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-# PCDMs
-Implementation code：[Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://arxiv.org/pdf/2310.06313.pdf), accepted at International Conference on Learning Representations (ICLR) 2024.
+原项目：https://github.com/tencent-ailab/PCDMs
 
+![f88da31cf992a48eed11c93b13285c6](https://github.com/StartHua/ComfyUI_PCDMs/assets/22284244/dee8343b-07f8-4732-9489-f23c26c2e5be)
 
-## Generated Results
-![PCDMs Motivation](imgs/compare_sota.png)
-
----
-![PCDMs results1](imgs/demo.png)
-
----
-![PCDMs results2](imgs/demo2.png)
-
-You can directly download our test results from Google Drive: (1) [PCDMs vs SOTA](https://drive.google.com/drive/folders/1q21tA3VsQqScecQ7m3_eUFxIPWUGYKAa?usp=drive_link) (2) [PCDMs Results](https://drive.google.com/drive/folders/1sjqMhZ79pugk2IHhW-whg_NASpx3BSew?usp=drive_link).
-
-The [PCDMs vs SOTA](https://drive.google.com/drive/folders/1q21tA3VsQqScecQ7m3_eUFxIPWUGYKAa?usp=drive_link) compares our method with several state-of-the-art methods e.g. ADGAN, PISE, GFLA, DPTN, CASD, NTED, PIDM. 
-Each row contains target_pose, source_image, ground_truth, ADGAN, PISE, GFLA, DPTN, CASD, NTED, PIDM, and PCDMs (ours) respectively.
-
-
-We present a simplified version of PCDMs and only use stage2, utilizing training data from TikTok and DeepFashion. 
-The weights can be obtained from [Google drive](https://drive.google.com/drive/folders/1hnPm-GzaAtQKjgG_glhlQZieJ5g5Xd-y?usp=drive_link).
-
-Download dwpose weights (`dw-ll_ucoco_384.pth`, `yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth`) following [this](https://github.com/IDEA-Research/DWPose?tab=readme-ov-file#-dwpose-for-controlnet).
-## Installation
-```
-# install diffusers & pose extractor
-pip install diffusers==0.24.0
-pip install controlnet-aux==0.0.7
-pip install transformers==4.32.1
-pip install accelerate==0.24.1
-
-# install DWPose which is dependent on MMDetection, MMCV and MMPose
-pip install -U openmim
-mim install mmengine
-mim install "mmcv>=2.0.1"
-mim install "mmdet>=3.1.0"
-mim install "mmpose>=1.1.0"
-
-# clone code
-git clone https://github.com/tencent-ailab/PCDMs.git
-
-# download the models
-cd PCDMs
-mv {weights} ./PCDMs_ckpt.pt
-
-# then you can use the notebook
-{pcdms_demo.ipynb}
-```
-
-
-## Methods
-![PCDMs inpainting](imgs/inpainting.png)
-
----
-![PCDMs Motivation](imgs/diagram.png)
-
----
-![PCDMs Framework](imgs/frameworkv2.png)
-
-## Dataset
-### Processed Data
-This [link](https://drive.google.com/drive/folders/17yqf2iV2y7Hwx-__uV_-cRaOm2XX8lWS?usp=drive_link) contains processed and prepared data that is ready for use.
-The data has been processed in the following ways:
-
-• Rename image
-
-• Split the train/test set
-
-• keypoints extracted with Openpose
-
-The folder structure of dataset should be as follows:
-
-```
-Deepfashion/
-├── all_data_png                        # including train and test images
-│   ├── img1.png          
-│   ├── ...
-│   ├── img52712.png         
-├── train_lst_256_png                   # including train images of 256 size
-│   ├── img1.png
-│   ├── ...
-│   ├── img48674.png
-├── train_lst_512_png                   # including train images of 512 size
-│   ├── img1.png
-│   ├── ...
-│   ├── img48674.png
-├── test_lst_256_png                    # including test images of 256 size
-│   ├── img1.png
-│   ├── ...
-│   ├── img4038.png
-├── test_lst_512_png                    # including test images of 512 size
-│   ├── img1.png
-│   ├── ...
-│   ├── img4038.png
-├── normalized_pose_txt.zip             # including pose coordinate of train and test set
-│   ├── pose_coordinate1.txt
-│   ├── ...
-│   ├── pose_coordinate40160.txt
-├── train_data.json                     
-├── test_data.json
-```
-
-### Original Data
- Download ```img_highres.zip``` of the DeepFashion Dataset from [In-shop Clothes Retrieval Benchmark](https://drive.google.com/drive/folders/0B7EVK8r0v71pYkd5TzBiclMzR00?resourcekey=0-fsjVShvqXP2517KnwaZ0zw). 
- 
- Unzip ```img_highres.zip```. You will need to ask for password from the dataset maintainers. 
-
-
-## Checkpoints Links
-We provide 3 stage checkpoints available [here](https://drive.google.com/drive/folders/1xDpSIBdP11UoXe0HoWGPWBfEnk1zRMVw?usp=drive_link).
-
-
-
-## How to Train/Test
-1. train/test stage1-prior
-```
-sh run_stage1.sh  & sh run_test_stage1.sh
-```
-
-2. train/test stage2-inpaint
-```
-sh run_stage2.sh  & sh run_test_stage2.sh
-```
-
-3. train/test stage3-refined
-```
-sh run_stage3.sh  & sh run_test_stage3.sh
-```
-
-## Citation
-If this work is useful to you, please consider citing our paper:
-```
-@article{shen2023advancing,
-  title={Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models},
-  author={Shen, Fei and Ye, Hu and Zhang, Jun and Wang, Cong and Han, Xiao and Yang, Wei},
-  journal={arXiv preprint arXiv:2310.06313},
-  year={2023}
-}
-
-```
-
-## Contact
-If you have any questions, please feel free to contact with me at shenfei140721@126.com.
+![b7de2de3fd00aff5085b6a5e54a5bca](https://github.com/StartHua/ComfyUI_PCDMs/assets/22284244/8c4d79f8-ba68-43c2-8bad-47a5ad0ddd00)