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.

翻译：尽管文本到图像生成（T2I）技术取得了显著进展，现有方法仍常面临生成图像中关系混淆等图文错配问题。当前解决方案包括通过交叉注意力操控增强组合理解能力，或集成大语言模型优化布局规划。然而，T2I模型固有的对齐能力仍存在不足。通过审视生成式建模与判别式建模之间的关联，我们提出T2I模型的判别能力可反映其生成过程中的图文对齐水平。基于此认识，我们主张强化T2I模型的判别能力，以实现更精准的文本到图像对齐生成。我们构建了基于T2I模型的判别式适配器，通过两项代表性任务探测其判别能力，并利用判别式微调提升图文对齐效果。作为判别式适配器的额外优势，自校正机制可在推理阶段利用判别式梯度优化生成图像与文本提示的对齐。在三个基准数据集（涵盖分布内与分布外场景）上的全面评估表明，本方法具有卓越的生成性能。同时，与其它生成模型相比，本方法在两个判别任务上取得了最先进的判别性能。