Salesforce, inc. (20240185035). SYSTEMS AND METHODS FOR TEXT-TO-IMAGE GENERATION USING LANGUAGE MODELS simplified abstract
Contents
SYSTEMS AND METHODS FOR TEXT-TO-IMAGE GENERATION USING LANGUAGE MODELS
Organization Name
Inventor(s)
Chen Xing of Palo Alto CA (US)
Stefano Ermon of Menlo Park CA (US)
Caiming Xiong of Menlo Park CA (US)
Ran Xu of Mountain View CA (US)
SYSTEMS AND METHODS FOR TEXT-TO-IMAGE GENERATION USING LANGUAGE MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185035 titled 'SYSTEMS AND METHODS FOR TEXT-TO-IMAGE GENERATION USING LANGUAGE MODELS
Simplified Explanation
The patent application describes a mechanism for enhancing text-to-image generation models by replacing existing text encoders with more powerful pre-trained language models.
- A translation network is trained to map features from the pre-trained language model output into the space of the target text encoder.
- The training preserves the rich structure of the pre-trained language model while allowing it to operate within the text-to-image generation model.
- The resulting modularized text-to-image model receives a prompt and generates an image representing the features contained in the prompt.
Potential Applications: - Improving the quality and accuracy of text-to-image generation models - Enhancing natural language processing capabilities in image generation tasks
Problems Solved: - Enhancing the performance of text-to-image generation models by leveraging pre-trained language models - Improving the integration of language and image processing in AI systems
Benefits: - Higher quality image generation based on textual prompts - Enhanced natural language understanding in image generation tasks
Commercial Applications: Title: Advanced Text-to-Image Generation Technology for Enhanced Visual Content Creation This technology can be used in industries such as advertising, graphic design, and content creation platforms to generate high-quality images based on textual descriptions, improving the efficiency and accuracy of visual content creation processes.
Questions about Advanced Text-to-Image Generation Technology: Question 1: How does this technology compare to traditional text-to-image generation models? Answer: This technology improves upon traditional models by incorporating more powerful pre-trained language models, resulting in higher-quality image generation based on textual prompts.
Question 2: What are the potential implications of using pre-trained language models in text-to-image generation tasks? Answer: By leveraging pre-trained language models, this technology can significantly enhance the natural language understanding capabilities of text-to-image generation models, leading to more accurate and contextually relevant image generation.
Original Abstract Submitted
embodiments described herein provide a mechanism for replacing existing text encoders in text-to-image generation models with more powerful pre-trained language models. specifically, a translation network is trained to map features from the pre-trained language model output into the space of the target text encoder. the training preserves the rich structure of the pre-trained language model while allowing it to operate within the text-to-image generation model. the resulting modularized text-to-image model receives prompt and generates an image representing the features contained in the prompt.
