After training the models, you could create a new Generator and load the trained state_dict via:
netG = Generator()
netG.load_state_dict(torch.load(path_to_generator_state_dict))

Afterwards, you could execute the forward pass using the same inputs as were used to train this model:
with torch.no_grad():
    noise = torch.randn(b_size, nz, 1, device=device)
    fake = netG(noise)