Delta-K: Boosting Multi-Instance Generation via Cross-Attention Augmentation

Abstract

While Diffusion Models excel in text-to-image synthesis, they frequently suffer from catastrophic concept omission when generating complex multi-instance scenes. Existing training-free methods attempt to resolve this by rescaling attention maps, which merely exacerbates unstructured noise without establishing coherent semantic representations. To address this, we propose Delta-K, a backbone-agnostic, plug-and-play inference framework that resolves omission by operating directly in the shared cross-attention Key space. Utilizing a lightweight Vision-Language Model (VLM) preview, we isolate a differential key ($\Delta K$) capturing the pure semantic signature of missing concepts, and proactively inject it during the early semantic planning phase. Governed by a dynamically optimized scheduling mechanism, Delta-K grounds diffuse noise into stable structural anchors while naturally preserving existing concepts via the inherent orthogonality of $\Delta K$. Extensive experiments validate its universal applicability, demonstrating that Delta-K significantly improves compositional alignment across both modern DiT and foundational U-Net architectures without requiring spatial masks, auxiliary training, or structural modifications.