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Human Preference Score: Better Aligning Text-to-Image Models with Human Preference

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arxiv 2303.14420 v2 pith:Q7GNXJV4 submitted 2023-03-25 cs.CV cs.AI

Human Preference Score: Better Aligning Text-to-Image Models with Human Preference

Xiaoshi Wu , Keqiang Sun , Feng Zhu , Rui Zhao , Hongsheng Li This is my paper
classification cs.CV cs.AI
keywords humanmodelsimagespreferencealignchoicesstablebetter
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, such as awkward combinations of limbs and facial expressions. To address this issue, we collect a dataset of human choices on generated images from the Stable Foundation Discord channel. Our experiments demonstrate that current evaluation metrics for generative models do not correlate well with human choices. Thus, we train a human preference classifier with the collected dataset and derive a Human Preference Score (HPS) based on the classifier. Using HPS, we propose a simple yet effective method to adapt Stable Diffusion to better align with human preferences. Our experiments show that HPS outperforms CLIP in predicting human choices and has good generalization capability toward images generated from other models. By tuning Stable Diffusion with the guidance of HPS, the adapted model is able to generate images that are more preferred by human users. The project page is available here: https://tgxs002.github.io/align_sd_web/ .

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