aiqbench analysis
This arXiv paper proposes CCG Diffusion, a training-free method that uses Combinatory Categorial Grammar to give text-to-image models explicit syntactic structure, improving attribute binding, 2D spatial relations, and prepositional-phrase ambiguity resolution on Stable Diffusion and Sana. The reported gains are benchmark results against a set of compared methods, so their practical weight depends on how broadly they hold across prompts and models. For readers tracking generative vision, the notable idea is that grammar-based control can be added at inference time without retraining.
CCG Diffusion: Combinatory Categorial Grammar-Driven Compositional and Syntactically Disambiguated Text-to-Image Generation
arXiv:2610.29415v1 Announce Type: cross Abstract: Text-to-image generation models do not explicitly receive the syntactic structure of the prompt, i.e., which word modifies which and where each phrase attaches. Consequently, they often struggle to bind attributes to the correct objects, to depict spatial relations and counts correctly, and to render syntactically ambiguous prompts in the intended interpretation. Existing syntax-based control methods mainly address attribute binding and have limited ability to control spatial relations and counts or to specify the interpretation of syntactically ambiguous prompts. They rely on phrase-structure or dependency grammars and require additional rules or constraints on the tree structure to extract modification relations between words or to specify a syntactic interpretation. We propose CCG Diffusion, a training-free method that controls image generation using Combinatory Categorial Grammar (CCG). In CCG, the category of each word carries rich syntactic information: it specifies what the word combines with and what phrase it forms. Modification relations between words can therefore be read directly from a CCG parse. Moreover, prepositional phrase (PP) attachment ambiguity, a representative syntactic ambiguity, can be resolved simply by fixing the preposition's category. CCG Diffusion applies the same two CCG-based mechanisms, prompt rewriting and attention guidance, both to controlling attribute binding, spatial relations, and counts and to resolving PP ambiguity. On two architecturally different backbones, Stable Diffusion and Sana, CCG Diffusion achieves the highest scores among the compared methods in attribute binding and 2D spatial relations and significantly outperforms all of them in PP ambiguity resolution.
Abstract:Text-to-image generation models do not explicitly receive the syntactic structure of the prompt, i.e., which word modifies which and where each phrase attaches. Consequently, they often struggle to bind attributes to the correct objects, to depict spatial relations and counts correctly, and to render syntactically ambiguous prompts in the intended interpretation. Existing syntax-based control methods mainly address attribute binding and have limited ability to control spatial relations and counts or to specify the interpretation of syntactically ambiguous prompts. They rely on phrase-structure or dependency grammars and require additional rules or constraints on the tree structure to extract modification relations between words or to specify a syntactic interpretation. We propose CCG Diffusion, a training-free method that controls image generation using Combinatory Categorial Grammar (CCG). In CCG, the category of each word carries rich syntactic information: it specifies what the word combines with and what phrase it forms. Modification relations between words can therefore be read directly from a CCG parse. Moreover, prepositional phrase (PP) attachment ambiguity, a representative syntactic ambiguity, can be resolved simply by fixing the preposition's category. CCG Diffusion applies the same two CCG-based mechanisms, prompt rewriting and attention guidance, both to controlling attribute binding, spatial relations, and counts and to resolving PP ambiguity. On two architecturally different backbones, Stable Diffusion and Sana, CCG Diffusion achieves the highest scores among the compared methods in attribute binding and 2D spatial relations and significantly outperforms all of them in PP ambiguity resolution.
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
arXiv:2610.29415 [cs.CV]
(or arXiv:2610.29415v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.29415 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 342 words