# FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models

## TL;DR

FastFit introduces a cacheable diffusion framework for multi-reference virtual try-on, achieving 3.5x faster inference and higher fidelity by reusing reference features across denoising steps.

## Contribution

The paper proposes a novel cacheable diffusion architecture with semi-attention and class embeddings, enabling efficient multi-reference virtual try-on and introduces the DressCode-MR dataset.

## Key findings

- FastFit achieves 3.5x speedup over existing methods.
- It surpasses state-of-the-art fidelity metrics.
- The DressCode-MR dataset supports complex multi-reference try-on research.

## Abstract

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.