18296002. MULTILINGUAL TEXT-TO-IMAGE GENERATION simplified abstract (Adobe Inc.)
Contents
MULTILINGUAL TEXT-TO-IMAGE GENERATION
Organization Name
Inventor(s)
Venkata Naveen Kumar Yadav Marri of Newark CA (US)
Ajinkya Gorakhnath Kale of San Jose CA (US)
MULTILINGUAL TEXT-TO-IMAGE GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18296002 titled 'MULTILINGUAL TEXT-TO-IMAGE GENERATION
Simplified Explanation: The patent application describes systems and methods for image processing using multilingual text prompts and diffusion models to generate images corresponding to the text prompts.
Key Features and Innovation:
- Obtaining a text prompt in a first language
- Encoding the text prompt using a multilingual encoder to obtain a multilingual text embedding
- Processing the multilingual text embedding using a diffusion prior model to obtain an image embedding
- Generating an image using a diffusion model based on the image embedding
Potential Applications: This technology can be used in various applications such as language translation, image generation based on text prompts, and multilingual content creation.
Problems Solved: This technology addresses the challenges of processing multilingual text prompts to generate corresponding images efficiently and accurately.
Benefits:
- Improved accuracy in generating images from multilingual text prompts
- Enhanced capabilities for multilingual content creation
- Streamlined image processing workflows
Commercial Applications: The technology can be utilized in industries such as digital marketing, e-commerce, and content creation platforms to enhance multilingual image generation and text-to-image capabilities.
Prior Art: Researchers can explore prior art related to multilingual text embedding, diffusion models in image processing, and language translation technologies to understand the existing knowledge in this field.
Frequently Updated Research: Stay updated on advancements in multilingual text processing, image generation models, and language translation technologies to leverage the latest developments in this area.
Questions about image processing with multilingual text prompts: 1. How does the diffusion prior model improve the accuracy of image generation from multilingual text prompts? 2. What are the potential limitations of using multilingual encoders in image processing with text prompts?
Original Abstract Submitted
Systems and methods for image processing are provided. One aspect of the systems and methods includes obtaining a text prompt in a first language. Another aspect of the systems and methods includes encoding the text prompt using a multilingual encoder to obtain a multilingual text embedding. Yet another aspect of the systems and methods includes processing the multilingual text embedding using a diffusion prior model to obtain an image embedding, wherein the diffusion prior model is trained to process multilingual text embeddings from the first language and a second language based on training data from the first language and the second language. Yet another aspect of the systems and methods includes generating an image using a diffusion model based on the image embedding, wherein the image includes an element corresponding to the text prompt.
