自动优化提示词，让AI自己搞定图文生成难题。
PromptIQ: Who Cares About Prompts? Let System Handle It -- A Component-Aware Framework for T2I Generation
- 用组件感知相似度自动检测并修正提示词结构错误
- 迭代生成直到用户满意，准确率提升显著
- 适合无提示工程经验的普通用户使用
文本到图像（T2I）模型在缺乏提示工程知识的情况下生成高质量图像仍具挑战性，往往因提示词结构不佳导致图像失真和语义错位。人类可轻松识别此类问题，但现有评估指标如CLIP无法捕捉结构不一致，暴露了当前评估方法的关键缺陷。为此，我们提出PromptIQ，一个自动化框架，通过新颖的组件感知相似度（CAS）度量来评估图像质量并优化提示词，能有效检测并惩罚结构错误。与传统方法不同，PromptIQ会持续生成并评估图像，直至用户满意，彻底消除试错式提示调优。实验表明，PromptIQ显著提升了生成质量与评估准确性，使T2I模型对无提示工程经验的用户更友好。
原文摘要 · Abstract (English)
Generating high-quality images without prompt engineering expertise remains a challenge for text-to-image (T2I) models, which often misinterpret poorly structured prompts, leading to distortions and misalignments. While humans easily recognize these flaws, metrics like CLIP fail to capture structural inconsistencies, exposing a key limitation in current evaluation methods. To address this, we introduce PromptIQ, an automated framework that refines prompts and assesses image quality using our novel Component-Aware Similarity (CAS) metric, which detects and penalizes structural errors. Unlike conventional methods, PromptIQ iteratively generates and evaluates images until the user is satisfied, eliminating trial-and-error prompt tuning. Our results show that PromptIQ significantly improves generation quality and evaluation accuracy, making T2I models more accessible for users with little to no prompt engineering expertise.
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