FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable
  Diffusion Models
Authors: name: Zheng Chong, name: Yanwei Lei, name: Shiyue Zhang, name: Zhuandi He, name: Zhen Wang, name: Xujie Zhang, name: Xiao Dong, name: Yiling Wu, name: Dongmei Jiang, name: Xiaodan Liang

Despite its great potential, virtual try-on technology is hindered from
real-world application by two major challenges: the inability of current
methods to support multi-reference outfit compositions (including garments and
accessories), and their significant inefficiency caused by the redundant
re-computation of reference features in each denoising step. To address these
challenges, we propose FastFit, a high-speed multi-reference virtual try-on
framework based on a novel cacheable diffusion architecture. By employing a
Semi-Attention mechanism and substituting traditional timestep embeddings with
class embeddings for reference items, our model fully decouples reference
feature encoding from the denoising process with negligible parameter overhead.
This allows reference features to be computed only once and losslessly reused
across all steps, fundamentally breaking the efficiency bottleneck and
achieving an average 3.5x speedup over comparable methods. Furthermore, to
facilitate research on complex, multi-reference virtual try-on, we introduce
DressCode-MR, a new large-scale dataset. It comprises 28,179 sets of
high-quality, paired images covering five key categories (tops, bottoms,
dresses, shoes, and bags), constructed through a pipeline of expert models and
human feedback refinement. Extensive experiments on the VITON-HD, DressCode,
and our DressCode-MR datasets show that FastFit surpasses state-of-the-art
methods on key fidelity metrics while offering its significant advantage in
inference efficiency.