Delta-K: Boosting Multi-Instance Generation via Cross-Attention Augmentation
Delta-K solves a problem where diffusion models fail to include missing concepts in complex scenes. It works by analyzing the prompt to find semantic signatures of omitted items and injecting this signature into the model's attention mechanism during generation. This dynamic injection stabilizes the image structure while guiding the model to correctly place new elements, improving scene coherence without retraining.