Spaces:
Build error
Build error
File size: 1,656 Bytes
8f87579 |
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 |
import argparse
import torch
from torchvision import utils
from model import Generator
from tqdm import tqdm
def generate(args, g_ema, device, mean_latent):
with torch.no_grad():
g_ema.eval()
for i in tqdm(range(args.pics)):
sample_z = torch.randn(args.sample, args.latent, device=device)
sample, _ = g_ema([sample_z], truncation=args.truncation, truncation_latent=mean_latent)
utils.save_image(
sample,
f'sample/{str(i).zfill(6)}.png',
nrow=1,
normalize=True,
range=(-1, 1),
)
if __name__ == '__main__':
device = 'cuda'
parser = argparse.ArgumentParser()
parser.add_argument('--size', type=int, default=1024)
parser.add_argument('--sample', type=int, default=1)
parser.add_argument('--pics', type=int, default=20)
parser.add_argument('--truncation', type=float, default=1)
parser.add_argument('--truncation_mean', type=int, default=4096)
parser.add_argument('--ckpt', type=str, default="stylegan2-ffhq-config-f.pt")
parser.add_argument('--channel_multiplier', type=int, default=2)
args = parser.parse_args()
args.latent = 512
args.n_mlp = 8
g_ema = Generator(
args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier
).to(device)
checkpoint = torch.load(args.ckpt)
g_ema.load_state_dict(checkpoint['g_ema'])
if args.truncation < 1:
with torch.no_grad():
mean_latent = g_ema.mean_latent(args.truncation_mean)
else:
mean_latent = None
generate(args, g_ema, device, mean_latent)
|