Graphic Template Customization Using Text Prompts and AI Elements
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Solution Overview
Problem
Traditional methods for generating graphic design documents require manual selection and placement of elements, which are time-consuming and depend on specialized design skills, making it difficult for less experienced users to achieve visually appealing and harmonious results.
Innovation Solution
A system utilizing machine learning models, including an image generation model, color palette generation model, text style component, and icon selection component, to automatically generate design elements that align with a specified theme, allowing for efficient customization of graphic design documents without extensive manual editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual selection and placement of design elements is used, then design precision and customization are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system enables self-service by allowing the graphic design document to automatically generate and customize its own elements through machine learning models. The input graphic design document provides the theme and structural framework, while the system autonomously selects and places appropriate images, icons, colors, and fonts without requiring manual intervention for each element.
Solution Approach 2:
The manual mechanical process of selecting and placing design elements is replaced with an automated machine learning system. The image generation model, icon selection component, color palette generation model, and text style component work together to automatically generate and position design elements based on the document's theme, substituting human manual operations with intelligent algorithms.
2Manufacturing precision
If manual design element selection is used, then design quality and thematic consistency are improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing the theme of the input graphic design document and autonomously selecting appropriate design elements. The machine learning models independently evaluate the document's characteristics and generate matching images, icons, colors, and fonts without requiring user expertise in design principles.
Solution Approach 2:
The machine learning models act as intermediaries between the user's simple theme input and the complex design element selection process. The image generation model, icon selection component, color palette generation model, and text style component serve as intermediary systems that translate high-level theme descriptions into specific design elements, shielding users from the complexity of manual design decisions.
3Productivity
If automated image generation is used, then productivity and ease of operation are improved, but manufacturing precision may deteriorate
Solution Approach 1:
The automated image generation model replaces manual image selection and creation processes. The model generates images, selects icons, creates color palettes, and determines text styles automatically based on the document theme, substituting human design operations with intelligent algorithms that maintain both speed and quality.
Solution Approach 2:
The system employs feedback mechanisms where the machine learning models continuously evaluate the generated design elements against the document's theme and make adjustments to improve precision. The models analyze the input graphic design document's characteristics and use this feedback to refine their selections and generations, ensuring thematic consistency while maintaining high productivity.
Data Source
AI summary
A computer-implemented method comprises receiving an input graphic design document and an input prompt, where the input graphic design document includes an image element, and wherein the input prompt indicates a target theme different from a theme of the input graphic design document. An image generation model generates a synthetic image based on the input prompt, wherein the synthetic image has the target theme, and a custom graphic design document is generated based on the input graphic design document and the synthetic image, wherein the custom graphic design document has the target theme and includes the synthetic image at a location of the image element of the input graphic design document.