DeCo-VTON

UNet checkpoints for DeCo-VTON, the official implementation of Rethinking Garment Conditioning in Diffusion-based Virtual Try-On: Decouple, Don't Denoise, accepted at ECCV 2026.

DeCo-VTON is a single-UNet virtual try-on model that separates garment conditioning from the denoising target.

• Code: https://github.com/Levinna/DeCo-VTON
• Paper: https://arxiv.org/abs/2511.18775

This repository was formerly named levinna/Re-CatVTON. The old repository URL redirects here, and the original checkpoint paths remain available for backward compatibility.

Checkpoints

Subfolder	Dataset	Resolution	Availability
VITON-HD-512/unet	VITON-HD	512×384	Available
DressCode-512/unet	DressCode	512×384	Available
VITON-HD-1024/unet	VITON-HD	1024×768	Checkpoint required; planned for a later release
DressCode-1024/unet	DressCode	1024×768	Checkpoint required; planned for a later release

The 1024 checkpoints are not currently included in this repository.

Legacy paths such as VITON-HD/checkpoint-16000/unet and DressCode/checkpoint-32000/unet are retained for existing users.

Installation

git clone https://github.com/Levinna/DeCo-VTON.git
cd DeCo-VTON
pip install -r requirements.txt
pip install -e .

Usage

This repository contains UNet checkpoints, not a standalone Diffusers pipeline. DiffusionPipeline.from_pretrained() cannot load them directly. Install DeCo-VTON and use DeCoVTONPipeline.from_vton_checkpoint() as shown below.

The pipeline loads the VAE from stabilityai/sd-vae-ft-mse and the scheduler configuration from stable-diffusion-v1-5/stable-diffusion-inpainting.

import torch
from decovton import DeCoVTONPipeline

pipe = DeCoVTONPipeline.from_vton_checkpoint(
    hf_repo="levinna/DeCo-VTON",
    subfolder="VITON-HD-512/unet",
    torch_dtype=torch.bfloat16,
).to("cuda")

See the GitHub repository for dataset preparation, inference, and evaluation instructions.

License

The model weights are licensed under CC BY-NC 4.0, reflecting the non-commercial terms of the VITON-HD and DressCode datasets. The accompanying source code is licensed separately under CC BY-NC-SA 4.0.

Citation

@article{na2025rethinking,
  title={Rethinking Garment Conditioning in Diffusion-based Virtual Try-On: Decouple, Don't Denoise},
  author={Na, Kihyun and Choi, Jinyoung and Kim, Injung},
  journal={arXiv preprint arXiv:2511.18775},
  year={2025}
}