🤖 AI Summary
This work addresses the challenges diffusion models face in generating images from complex text prompts involving multiple entities, attributes, and spatial relationships—often resulting in missing objects, misaligned attributes, or incorrect layouts. To tackle this, the authors propose a training-free, stage-aware compositional binding framework that parses the input prompt into a compositional graph, establishing a global layout and binding attributes during early denoising stages while progressively relaxing constraints in later stages to preserve fine details. The method innovatively incorporates dynamic guidance strength adjustment and a contrastive optimization mechanism to enable precise cross-entity attribute binding. Evaluated on benchmarks such as T2I-CompBench++ and GenEval, the approach significantly improves attribute binding accuracy, spatial relation fidelity, and complex scene generation capability, all while maintaining high visual quality.
📝 Abstract
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.