让文生图模型精准理解复杂指令中的对象关系。
CoBind: Stage-Aware Compositional Binding for Training-Free Text-to-Image Generation
- 构建组合图解析提示词，分阶段绑定对象与属性。
- 在多个数据集上显著提升属性与空间关系准确率。
- 无需训练，适合希望提升生成精度的研究者。
基于扩散的文生图模型在处理包含多个实体、属性和关系的复杂提示时，常出现物体遗漏、属性错配或空间布局颠倒等问题。现有无训练方法主要增强词级注意力，但未显式建模属性归属关系或不同去噪阶段的约束强度。我们提出 CoBind，一种面向阶段感知的无训练组合绑定框架。CoBind 将提示词解析为实体、属性与关系组成的组合图，先通过实体完整性和关系约束建立全局布局，再通过对比跨实体优化将属性绑定至目标实体。结构引导在后期去噪步骤中逐步放松，以保留纹理与视觉细节。同时，根据各约束的满足度动态调整引导强度，减少不必要的潜在空间更新。CoBind 无需重训练或额外标注。在 T2I-CompBench++、GenEval 及多种扩散模型骨干网络上的实验表明，其在属性绑定、空间关系与复杂组合生成方面均实现一致提升，同时保持良好视觉质量。
原文摘要 · Abstract (English)
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.
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