Template-Based Ad Image Generation With Prompt Optimization
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Solution Overview
Problem
The production process of image advertisement materials is complex, leading to low efficiency and poor quality, which affects the effectiveness of Internet advertising.
Innovation Solution
An image generation method involving determining an image template based on product information, generating elements using text generation and optimization models, and synthesizing these elements to create a high-quality image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to produce image advertisement materials, then the production process can be completed, but the production efficiency is low and the quality is poor
Solution Approach 1:
The patent segments the image generation process into distinct modules: text generation model for creating element descriptions, content optimization model for refining prompts, text-to-image generation model for creating visual elements, and template synthesis model for assembling final images. This modular segmentation enables parallel processing and independent optimization of each stage, significantly improving production efficiency while maintaining quality control through specialized processing at each step.
2Productivity
If the production process is simplified, then efficiency improves, but quality control and compliance review become difficult
Solution Approach 1:
The patent implements preliminary action through the content optimization model that refines prompts before image generation, and through the independent review mechanism that checks element compliance before synthesis. The template synthesis model also performs quality verification by ensuring generated elements meet predefined standards before final assembly. This preliminary processing ensures quality control is built into the workflow rather than added as a separate step, maintaining reliability while enabling efficient automated production.
3Manufacturing precision
If multiple models and processing steps are used to improve quality, then image element quality improves, but the device complexity increases
Solution Approach 1:
The patent applies universality through the template synthesis model that serves multiple functions: it assembles generated elements into final images, verifies quality standards, ensures compliance with advertising regulations, and manages the integration of different model outputs. The content optimization model also performs multiple roles by both refining prompts and preparing them for text-to-image conversion. This multi-functionality reduces the need for separate specialized components, managing system complexity while maintaining high image element quality.
Data Source
AI summary
Embodiments of this specification disclose image generation methods, apparatuses, electronic devices, and storage media. An example method includes: determining, based on product information of a product, an image template that corresponds to the product; generating several first elements based on a prompt library by using a pre-accessed text generation model; optimizing prompts in the prompt library by using a pre-accessed content optimization model, to obtain optimized prompts; generating, by using a pre-accessed text-to-image generation model, several second elements that correspond to the optimized prompts; determining, from the several first elements and the several second elements, image materials that correspond to the product information; and performing, by using the image template, synthesis processing on the image materials that correspond to the product information, to obtain a synthesized image.