View source for Salesforce, inc. (20240185035). SYSTEMS AND METHODS FOR TEXT-TO-IMAGE GENERATION USING LANGUAGE MODELS simplified abstract

=SYSTEMS AND METHODS FOR TEXT-TO-IMAGE GENERATION USING LANGUAGE MODELS=


==Organization Name==

[[:Category:salesforce, inc.|salesforce, inc.]]


[[Category:salesforce, inc.]]

==Inventor(s)==

[[:Category:Ning Yu of Palo Alto CA (US)|Ning Yu of Palo Alto CA (US)]][[Category:Ning Yu of Palo Alto CA (US)]]

[[:Category:Can Qin of Somervile MA (US)|Can Qin of Somervile MA (US)]][[Category:Can Qin of Somervile MA (US)]]

[[:Category:Chen Xing of Palo Alto CA (US)|Chen Xing of Palo Alto CA (US)]][[Category:Chen Xing of Palo Alto CA (US)]]

[[:Category:Shu Zhang of Fremont CA (US)|Shu Zhang of Fremont CA (US)]][[Category:Shu Zhang of Fremont CA (US)]]

[[:Category:Stefano Ermon of Menlo Park CA (US)|Stefano Ermon of Menlo Park CA (US)]][[Category:Stefano Ermon of Menlo Park CA (US)]]

[[:Category:Caiming Xiong of Menlo Park CA (US)|Caiming Xiong of Menlo Park CA (US)]][[Category:Caiming Xiong of Menlo Park CA (US)]]

[[:Category:Ran Xu of Mountain View CA (US)|Ran Xu of Mountain View CA (US)]][[Category: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.



[[Category:G06N3/0455]]
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