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# Magic Clothing
This repository is the official implementation of Magic Clothing
Magic Clothing is a branch version of [OOTDiffusion](https://github.com/levihsu/OOTDiffusion), focusing on controllable garment-driven image synthesis
Please refer to our [previous paper](https://arxiv.org/abs/2403.01779) for more details
> **Magic Clothing: Controllable Garment-Driven Image Synthesis** (coming soon)<br>
> [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>
> \* Equal contribution<br>
> Xiao-i Research
## News
🔥 [2024/3/8] We released the model weights trained on the 768 resolution. The strength of clothing and text prompts can be independently adjusted.
🤗 [Hugging Face link](https://huggingface.co/ShineChen1024/MagicClothing)
🔥 [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.
Have fun with **gradio_ipadapter_openpose.py**
🔥 [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.
Have fun with **gradio_ipadapter_faceid.py**
## Installation
1. Clone the repository
```sh
git clone https://github.com/ShineChen1024/MagicClothing.git
```
2. Create a conda environment and install the required packages
```sh
conda create -n magicloth python==3.10
conda activate magicloth
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
```
## Inference
1. Python demo
> 512 weights
```sh
python inference.py --cloth_path [your cloth path] --model_path [your model path]
```
> 768 weights
```sh
python inference.py --cloth_path [your cloth path] --model_path [your model path] --enable_cloth_guidance
```
2. Gradio demo
> 512 weights
```sh
python gradio_generate.py --model_path [your model path]
```
> 768 weights
```sh
python gradio_generate.py --model_path [your model path] --enable_cloth_guidance
```
## TODO List
- [ ] Paper
- [x] Gradio demo
- [x] Inference code
- [x] Model weights
- [ ] Training code
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