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Text-conditioned image generation models often generate incorrectassociations between entities and their visual attributes. This reflects animpaired mapping between linguistic binding of entities and modifiers in theprompt and visual binding of the corresponding elements in the generated image.As one notable example, a query like "a pink sunflower and a yellow flamingo"may incorrectly produce an image of a yellow sunflower and a pink flamingo. Toremedy this issue, we propose SynGen, an approach which first syntacticallyanalyses the prompt to identify entities and their modifiers, and then uses anovel loss function that encourages the cross-attention maps to agree with thelinguistic binding reflected by the syntax. Specifically, we encourage largeoverlap between attention maps of entities and their modifiers, and smalloverlap with other entities and modifier words. The loss is optimized duringinference, without retraining or fine-tuning the model. Human evaluation onthree datasets, including one new and challenging set, demonstrate significantimprovements of SynGen compared with current state of the art methods. Thiswork highlights how making use of sentence structure during inference canefficiently and substantially improve the faithfulness of text-to-imagegeneration.

Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment