CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers
  Up
Authors: name: Songhua Liu, name: Zhenxiong Tan, name: Xinchao Wang

Diffusion Transformers (DiT) have become a leading architecture in image
generation. However, the quadratic complexity of attention mechanisms, which
are responsible for modeling token-wise relationships, results in significant
latency when generating high-resolution images. To address this issue, we aim
at a linear attention mechanism in this paper that reduces the complexity of
pre-trained DiTs to linear. We begin our exploration with a comprehensive
summary of existing efficient attention mechanisms and identify four key
factors crucial for successful linearization of pre-trained DiTs: locality,
formulation consistency, high-rank attention maps, and feature integrity. Based
on these insights, we introduce a convolution-like local attention strategy
termed CLEAR, which limits feature interactions to a local window around each
query token, and thus achieves linear complexity. Our experiments indicate
that, by fine-tuning the attention layer on merely 10K self-generated samples
for 10K iterations, we can effectively transfer knowledge from a pre-trained
DiT to a student model with linear complexity, yielding results comparable to
the teacher model. Simultaneously, it reduces attention computations by 99.5%
and accelerates generation by 6.3 times for generating 8K-resolution images.
Furthermore, we investigate favorable properties in the distilled attention
layers, such as zero-shot generalization cross various models and plugins, and
improved support for multi-GPU parallel inference. Models and codes are
available here: https://github.com/Huage001/CLEAR.