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imgs = torch.stack(imgs, 0).to(device)
g_ema = Generator(args.size, 512, 8)
g_ema.load_state_dict(torch.load(args.ckpt)["g_ema"], strict=False)
g_ema.eval()
g_ema = g_ema.to(device)
with torch.no_grad():
noise_sample = torch.randn(n_mean_latent, 512, device=device)
latent_out = g_ema.style(noise_sample)
latent_mean = latent_out.mean(0)
latent_std = ((latent_out - latent_mean).pow(2).sum() / n_mean_latent) ** 0.5
percept = lpips.PerceptualLoss(
model="net-lin", net="vgg", use_gpu=device.startswith("cuda")
)
noises_single = g_ema.make_noise()
noises = []
for noise in noises_single:
noises.append(noise.repeat(imgs.shape[0], 1, 1, 1).normal_())
latent_in = latent_mean.detach().clone().unsqueeze(0).repeat(imgs.shape[0], 1)
if args.w_plus:
latent_in = latent_in.unsqueeze(1).repeat(1, g_ema.n_latent, 1)
latent_in.requires_grad = True
for noise in noises: