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We haven't tried to combine spetral norm with our method, but depending on the spectral norm implementation your problems might be related to how our code handles trainable parameters. Here and here we set all parameters of the generator / discriminator to trainable, which might also affect the u and v vectors of the spectral norm (which shouldn't be trainable). You can use buffers instead, but then you have to make sure that the running average still works, which currently only operates on the parameters.
I'm curious if you've tried the Spectral Normalization technic for your approach? At least at resolution 128^2 it didn't work for me
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