diff options
| -rw-r--r-- | modules/hypernetworks/hypernetwork.py | 54 | ||||
| -rw-r--r-- | modules/hypernetworks/ui.py | 2 | ||||
| -rw-r--r-- | modules/shared.py | 1 |
3 files changed, 51 insertions, 6 deletions
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6e1a10cf..b1f308e2 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -22,6 +22,8 @@ from collections import defaultdict, deque from statistics import stdev, mean
+optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
+
class HypernetworkModule(torch.nn.Module):
multiplier = 1.0
activation_dict = {
@@ -142,6 +144,8 @@ class Hypernetwork: self.use_dropout = use_dropout
self.activate_output = activate_output
self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
+ self.optimizer_name = None
+ self.optimizer_state_dict = None
for size in enable_sizes or []:
self.layers[size] = (
@@ -163,6 +167,7 @@ class Hypernetwork: def save(self, filename):
state_dict = {}
+ optimizer_saved_dict = {}
for k, v in self.layers.items():
state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -178,8 +183,15 @@ class Hypernetwork: state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
state_dict['activate_output'] = self.activate_output
state_dict['last_layer_dropout'] = self.last_layer_dropout
-
+
+ if self.optimizer_name is not None:
+ optimizer_saved_dict['optimizer_name'] = self.optimizer_name
+
torch.save(state_dict, filename)
+ if shared.opts.save_optimizer_state and 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):
self.filename = filename
@@ -202,6 +214,18 @@ class Hypernetwork: print(f"Activate last layer is set to {self.activate_output}")
self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
+ 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')
+ print(f"Optimizer name is {self.optimizer_name}")
+ 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:
+ print("Loaded existing optimizer from checkpoint")
+ else:
+ print("No saved optimizer exists in checkpoint")
+
for size, sd in state_dict.items():
if type(size) == int:
self.layers[size] = (
@@ -223,7 +247,7 @@ def list_hypernetworks(path): name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed.
if name != "None":
- res[name] = filename
+ res[name + f"({sd_models.model_hash(filename)})"] = filename
return res
@@ -358,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log shared.state.textinfo = "Initializing hypernetwork training..."
shared.state.job_count = steps
+ hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
@@ -404,8 +429,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log weights = hypernetwork.weights()
for weight in weights:
weight.requires_grad = True
- # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
- optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+ # Here we use optimizer from saved HN, or we can specify as UI option.
+ if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
+ optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
+ else:
+ print(f"Optimizer type {optimizer_name} is not defined!")
+ optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+ optimizer_name = 'AdamW'
+ if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer.
+ try:
+ optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
+ except RuntimeError as e:
+ print("Cannot resume from saved optimizer!")
+ print(e)
steps_without_grad = 0
@@ -467,7 +503,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # Before saving, change name to match current checkpoint.
hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+ hypernetwork.optimizer_name = optimizer_name
+ if shared.opts.save_optimizer_state:
+ hypernetwork.optimizer_state_dict = optimizer.state_dict()
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
+ hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
"loss": f"{previous_mean_loss:.7f}",
@@ -530,8 +570,12 @@ Last saved image: {html.escape(last_saved_image)}<br/> report_statistics(loss_dict)
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+ hypernetwork.optimizer_name = optimizer_name
+ if shared.opts.save_optimizer_state:
+ hypernetwork.optimizer_state_dict = optimizer.state_dict()
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
-
+ del optimizer
+ hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
return hypernetwork, filename
def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index aad09ffc..c2d4b51c 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared from modules.hypernetworks import hypernetwork
not_available = ["hardswish", "multiheadattention"]
-keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
# Remove illegal characters from name.
diff --git a/modules/shared.py b/modules/shared.py index 7a20c3af..71587557 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -320,6 +320,7 @@ options_templates.update(options_section(('system', "System"), { options_templates.update(options_section(('training', "Training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+ "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
|