  Current metrics for text-to-image models typically rely on statistical
metrics which inadequately represent the real preference of humans. Although
recent work attempts to learn these preferences via human annotated images,
they reduce the rich tapestry of human preference to a single overall score.
However, the preference results vary when humans evaluate images with different
aspects. Therefore, to learn the multi-dimensional human preferences, we
propose the Multi-dimensional Preference Score (MPS), the first
multi-dimensional preference scoring model for the evaluation of text-to-image
models. The MPS introduces the preference condition module upon CLIP model to
learn these diverse preferences. It is trained based on our Multi-dimensional
Human Preference (MHP) Dataset, which comprises 918,315 human preference
choices across four dimensions (i.e., aesthetics, semantic alignment, detail
quality and overall assessment) on 607,541 images. The images are generated by
a wide range of latest text-to-image models. The MPS outperforms existing
scoring methods across 3 datasets in 4 dimensions, enabling it a promising
metric for evaluating and improving text-to-image generation.
