PromptIQ: Who Cares About Prompts? Let System Handle It -- A Component-Aware Framework for T2I Generation
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913829548457984 |
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| author | Chhetri, Nisan Sainju, Arpan |
| author_facet | Chhetri, Nisan Sainju, Arpan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06467 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PromptIQ: Who Cares About Prompts? Let System Handle It -- A Component-Aware Framework for T2I Generation Chhetri, Nisan Sainju, Arpan Computer Vision and Pattern Recognition Human-Computer Interaction 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. |
| title | PromptIQ: Who Cares About Prompts? Let System Handle It -- A Component-Aware Framework for T2I Generation |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.06467 |