Paint images using famous artistic styles in seconds (or less!)
quickpaint using pre-trained styles (models) or trainstyle a new model on a new style
a TensorFlow implementation for feed forward CNN fast neural style transfer, based on [1-3].
works with different versions of pre-trained TF models, many pre-trained models included (more to come)
| styles | outputs | outputs | styles |
|---|---|---|---|
rain princess  |
 |
 |
the shipwreck of the minotaur  |
wave  |
 |
 |
mosaic  |
the scream  |
 |
 |
composition vii  |
la muse  |
 |
 |
cubist  |
udnie  |
 |
 |
feathers  |
GUI:
Command line:
python quickpaint.py -i [ input (content) ] -o [ output (stylized content) ] -m [ model (style) ] -ma [ mask ] -bl [ blend ]
Example: python quickpaint.py -i inputs/stanford.jpg -o outputs/stanford_cubist.jpg -m pre-trained_models/cubist.model -ma 1 -bl 0.05
required arguments:
-i, --input dir or file to transform (content)
-o, --output destination (dir or file) of transformed input (stylized content)
-m, --model path to load model (.ckpt or .model/.meta) from
optional arguments:
-h, --help show this help message and exit
-d, --device device to perform compute on (default: /gpu:0)
-b, --batch-size batch size for feed-forwarding (default: 4)
-a, --model-arch model architecture if models in form (.model) are used, (default: pre-trained_models/model.meta)
-ma, --mask create binary mask from input (@ 1% of max) and mask output (default: 0 -> no masking)
-bl, --blend multiply the original image with the output using a weighting (factor) -- to preserve original colors / contrast (default: 0 -> no blending)
Command line:
python trainstyle.py -s [ style ] -c [ checkpoint dir ] -o [ output test image ] -i [ checkpoint iterations ] -t [ training set path ]
-od [ output test dir ] -cw [ content weight ] -tv [ tv weight ] -sw [ style weight ] - b [ batch size ] -l [ learning rate ] -n [ network path ]
Example: python trainstyle.py -s styles/the_scream.jpg -c checkpoint -o stanford.jpg
-od test -cw 1.5e1 -i 1000 -b 20
required arguments:
-c, --checkpoint-dir dir to save checkpoint in
-s, --style desired style image path
optional arguments:
-h, --help show this help message and exit
-t, --train-path path to training images folder
-o, --output output test image at every checkpoint path
-od, --output-dir output test images dir
-e, --epochs # of epochs
-b, --batch-size batch size
-i, --checkpoint-iterations checkpoint frequency
-n, --net-path path to VGG19 network (default data/imagenet-vgg-verydeep-19.mat)
-cw, --content-weight content weight (default 7.5)
-sw, --style-weight style weight (default 100.0)
-tw, --tv-weight total variation regularization weight (default 200.0)
-l, --learning-rate learning rate (default 0.001)
- Python 2.7.9
- TensorFlow 0.11.0 >=
- scipy 0.18.1
- numpy 1.11.2
-
COCO dataset (training data) http://msvocds.blob.core.windows.net/coco2014/train2014.zip
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VGG19 imagenet weights http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat
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Implementation based on fast-style-transfer: https://github.com/lengstrom/fast-style-transfer by lengstrom (engstrom at mit dot edu)
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TF vgg network from: https://github.com/anishathalye/neural-style by anishathalye (http://www.anishathalye.com)
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Pre-trained models acquired from:
[1] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155. https://arxiv.org/abs/1603.08155
check out Justin's repo for a theano/Lua implementation: https://github.com/jcjohnson/fast-neural-style
[2] Ulyanov, Dmitry, Andrea Vedaldi, and Victor Lempitsky. "Instance Normalization: The Missing Ingredient for Fast Stylization." arXiv preprint arXiv:1607.08022. https://arxiv.org/abs/1607.08022
[3] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576. https://arxiv.org/abs/1508.06576