PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference
  Time by Leveraging Sparsity
Authors: name: Kwanyoung Kim, name: Byeongsu Sim

Diffusion models have shown impressive results in generating high-quality
conditional samples using guidance techniques such as Classifier-Free Guidance
(CFG). However, existing methods often require additional training or neural
function evaluations (NFEs), making them incompatible with guidance-distilled
models. Also, they rely on heuristic approaches that need identifying target
layers. In this work, we propose a novel and efficient method, termed PLADIS,
which boosts pre-trained models (U-Net/Transformer) by leveraging sparse
attention. Specifically, we extrapolate query-key correlations using softmax
and its sparse counterpart in the cross-attention layer during inference,
without requiring extra training or NFEs. By leveraging the noise robustness of
sparse attention, our PLADIS unleashes the latent potential of text-to-image
diffusion models, enabling them to excel in areas where they once struggled
with newfound effectiveness. It integrates seamlessly with guidance techniques,
including guidance-distilled models. Extensive experiments show notable
improvements in text alignment and human preference, offering a highly
efficient and universally applicable solution.