Nvidia corporation (20240249446). TEXT-TO-IMAGE DIFFUSION MODEL WITH COMPONENT LOCKING AND RANK-ONE EDITING simplified abstract
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 20240249446 titled 'TEXT-TO-IMAGE DIFFUSION MODEL WITH COMPONENT LOCKING AND RANK-ONE EDITING
- Simplified Explanation:**
A text-to-image machine learning model is personalized on a per-user basis to generate images based on specific user-provided concepts, allowing users to modify the appearance or composition of the images using free text prompts.
- Key Features and Innovation:**
- Personalization of text-to-image diffusion models for fine-grained details in generated images.
- Component locking and rank-one editing to improve image quality without full model fine-tuning.
- Reduction of memory footprint and adverse effects on the model.
- Potential Applications:**
This technology can be applied in various fields such as e-commerce, entertainment, virtual reality, and education for personalized image generation based on user preferences.
- Problems Solved:**
- Lack of personalized image generation in existing text-to-image models.
- High cost and adverse effects of full model fine-tuning for personalization.
- Inability to generate images with fine-grained details matching user-provided concepts.
- Benefits:**
- Enhanced user experience with personalized image generation.
- Improved image quality with fine-grained details.
- Reduced memory footprint and adverse effects on the model.
- Commercial Applications:**
Potential commercial applications include personalized product visualization in e-commerce, customized content creation in entertainment, and interactive storytelling in virtual reality experiences.
- Questions about the Technology:**
1. How does component locking and rank-one editing improve the personalization of text-to-image diffusion models? 2. What are the potential market implications of this technology in different industries?
- Frequently Updated Research:**
Stay updated on advancements in machine learning algorithms for text-to-image generation and personalized image creation techniques.
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.
