Trainer for DeepFashion Dataset

.gitignore

@@ -0,0 +1,3 @@
+/dataset
+/deepfashion_dataset
+/model_files

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,208 @@
+
+<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!
+
+---
+
+
+![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).
+
+

.ipynb_checkpoints/deepfashion_train_tagged-checkpoint.json

@@ -0,0 +1,3 @@
+{
+    "data": []
+}