Text-to-Image Fashion Design Generation With Super-Resolution
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Solution Overview
Problem
The fast fashion industry faces excessive competition and high barriers to entry due to rapid production cycles and the need for technologies that can generate high-resolution design images reflecting current fashion trends based on textual inputs.
Innovation Solution
A method and apparatus that learns from matched images and texts to generate designs by identifying features, interpreting user inputs, and converting resolution to high-quality images using generative adversarial networks (GANs) and super-resolution generative adversarial networks (SRGANs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fast fashion brands use mass production systems with numerous designers to generate design images, then the quantity of designs increases and trendy concepts are applied, but excessive competition is caused and high barriers to entry are created for start-ups
Solution Approach 1:
The patent replaces the mechanical system of human designers creating designs manually with an automated AI-based image generation system. The system uses neural networks and machine learning models to automatically generate design images from text inputs, eliminating the need for numerous human designers and reducing operational complexity while maintaining high productivity
Solution Approach 2:
The patent enables users to generate design images independently by inputting their own text descriptions without requiring access to complex design software or expertise. The system automatically processes the text input and generates corresponding design images, allowing end-users to perform design creation tasks themselves
2Ease of operation
If technologies generate design images automatically based on texts, then general users can generate designs easily, but the resolution and commercial quality of the generated images are insufficient
Solution Approach 1:
The patent divides the image generation process into multiple sequential stages: first generating a base design image from text input, then separately processing super-resolution enhancement, and finally applying post-processing adjustments. This segmentation allows each stage to be optimized independently - the first stage prioritizes ease of generation while subsequent stages progressively improve resolution and quality
Solution Approach 2:
The patent performs preliminary generation of design images at lower resolution to establish the basic design concept and structure quickly. This preliminary action allows users to obtain design images immediately for conceptual purposes, while the system subsequently applies enhancement processes to achieve commercial-quality resolution when needed
3Manufacturing precision
If high-resolution design images are generated to reflect rapidly changing fashion trends, then commercial quality is improved, but the production time and computational resources increase
Solution Approach 1:
The patent implements a multi-stage periodic processing approach where design images undergo sequential enhancement cycles. The base image is generated first, then subjected to periodic super-resolution enhancement passes that progressively refine the image quality. This allows the system to balance generation speed with final resolution by applying computational intensity only when and where needed
Data Source
AI summary
The present disclosure relates to a method for generating a design based on a learned condition performed by a design generation apparatus based on a learned condition. According to an embodiment of the present disclosure, the method may comprise acquiring an image from information including images and texts; learning features of the acquired image; extracting texts from the information and matching the extracted texts with the learned features of the image; learning a condition for a design to be generated based on the image through the matching; receiving texts inputted by a user for design image generation; identifying a condition corresponding to the user's texts; and generating a design image based on the identified condition.