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Swapping Autoencoder Interface

An interactive interface for swapping autoencoder

license MIT

πŸ’‘ Project Description

Our project builds on the paper Swapping Autoencoder for Deep Image Manipulation by Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang. Our goal with this project was to make it easier for artists to use it as a tool. In that effort, we have introduced 3 interfaces to interact with a pre-trained model and edit images.

πŸ“Ί Preview

Screenshot

Some images we genarated with the streamlit inerface

πŸ“Œ Prerequisites

πŸ’» System requirement :

  1. Nvidia GPU + CUDA.
  2. Operating System : Any (Windows / Linux / Mac).

πŸ’Ώ Software requirement :

  1. python 3.8
  2. poetry (Check out poetry here)

πŸ”§ Installation

Step One - install python dependencies

$ poetry install

Step Two - Download pretrained models

Head over to the Testing and Evaluation section of the official implementation of the paper and download the pretrained models and unzip them, put the checkpoints at ./checkpoints/, you can change this location by specifying it at api/const.py:7

🏁 Quick Start

Streamlit Interface

$ streamlit run streamlit_interface.py

πŸ“¦ Inside the box

Checkout our wiki for more details

πŸ“œ License

saxenabhishek/swapping-autoencoder-pytorch is available under the MIT license. See the LICENSE file for more info.

🀝 Contributing

Please read Contributing.md for details on our code of conduct, and the process for submitting pull requests to us.

βš™οΈ Maintainers

Abhishek Saxena
Abhishek Saxena

πŸ’₯ Contributors

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Interface built to use the Swapping Autoencoder model.

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  • Python 92.6%
  • Cuda 6.4%
  • C++ 1.0%