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We introduce DEsignBench, a text-to-image (T2I) generation benchmark tailoredfor visual design scenarios. Recent T2I models like DALL-E 3 and others, havedemonstrated remarkable capabilities in generating photorealistic images thatalign closely with textual inputs. While the allure of creating visuallycaptivating images is undeniable, our emphasis extends beyond mere aestheticpleasure. We aim to investigate the potential of using these powerful models inauthentic design contexts. In pursuit of this goal, we develop DEsignBench,which incorporates test samples designed to assess T2I models on both "designtechnical capability" and "design application scenario." Each of these twodimensions is supported by a diverse set of specific design categories. Weexplore DALL-E 3 together with other leading T2I models on DEsignBench,resulting in a comprehensive visual gallery for side-by-side comparisons. ForDEsignBench benchmarking, we perform human evaluations on generated images inDEsignBench gallery, against the criteria of image-text alignment, visualaesthetic, and design creativity. Our evaluation also considers otherspecialized design capabilities, including text rendering, layout composition,color harmony, 3D design, and medium style. In addition to human evaluations,we introduce the first automatic image generation evaluator powered by GPT-4V.This evaluator provides ratings that align well with human judgments, whilebeing easily replicable and cost-efficient. A high-resolution version isavailable athttps://github.com/design-bench/design-bench.github.io/raw/main/designbench.pdf?download=

DEsignBench: Exploring and Benchmarking DALL-E 3 for Imagining Visual Design