| --- |
| license: mit |
| --- |
| |
| # 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. |
