Text-to-image attention stops hugging the first token: diffusion models pile it on modality boundaries and image corners
Across the text-to-image Transformers FLUX.1, Z-Image and Infinity-2B, attention sinks land in architecture-specific places: the autoregressive model concentrates on its first global token, while the bidirectional diffusion models concentrate near modality boundaries, inside text regions and at image corners.