Prompt Construction Unit for AI Graphic Design
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Solution Overview
Problem
Users of image generative models face challenges in controlling the output images, as they often differ from user expectations despite detailed prompts, leading to repetitive generation attempts for satisfactory results.
Innovation Solution
The system improves user control by constructing a second prompt that extracts an artifact and theme from textual content, appending them to an instruction string to guide the generative model in selecting a design template and generating images by replacing visual elements based on the theme while preserving the graphic layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users provide detailed prompts to image generative models, then they expect accurate image generation, but the output images still differ from user expectations leading to repetitive generation attempts
Solution Approach 1:
The system performs preliminary actions by extracting artifacts and themes from user prompts before image generation, and by pre-processing prompts through a prompt construction unit that formats and structures the prompts. This preparation ensures the generative model receives optimized inputs that align with user expectations, reducing the need for repetitive generation attempts.
Solution Approach 2:
The patent introduces an intermediary prompt construction unit that mediates between the user's detailed prompt and the generative model. This intermediary component processes, extracts, and reformats the prompt to include artifact and theme information, acting as a bridge that translates user intentions into model-friendly instructions, thereby improving generation accuracy without requiring repeated attempts.
2Ease of operation
If the system extracts and processes artifacts and themes from prompts, then user control over image generation is improved, but system complexity increases
Solution Approach 1:
The system segments the prompt processing function into distinct components: an extraction unit that identifies artifacts and themes, and a prompt construction unit that formats the processed information. This segmentation allows complex prompt processing to be broken down into manageable functional blocks, improving user control while keeping the architecture organized and maintainable.
Solution Approach 2:
The prompt construction unit serves multiple functions: it extracts artifacts, identifies themes, formats prompts, and prepares inputs for the generative model. By making this component multi-functional, the system achieves improved user control without proportionally increasing complexity, as one component handles multiple processing tasks.
3Productivity
If the system uses design templates with preserved graphic layouts, then visual content creation efficiency is improved, but creativity flexibility may be reduced
Solution Approach 1:
The system applies local quality by preserving the graphic layout (structural elements) of design templates while allowing replacement of specific visual elements (content elements). This selective approach maintains the efficient template structure in key areas while enabling creative flexibility in specific regions, balancing productivity with adaptability.
Solution Approach 2:
The system creates a dynamic process where design templates serve as flexible frameworks rather than fixed structures. The graphic layout is preserved as a dynamic scaffold that can accommodate various visual elements, allowing the system to maintain template efficiency while adapting to different creative requirements through element replacement.
Data Source
AI summary
A data processing system implements receiving, via a user interface of a client device of a user, a first prompt requesting an image to be generated for the user by a generative model, the first prompt including textual content. The system further implements constructing a second prompt by a prompt construction unit as an input to the generative model, the prompt construction unit constructing the second prompt by extracting an artifact and a theme from the textual content and appending the artifact and the theme to an instruction string, the instruction string comprising instructions to the generative model to determine a design template matching the artifact, and to generate the image by replacing visual element(s) of the design template based on the theme while preserving a graphic layout of the design template; providing the image to the client device; and causing the user interface to present the image.