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Fix merge conflicts
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@ -373,6 +373,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
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scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
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clip_grad_mode_value = clip_grad_mode == "value"
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clip_grad_mode_norm = clip_grad_mode == "norm"
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clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm
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if clip_grad_enabled:
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clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
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# dataset loading may take a while, so input validations and early returns should be done before this
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# dataset loading may take a while, so input validations and early returns should be done before this
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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with torch.autocast("cuda"):
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@ -389,21 +395,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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previous_mean_loss = 0
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previous_mean_loss = 0
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print("Mean loss of {} elements".format(size))
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print("Mean loss of {} elements".format(size))
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last_saved_file = "<none>"
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last_saved_image = "<none>"
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forced_filename = "<none>"
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ititial_step = hypernetwork.step or 0
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ititial_step = hypernetwork.step or 0
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if ititial_step > steps:
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if ititial_step > steps:
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return hypernetwork, filename
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return hypernetwork, filename
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clip_grad_mode_value = clip_grad_mode == "value"
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clip_grad_mode_norm = clip_grad_mode == "norm"
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clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm
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if clip_grad_enabled:
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clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
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scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
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weights = hypernetwork.weights()
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weights = hypernetwork.weights()
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for weight in weights:
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for weight in weights:
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