SmoothCache: A Universal Inference Acceleration Technique for Diffusion
  Transformers
Authors: name: Joseph Liu, name: Joshua Geddes, name: Ziyu Guo, name: Haomiao Jiang, name: Mahesh Kumar Nandwana
Organization: Roblox Corporation

Diffusion Transformers (DiT) have emerged as powerful generative models for
various tasks, including image, video, and speech synthesis. However, their
inference process remains computationally expensive due to the repeated
evaluation of resource-intensive attention and feed-forward modules. To address
this, we introduce SmoothCache, a model-agnostic inference acceleration
technique for DiT architectures. SmoothCache leverages the observed high
similarity between layer outputs across adjacent diffusion timesteps. By
analyzing layer-wise representation errors from a small calibration set,
SmoothCache adaptively caches and reuses key features during inference. Our
experiments demonstrate that SmoothCache achieves 8% to 71% speed up while
maintaining or even improving generation quality across diverse modalities. We
showcase its effectiveness on DiT-XL for image generation, Open-Sora for
text-to-video, and Stable Audio Open for text-to-audio, highlighting its
potential to enable real-time applications and broaden the accessibility of
powerful DiT models.