18385840. TEXT-TO-IMAGE DIFFUSION MODEL WITH COMPONENT LOCKING AND RANK-ONE EDITING simplified abstract (NVIDIA Corporation)
Contents
TEXT-TO-IMAGE DIFFUSION MODEL WITH COMPONENT LOCKING AND RANK-ONE EDITING
Organization Name
Inventor(s)
Yuval Atzmon of Hod Hasharon (IL)
Yoad Tewel of Tel Aviv-Yafo (IL)
Gal Chechik of Ramat Hasharon (IL)
TEXT-TO-IMAGE DIFFUSION MODEL WITH COMPONENT LOCKING AND RANK-ONE EDITING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18385840 titled 'TEXT-TO-IMAGE DIFFUSION MODEL WITH COMPONENT LOCKING AND RANK-ONE EDITING
Simplified Explanation: The patent application describes a method to personalize text-to-image machine learning models on a per-user basis by allowing users to generate images of specific concepts using free text prompts without the need for full fine-tuning of the model.
Key Features and Innovation:
- Personalization of text-to-image models on a per-user basis.
- Generation of images matching specific user-provided concepts.
- Component locking and rank-one editing for fine-grained details in generated images.
- Reduction of memory footprint and adverse effects on the model.
Potential Applications: This technology can be used in various fields such as e-commerce, virtual reality, gaming, and content creation platforms.
Problems Solved:
- Personalizing text-to-image models without full fine-tuning.
- Generating images with specific user-provided concepts accurately.
- Improving fine-grained details in generated images.
Benefits:
- Enhanced user experience through personalized image generation.
- Reduced memory footprint and adverse effects on the model.
- Improved accuracy in generating images matching user-provided concepts.
Commercial Applications: Potential commercial uses include personalized content creation platforms, customized product visualization tools, and virtual reality applications. This technology can have significant market implications in the fields of e-commerce and entertainment.
Prior Art: Readers can explore prior art related to text-to-image machine learning models, personalization techniques in machine learning, and image generation methods.
Frequently Updated Research: Stay updated on advancements in text-to-image machine learning models, personalization techniques, and image generation algorithms.
Questions about Text-to-Image Personalization: 1. How does personalized text-to-image generation benefit users? 2. What are the potential challenges in implementing personalized text-to-image models?
Original Abstract Submitted
A text-to-image machine learning model takes a user input text and generates an image matching the given description. While text-to-image models currently exist, there is a desire to personalize these models on a per-user basis, including to configure the models to generate images of specific, unique user-provided concepts (via images of specific objects or styles) while allowing the user to use free text “prompts” to modify their appearance or compose them in new roles and novel scenes. Current personalization solutions either generate images with only coarse-grained resemblance to the provided concept(s) or require fine tuning of the entire model which is costly and can adversely affect the model. The present description employs component locking and/or rank-one editing for personalization of text-to-image diffusion models, which can improve the fine-grained details of the concepts in the generated images, reduce the memory footprint update of the underlying model instead of full fine-tuning, and reduce adverse effects to the model.
