File size: 5,244 Bytes
a00ee36 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
"""Generate style mixing image matrix using pretrained network pickle."""
import os
import re
from typing import List
import click
import dnnlib
import numpy as np
import PIL.Image
import torch
import legacy
# ----------------------------------------------------------------------------
def num_range(s: str) -> List[int]:
"""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 list(range(int(m.group(1)), int(m.group(2)) + 1))
vals = s.split(",")
return [int(x) for x in vals]
# ----------------------------------------------------------------------------
@click.command()
@click.option("--network", "network_pkl", help="Network pickle filename", required=True)
@click.option(
"--rows",
"row_seeds",
type=num_range,
help="Random seeds to use for image rows",
required=True,
)
@click.option(
"--cols",
"col_seeds",
type=num_range,
help="Random seeds to use for image columns",
required=True,
)
@click.option(
"--styles",
"col_styles",
type=num_range,
help="Style layer range",
default="0-6",
show_default=True,
)
@click.option(
"--trunc",
"truncation_psi",
type=float,
help="Truncation psi",
default=1,
show_default=True,
)
@click.option(
"--noise-mode",
help="Noise mode",
type=click.Choice(["const", "random", "none"]),
default="const",
show_default=True,
)
@click.option("--outdir", type=str, required=True)
def generate_style_mix(
network_pkl: str,
row_seeds: List[int],
col_seeds: List[int],
col_styles: List[int],
truncation_psi: float,
noise_mode: str,
outdir: str,
):
"""Generate images using pretrained network pickle.
Examples:
\b
python style_mixing.py --outdir=out --rows=85,100,75,458,1500 --cols=55,821,1789,293 \\
--network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl
"""
print('Loading networks from "%s"...' % network_pkl)
device = torch.device("cuda")
with dnnlib.util.open_url(network_pkl) as f:
G = legacy.load_network_pkl(f)["G_ema"].to(device) # type: ignore
os.makedirs(outdir, exist_ok=True)
print("Generating W vectors...")
all_seeds = list(set(row_seeds + col_seeds))
all_z = np.stack([np.random.RandomState(seed).randn(G.z_dim) for seed in all_seeds])
all_w = G.mapping(torch.from_numpy(all_z).to(device), None)
w_avg = G.mapping.w_avg
all_w = w_avg + (all_w - w_avg) * truncation_psi
w_dict = {seed: w for seed, w in zip(all_seeds, list(all_w))}
print("Generating images...")
all_images = G.synthesis(all_w, noise_mode=noise_mode)
all_images = (
(all_images.permute(0, 2, 3, 1) * 127.5 + 128)
.clamp(0, 255)
.to(torch.uint8)
.cpu()
.numpy()
)
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].clone()
w[col_styles] = w_dict[col_seed][col_styles]
image = G.synthesis(w[np.newaxis], noise_mode=noise_mode)
image = (
(image.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
)
image_dict[(row_seed, col_seed)] = image[0].cpu().numpy()
print("Saving images...")
os.makedirs(outdir, exist_ok=True)
for (row_seed, col_seed), image in image_dict.items():
PIL.Image.fromarray(image, "RGB").save(f"{outdir}/{row_seed}-{col_seed}.png")
print("Saving image grid...")
W = G.img_resolution
H = G.img_resolution
canvas = PIL.Image.new(
"RGB", (W * (len(col_seeds) + 1), H * (len(row_seeds) + 1)), "black"
)
for row_idx, row_seed in enumerate([0] + row_seeds):
for col_idx, col_seed in enumerate([0] + col_seeds):
if row_idx == 0 and col_idx == 0:
continue
key = (row_seed, col_seed)
if row_idx == 0:
key = (col_seed, col_seed)
if col_idx == 0:
key = (row_seed, row_seed)
canvas.paste(
PIL.Image.fromarray(image_dict[key], "RGB"), (W * col_idx, H * row_idx)
)
canvas.save(f"{outdir}/grid.png")
# ----------------------------------------------------------------------------
if __name__ == "__main__":
generate_style_mix() # pylint: disable=no-value-for-parameter
# ----------------------------------------------------------------------------
|