Modifed code (to make things clear) of the paper https://arxiv.org/abs/1904.12785, CVPR 2019
Webdemo available at: http://style.ttic.edu
- python3 >= 3.5
- pytorch >= 1.0
- imageio >= 2.2
- numpy >= 1.1
python styleTransfer.py --content_path images/content_im.jpg --style_path images/style_im.jpg --content_weight 0.5
The default content weight is 1.0 (for the images provided my personal favorite is 0.5, but generally 1.0 works well for most inputs). The content weight is actually multiplied by 16, see section 2.5 of paper for explanation.
The resolution of the output can be set on line 80 of styleTransfer.py; the current scale is 5, and produces outputs that are 512 pixels on the long side, setting it to 4 or 6 will produce outputs that are 256 or 1024 pixels on the long side respectively, most GPUs will run out of memory for settings of this variable above 6.
The output will appear in the same folder as 'styleTransfer.py' and be named 'output.png'
python styleTransfer.py --content_path images/content_im.jpg --style_path images/style_im.jpg --content_weight 0.5 --content_guidance_path images/content_guidance.jpg --style_guidance_path images/style_guidance.jpg
guidance should take the form of two masks such as these:
| Content Mask | Style Mask |
|---|---|
 |
 |
where regions that you wish to map onto each other have the same color.