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use hash to check valid optim

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This commit is contained in:
aria1th 2022-11-03 14:49:26 +09:00
parent 0b143c1163
commit 1764ac3c8b
1 changed files with 10 additions and 5 deletions
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15
modules/hypernetworks/hypernetwork.py
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@ -177,11 +177,12 @@ class Hypernetwork:
state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
if self.optimizer_name is not None: if self.optimizer_name is not None:
optimizer_saved_dict['optimizer_name'] = self.optimizer_name optimizer_saved_dict['optimizer_name'] = self.optimizer_name
if self.optimizer_state_dict:
optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
torch.save(optimizer_saved_dict, filename + '.optim')
torch.save(state_dict, filename) torch.save(state_dict, filename)
if self.optimizer_state_dict:
optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
torch.save(optimizer_saved_dict, filename + '.optim')
def load(self, filename): def load(self, filename):
self.filename = filename self.filename = filename
@ -204,7 +205,10 @@ class Hypernetwork:
optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {} optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
print(f"Optimizer name is {self.optimizer_name}") print(f"Optimizer name is {self.optimizer_name}")
self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
else:
self.optimizer_state_dict = None
if self.optimizer_state_dict: if self.optimizer_state_dict:
print("Loaded existing optimizer from checkpoint") print("Loaded existing optimizer from checkpoint")
else: else:
@ -229,7 +233,7 @@ def list_hypernetworks(path):
name = os.path.splitext(os.path.basename(filename))[0] name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed. # Prevent a hypothetical "None.pt" from being listed.
if name != "None": if name != "None":
res[name] = filename res[name + f"({sd_models.model_hash(filename)})"] = filename
return res return res
@ -375,6 +379,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
else: else:
hypernetwork_dir = None hypernetwork_dir = None
hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
if create_image_every > 0: if create_image_every > 0:
images_dir = os.path.join(log_directory, "images") images_dir = os.path.join(log_directory, "images")
os.makedirs(images_dir, exist_ok=True) os.makedirs(images_dir, exist_ok=True)