CoBind: Stage-Aware Compositional Binding for Training-Free Text-to-Image Generation
Quick Answer
CoBind is a training-free framework that enhances text-to-image generation by establishing a composition graph for entities, attributes, and relations.
Quick Take
It improves attribute binding and spatial relations while maintaining visual quality across various benchmarks, including T2I-CompBench++ and GenEval, without requiring retraining or additional annotations.
Key Points
- CoBind uses a composition graph to parse prompts for better entity-attribute relations.
- It employs contrastive cross-entity optimization for effective attribute binding.
- The framework adapts guidance strength based on constraint satisfaction during denoising.
- Experiments show consistent improvements in complex compositional generation.
- No retraining or additional annotations are required for implementation.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Diffusion-based text-to-image models often fail on complex prompts involving multiple entities, attributes, and relations, producing object omissions, incorrect attribute assignments, or reversed spatial layouts. Existing training-free methods mainly strengthen token-level attention, but do not explicitly model which attributes belong to which entities or when different constraints should be enforced during denoising.
We introduce \textbf{CoBind}, a training-free framework for stage-aware compositional binding. CoBind parses a prompt into a composition graph of entities, attributes, and relations. It first establishes the global layout using entity-completeness and relation constraints, then binds attributes to their target entities through contrastive cross-entity optimization. Structural guidance is gradually relaxed in later denoising steps to preserve textures and visual details. CoBind also adapts the guidance strength according to the current satisfaction of each constraint, reducing unnecessary latent updates.
CoBind requires no retraining or additional annotations. Experiments on T2I-CompBench++, GenEval, and multiple diffusion backbones show consistent improvements in attribute binding, spatial relations, and complex compositional generation while maintaining competitive visual quality.
| Comments: | 22 pages |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.16307 [cs.CV] |
| (or arXiv:2607.16307v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16307 arXiv-issued DOI via DataCite |
Submission history
From: Kaijie Chen [view email]
[v1]
Tue, 14 Jul 2026 12:18:43 UTC (2,034 KB)
— Originally published at arxiv.org
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