I just found a project that allows controlling a bunch of StyleGAN features through UI knobs:

[puzer]

I just found a project that allows controlling a bunch of StyleGAN features through UI knobs:

[progmars]

I, too, got curious and played with GANSpace a bit.

The conclusions are as follows.
While limiting components to layers helps a lot for disentangling some minor features (color, background(, still it is not good enough for major features (age, gender, beard, glasses) because they are too much entangled with each other.

I suspect the source of the problem is that GAN was trained on images of different people with different features, so it's difficult to isolate a feature, let's say "beard".

I think it would work much better if GAN was trained on images of the same person with various degrees of features that are interesting to us. For example, on a series of photos of the same person with different degrees of beard, age, etc. Of course, it's impossible to collect such a dataset from real people, but it might be possible to use high-definition 3D model renders. Still, it would be a very time-consuming endeavor to create enough models.

I couldn't try InterfaceGAN, though, because my GPU doesn't have enough RAM (4GB leads to infamous "RuntimeError: CUDA out of memory.").

[ramapinnimty]

@pbaylies, @pender, @rolux Please help me by telling a way to disentangle the features. Thanks in advance.

[ChengBinJin]

@ramapinnimty You can refer to the GANSpace and InterfaceGAN. Both of the methods focus on disentangled directions. The first one uses PCA with unsupervised, the second one uses the idea of linear projection.