# StyleGAN V2

## StyleGAN V2 introduction

The task of StyleGAN V2 is image generation. Given a vector of a specific length, generate the image corresponding to the vector. It is an upgraded version of StyleGAN, which solves the problem of artifacts generated by StyleGAN.

<div align="center">
  <img src="../../imgs/stylegan2-teaser-1024x256.png" width="500"/>
</div>

StyleGAN V2 can mix multi-level style vectors. Its core is adaptive style decoupling.

Compared with StyleGAN, its main improvement is:

- The quality of the generated image is significantly better (higher FID score, fewer artifacts)
- Propose a new method to replace progressive training, with more perfect details such as teeth and eyes
- Style mixing improved
- Smoother interpolation
- Train faster

## How to use

### Generate

The user can generate different results by replacing the value of the seed or removing the seed. Use the following command to generate images：

```
cd applications/
python -u tools/styleganv2.py \
       --output_path <DIRECTORY TO STORE OUTPUT IMAGE> \
       --weight_path <YOUR PRETRAINED MODEL PATH> \
       --model_type ffhq-config-f \
       --seed 233 \
       --size 1024 \
       --style_dim 512 \
       --n_mlp 8 \
       --channel_multiplier 2 \
       --n_row 3 \
       --n_col 5 \
       --cpu
```

**params:**
- output_path: the directory where the generated images are stored
- weight_path: pretrained model path
- model_type: inner model type in PaddleGAN. If you use an existing model type, `weight_path` will have no effect.
  Currently available: `ffhq-config-f`, `animeface-512`
- seed: random number seed
- size: model parameters, output image resolution
- style_dim: model parameters, dimensions of style z
- n_mlp: model parameters, the number of multi-layer perception layers for style z
- channel_multiplier: model parameters, channel product, affect model size and the quality of generated pictures
- n_row: the number of rows of the sampled image
- n_col: the number of columns of the sampled picture
- cpu: whether to use cpu inference, if not, please remove it from the command

### Train (TODO)

In the future, training scripts will be added to facilitate users to train more types of StyleGAN V2 image generators.


## Results

Random Samples:

![Samples](../../imgs/stylegan2-sample.png)

Random Style Mixing:

![Random Style Mixing](../../imgs/stylegan2-sample-mixing-0.png)


## Reference

```
@inproceedings{Karras2019stylegan2,
  title     = {Analyzing and Improving the Image Quality of {StyleGAN}},
  author    = {Tero Karras and Samuli Laine and Miika Aittala and Janne Hellsten and Jaakko Lehtinen and Timo Aila},
  booktitle = {Proc. CVPR},
  year      = {2020}
}

```