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license: cc-by-nc-sa-4.0 |
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--- |
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# Magic Clothing |
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This repository is the official implementation of [Magic Clothing](https://github.com/ShineChen1024/MagicClothing) |
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Magic Clothing is a branch version of [OOTDiffusion](https://github.com/levihsu/OOTDiffusion), focusing on controllable garment-driven image synthesis |
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<!-- Please refer to our [previous paper](https://arxiv.org/abs/2403.01779) for more details --> |
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> **Magic Clothing: Controllable Garment-Driven Image Synthesis** [[arXiv paper](https://arxiv.org/abs/2404.09512)]<br> |
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> [Weifeng Chen](https://github.com/ShineChen1024)\*, [Tao Gu](https://github.com/T-Gu)\*, [Yuhao Xu](http://levihsu.github.io/), [Chengcai Chen](https://www.researchgate.net/profile/Chengcai-Chen)<br> |
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> \* Equal contribution<br> |
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> Xiao-i Research |
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## News |
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π₯ [2024/4/19] An 1024 version trained on both VTON-HD and DressCode for early access branch is avaliable now! |
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π₯ [2024/4/19] We support AnimateDiff now for generating GIF! |
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π₯ [2024/4/16] Our [paper](https://arxiv.org/abs/2404.09512) is available now! |
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π₯ [2024/3/8] We released the model weights trained on the 768 resolution. The strength of clothing and text prompts can be independently adjusted. |
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π€ [Hugging Face link](https://huggingface.co/ShineChen1024/MagicClothing) |
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π₯ [2024/2/28] We support [IP-Adapter-FaceID](https://huggingface.co/h94/IP-Adapter-FaceID) with [ControlNet-Openpose](https://github.com/lllyasviel/ControlNet-v1-1-nightly)! A portrait and a reference pose image can be used as additional conditions. |
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Have fun with ***gradio_ipadapter_openpose.py*** |
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π₯ [2024/2/23] We support [IP-Adapter-FaceID](https://huggingface.co/h94/IP-Adapter-FaceID) now! A portrait image can be used as an additional condition. |
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Have fun with ***gradio_ipadapter_faceid.py*** |
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 |
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## Installation |
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1. Clone the repository |
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```sh |
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git clone https://github.com/ShineChen1024/MagicClothing.git |
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``` |
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2. Create a conda environment and install the required packages |
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```sh |
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conda create -n magicloth python==3.10 |
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conda activate magicloth |
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pip install torch==2.0.1 torchvision==0.15.2 numpy==1.25.1 diffusers==0.25.1 opencv-python==4.9.0.80 transformers==4.31.0 gradio==4.16.0 safetensors==0.3.1 controlnet-aux==0.0.6 accelerate==0.21.0 |
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``` |
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## Inference |
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1. Python demo |
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> 512 weights |
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```sh |
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python inference.py --cloth_path [your cloth path] --model_path [your model checkpoints path] |
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``` |
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> 768 weights |
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```sh |
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python inference.py --cloth_path [your cloth path] --model_path [your model checkpoints path] --enable_cloth_guidance |
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``` |
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2. Gradio demo |
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> 512 weights |
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```sh |
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python gradio_generate.py --model_path [your model checkpoints path] |
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``` |
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> 768 weights |
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```sh |
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python gradio_generate.py --model_path [your model checkpoints path] --enable_cloth_guidance |
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``` |
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## Citation |
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``` |
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@article{chen2024magic, |
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title={Magic Clothing: Controllable Garment-Driven Image Synthesis}, |
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author={Chen, Weifeng and Gu, Tao and Xu, Yuhao and Chen, Chengcai}, |
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journal={arXiv preprint arXiv:2404.09512}, |
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year={2024} |
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} |
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``` |
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## TODO List |
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- [x] Paper |
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- [x] Gradio demo |
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- [x] Inference code |
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- [x] Model weights |
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- [ ] Training code |
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