The progress in the generation of synthetic images has made it crucial to
assess their quality. While several metrics have been proposed to assess the
rendering of images, it is crucial for Text-to-Image (T2I) models, which
generate images based on a prompt, to consider additional aspects such as to
which extent the generated image matches the important content of the prompt.
Moreover, although the generated images usually result from a random starting
point, the influence of this one is generally not considered. In this article,
we propose a new metric based on prompt templates to study the alignment
between the content specified in the prompt and the corresponding generated
images. It allows us to better characterize the alignment in terms of the type
of the specified objects, their number, and their color. We conducted a study
on several recent T2I models about various aspects. An additional interesting
result we obtained with our approach is that image quality can vary drastically
depending on the latent noise used as a seed for the images. We also quantify
the influence of the number of concepts in the prompt, their order as well as
their (color) attributes. Finally, our method allows us to identify some latent
seeds that produce better images than others, opening novel directions of
research on this understudied topic.

通过针对 prompt 模板的新度量方法，本文对多种 Text-to-Image（T2I）模型的对齐性进行了研究，并发现潜在噪声和提示概念数量、顺序和属性等因素对图像生成的影响。