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@ -177,12 +177,13 @@ class Hypernetwork:
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state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
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if self.optimizer_name is not None:
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optimizer_saved_dict['optimizer_name'] = self.optimizer_name
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torch.save(state_dict, filename)
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if self.optimizer_state_dict:
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optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
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optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
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torch.save(optimizer_saved_dict, filename + '.optim')
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torch.save(state_dict, filename)
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def load(self, filename):
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self.filename = filename
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if self.name is None:
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@ -204,7 +205,10 @@ class Hypernetwork:
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optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
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self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
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print(f"Optimizer name is {self.optimizer_name}")
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if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
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self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
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else:
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self.optimizer_state_dict = None
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if self.optimizer_state_dict:
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print("Loaded existing optimizer from checkpoint")
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else:
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@ -229,7 +233,7 @@ def list_hypernetworks(path):
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name = os.path.splitext(os.path.basename(filename))[0]
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# Prevent a hypothetical "None.pt" from being listed.
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if name != "None":
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res[name] = filename
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res[name + f"({sd_models.model_hash(filename)})"] = filename
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return res
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@ -375,6 +379,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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else:
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hypernetwork_dir = None
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hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
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if create_image_every > 0:
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images_dir = os.path.join(log_directory, "images")
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os.makedirs(images_dir, exist_ok=True)
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