Current diffusion models create photorealistic images given a text prompt as
input but struggle to correctly bind attributes mentioned in the text to the
right objects in the image. This is evidenced by our novel image-graph
alignment model called EPViT (Edge Prediction Vision Transformer) for the
evaluation of image-text alignment. To alleviate the above problem, we propose
focused cross-attention (FCA) that controls the visual attention maps by
syntactic constraints found in the input sentence. Additionally, the syntax
structure of the prompt helps to disentangle the multimodal CLIP embeddings
that are commonly used in T2I generation. The resulting DisCLIP embeddings and
FCA are easily integrated in state-of-the-art diffusion models without
additional training of these models. We show substantial improvements in T2I
generation and especially its attribute-object binding on several
datasets.\footnote{Code and data will be made available upon acceptance.

通过提出聚焦交叉注意力（FCA）和句子中的句法约束，本研究解决了当前扩散模型在图像中正确绑定文本提及的属性到正确对象上的困难，并在多个数据集上展示了图像与文本之间的对齐领域的重大改进。