Awesome
Style transfer
This is tensorflow implementation of 'Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer' which generates stylized image in high resulution such as 1024 pixels.
Download program
$ git clone https://github.com/fullfanta/multimodal_style_transfer.git
Train
To train network, I use MS coco dataset.
$ cd multimodal_style_transfer
$ bash get_coco.sh
- downloaded image is in 'data/train2014'.
For stylization, pretrained VGG16 is necessary.
$ bash get_vgg16.sh
Then training is SIMPLE.
$ python train.py
- If you have multiple GPU cards, use CUDA_VISIBLE_DEVICES to specify GPU card.
- Trained model is in summary.
During training, you can see generated images through tensorboard.
$ tensorboard --logdir=summary
Freeze model
$ sh freeze.sh 10000
- parameter is iteration number among saved check point files.
- It generates pb file which contains weights as contant.
Test
$ python stylize.py --model=models/starry_night.pb --input_image=test_images/jolie.jpg
- It generates hierarchical stylized images and save them to 'test_images/jolie_output_1.jpg', 'test_images/jolie_output_2.jpg', and 'test_images/jolie_output_3.jpg'. Their sizes are 256, 512 and 1024 in short edge.
- Parameters:
--model : freezed model path
--input_image : image file path to stylize
--hierarchical_short_edges : three short edge length to generate images. (default is 256, 512, 1024)
Examples
| Input | Output(256px) | Output(512px) | Output(1024px) | |
|---|---|---|---|---|
| Angelina Jolie | <img src='test_images/jolie.jpg' width='256px'> | <img src='test_images/jolie_output_1.jpg' width='256px'> | <img src='test_images/jolie_output_2.jpg' width='256px'> | <img src='test_images/jolie_output_3.jpg' width='256px'> |
| Dinosour | <img src='test_images/dinosour.png' width='256px'> | <img src='test_images/dinosour_output_1.jpg' width='256px'> | <img src='test_images/dinosour_output_2.jpg' width='256px'> | <img src='test_images/dinosour_output_3.jpg' width='256px'> |
| Ryan | <img src='test_images/ryan.png' width='256px'> | <img src='test_images/ryan_output_1.jpg' width='256px'> | <img src='test_images/ryan_output_2.jpg' width='256px'> | <img src='test_images/ryan_output_3.jpg' width='256px'> |
| Cheez | <img src='test_images/cheez.png' width='256px'> | <img src='test_images/cheez_output_1.jpg' width='256px'> | <img src='test_images/cheez_output_2.jpg' width='256px'> | <img src='test_images/cheez_output_3.jpg' width='256px'> |
| Herb | <img src='test_images/herb.png' width='256px'> | <img src='test_images/herb_output_1.jpg' width='256px'> | <img src='test_images/herb_output_2.jpg' width='256px'> | <img src='test_images/herb_output_3.jpg' width='256px'> |
Acknowledgement
- For instance normalization, I refer 'https://github.com/ghwatson/faststyle'.
- For pretrained VGG16 network, I refer 'https://www.cs.toronto.edu/~frossard/post/vgg16/'.