Attention Sinks in Diffusion Transformers: A Causal Analysis

Code

• github.com/wfz666/icml26-attention-sink
  OfficialIn paper★ 0

Abstract

Attention sinks -- tokens that receive disproportionate attention mass -- are assumed to be functionally important in autoregressive language models, but their role in diffusion transformers remains unclear. We present a causal analysis in text-to-image diffusion, dynamically identifying dominant attention recipients per timestep and suppressing them via paired, training-free interventions on the score and value paths. Across 553 GenEval prompts on Stable Diffusion~3 (with SDXL corroboration), removing these sinks does not degrade text-image alignment (CLIP-T) or preference proxies (ImageReward, HPS-v2) at k=1; only under stronger interventions (k\!\!10) does HPS-v2 exhibit a metric-dependent boundary, while CLIP-T remains robust throughout. The perceptual shifts induced by suppression are nonetheless sink-specific -- \!6 larger than equal-budget random masking -- revealing an empirical dissociation between trajectory-level perturbation and semantic alignment in diffusion transformers. Code available at https://github.com/wfz666/ICML26-attention-sink.

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