# pytorch-AdaIN

**Repository Path**: sing_jay_lee/pytorch-AdaIN

## Basic Information

- **Project Name**: pytorch-AdaIN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-29
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# pytorch-AdaIN

This is an unofficial pytorch implementation of a paper, Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization [Huang+, ICCV2017].
I'm really grateful to the [original implementation](https://github.com/xunhuang1995/AdaIN-style) in Torch by the authors, which is very useful.

![Results](results.png)

## Requirements
Please install requirements by `pip install -r requirements.txt`

- Python 3.5+
- PyTorch 0.4+
- TorchVision
- Pillow

(optional, for training)
- tqdm
- TensorboardX

## Usage

### Download models
Download [vgg_normalized.pth](https://drive.google.com/open?id=108uza-dsmwvbW2zv-G73jtVcMU_2Nb7Y)/[decoder.pth](https://drive.google.com/open?id=1w9r1NoYnn7tql1VYG3qDUzkbIks24RBQ) and put them under `models/`.

### Test
Use `--content` and `--style` to provide the respective path to the content and style image.
```
CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --content input/content/cornell.jpg --style input/style/woman_with_hat_matisse.jpg
```

You can also run the code on directories of content and style images using `--content_dir` and `--style_dir`. It will save every possible combination of content and styles to the output directory.
```
CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --content_dir input/content --style_dir input/style
```

This is an example of mixing four styles by specifying `--style` and `--style_interpolation_weights` option.
```
CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --content input/content/avril.jpg --style input/style/picasso_self_portrait.jpg,input/style/impronte_d_artista.jpg,input/style/trial.jpg,input/style/antimonocromatismo.jpg --style_interpolation_weights 1,1,1,1 --content_size 512 --style_size 512 --crop
```

Some other options:
* `--content_size`: New (minimum) size for the content image. Keeping the original size if set to 0.
* `--style_size`: New (minimum) size for the content image. Keeping the original size if set to 0.
* `--alpha`: Adjust the degree of stylization. It should be a value between 0.0 and 1.0 (default).
* `--preserve_color`: Preserve the color of the content image.


### Train
Use `--content_dir` and `--style_dir` to provide the respective directory to the content and style images.
```
CUDA_VISIBLE_DEVICES=<gpu_id> python train.py --content_dir <content_dir> --style_dir <style_dir>
```

For more details and parameters, please refer to --help option.

I share the model trained by this code [here](https://drive.google.com/open?id=18wgz9-7sofBocdr2OdRSK2OK19BaQrYA)

## References
- [1]: X. Huang and S. Belongie. "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.", in ICCV, 2017.
- [2]: [Original implementation in Torch](https://github.com/xunhuang1995/AdaIN-style)