Align Beyond Prompts: Evaluating World Knowledge Alignment in Text-to-Image Generation

• URL: http://arxiv.org/abs/2505.18730v1
• Date: Sat, 24 May 2025 14:56:09 GMT
• Title: Align Beyond Prompts: Evaluating World Knowledge Alignment in Text-to-Image Generation
• Authors: Wenchao Zhang, Jiahe Tian, Runze He, Jizhong Han, Jiao Dai, Miaomiao Feng, Wei Mi, Xiaodan Zhang,
• Abstract summary: We introduce Align Beyond Prompts (ABP), a benchmark to measure alignment of generated images with real-world knowledge beyond prompts.<n>ABP comprises over 2,000 meticulously crafted prompts, covering real-world knowledge across six distinct scenarios.<n>ABPScore is a metric that utilizes existing Multimodal Large Language Models (MLLMs) to assess the alignment between generated images and world knowledge beyond prompts.
• Score: 10.583920883457635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent text-to-image (T2I) generation models have advanced significantly, enabling the creation of high-fidelity images from textual prompts. However, existing evaluation benchmarks primarily focus on the explicit alignment between generated images and prompts, neglecting the alignment with real-world knowledge beyond prompts. To address this gap, we introduce Align Beyond Prompts (ABP), a comprehensive benchmark designed to measure the alignment of generated images with real-world knowledge that extends beyond the explicit user prompts. ABP comprises over 2,000 meticulously crafted prompts, covering real-world knowledge across six distinct scenarios. We further introduce ABPScore, a metric that utilizes existing Multimodal Large Language Models (MLLMs) to assess the alignment between generated images and world knowledge beyond prompts, which demonstrates strong correlations with human judgments. Through a comprehensive evaluation of 8 popular T2I models using ABP, we find that even state-of-the-art models, such as GPT-4o, face limitations in integrating simple real-world knowledge into generated images. To mitigate this issue, we introduce a training-free strategy within ABP, named Inference-Time Knowledge Injection (ITKI). By applying this strategy to optimize 200 challenging samples, we achieved an improvement of approximately 43% in ABPScore. The dataset and code are available in https://github.com/smile365317/ABP.

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