Loading modules/hypernetworks/hypernetwork.py +6 −11 Original line number Diff line number Diff line Loading @@ -373,6 +373,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) clip_grad_mode_value = clip_grad_mode == "value" clip_grad_mode_norm = clip_grad_mode == "norm" clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm if clip_grad_enabled: clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): Loading @@ -389,21 +395,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) last_saved_file = "<none>" last_saved_image = "<none>" forced_filename = "<none>" ititial_step = hypernetwork.step or 0 if ititial_step > steps: return hypernetwork, filename clip_grad_mode_value = clip_grad_mode == "value" clip_grad_mode_norm = clip_grad_mode == "norm" clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm if clip_grad_enabled: clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) weights = hypernetwork.weights() for weight in weights: Loading Loading
modules/hypernetworks/hypernetwork.py +6 −11 Original line number Diff line number Diff line Loading @@ -373,6 +373,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) clip_grad_mode_value = clip_grad_mode == "value" clip_grad_mode_norm = clip_grad_mode == "norm" clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm if clip_grad_enabled: clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): Loading @@ -389,21 +395,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) last_saved_file = "<none>" last_saved_image = "<none>" forced_filename = "<none>" ititial_step = hypernetwork.step or 0 if ititial_step > steps: return hypernetwork, filename clip_grad_mode_value = clip_grad_mode == "value" clip_grad_mode_norm = clip_grad_mode == "norm" clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm if clip_grad_enabled: clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) weights = hypernetwork.weights() for weight in weights: Loading
