T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation

Kaiyue Sun, Rongyao Fang, Duan Chengqi, Xian Liu, Xihui Liu

T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation: 26 upvotes on Hugging Face Daily Papers, #6 of 23 papers on 2025-08-26. Day-by-day upvote history.

We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of text-to-image (T2I) models. It consists of four dimensions: Idiom Interpretation, Textual Image Design, Entity-Reasoning and Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the reasoning accuracy and image quality. We benchmark various T2I generation models, and provide comprehensive analysis on their performances.

Paper page on Hugging Face · arXiv

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