Qihoo-T2X: An Efficiency-Focused Diffusion Transformer via Proxy Tokens
  for Text-to-Any-Task
Authors: name: Jing Wang, name: Ao Ma, name: Jiasong Feng, name: Dawei Leng, name: Yuhui Yin, name: Xiaodan Liang

The global self-attention mechanism in diffusion transformers involves
redundant computation due to the sparse and redundant nature of visual
information, and the attention map of tokens within a spatial window shows
significant similarity. To address this redundancy, we propose the Proxy Token
Diffusion Transformer (PT-DiT), which employs sparse representative token
attention (where the number of representative tokens is much smaller than the
total number of tokens) to model global visual information efficiently.
Specifically, in each transformer block, we randomly sample one token from each
spatial-temporal window to serve as a proxy token for that region. The global
semantics are captured through the self-attention of these proxy tokens and
then injected into all latent tokens via cross-attention. Simultaneously, we
introduce window and shift window attention to address the limitations in
detail modeling caused by the sparse attention mechanism. Building on the
well-designed PT-DiT, we further develop the Qihoo-T2X family, which includes a
variety of models for T2I, T2V, and T2MV tasks. Experimental results show that
PT-DiT achieves competitive performance while reducing the computational
complexity in both image and video generation tasks (e.g., a 48% reduction
compared to DiT and a 35% reduction compared to Pixart-alpha). Our source code
is available at https://github.com/360CVGroup/Qihoo-T2X.