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| # 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() | |