Generative AI Personalization for Physical Products
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Solution Overview
Problem
Current methods for personalizing physical articles, such as clothing and accessories, fail to effectively capture users' imagined styles and preferences in a technically-friendly, user-friendly, and cost-effective manner.
Innovation Solution
A computer-implemented method using generative AI, specifically a diffusion model enhanced by a diffusion control model and a textual inversion model, to generate images based on user inputs such as text or drawings, allowing for the creation of personalized images in a selected style, with the ability to receive feedback for iterative improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional image printing methods are used for personalization, then the process is simple and cost-effective, but it requires a specific image to be provided and leaves little room for artistic representation
Solution Approach 1:
The patent replaces traditional mechanical printing systems with a generative AI system that uses diffusion models to create images from text descriptions. This substitution enables artistic representation without requiring pre-provided images, transforming the personalization process from image-based to concept-based creation.
Solution Approach 2:
The patent introduces an intermediary system consisting of a diffusion model and control network that mediates between user text inputs and final image outputs. This intermediary layer translates textual descriptions into visual art, enabling creative freedom while maintaining system manageability through structured processing stages.
2Ease of operation
If generative AI models are used to create personalized images from text inputs, then artistic representation and user creativity are enhanced, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex generative AI system into distinct functional components: a diffusion model for image generation, a control network for style guidance, and a user interface for text input. This segmentation allows each component to be optimized independently while presenting a simple unified interface to users.
Solution Approach 2:
The patent implements feedback mechanisms where the system iteratively refines image generation based on user inputs and intermediate results. The control network receives feedback about desired styles and adjustments, modifying the generation process to achieve the target artistic representation while managing computational complexity through targeted iterations.
3Manufacturing precision
If iterative training with feedback is implemented to improve image quality, then the accuracy and quality of generated images improve, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary training of the diffusion model and control network before actual use, pre-training them on extensive datasets of images and styles. This preliminary action establishes a foundation of knowledge that enables rapid, high-quality generation during actual personalization tasks without requiring time-consuming training during each user interaction.
Solution Approach 2:
The patent employs partial training strategies where the control network is trained specifically on style-related data rather than all possible image parameters. This partial action approach focuses computational resources on the most critical aspects of image generation quality, achieving high precision while reducing overall training time and resource requirements.
Data Source
AI summary
A computer-implemented method generates a requested image based on an image style. An image style is selected, and an image generation model is trained using the selected image style. In some examples, the image generation model is a diffusion model. An image request input is received (e.g., text input, drawing input, and/or voice input) and, based on the received image request input, an image is generated using the trained image generation model. The generated image is in the selected image style. The generated image is then output in response to the received image request input. Further, in some examples, feedback associated with the generated image is received and the image generation model is further trained based on the received feedback to improve the quality of its image generation. Additionally, in some examples, the generated image is applied to an item for sale, enabling users to personalized items with generated images.