def loadStyleGAN():
sys.path.append(StyleGAN1_root)
ckpt_root = join(StyleGAN1_root, 'checkpoint')
from model import StyledGenerator
from generate import get_mean_style
import math
generator = StyledGenerator(512).to("cuda")
# generator.load_state_dict(torch.load(r"E:\Github_Projects\style-based-gan-pytorch\checkpoint\stylegan-256px-new.model")['g_running'])
generator.load_state_dict(
torch.load(join(StyleGAN1_root,
"checkpoint\stylegan-256px-new.model"))['g_running'])
generator.eval()
for param in generator.parameters():
param.requires_grad_(False)
return generator
"""
This is the smaller explicit version of StyleGAN. Very easy to work with
"""
#%%
sys.path.append("E:\Github_Projects\style-based-gan-pytorch")
sys.path.append("D:\Github\style-based-gan-pytorch")
from model import StyledGenerator
from generate import get_mean_style
import math
#%%
generator = StyledGenerator(512).to("cuda")
# generator.load_state_dict(torch.load(r"E:\Github_Projects\style-based-gan-pytorch\checkpoint\stylegan-256px-new.model")['g_running'])
generator.load_state_dict(torch.load(r"D:\Github\style-based-gan-pytorch\checkpoint\stylegan-256px-new.model")[
'g_running'])
generator.eval()
for param in generator.parameters():
param.requires_grad_(False)
mean_style = get_mean_style(generator, "cuda")
step = int(math.log(256, 2)) - 2
#%%
feat = torch.randn(1, 512, requires_grad=False).to("cuda")
image = generator(
feat,
step=step,
alpha=1,
mean_style=mean_style,
style_weight=0.7,
)
#%%
class StyleGAN_wrapper(): # nn.Module
def __init__(self, StyleGAN, ):
