Despite advancements in text-to-image generation (T2I), prior methods often
face text-image misalignment problems such as relation confusion in generated
images. Existing solutions involve cross-attention manipulation for better
compositional understanding or integrating large language models for improved
layout planning. However, the inherent alignment capabilities of T2I models are
still inadequate. By reviewing the link between generative and discriminative
modeling, we posit that T2I models' discriminative abilities may reflect their
text-image alignment proficiency during generation. In this light, we advocate
bolstering the discriminative abilities of T2I models to achieve more precise
text-to-image alignment for generation. We present a discriminative adapter
built on T2I models to probe their discriminative abilities on two
representative tasks and leverage discriminative fine-tuning to improve their
text-image alignment. As a bonus of the discriminative adapter, a
self-correction mechanism can leverage discriminative gradients to better align
generated images to text prompts during inference. Comprehensive evaluations
across three benchmark datasets, including both in-distribution and
out-of-distribution scenarios, demonstrate our method's superior generation
performance. Meanwhile, it achieves state-of-the-art discriminative performance
on the two discriminative tasks compared to other generative models.

通过增强 T2I 模型的判别能力来实现更精确的文本与图像对齐，我们提出了一种基于 T2I 模型构建的判别适配器，并利用判别微调来改善文本和图像之间的对齐。综合评估结果表明，我们的方法在生成性能上优于其他模型，并在两个判别性任务方面达到了最先进的性能。