Nvidia corporation (20240221242). USING STABLE DIFFUSION TO GENERATE SEAMLESS CONTENT TILE SETS IN CONTENT GENERATION SYSTEMS AND APPLICATIONS simplified abstract
USING STABLE DIFFUSION TO GENERATE SEAMLESS CONTENT TILE SETS IN CONTENT GENERATION SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Alex Greenen of Los Altos CA (US)
Manuel Kraemer of San Jose CA (US)
USING STABLE DIFFUSION TO GENERATE SEAMLESS CONTENT TILE SETS IN CONTENT GENERATION SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240221242 titled 'USING STABLE DIFFUSION TO GENERATE SEAMLESS CONTENT TILE SETS IN CONTENT GENERATION SYSTEMS AND APPLICATIONS
The approaches presented in this patent application involve utilizing a network that learns to generate content tiles representing a specific type of content, such as texture, while adhering to a set of rules or boundary conditions. The network, which can be a diffusion network, adapts to these conditions over multiple iterations.
- The network is trained using an indication of the type of content and a set of noisy prior images as input, allowing it to generate a series of content images.
- These content images can then be placed using a random selection process, ensuring that each selection meets the respective boundary conditions.
- This method enables the use of a small number of content tiles to create a texture region with a pattern that may not be easily discernible to the human eye.
Potential Applications: - Textile design - Graphic design - Computer-generated imagery (CGI) - Augmented reality (AR) applications
Problems Solved: - Efficient generation of content tiles - Ensuring adherence to specific boundary conditions - Creating visually appealing textures with minimal input
Benefits: - Streamlined content generation process - Enhanced design possibilities - Improved visual aesthetics in digital creations
Commercial Applications: Title: Innovative Content Generation Technology for Textures This technology can be utilized in industries such as fashion, interior design, and digital art to create unique and visually appealing textures for various products and applications. The market implications include increased efficiency in design processes and the ability to offer customizable textures to consumers.
Prior Art: Further research can be conducted in the fields of machine learning, computer vision, and digital art to explore similar approaches to content generation and texture creation.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms, image processing techniques, and design software to enhance the capabilities of this content generation technology.
Questions about Content Generation Technology for Textures: 1. How does this technology improve the efficiency of texture creation compared to traditional methods? 2. What are the potential limitations or challenges faced when implementing this content generation approach in different industries?
Original Abstract Submitted
approaches presented herein can utilize a network that learns to generate a set of content tiles that represent a type of content (e.g., texture) and satisfy a set of rules or boundary conditions. the network can be a diffusion network that learns or adapts to the boundary conditions over several iterations. an indication of a type of content, along with a set of noisy prior images, can then be provided as input to the trained diffusion network, which can generate a set of content images. the content images can then be placed using a random (or other) selection process, as long as each selection satisfies the respective boundary conditions. such an approach enables a small number of content tiles to be used for a texture region with a repeatability or pattern that may not be obviously detectable by a typical human viewer.
