Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing evaluation metrics. To address this issue, we introduce Human Preference Dataset v2 (HPD v2), a large-scale dataset that captures human preferences on images from a wide range of sources. HPD v2 comprises 798,090 human preference choices on 433,760 pairs of images, making it the largest dataset of its kind. The text prompts and images are deliberately collected to eliminate potential bias, which is a common issue in previous datasets. By fine-tuning CLIP on HPD v2, we obtain Human Preference Score v2 (HPS v2), a scoring model that can more accurately predict human preferences on generated images. Our experiments demonstrate that HPS v2 generalizes better than previous metrics across various image distributions and is responsive to algorithmic improvements of text-to-image generative models, making it a preferable evaluation metric for these models. We also investigate the design of the evaluation prompts for text-to-image generative models, to make the evaluation stable, fair and easy-to-use. Finally, we establish a benchmark for text-to-image generative models using HPS v2, which includes a set of recent text-to-image models from the academic, community and industry. The code and dataset is available at https://github.com/tgxs002/HPSv2 .

翻译：近期文本到图像生成模型能够从文本输入生成高保真图像，但现有评估指标无法准确衡量这些生成图像的质量。为解决这一问题，我们提出了人类偏好数据集 v2（HPD v2），这是一个覆盖广泛来源图像的人类偏好大规模数据集。HPD v2 包含对 433,760 对图像的 798,090 个人类偏好选择，是同类数据集中规模最大的。文本提示和图像经过精心收集以消除潜在偏差，这是以往数据集中的常见问题。通过在 HPD v2 上微调 CLIP，我们获得了人类偏好评分 v2（HPS v2），这是一个能更准确预测生成图像人类偏好的评分模型。实验表明，HPS v2 在多种图像分布上比以往指标具有更好的泛化能力，并且对文本到图像生成模型的算法改进具有响应性，使其成为这些模型的更优评估指标。我们还探讨了文本到图像生成模型评估提示的设计，以确保评估的稳定性、公平性和易用性。最后，我们利用 HPS v2 建立了文本到图像生成模型的基准，涵盖来自学术界、社区和工业界的多个近期文本到图像模型。代码和数据集可在 https://github.com/tgxs002/HPSv2 获取。