Command Set One
Images used:
fig4_content.jpg: https://github.com/leongatys/NeuralImageSynthesis/blob/master/Images/ControlPaper/fig4_content.jpg
Fine style: https://github.com/leongatys/NeuralImageSynthesis/blob/master/Images/ControlPaper/fig4_style1.jpg
Course style: https://github.com/leongatys/NeuralImageSynthesis/blob/master/Images/ControlPaper/fig4_style2.jpg
Step 1:
python linear-color-transfer.py --target_image coarse_style.png --source_image fig4_content.jpg --output_image coarse_pca.png
python linear-color-transfer.py --target_image fine_style.png --source_image fig4_content.jpg --output_image fine_pca.png
Step 2 (Gatys called the output from this step, "stylemix", but I used a generic name from a the list of experiments I was running):
th neural_style.lua -tv_weight 0 -content_weight 0 -style_weight 10000 -output_image out5.png -num_iterations 550 -content_image coarse_pca.png -style_image fine_pca.png -image_size 1536 -content_layers relu2_1,relu4_2 -style_layers relu2_1,relu4_2 -save_iter 50 -print_iter 50 -seed 876 -init image -backend cudnn -cudnn_autotune
Step 2.5 (I don't think Gatys' code does this, but I thought it would make the colors look better):
python linear-color-transfer.py --target_image out5.png --source_image fig4_content.jpg --output_image out5_pca.png
Step 3:
Then I tried to mimic Gaty's two step process where the first image is generated at 512px:
th neural_style.lua -style_weight 10000 -output_image out_final.png -num_iterations 1000 -content_image fig4_content.jpg -style_image out5_pca.png -image_size 512 -save_iter 0 -print_iter 50 -seed 876 -init image -backend cudnn -cudnn_autotune
th neural_style.lua -style_weight 10000 -output_image out_final_hr.png -num_iterations 550 -content_image fig4_content.jpg -init_image out_final.png -style_image out5_pca.png -image_size 1536 -save_iter 0 -print_iter 50 -seed 876 -init image -backend cudnn -cudnn_autotune
Those commands in that order should give you the exact same output as I got.