Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion
  Models
Wentian Zhang Haozhe Liu Jinheng Xie Francesco Faccio Mike Zheng Shou Jürgen Schmidhuber
This study explores the role of cross-attention during inference in
text-conditional diffusion models. We find that cross-attention outputs
converge to a fixed point after few inference steps. Accordingly, the time
point of convergence naturally divides the entire inference process into two
stages: an initial semantics-planning stage, during which, the model relies on
cross-attention to plan text-oriented visual semantics, and a subsequent
fidelity-improving stage, during which the model tries to generate images from
previously planned semantics. Surprisingly, ignoring text conditions in the
fidelity-improving stage not only reduces computation complexity, but also
maintains model performance. This yields a simple and training-free method
called TGATE for efficient generation, which caches the cross-attention output
once it converges and keeps it fixed during the remaining inference steps. Our
empirical study on the MS-COCO validation set confirms its effectiveness. The
source code of TGATE is available at https://github.com/HaozheLiu-ST/T-GATE.