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

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arxiv 2508.20586 v1 pith:ORYQLZ3R submitted 2025-08-28 cs.CV

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

Zheng Chong , Yanwei Lei , Shiyue Zhang , Zhuandi He , Zhen Wang , Xujie Zhang , Xiao Dong , Yiling Wu
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Dongmei Jiang Xiaodan Liang
This is my paper
classification cs.CV
keywords multi-referencereferencetry-onvirtualfastfitmethodscacheablechallenges
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CtrlVTON: Controllable Virtual Try-On via Visual-Instance-Prompt Segmentation

    cs.CV 2026-07 accept novelty 7.0

    CtrlVTON recasts virtual try-on as mask-conditioned editing and introduces VIP-SAM for instance-level garment segmentation, beating proprietary editors on layout fidelity while matching garment quality.

  2. Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On

    cs.CV 2026-07 conditional novelty 6.0

    Oxygen-TryOn performs any-item, multi-reference virtual try-on via understanding-driven generation, reporting state-of-the-art scores on public and internal benchmarks.

  3. VersaVogue: Visual Expert Orchestration and Preference Alignment for Unified Fashion Synthesis

    cs.CV 2026-04 unverdicted novelty 6.0

    VersaVogue unifies garment generation and virtual dressing via trait-routing attention with mixture-of-experts and an automated multi-perspective preference optimization pipeline that uses DPO without human labels.

  4. VTEdit-Bench: A Comprehensive Benchmark for Multi-Reference Image Editing Models in Virtual Try-On

    cs.CV 2026-03 conditional novelty 6.0

    VTEdit-Bench and VTEdit-QA show top universal multi-reference editors match specialized VTON models on standard tasks and transfer more stably to harder multi-person/multi-cloth settings, yet still fail under complex ...

  5. FDM-MFVT: Few-step Sampling Diffusion Model for Mask-Free Virtual Try-On

    cs.CV 2026-06 unverdicted novelty 5.0

    FDM-MFVT is a few-step mask-free virtual try-on diffusion model using OANO and IDT modules plus a new 30,000-pair MFVT dataset, claiming better efficiency and quality than baselines.

  6. Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items

    cs.CV 2026-04 unverdicted novelty 4.0

    Tstars-Tryon 1.0 is a deployed virtual try-on system claiming high robustness, photorealism, multi-reference flexibility, and near real-time speed for diverse fashion items.

  7. Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items

    cs.CV 2026-04 unverdicted novelty 3.0

    Tstars-Tryon 1.0 is a robust, photorealistic virtual try-on system with multi-image support and near real-time speed, deployed at industrial scale on Taobao and accompanied by a released benchmark.