VimalrajS04/Major_pro
By VimalrajS04 · ↓ 0 · ♥ 0 · Updated 2025-09-23
What's in this repo
This page lists the files in VimalrajS04/Major_pro, including parsing_atr.onnx, parsing_lip.onnx.
- 2 ONNX models (.onnx files)
- 952 files, including 560 .py, 194 .yaml, 59 .md, 24 .jpg, 17 .h, 17 .sh
- Last updated 2025-09-23
ONNX files
| File | Size | Download |
|---|---|---|
| ckpt/humanparsing/parsing_atr.onnx | — | Download |
| ckpt/humanparsing/parsing_lip.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
license: cc-by-nc-sa-4.0 language: en tags:
- virtual-try-on
- diffusion
- idm-vton
IDM-VTON: Improving Diffusion Models for Authentic Virtual Try-on in the Wild
This is the official implementation of the paper "Improving Diffusion Models for Authentic Virtual Try-on in the Wild".
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.
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.
We provide pre-computed densepose images and captions for garments here.
We used detectron2 for obtaining densepose images, refer here 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.
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 
Download checkpoints for human parsing here.
Place the checkpoints under the ckpt folder.
ckpt
|-- densepose
|-- model_final_162be9.pkl
|-- humanparsing
|-- parsing_atr.onnx
|-- parsing_lip.onnx
|-- openpose
|-- ckpts
|-- body_pose_model.pth
Run the following command:
python gradio_demo/app.py
Acknowledgements
Thanks ZeroGPU for providing free GPU.
Thanks IP-Adapter for base codes.
Thanks OOTDiffusion and DCI-VTON for masking generation.
Thanks SCHP for human segmentation.
Thanks Densepose for human densepose.
Star History
Citation
@article{choi2024improving,
title={Improving Diffusion Models for Authentic Virtual Try-on in the Wild},
author={Choi, Yisol and Kwak, Sangkyung and Lee, Kyungmin and Choi, Hyungwon and Shin, Jinwoo},
journal={arXiv preprint arXiv:2403.05139},
year={2024}
}
License
The codes and checkpoints in this repository are under the CC BY-NC-SA 4.0 license.
Model info
Created: 2025-09-23
Updated: 2025-09-23
View on Hugging FaceFiles (952)
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