The behavior at low strength is not what I would expect. There are very strong effects on the input image. Those effects are independent of the style applied.
That's a good observation. Theoretically, 0 strength should give you the content image. What I think is happening is that the algorithm is not trained to generate style vectors for photorealistic images, and so the mapping it learns from pixels to style vector doesn't work well. Maybe a term should be added to the loss function to generate itself when content and style image are equal.
Maybe train it 'backwards'? I mean take a painting [1] as the input and perturb it with a photographic image toward photo realism. [2]
[1]: Assumptions about the 'quantity of art' inherent in painting versus photography seem to be playing a role. Both are typically flat images and are computationally fungible. So maybe photo styled to photo might be a different training set. It's not that radically different from etching applied to photograph.
[2]: anyway, still an awesome project. What I imagined is something that could have subtle effects on a photograph as an artistic tool for photographers...because that's my bias.