nn.load_parameters("styleGAN2_G_params.h5")
rgb_output = generate(batch_size, style_noises,
args.stochastic_seed, args.truncation_psi)
rgb_output.forward()
// convert to uint8 to save an image file
image = convert_images_to_uint8(rgb_output, drange=[-1, 1])
if args.output_filename is None:
After Change
rnd = np.random.RandomState(args.seed_mix)
z2 = rnd.randn(batch_size, 512)
style_noises = [nn.NdArray.from_numpy_array(z)]
style_noises += [nn.NdArray.from_numpy_array(z2)]
else:
// no style mixing (single noise / style is used)
print(f"using style noise seed {args.seed} for entire layers.")
style_noises = [nn.NdArray.from_numpy_array(z) for _ in range(2)]
nn.set_auto_forward(True)
nn.load_parameters("styleGAN2_G_params.h5")
rgb_output = generate(batch_size, style_noises,
args.stochastic_seed, args.mix_after, args.truncation_psi)
// convert to uint8 to save an image file
image = convert_images_to_uint8(rgb_output, drange=[-1, 1])
if args.output_filename is None:
if not args.mixing:
filename = f"seed{args.seed}"
else:
filename = f"seed{args.seed}_{args.seed_mix}"
else:
filename = args.output_filename
os.makedirs(args.output_dir, exist_ok=True)
for i in range(batch_size):
filepath = os.path.join(args.output_dir, f"{filename}_{i}.png")
imsave(filepath, image[i], channel_first=True)
print(f"Genetation completed. Saved {filepath}.")
