code: # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
  #
  # This work is licensed under the Creative Commons Attribution-NonCommercial
  # 4.0 International License. To view a copy of this license, visit
  # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
  # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
  
  """Minimal script for generating an image using pre-trained StyleGAN generator."""
  
  import os
  import pickle
  import numpy as np
  import PIL.Image
  import dnnlib
  import dnnlib.tflib as tflib
  import config
  
  
  def main():
      # Initialize TensorFlow.
      tflib.init_tf()
  
      # Load pre-trained network.
      url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
      with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
          _G, _D, Gs = pickle.load(f)
          # _G = Instantaneous snapshot of the generator. Mainly useful for resuming a previous training run.
          # _D = Instantaneous snapshot of the discriminator. Mainly useful for resuming a previous training run.
          # Gs = Long-term average of the generator. Yields higher-quality results than the instantaneous snapshot.
  
      # Print network details.
      Gs.print_layers()
  
      # Pick latent vector.
      rnd = np.random.RandomState(5)
      latents = rnd.randn(1, Gs.input_shape[1])
  
      # Generate image.
      fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
      images = Gs.run(latents, None, truncation_psi=0.7, randomize_noise=True, output_transform=fmt)
  
      # Save image.
      os.makedirs(config.result_dir, exist_ok=True)
      png_filename = os.path.join(config.result_dir, 'example.png')
      PIL.Image.fromarray(images[0], 'RGB').save(png_filename)
  
  if __name__ == "__main__":
      main()
  
apis: numpy.random.RandomState