limitations of current text-to-image generation technologies?

Current limitations may include difficulties in capturing nuanced details, generating realistic textures, and accurately interpreting abstract concepts from text prompts. Ongoing research and development efforts seek to address these limitations and enhance the capabilities of text-to-image generation systems.

Original Abstract Submitted

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating images. In one aspect, a method includes: receiving an input text prompt including a sequence of text tokens in a natural language; processing the input text prompt using a text encoder neural network to generate a set of contextual embeddings of the input text prompt; and processing the contextual embeddings through a sequence of generative neural networks to generate a final output image that depicts a scene that is described by the input text prompt.