FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable
  Diffusion Models
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.
68b50746851c6e7b001eca0f
Zheng Chong
claimed_verified
2025-09-03T09:37:58.845Z
646446517572c66a8e652e94
https://cdn-avatars.huggingface.co/v1/production/uploads/646446517572c66a8e652e94/A4LIdECGW03ixc0HfIhfo.png
ZhengChong
user
zhengchong
68b50746851c6e7b001eca10
Yanwei Lei
68b50746851c6e7b001eca11
Shiyue Zhang
68b50746851c6e7b001eca12
Zhuandi He
68b50746851c6e7b001eca13
Zhen Wang
68b50746851c6e7b001eca14
Xujie Zhang
68b50746851c6e7b001eca15
Xiao Dong
68b50746851c6e7b001eca16
Yiling Wu
68b50746851c6e7b001eca17
Dongmei Jiang
68b50746851c6e7b001eca18
Xiaodan Liang
FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable
  Diffusion Models
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.
FastFit, a high-speed virtual try-on framework using a cacheable diffusion architecture with a Semi-Attention mechanism, achieves significant speedup and maintains high fidelity in multi-reference outfit compositions.
diffusion architecture Semi-Attention mechanism timestep embeddings class embeddings reference feature encoding denoising process DressCode-MR VITON-HD DressCode fidelity metrics inference efficiency