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原文摘要

Text-to-image (T2I) models struggle with compositional prompts requiring multiple objects, bound attributes, accurate counts, or spatial relations. Existing benchmarks face limits from fixed detector vocabularies, aggregated scores, and varying human-validation protocols. We introduce SANEval (Spatial, Attribute, and Numeracy Evaluation), an open-vocabulary compositional benchmark with two key contributions: (i) a public dataset of ~5,250 prompts paired with images from six state-of-the-art T2I models, split into algorithmically generated Simple and fully human-written Hard partitions across five compositional tasks, and (ii) a modular evaluation pipeline combining an LLM prompt parser, an open-vocabulary detector with LLM synonym expansion, and scorers for attribute binding, spatial relations, and numeracy. Each scorer emits numerical scores and diagnostic feedback for missing, extra, or mis-bound elements. Validated against human ratings (n = 500), SANEval achieves positive Pearson correlations across all categories (overall r = 0.222), outperforming CompBench++ (-0.016) and GenEval (-0.030). A manual audit of 24,012 detections shows 96.58% precision, with ablation confirming that synonym expansion is critical to pipeline performance. Dataset: [https://huggingface.co/datasets/saneval/saneval-release](https://huggingface.co/datasets/saneval/saneval-release). Pipeline: [https://github.com/nielseni6/saneval](https://github.com/nielseni6/saneval).