Ok, here I come with updated results. They are already better with state_dict(), but the pruned model still takes more memory. I just added:
torch.save(enc_pruned.state_dict(), 'pruned_model.pth')
to all the models being saved.
And this is the output:
25629256 -rw-r--r-- 1 users 550M Aug 18 08:43 pruned_model.pth
25629254 -rw-r--r-- 1 users 275M Aug 18 08:43 model_repeated.pth
25629253 -rw-r--r-- 1 users  31M Aug 18 08:43 orig_model.pth

So, since the size is precisely twice the size of the original repeated, might it come from the mask? Is it then pruning correctly working?
Thanks again for all your help!!