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import dnnlib
import numpy as np
import PIL.Image
import torch
import legacy
import pickle
import torchvision.transforms as transforms
from PIL import Image
network_pkl = '/home/rahul/Downloads/network-snapshot-003200.pkl'
with open(network_pkl, 'rb') as f:
G = pickle.load(f)['G_ema'].cpu() # torch.nn.Module
z = torch.randn([1, G.z_dim]).cpu() # latent codes
c = None # class labels (not used in this example)
img = G(z, c)
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
#um = torch..nn.Upsample(scale_factor=2, mode='bilinear')
#img=um(img)
image=PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
transform = transforms.Resize((image.height * 2, image.width * 2), interpolation=transforms.InterpolationMode.BILINEAR)
upscaled_image = transform(image)
upscaled_image.save('/home/rahul/Downloads/seed1.png')
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