# StyleGAN V2 Mixing Module

## StyleGAN V2 Mixing introduction

The task of StyleGAN V2 is image generation while the Mixing module uses its style vector to achieve the mixing of two generated images with different levels and different proportions.

## How to use

### Mixing


The user can use the following command to fit images：

```
cd applications/
python -u tools/styleganv2mixing.py \
       --latent1 <PATH TO FIRST STYLE VECTOR> \
       --latent2 <PATH TO SECOND STYLE VECTOR> \
       --weights \
                 0.5 0.5 0.5 0.5 0.5 0.5 \
                 0.5 0.5 0.5 0.5 0.5 0.5 \
                 0.5 0.5 0.5 0.5 0.5 0.5 \
       --output_path <DIRECTORY TO STORE OUTPUT IMAGE> \
       --weight_path <YOUR PRETRAINED MODEL PATH> \
       --model_type ffhq-config-f \
       --size 1024 \
       --style_dim 512 \
       --n_mlp 8 \
       --channel_multiplier 2 \
       --cpu
```

**params:**
- latent1: The path of the first style vector. Come from `dst.npy` generated by Pixel2Style2Pixel or `dst.fitting.npy` generated by StyleGANv2 Fitting module
- latent2: The path of the second style vector. The source is the same as the first style vector
- weights: The two style vectors are mixed in different proportions at different levels. For a resolution of 1024, there are 18 levels. For a resolution of 512, there are 16 levels, and so on.
  The more in front, the more it affects the whole of the mixed image. The more behind, the more it affects the details of the mixed image.
- need_align: whether to crop the image to an image that can be recognized by the model. For an image that has been cropped, such as the `src.png` that is pre-generated when Pixel2Style2Pixel is used to generate the style vector, the need_align parameter may not be filled in
- start_lr: learning rate at the begin of training
- final_lr: learning rate at the end of training
- latent_level: The style vector level involved in fitting is 0~17 at 1024 resolution, 0~15 at 512 resolution, and so on. The lower the level, the more biased toward the overall style change. The higher the level, the more biased toward the detail style change
- step: the number of steps required to fit the image, the larger the number of steps, the longer it takes and the better the effect
- mse_weight: weight of MSE loss
- pre_latent: The pre-made style vector files are saved to facilitate better fitting. The default is empty, you can fill in the file path of `dst.npy` generated by Pixel2Style2Pixel
- 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 recommended use: `ffhq-config-f`
- 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
- cpu: whether to use cpu inference, if not, please remove it from the command

## Fitting Results

The image corresponding to the first style vector:

<div align="center">
    <img src="../../imgs/stylegan2fitting-sample.png" width="300"/>
</div>

The image corresponding to the second style vector:

<div align="center">
    <img src="../../imgs/stylegan2fitting-sample2.png" width="256"/>
</div>

The result of mixing two style vectors in a specific ratio:

<div align="center">
    <img src="../../imgs/stylegan2mixing-sample.png" width="256"/>
</div>

## Reference

- 1. [Analyzing and Improving the Image Quality of StyleGAN](https://arxiv.org/abs/1912.04958)

  ```
  @article{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}
  }
  ```
- 2. [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](hhttps://arxiv.org/abs/2008.00951)

  ```
  @article{richardson2020encoding,
    title={Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation},
    author={Richardson, Elad and Alaluf, Yuval and Patashnik, Or and Nitzan, Yotam and Azar, Yaniv and Shapiro, Stav and Cohen-Or, Daniel},
    journal={arXiv preprint arXiv:2008.00951},
    year={2020}
  }
  ```