Virtual Try-on (VTON) has become a core capability for online retail, where realistic try-on results provide reliable fit guidance, reduce returns, and benefit both consumers and merchants. Diffusion-based VTON methods achieve photorealistic synthesis, yet often rely on intricate architectures such as auxiliary reference networks and suffer from slow sampling, making the trade-off between fidelity and efficiency a persistent challenge. We approach VTON as a structured image editing problem that demands strong conditional generation under three key requirements: subject preservation, faithful texture transfer, and seamless harmonization. Under this perspective, our training framework is generic and transfers to broader image editing tasks. Moreover, the paired data produced by VTON constitutes a rich supervisory resource for training general-purpose editors. We present PROMO, a promptable virtual try-on framework built upon a Flow Matching DiT backbone with latent multi-modal conditional concatenation. By leveraging conditioning efficiency and self-reference mechanisms, our approach substantially reduces inference overhead. On standard benchmarks, PROMO surpasses both prior VTON methods and general image editing models in visual fidelity while delivering a competitive balance between quality and speed. These results demonstrate that flow-matching transformers, coupled with latent multi-modal conditioning and self-reference acceleration, offer an effective and training-efficient solution for high-quality virtual try-on.
Figures & tables
Figure 2: Overview of scenarios handled by our PROMO framework. The left panel illustrates three common scenarios that customers encounter during online shopping: 1) model image available without a specific garment image, 2) garment image available without a model, and 3) both model and garment images available. Our system addresses the missing information in each scenario by generating the necessary conditional inputs required by our model. Notably, for scenarios without model images, our system features pure image-reference capability, allowing text prompts to be omitted while maintaining a robust baseline performance.
Figure 3: Compared to directly using the original DensePose [ 11 ] , our method better estimates plausible body shapes under loose clothing, effectively preventing information leakage.
Figure 4: We downsample the extracted human parsing to match the resolution of the latent space. Compared to the standard supervision in (a), we adopt a weighted loss design in (b).
Figure 5: Our method for efficient spatial condition injection. We directly paste the pose condition onto the agnostic image, then perform downsampling, eventually to reduce the 2N tokens to N/4 tokens, achieving 87.5% token reduction.
Figure 6: Overview of the Style Prompt System . A finetuned VLM processes an image of a person wearing the source garment, extracting person and garment attributes into a JSON format. This structured JSON is then converted into a natural language prompt for our pipeline.
Figure 7: Attention mechanism: zt and Tstyle have global visibility, while Ci only attends to itself.
Figure 8: Temporal Self-Reference mechanism.
Figure 9: Visualization of comparison between ours and baseline models on DressCode dataset. Best viewed when zoomed in.
Figure 10: Visualization of comparison on virtual try-on between our PROMO between state-of-the-art image-editing model on DressCode dataset. Best viewed when zoomed in.
Methods
DressCode
VITON-HD
Paired
Unpaired
Paired
Unpaired
SSIM ↑
LPIPS ↓
FID ↓
KID ↓
FID ↓
KID ↓
SSIM ↑
LPIPS ↓
FID ↓
KID ↓
FID ↓
KID ↓
LaDI-VTON [ 28 ]
0.7564
0.3800
5.4700
1.9270
7.3475
2.4690
0.8721
0.1531
6.8500
1.3766
9.6131
2.0207
CatVTON [ 6 ]
0.8944
0.1600
6.5372
3.9591
8.4567
4.4897
0.8673
0.1882
9.4412
4.7389
12.5716
5.4555
OOTDiffusion [ 47 ]
0.8883
0.0800
3.6623
0.8550
7.0463
2.7910
0.7919
0.1905
32.8944
20.0843
39.9626
26.0686
Any2AnyTryon [ 12 ]
0.9107
0.1208
3.0828
1.0565
5.5404
1.5258
0.8705
0.1569
7.1238
2.1797
10.3120
2.7086
Table 1: Quantitative results on VITON-HD and DressCode datasets. We compare the metrics under both paired (model’s clothing is the same as the given cloth image) and unpaired settings (model’s clothing differs) with other methods.
Methods
DressCode
VITON-HD
Paired
Unpaired
Paired
Unpaired
SSIM ↑
LPIPS ↓
FID ↓
KID ↓
FID ↓
KID ↓
SSIM ↑
LPIPS ↓
FID ↓
KID ↓
FID ↓
KID ↓
Qwen-Image-Edit-2509 [ 44 ]
0.8370
0.1831
13.5464
1.9944
13.9163
2.7315
0.7790
0.2174
20.6403
0.5018
18.7954
1.1485
Seedream 4.0 [ 36 ]
0.8217
0.1816
16.8140
7.2200
25.4943
6.1900
0.7600
0.2352
26.9076
9.3880
26.8181
8.4790
Nanobanana [ 10 ]
0.8647
0.1186
10.7050
1.0860
10.6720
0.7160
0.8100
0.1520
19.1688
1.1299
20.4191
0.4411
PROMO (Ours)
0.8919
0.0885
8.9268
0.4674
10.1217
0.5763
0.8439
0.1365
15.6658
2.0513
18.0356
1.5828
Table 2: Quantitative results on VITON-HD and DressCode datasets. Comparison between our model with image-editing methods.
Method
Texture ↑
Body Shape ↑
Style ↑
Color ↑
Overall ↑
Huiwa
94.42
88.85
94.80
99.04
78.85
Kling
87.12
93.46
79.87
96.53
60.19
Douyin
96.73
79.04
85.19
95.77
61.54
PROMO (Ours)
93.65
94.62
96.92
97.88
84.42
Table 3: Excellence rates (%) of different methods on in-the-wild dataset. A result is considered excellent if more than half annotators rate it as such. Overall indicates the percentage of images that are excellent in all four dimensions. Bold denotes the best result and underline denotes the second best.
Paired
Unpaired
SSIM ↑
LPIPS ↓
FID ↓
KID ↓
FID ↓
KID ↓
- w/o parsing area loss
0.8896
0.0872
3.2847
0.5076
4.6390
0.9535
- w/o style prompt
0.8896
0.0926
3.7178
0.8926
5.3516
0.6180
- w/o 3D-RoPE
0.8702
0.1304
6.7293
1.7160
7.8175
2.2801
PROMO (Ours)
0.8913
0.0887
3.3103
0.4902
5.3516
0.4992
Table 4: Ablation study on DressCode dataset. The best and second-best results are highlighted in bold and underlined , respectively.
Figure 11: Visualization of ablation study on parsing area loss. Best viewed when zoomed in.
Figure 12: Visualization of ablation study on 3D-RoPE. Without 3D-RoPE, the model fails to correctly distinguish subject transfer and spatial alignment relationships between conditions.
Inference time (s)
Paired
Unpaired
SSIM ↑
LPIPS ↓
FID ↓
KID ↓
FID ↓
KID ↓
- w/o T.S.R
22.2378
0.8913
0.0887
3.3102
0.7975
4.7391
0.5313
- w/o S.T.M
11.1030
0.8916
0.0873
3.4878
0.5126
4.8546
0.4907
PROMO (Ours)
9.1775
0.8913
0.0887
3.3103
0.4902
4.7393
0.4992
Table 5: Quantitative ablation results on DressCode, including quality metrics and inference time. T.S.R is for Temporal Self-Reference and S.T.M is for Spatial Token Merging. Inference time is averaged over 20 runs on an H800 GPU.
Virtual try-on (VTON) requires precise pixel-level fidelity, yet mainstream Diffusion Transformers (DiTs) often suffer from texture degradation and structural drift. We identify symmetric interactions in standard joint-attention mechanisms as a source of these failures. Although such interactions support semantic flexibility in general-purpose editing, they allow stochastic noise to corrupt deterministic garment features in VTON. We analyze this problem through asymmetric information dynamics and introduce two diagnostic indicators: Conditional Attention Entropy (CAE) for feature unbiasedness and Injected Information Flux (IIF) for injection effectiveness. Our analysis suggests that symmetric bidirectional attention can corrupt conditional features and attenuate the conditional signal. To address these limitations, we propose RealFit, a framework that combines Unidirectional Information Flow (UIF) with Decoupled Timestep Modulation (DTM). UIF isolates the garment condition from stochastic noise to preserve garment identity, while DTM optimizes the modulation scale to maintain a strong conditional signal. The resulting time-invariant condition branch enables a conditional KV cache that reduces inference time by approximately 75%. RealFit offers a principled approach to conditional generation with state-of-the-art fidelity and efficiency.
Diffusion-based virtual try-on (VTON) often relies on segmentation-derived masks that become unreliable when the reference garment differs substantially from the target person's clothing. Existing methods may also lose fine details and commonly focus on single-garment transfer. We present TAMF-VTON, an inference-time mask-free framework built on Qwen-Image-Edit-2509. Given a person image, one or more garment images, and a text instruction, it performs single- or multi-garment transfer without masks, parsing maps, pose representations, or spatial controls at inference. Its central component is token-wise sparse Mixture-of-Experts adaptation, which provides input-dependent capacity without predefined expert roles. Timestep-weighted frequency supervision regularizes high-frequency reconstruction, while adaptive offline data construction supplies cross-category and cross-silhouette training tuples; neither is used at inference. TAMF-VTON achieves the best scores on most reported paired and unpaired metrics among evaluated VTON baselines on the standard single-garment VITON-HD and DressCode benchmarks. Selected qualitative results illustrate multi-garment composition, subject-to-subject transfer, diverse scenes, body-shape variation, and illumination changes. With INT4 on an RTX 4090, TAMF-VTON generates 1K-resolution images under 15 seconds and 2K-resolution images under 30 seconds. Our project can be accessed via https://www.style3d.ai/ai-photoshoot/virtual-clothing-try-on.
Jie Wang, Qian He, Gaofeng He +2
State Key Lab of CAD & CG, Zhejiang University and Style3D Research, China · Style3D Research, China · State Key Lab of CAD & CG, Zhejiang University, China
Despite the rapid advancement of Virtual Try-On (VTON) and Try-Off (VTOFF) technologies, existing VTON methods face challenges with fine-grained detail preservation, generalization to complex scenes, complicated pipeline, and efficient inference. To tackle these problems, we propose OmniDiT, an omni Virtual Try-On framework based on the Diffusion Transformer, which combines try-on and try-off tasks into one unified model. Specifically, we first establish a self-evolving data curation pipeline to continuously produce data, and construct a large VTON dataset Omni-TryOn, which contains over 380k diverse and high-quality garment-model-tryon image pairs and detailed text prompts. Then, we employ the token concatenation and design an adaptive position encoding to effectively incorporate multiple reference conditions. To relieve the bottleneck of long sequence computation, we are the first to introduce Shifted Window Attention into the diffusion model, thus achieving a linear complexity. To remedy the performance degradation caused by local window attention, we utilize multiple timestep prediction and an alignment loss to improve generation fidelity. Experiments reveal that, under various complex scenes, our method achieves the best performance in both the model-free VTON and VTOFF tasks and a performance comparable to current SOTA methods in the model-based VTON task.
Weixuan Zeng, Pengcheng Wei, Huaiqing Wang +8
The Chinese University of Hong Kong, ShenZhen · Beihang University · KuaiShou