On a 1080 Ti it takes 45-90 minutes to train networks for the various tasks on 256px images (depending on some quality parameters and which task). Each task also requires training individually so if you'd like to try them all for a given image you'll need to train 6 times.
Also the pyramid of GANs approach is very memory hungry. I was only able to get up to 724px images with 11 GB of VRAM. This was also only possible with a higher scale factor (sparser pyramid) which sacrifices a lot of quality and is incredibly noticeable at larger image sizes. I only tried for larger sizes with the animation task though, perhaps there is a way to combine the super resolution and animation task and achieve better results. Training on larger sizes was taking upwards of 6-8 hours.
All of this was tested with the official repo about a month ago.
Would you say that could be a downside of the single image approach? Rather than feeding images into a generalized model you’re training a whole model per image which is costly to scale?
The harmonization aspect of the paper actually makes it very useful. There certainly are cases where you want to introduce an image component as an overlay and want the style to integrate.
Really cool stuff, and with code!
They ran it on the Berkeley Segmentation Dataset; the human faces came out a little interesting...