reverting readme

README.md

@@ -1,208 +1,11 @@
-
-<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!
-
+title: IDM VITON PARO
+emoji: 🔥
+colorFrom: red
+colorTo: purple
+sdk: gradio
+sdk_version: 4.36.1
+app_file: app.py
+pinned: false
 ---
 
-
-![teaser2](assets/teaser2.png)&nbsp;
-![teaser](assets/teaser.png)&nbsp;
-
-
-## TODO LIST
-
-
-- [x] demo model
-- [x] inference code
-- [ ] training code
-
-
-
-## 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.
-
-Structure of the Dataset directory should be as follows.
-
-```
-
-train
-|-- ...
-
-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
-    |-- ...
-```
-
-
-## 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.shields.io/github/stars/gradio-app/gradio'></a>
-
-Download checkpoints for human parsing [here](https://huggingface.co/spaces/yisol/IDM-VTON-local/tree/main/ckpt).
-
-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
-python gradio_demo/app.py
-```
-
-
-
-
-
-
-## Acknowledgements
-
-
-Thanks [ZeroGPU](https://huggingface.co/zero-gpu-explorers) for providing free GPU.
-
-Thanks [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter) for base codes.
-
-Thanks [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) and [DCI-VTON](https://github.com/bcmi/DCI-VTON-Virtual-Try-On) for masking generation.
-
-Thanks [SCHP](https://github.com/GoGoDuck912/Self-Correction-Human-Parsing) for human segmentation.
-
-Thanks [Densepose](https://github.com/facebookresearch/DensePose) for human densepose.
-
-
-
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=yisol/IDM-VTON&type=Date)](https://star-history.com/#yisol/IDM-VTON&Date)
-
-
-
-## Citation
-```
-@article{choi2024improving,
-  title={Improving Diffusion Models for Virtual Try-on},
-  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](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
-
-
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference