Holistic Evaluation of Text-To-Image Models
Tony Lee Michihiro Yasunaga Chenlin Meng Yifan Mai Joon Sung Park Agrim Gupta Yunzhi Zhang Deepak Narayanan Hannah Benita Teufel Marco Bellagente Minguk Kang Taesung Park Jure Leskovec Jun-Yan Zhu Li Fei-Fei Jiajun Wu Stefano Ermon Percy Liang
The stunning qualitative improvement of recent text-to-image models has led
to their widespread attention and adoption. However, we lack a comprehensive
quantitative understanding of their capabilities and risks. To fill this gap,
we introduce a new benchmark, Holistic Evaluation of Text-to-Image Models
(HEIM). Whereas previous evaluations focus mostly on text-image alignment and
image quality, we identify 12 aspects, including text-image alignment, image
quality, aesthetics, originality, reasoning, knowledge, bias, toxicity,
fairness, robustness, multilinguality, and efficiency. We curate 62 scenarios
encompassing these aspects and evaluate 26 state-of-the-art text-to-image
models on this benchmark. Our results reveal that no single model excels in all
aspects, with different models demonstrating different strengths. We release
the generated images and human evaluation results for full transparency at
https://crfm.stanford.edu/heim/v1.1.0 and the code at
https://github.com/stanford-crfm/helm, which is integrated with the HELM
codebase.