Dynamic Diffusion Transformer
Wangbo Zhao Yizeng Han Jiasheng Tang Kai Wang Yibing Song Gao Huang Fan Wang Yang You
Diffusion Transformer (DiT), an emerging diffusion model for image generation,
has demonstrated superior performance but suffers from substantial computational
costs. Our investigations reveal that these costs stem from the static inference
paradigm, which inevitably introduces redundant computation in certain diffusion
timesteps and spatial regions. To address this inefficiency, we propose Dynamic
Diffusion Transformer (DyDiT), an architecture that dynamically adjusts its compu-
tation along both timestep and spatial dimensions during generation. Specifically,
we introduce a Timestep-wise Dynamic Width (TDW) approach that adapts model
width conditioned on the generation timesteps. In addition, we design a Spatial-
wise Dynamic Token (SDT) strategy to avoid redundant computation at unnecessary
spatial locations. Extensive experiments on various datasets and different-sized
models verify the superiority of DyDiT. Notably, with <3% additional fine-tuning it-
erations, our method reduces the FLOPs of DiT-XL by 51%, accelerates generation
by 1.73×, and achieves a competitive FID score of 2.07 on ImageNet.
Diffusion Transformer, Dynamic Neural Network, Efficiency