Cross Attention Map

Cross-attention maps, derived from transformer-based models like Stable Diffusion, represent the relationships between different parts of an image and textual descriptions, providing a powerful tool for understanding and manipulating image generation and editing. Current research focuses on leveraging these maps for tasks such as training-free segmentation, localized image editing, and improving the fidelity and control of multi-object generation in diffusion models, often employing iterative refinement or attention-based regularization techniques. This work is significant because it enhances the controllability and interpretability of generative AI, leading to more efficient and effective image synthesis and manipulation across various applications, including image editing, segmentation, and video generation.