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@ -309,7 +309,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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with torch.autocast("cuda"):
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c = stack_conds([entry.cond for entry in entries]).to(devices.device)
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c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
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# c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
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x = torch.stack([entry.latent for entry in entries]).to(devices.device)
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loss = shared.sd_model(x, c)[0]
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del x
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@ -331,7 +331,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
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"loss": f"{mean_loss:.7f}",
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"learn_rate": f"{scheduler.learn_rate:.7f}"
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"learn_rate": scheduler.learn_rate
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})
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if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
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