Readme Changes

README-2.md

@@ -0,0 +1,162 @@
+
+<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-blue'></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!
+
+---
+
+
+<!-- ![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
+```
+
+
+## Acknowledgements
+
+For the [demo](https://huggingface.co/spaces/yisol/IDM-VTON), GPUs are supported from [ZeroGPU](https://huggingface.co/zero-gpu-explorers), and masking generation codes are based on [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) and [DCI-VTON](https://github.com/bcmi/DCI-VTON-Virtual-Try-On).
+
+Parts of our code are based on [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter).
+
+
+
+## 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).
+
+
+

README.md

@@ -1,162 +1,12 @@
-
-<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-blue'></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: AILUSION VTON DEMO V1
+emoji: Demo
+colorForm: yellow
+colorTo: green
+sdk: gradio
+sdk_version: 4.28.2
+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
-```
-
-
-## Acknowledgements
-
-For the [demo](https://huggingface.co/spaces/yisol/IDM-VTON), GPUs are supported from [ZeroGPU](https://huggingface.co/zero-gpu-explorers), and masking generation codes are based on [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) and [DCI-VTON](https://github.com/bcmi/DCI-VTON-Virtual-Try-On).
-
-Parts of our code are based on [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter).
-
-
-
-## 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).
-
-
-
+AILUSION V1 DEMO Virtual Try ON