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[ECCV2024] IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
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| 2026-07-31 | 5120 |
| 2026-08-02 | 5120 |
| 2026-08-06 | 5132 |
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<div align="center">
<h1>IDM-VTON: Improving Diffusion Models for Authentic Virtual Try-on in the Wild</h1>
<a href='https://idm-vton.github.io'><img src='https://img.shields.io/badge/Project-Page-green'></a>
<a href='https://arxiv.org/abs/2403.05139'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
<a href='https://huggingface.co/spaces/yisol/IDM-VTON'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-yellow'></a>
<a href='https://huggingface.co/yisol/IDM-VTON'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
</div>
This is the official implementation of the paper ["Improving Diffusion Models for Authentic Virtual Try-on in the Wild"](https://arxiv.org/abs/2403.05139).
Star ⭐ us if you like it!
---
## Requirements
```
git clone https://github.com/yisol/IDM-VTON.git
cd IDM-VTON
conda env create -f environment.yaml
conda activate idm
```
## Data preparation
### VITON-HD
You can download VITON-HD dataset from [VITON-HD](https://github.com/shadow2496/VITON-HD).
After download VITON-HD dataset, move vitonhd_test_tagged.json into the test folder, and move vitonhd_train_tagged.json into the train folder.
Structure of the Dataset directory should be as follows.
```
train
|-- image
|-- image-densepose
|-- agnostic-mask
|-- cloth
|-- vitonhd_train_tagged.json
test
|-- image
|-- image-densepose
|-- agnostic-mask
|-- cloth
|-- vitonhd_test_tagged.json
```
### DressCode
You can download DressCode dataset from [DressCode](https://github.com/aimagelab/dress-code).
We provide pre-computed densepose images and captions for garments [here](https://kaistackr-my.sharepoint.com/:u:/g/personal/cpis7_kaist_ac_kr/EaIPRG-aiRRIopz9i002FOwBDa-0-BHUKVZ7Ia5yAVVG3A?e=YxkAip).
We used [detectron2](https://github.com/facebookresearch/detectron2) for obtaining densepose images, refer [here](https://github.com/sangyun884/HR-VITON/issues/45) for more details.
After download the DressCode dataset, place image-densepose directories and caption text files as follows.
```
DressCode
|-- dresses
|-- images
|-- image-densepose
|-- dc_caption.txt
|-- ...
|-- lower_body
|-- images
|-- image-densepose
|-- dc_caption.txt
|-- ...
|-- upper_body
|-- images
|-- image-densepose
|-- dc_caption.txt
|-- ...
```
## Training
### Preparation
Download pre-trained ip-adapter for sdxl(IP-Adapter/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin) and image encoder(IP-Adapter/models/image_encoder) [here](https://github.com/tencent-ailab/IP-Adapter).
```
git clone https://huggingface.co/h94/IP-Adapter
```
Move ip-adapter to ckpt/ip_adapter, and image encoder to ckpt/image_encoder.
Start training using python file with arguments,
```
accelerate launch train_xl.py \
--gradient_checkpointing --use_8bit_adam \
--output_dir=result --train_batch_size=6 \
--data_dir=DATA_DIR
```
or, you can simply run with the script file.
```
sh train_xl.sh
```
## Inference
### VITON-HD
Inference using python file with arguments,
```
accelerate launch inference.py \
--width 768 --height 1024 --num_inference_steps 30 \
--output_dir "result" \
--unpaired \
--data_dir "DATA_DIR" \
--seed 42 \
--test_batch_size 2 \
--guidance_scale 2.0
```
or, you can simply run with the script file.
```
sh inference.sh
```
### DressCode
For DressCode dataset, put the category you want to generate images via category argument,
```
accelerate launch inference_dc.py \
--width 768 --height 1024 --num_inference_steps 30 \
--output_dir "result" \
--unpaired \
--data_dir "DATA_DIR" \
--seed 42
--test_batch_size 2
--guidance_scale 2.0
--category "upper_body"
```
or, you can simply run with the script file.
```
sh inference.sh
```
## Start a local gradio demo <a href='https://github.com/gradio-app/gradio'><img src='https://img.shieldExcerpt of 5,602 characters
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matched fp:08ad99495c74a868, llm:description: [ECCV2024] IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
matched fp:08ad99495c74a868, llm:description: [ECCV2024] IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
matched fp:08ad99495c74a868, llm:description: [ECCV2024] IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild