update readme

README.md

@@ -1,39 +1,20 @@
-# Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval PyTorch
-![](ris_teaser.png)
+# StyleGAN of All Trades: Image Manipulation withOnly Pretrained StyleGAN
+![](teaser.pdf)
 
-This is the PyTorch implementation of [Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval](). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mchong6/RetrieveInStyle/blob/main/RIS_colab.ipynb)
+This is the PyTorch implementation of [StyleGAN of All Trades: Image Manipulation withOnly Pretrained StyleGAN](). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mchong6/SOAT/blob/main/infinity.ipynb)
 
 
 >**Abstract:**<br>
->We present Retrieve in Style (RIS), an unsupervised framework for fine-grained facial feature transfer and retrieval on real images Recent work shows that it is possible to learn a catalog that allows local semantic transfers of facial features on generated images by capitalizing on the disentanglement property of the StyleGAN latent space. RIS improves existing art on: 
->1) feature disentanglement and allows for challenging transfers (\ie, hair and pose) that were not shown possible in SoTA methods.
->2) eliminating the needs for per-image hyperparameter tuning, and for computing a catalog over a large batch of images.
->3) enabling face retrieval using the proposed facial features (\eg, eyes), and to our best knowledge, is the first work to retrieve face images at the fine-grained level.
->4) robustness and natural application to real images. 
->Our qualitative and quantitative analyses show RIS achieves both high-fidelity feature transfers and accurate fine-grained retrievals on real images. 
->We discuss the responsible application of RIS.
+Recently, StyleGAN has enabled various image manipulation and editing tasks thanks to the high-quality generation and the disentangled latent space. However, additional architectures or task-specific training paradigms are usually required for different tasks. In this work, we take a deeper look at the spatial properties of StyleGAN. We show that with a pretrained StyleGAN along with some operations, without any additional architecture, we can perform comparably to the state-of-the-art methods on various tasks, including image blending, panorama generation, generation from a single image, controllable and local multimodal image to image translation, and attributes transfer.
 
-## Dependency
-Our codebase is based off [stylegan2 by rosalinity](https://github.com/rosinality/stylegan2-pytorch). 
-```bash
-conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=<CUDA_VERSION>
-pip install tqdm gdown scikit-learn scipy lpips dlib opencv-python
-```
 
 ## How to use
-Everything to get started is in the [colab notebook](https://colab.research.google.com/github/mchong6/RetrieveInStyle/blob/main/RIS_colab.ipynb).
+Everything to get started is in the [colab notebook](https://colab.research.google.com/github/mchong6/SOAT/blob/main/infinity.ipynb).
 
 ## Citation
 If you use this code or ideas from our paper, please cite our paper:
 ```
-@article{chong2021retrieve,
-  title={Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval},
-  author={Chong, Min Jin and Chu, Wen-Sheng and Kumar, Abhishek},
-  journal={arXiv preprint arXiv:2107.06256},
-  year={2021}
-}
-{"mode":"full","isActive":false}
 ```
 
 ## Acknowledgments
-This code borrows from [StyleGAN2 by rosalinity](https://github.com/rosinality/stylegan2-pytorch), [Editing in Style](https://github.com/IVRL/GANLocalEditing), [StyleClip](https://github.com/orpatashnik/StyleCLIP), [PTI](https://github.com/danielroich/PTI). Encoder used is borrowed directly from [encoder4editing](https://github.com/omertov/encoder4editing).
+This code borrows from [StyleGAN2 by rosalinity](https://github.com/rosinality/stylegan2-pytorch)