The references you stated indeed are the right way to go regarding small dataset image synthesis.
I'd research the space of few-shot image synthesis for what is out there, but something along the line of StyleGAN2 is a logical direction to look into.
In addition, I'd like to state specifically that training a synthesizer on only 10-50 images is always going to be very difficult. Is your goal going to be to make slight adaptations to the images? Or to synthesize completely new images? Image data is very high dimensional, and generalising high dimensional data from very few samples is near impossible, no matter what technique you employ.