Presto Renderized with Pretrained Models

This is the best way to train models and create renderized artworks quickly.

Prerequisites

• Torch7 + loadcaffe
• cudnn + torch.cudnn (optionally)
• display (optionally)

Download VGG-19.

cd data/pretrained && bash download_models.sh && cd ../..

Training

Preparing image dataset

I recommend to download the MSCOCO: Training Images: http://msvocds.blob.core.windows.net/coco2014/train2014.zip Validation Images: http://msvocds.blob.core.windows.net/coco2014/val2014.zip

Unzip the Images into dataset:

dataset/train
dataset/val

Download and create Symbolic Link to dummy:

wget http://msvocds.blob.core.windows.net/coco2014/train2014.zip
wget http://msvocds.blob.core.windows.net/coco2014/val2014.zip
unzip train2014.zip
unzip val2014.zip
mkdir -p dataset/train
mkdir -p dataset/val
ln -s `pwd`/val2014 dataset/val/dummy
ln -s `pwd`/train2014 dataset/train/dummy

Training a network

th train.lua -data <path to any image dataset>  -style_image path/to/img.jpg

I recommend this params for test:

th train.lua -data datasets/ -style_image $style -style_size 1024  -model johnson -batch_size 4 -learning_rate 1e-2 -style_weight 10 -style_layers relu1_2,relu2_2,relu3_2,relu4_2 -content_layers relu4_2 -backend cudnn -save_every 10000

Testing

th fast.lua -input_image source.jpg -model_t7 ./models/model.t7 -save_path out.png -image_size 0 -keep_color 0

-input_image Image to stylize. []
-image_size  Resize input image to. Do not resize if 0. [0]
-model_t7    Path to trained model.t7 []
-save_path   Path to save stylized image. [stylized.jpg]
-cpu         Use this flag to run on CPU [false]
-keep_color  Use keep color to keep original color. [0]

YOU could try Birth, Composition, Illegal Beauty, Lilies & Mononoke. We put our pretrained models in ./models/

Process

Stylize an image.

th stylization_process.lua -model data/out/model.t7 -input_image data/readme_pics/kitty.jpg -noise_depth 3

Again, noise_depth should be consistent with training setting.

Hardware

• The code was tested with 8GB NVIDIA GTX 1080 on Ubuntu 16.04 && Centos 7.
• You may decrease batch_size, image_size if the model do not fit your GPU memory.
• The pretrained models do not need much memory to sample.

Credits

The code is based on Justin Johnson's great code for artistic style. The work was supported by Yandex and Skoltech. Thanks to DmitryUlyanov!!