
# User-Controllable Latent Transformer for StyleGAN Image Layout Editing

Yuki Endo: "User-Controllable Latent Transformer for StyleGAN Image Layout Editing," Computer Graphpics Forum (Pacific Graphics 2022) [[Project](http://www.cgg.cs.tsukuba.ac.jp/~endo/projects/UserControllableLT)] [[PDF (preprint)](http://arxiv.org/abs/2208.12408)]

## Prerequisites  
1. Python 3.8
2. PyTorch 1.9.0
3. Flask
4. Others (see env.yml)

## Preparation
Download and decompress <a href="https://drive.google.com/file/d/1lBL_J-uROvqZ0BYu9gmEcMCNyaPo9cBY/view?usp=sharing">our pre-trained models</a>.

## Inference with our pre-trained models
We provide an interactive interface based on Flask. This interface can be locally launched with
```
python interface/flask_app.py --checkpoint_path=pretrained_models/latent_transformer/cat.pt
```
The interface can be accessed via http://localhost:8000/.

## Training
The latent transformer can be trained with
```
python scripts/train.py --exp_dir=results --stylegan_weights=pretrained_models/stylegan2-cat-config-f.pt
```
To perform training with your dataset, you need first to train StyleGAN2 on your dataset using [rosinality's code](https://github.com/rosinality/stylegan2-pytorch) and then run the above script with specifying the trained weights. 

## Citation
Please cite our paper if you find the code useful:
```
@Article{endoPG2022,
Title = {User-Controllable Latent Transformer for StyleGAN Image Layout Editing},
Author = {Yuki Endo},
Journal = {Computer Graphics Forum},
volume = {41},
number = {7},
pages = {395-406},
doi = {10.1111/cgf.14686},
Year = {2022}
}
```

## Acknowledgements
This code heavily borrows from the [pixel2style2pixel](https://github.com/eladrich/pixel2style2pixel) and [expansion](https://github.com/gengshan-y/expansion) repositories. 
{"license": "mit"}
arxiv:2208.12408 license:mit has_space region:us
TAGS
#arxiv-2208.12408 #license-mit #has_space #region-us 


# User-Controllable Latent Transformer for StyleGAN Image Layout Editing

Yuki Endo: "User-Controllable Latent Transformer for StyleGAN Image Layout Editing," Computer Graphpics Forum (Pacific Graphics 2022) [Project] [PDF (preprint)]

## Prerequisites  
1. Python 3.8
2. PyTorch 1.9.0
3. Flask
4. Others (see URL)

## Preparation
Download and decompress <a href="URL pre-trained models</a>.

## Inference with our pre-trained models
We provide an interactive interface based on Flask. This interface can be locally launched with

The interface can be accessed via http://localhost:8000/.

## Training
The latent transformer can be trained with

To perform training with your dataset, you need first to train StyleGAN2 on your dataset using rosinality's code and then run the above script with specifying the trained weights. 

Please cite our paper if you find the code useful:


## Acknowledgements
This code heavily borrows from the pixel2style2pixel and expansion repositories. 
# User-Controllable Latent Transformer for StyleGAN Image Layout Editing

Yuki Endo: "User-Controllable Latent Transformer for StyleGAN Image Layout Editing," Computer Graphpics Forum (Pacific Graphics 2022) [Project] [PDF (preprint)] ## Prerequisites  
1. Python 3.8
2. PyTorch 1.9.0
3. Flask
4. Others (see URL) ## Preparation
Download and decompress <a href="URL pre-trained models</a>. ## Inference with our pre-trained models
We provide an interactive interface based on Flask. This interface can be locally launched with

The interface can be accessed via http://localhost:8000/. ## Training
The latent transformer can be trained with

To perform training with your dataset, you need first to train StyleGAN2 on your dataset using rosinality's code and then run the above script with specifying the trained weights. 

Please cite our paper if you find the code useful: ## Acknowledgements
This code heavily borrows from the pixel2style2pixel and expansion repositories.
TAGS
#arxiv-2208.12408 #license-mit #has_space #region-us 
 # User-Controllable Latent Transformer for StyleGAN Image Layout Editing

Yuki Endo: "User-Controllable Latent Transformer for StyleGAN Image Layout Editing," Computer Graphpics Forum (Pacific Graphics 2022) [Project] [PDF (preprint)] ## Prerequisites  
1. Python 3.8
2. PyTorch 1.9.0
3. Flask
4. Others (see URL) ## Preparation
Download and decompress <a href="URL pre-trained models</a>. ## Inference with our pre-trained models
We provide an interactive interface based on Flask. This interface can be locally launched with

The interface can be accessed via http://localhost:8000/. ## Training
The latent transformer can be trained with

To perform training with your dataset, you need first to train StyleGAN2 on your dataset using rosinality's code and then run the above script with specifying the trained weights. 

Please cite our paper if you find the code useful: ## Acknowledgements
This code heavily borrows from the pixel2style2pixel and expansion repositories.