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Official implementation of Magic Clothing: Controllable Garment-Driven Image Synthesis

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Magic Clothing

This repository is the official implementation of Magic Clothing

Magic Clothing is a branch version of OOTDiffusion, focusing on controllable garment-driven image synthesis

Magic Clothing: Controllable Garment-Driven Image Synthesis [arXiv paper]
Weifeng Chen*, Tao Gu*, Yuhao Xu, Chengcai Chen
* Equal contribution
Xiao-i Research

News

🔥 [2024/4/23] In response to the enthusiasm for virtual try-on, we release gradio_virtual_tryon, and see our [guidance]!

🔥 [2024/4/19] An 1024 version trained on both VTON-HD and DressCode for early access branch is avaliable now!

🔥 [2024/4/19] We support AnimateDiff now for generating GIF!

🔥 [2024/4/16] Our paper is available now!

🔥 [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

🔥 [2024/2/28] We support IP-Adapter-FaceID with ControlNet-Openpose! 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 now! A portrait image can be used as an additional condition.

Have fun with gradio_ipadapter_faceid.py

demo  workflow 

Virtual Try-On Demo

图片1 图片2 图片3 图片4 图片5 图片6 图片7 图片8 图片9

1024 version for upper-body lower-body and full-body clothes Demo

图片1 图片2 图片3 图片4 图片5 图片6

AnimateDiff Demo 'a beautiful girl with a smile'

图片1 图片2 图片3

Installation

  1. Clone the repository
git clone https://github.com/ShineChen1024/MagicClothing.git
  1. Create a conda environment and install the required packages
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

python inference.py --cloth_path [your cloth path] --model_path [your model checkpoints path]

768 weights

python inference.py --cloth_path [your cloth path] --model_path [your model checkpoints path] --enable_cloth_guidance
  1. Gradio demo

512 weights

python gradio_generate.py --model_path [your model checkpoints path] 

768 weights

python gradio_generate.py --model_path [your model checkpoints path] --enable_cloth_guidance

Citation

@article{chen2024magic,
  title={Magic Clothing: Controllable Garment-Driven Image Synthesis},
  author={Chen, Weifeng and Gu, Tao and Xu, Yuhao and Chen, Chengcai},
  journal={arXiv preprint arXiv:2404.09512},
  year={2024}
}

TODO List

  • Paper
  • Gradio demo
  • Inference code
  • Model weights
  • Training code

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