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run_generator.py
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run_generator.py
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# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, visit
# https://nvlabs.github.io/stylegan2/license.html
import time
import argparse
import numpy as np
import PIL.Image
import dnnlib
import dnnlib.tflib as tflib
import re
import sys
import pretrained_networks
#----------------------------------------------------------------------------
def generate_images(network_pkl, seeds, truncation_psi):
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed))
#----------------------------------------------------------------------------
def bench_genimg(network_pkl, seeds, truncation_psi):
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
start_time = time.time()
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
#PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed))
end_time = time.time()
print('Total time:', end_time - start_time)
#----------------------------------------------------------------------------
def interpolation_example(network_pkl, src_seeds, tar_seeds, truncation_psi):
assert len(src_seeds) == len(tar_seeds)
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
l = len(src_seeds)
for seed_idx, (seed0, seed1) in enumerate(zip(src_seeds, tar_seeds)):
print('Generating images for seeds %d and %d (%d/%d) ...' % (seed0, seed1, seed_idx + 1, l))
rnd0 = np.random.RandomState(seed0)
rnd1 = np.random.RandomState(seed1)
z0 = rnd0.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
z1 = rnd1.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars({var: rnd0.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
for prog, z in enumerate(np.linspace(z0, z1, 150)):
print(f"{prog + 1}/150\r", end='')
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('int%04d-%04d-%03d.png' % (seed0, seed1, prog)))
#----------------------------------------------------------------------------
def style_mixing_example(network_pkl, row_seeds, col_seeds, truncation_psi, col_styles, minibatch_size=4):
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
w_avg = Gs.get_var('dlatent_avg') # [component]
Gs_syn_kwargs = dnnlib.EasyDict()
Gs_syn_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_syn_kwargs.randomize_noise = False
Gs_syn_kwargs.minibatch_size = minibatch_size
print('Generating W vectors...')
all_seeds = list(set(row_seeds + col_seeds))
all_z = np.stack([np.random.RandomState(seed).randn(*Gs.input_shape[1:]) for seed in all_seeds]) # [minibatch, component]
all_w = Gs.components.mapping.run(all_z, None) # [minibatch, layer, component]
all_w = w_avg + (all_w - w_avg) * truncation_psi # [minibatch, layer, component]
w_dict = {seed: w for seed, w in zip(all_seeds, list(all_w))} # [layer, component]
print('Generating images...')
all_images = Gs.components.synthesis.run(all_w, **Gs_syn_kwargs) # [minibatch, height, width, channel]
image_dict = {(seed, seed): image for seed, image in zip(all_seeds, list(all_images))}
print('Generating style-mixed images...')
for row_seed in row_seeds:
for col_seed in col_seeds:
w = w_dict[row_seed].copy()
w[col_styles] = w_dict[col_seed][col_styles]
image = Gs.components.synthesis.run(w[np.newaxis], **Gs_syn_kwargs)[0]
image_dict[(row_seed, col_seed)] = image
print('Saving images...')
for (row_seed, col_seed), image in image_dict.items():
PIL.Image.fromarray(image, 'RGB').save(dnnlib.make_run_dir_path('%d-%d.png' % (row_seed, col_seed)))
print('Saving image grid...')
_N, _C, H, W = Gs.output_shape
canvas = PIL.Image.new('RGB', (W * (len(col_seeds) + 1), H * (len(row_seeds) + 1)), 'black')
for row_idx, row_seed in enumerate([None] + row_seeds):
for col_idx, col_seed in enumerate([None] + col_seeds):
if row_seed is None and col_seed is None:
continue
key = (row_seed, col_seed)
if row_seed is None:
key = (col_seed, col_seed)
if col_seed is None:
key = (row_seed, row_seed)
canvas.paste(PIL.Image.fromarray(image_dict[key], 'RGB'), (W * col_idx, H * row_idx))
canvas.save(dnnlib.make_run_dir_path('grid.png'))
#----------------------------------------------------------------------------
def _parse_num_range(s):
'''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.'''
range_re = re.compile(r'^(\d+)-(\d+)$')
m = range_re.match(s)
if m:
return range(int(m.group(1)), int(m.group(2))+1)
vals = s.split(',')
return [int(x) for x in vals]
#----------------------------------------------------------------------------
_examples = '''examples:
# Generate ffhq uncurated images (matches paper Figure 12)
python %(prog)s generate-images --network=gdrive:networks/stylegan2-ffhq-config-f.pkl --seeds=6600-6625 --truncation-psi=0.5
# Generate ffhq curated images (matches paper Figure 11)
python %(prog)s generate-images --network=gdrive:networks/stylegan2-ffhq-config-f.pkl --seeds=66,230,389,1518 --truncation-psi=1.0
# Generate uncurated car images (matches paper Figure 12)
python %(prog)s generate-images --network=gdrive:networks/stylegan2-car-config-f.pkl --seeds=6000-6025 --truncation-psi=0.5
# Generate style mixing example (matches style mixing video clip)
python %(prog)s style-mixing-example --network=gdrive:networks/stylegan2-ffhq-config-f.pkl --row-seeds=85,100,75,458,1500 --col-seeds=55,821,1789,293 --truncation-psi=1.0
'''
#----------------------------------------------------------------------------
def main():
parser = argparse.ArgumentParser(
description='''StyleGAN2 generator.
Run 'python %(prog)s <subcommand> --help' for subcommand help.''',
epilog=_examples,
formatter_class=argparse.RawDescriptionHelpFormatter
)
subparsers = parser.add_subparsers(help='Sub-commands', dest='command')
parser_generate_images = subparsers.add_parser('generate-images', help='Generate images')
parser_generate_images.add_argument('--network', help='Network pickle filename', dest='network_pkl', required=True)
parser_generate_images.add_argument('--seeds', type=_parse_num_range, help='List of random seeds', required=True)
parser_generate_images.add_argument('--truncation-psi', type=float, help='Truncation psi (default: %(default)s)', default=0.5)
parser_generate_images.add_argument('--result-dir', help='Root directory for run results (default: %(default)s)', default='results', metavar='DIR')
parser_bench= subparsers.add_parser('bench', help='Benchmark generate images')
parser_bench.add_argument('--network', help='Network pickle filename', dest='network_pkl', required=True)
parser_bench.add_argument('--seeds', type=_parse_num_range, help='List of random seeds', required=True)
parser_bench.add_argument('--truncation-psi', type=float, help='Truncation psi (default: %(default)s)', default=0.5)
parser_bench.add_argument('--result-dir', help='Root directory for run results (default: %(default)s)', default='results', metavar='DIR')
parser_style_mixing_example = subparsers.add_parser('style-mixing-example', help='Generate style mixing video')
parser_style_mixing_example.add_argument('--network', help='Network pickle filename', dest='network_pkl', required=True)
parser_style_mixing_example.add_argument('--row-seeds', type=_parse_num_range, help='Random seeds to use for image rows', required=True)
parser_style_mixing_example.add_argument('--col-seeds', type=_parse_num_range, help='Random seeds to use for image columns', required=True)
parser_style_mixing_example.add_argument('--col-styles', type=_parse_num_range, help='Style layer range (default: %(default)s)', default='0-6')
parser_style_mixing_example.add_argument('--truncation-psi', type=float, help='Truncation psi (default: %(default)s)', default=0.5)
parser_style_mixing_example.add_argument('--result-dir', help='Root directory for run results (default: %(default)s)', default='results', metavar='DIR')
parser_interpolation_example = subparsers.add_parser('interpolation-example', help='Generate interpolation video')
parser_interpolation_example.add_argument('--network', help='Network pickle filename', dest='network_pkl', required=True)
parser_interpolation_example.add_argument('--src-seeds', type=_parse_num_range, help='Random seeds to use for source images', required=True)
parser_interpolation_example.add_argument('--tar-seeds', type=_parse_num_range, help='Random seeds to use for target images', required=True)
parser_interpolation_example.add_argument('--truncation-psi', type=float, help='Truncation psi (default: %(default)s)', default=0.5)
parser_interpolation_example.add_argument('--result-dir', help='Root directory for run results (default: %(default)s)', default='results', metavar='DIR')
args = parser.parse_args()
kwargs = vars(args)
subcmd = kwargs.pop('command')
if subcmd is None:
print ('Error: missing subcommand. Re-run with --help for usage.')
sys.exit(1)
sc = dnnlib.SubmitConfig()
sc.num_gpus = 1
sc.submit_target = dnnlib.SubmitTarget.LOCAL
sc.local.do_not_copy_source_files = True
sc.run_dir_root = kwargs.pop('result_dir')
sc.run_desc = subcmd
func_name_map = {
'generate-images': 'run_generator.generate_images',
'style-mixing-example': 'run_generator.style_mixing_example',
'interpolation-example': 'run_generator.interpolation_example',
'bench': 'run_generator.bench_genimg'
}
dnnlib.submit_run(sc, func_name_map[subcmd], **kwargs)
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------