I blame code autocomplete
This commit is contained in:
AngelBottomless
GitHub
parent
0abb39f461
commit
0d07cbfa15
1 changed files with 27 additions and 49 deletions
|
|
@ -33,12 +33,9 @@ class HypernetworkModule(torch.nn.Module):
|
|||
"tanh": torch.nn.Tanh,
|
||||
"sigmoid": torch.nn.Sigmoid,
|
||||
}
|
||||
activation_dict.update(
|
||||
{cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if
|
||||
inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
|
||||
activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
|
||||
|
||||
def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
|
||||
add_layer_norm=False, use_dropout=False):
|
||||
def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
|
||||
super().__init__()
|
||||
|
||||
assert layer_structure is not None, "layer_structure must not be None"
|
||||
|
|
@ -49,7 +46,7 @@ class HypernetworkModule(torch.nn.Module):
|
|||
for i in range(len(layer_structure) - 1):
|
||||
|
||||
# Add a fully-connected layer
|
||||
linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i + 1])))
|
||||
linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
|
||||
|
||||
# Add an activation func
|
||||
if activation_func == "linear" or activation_func is None:
|
||||
|
|
@ -61,7 +58,7 @@ class HypernetworkModule(torch.nn.Module):
|
|||
|
||||
# Add layer normalization
|
||||
if add_layer_norm:
|
||||
linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1])))
|
||||
linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
|
||||
|
||||
# Add dropout expect last layer
|
||||
if use_dropout and i < len(layer_structure) - 3:
|
||||
|
|
@ -130,8 +127,7 @@ class Hypernetwork:
|
|||
filename = None
|
||||
name = None
|
||||
|
||||
def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None,
|
||||
add_layer_norm=False, use_dropout=False):
|
||||
def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
|
||||
self.filename = None
|
||||
self.name = name
|
||||
self.layers = {}
|
||||
|
|
@ -146,10 +142,8 @@ class Hypernetwork:
|
|||
|
||||
for size in enable_sizes or []:
|
||||
self.layers[size] = (
|
||||
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
|
||||
self.add_layer_norm, self.use_dropout),
|
||||
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
|
||||
self.add_layer_norm, self.use_dropout),
|
||||
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
|
||||
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
|
||||
)
|
||||
|
||||
def weights(self):
|
||||
|
|
@ -196,15 +190,13 @@ class Hypernetwork:
|
|||
self.add_layer_norm = state_dict.get('is_layer_norm', False)
|
||||
print(f"Layer norm is set to {self.add_layer_norm}")
|
||||
self.use_dropout = state_dict.get('use_dropout', False)
|
||||
print(f"Dropout usage is set to {self.use_dropout}")
|
||||
print(f"Dropout usage is set to {self.use_dropout}" )
|
||||
|
||||
for size, sd in state_dict.items():
|
||||
if type(size) == int:
|
||||
self.layers[size] = (
|
||||
HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
|
||||
self.add_layer_norm, self.use_dropout),
|
||||
HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
|
||||
self.add_layer_norm, self.use_dropout),
|
||||
HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
|
||||
HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
|
||||
)
|
||||
|
||||
self.name = state_dict.get('name', self.name)
|
||||
|
|
@ -316,7 +308,7 @@ def statistics(data):
|
|||
std = 0
|
||||
else:
|
||||
std = stdev(data)
|
||||
total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})"
|
||||
total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
|
||||
recent_data = data[-32:]
|
||||
if len(recent_data) < 2:
|
||||
std = 0
|
||||
|
|
@ -326,7 +318,7 @@ def statistics(data):
|
|||
return total_information, recent_information
|
||||
|
||||
|
||||
def report_statistics(loss_info: dict):
|
||||
def report_statistics(loss_info:dict):
|
||||
keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
|
||||
for key in keys:
|
||||
try:
|
||||
|
|
@ -338,18 +330,14 @@ def report_statistics(loss_info: dict):
|
|||
print(e)
|
||||
|
||||
|
||||
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width,
|
||||
training_height, steps, create_image_every, save_hypernetwork_every, template_file,
|
||||
preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps,
|
||||
preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
|
||||
|
||||
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
|
||||
# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
|
||||
from modules import images
|
||||
|
||||
save_hypernetwork_every = save_hypernetwork_every or 0
|
||||
create_image_every = create_image_every or 0
|
||||
textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps,
|
||||
save_hypernetwork_every, create_image_every, log_directory,
|
||||
name="hypernetwork")
|
||||
textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
|
||||
|
||||
path = shared.hypernetworks.get(hypernetwork_name, None)
|
||||
shared.loaded_hypernetwork = Hypernetwork()
|
||||
|
|
@ -388,20 +376,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
|
|||
# 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"):
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width,
|
||||
height=training_height,
|
||||
repeats=shared.opts.training_image_repeats_per_epoch,
|
||||
placeholder_token=hypernetwork_name,
|
||||
model=shared.sd_model, device=devices.device,
|
||||
template_file=template_file, include_cond=True,
|
||||
batch_size=batch_size)
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
|
||||
|
||||
if unload:
|
||||
shared.sd_model.cond_stage_model.to(devices.cpu)
|
||||
shared.sd_model.first_stage_model.to(devices.cpu)
|
||||
|
||||
size = len(ds.indexes)
|
||||
loss_dict = defaultdict(lambda: deque(maxlen=1024))
|
||||
loss_dict = defaultdict(lambda : deque(maxlen = 1024))
|
||||
losses = torch.zeros((size,))
|
||||
previous_mean_losses = [0]
|
||||
previous_mean_loss = 0
|
||||
|
|
@ -510,7 +492,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
|
|||
preview_text = p.prompt
|
||||
|
||||
processed = processing.process_images(p)
|
||||
image = processed.images[0] if len(processed.images) > 0 else None
|
||||
image = processed.images[0] if len(processed.images)>0 else None
|
||||
|
||||
if unload:
|
||||
shared.sd_model.cond_stage_model.to(devices.cpu)
|
||||
|
|
@ -518,10 +500,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
|
|||
|
||||
if image is not None:
|
||||
shared.state.current_image = image
|
||||
last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt,
|
||||
shared.opts.samples_format, processed.infotexts[0],
|
||||
p=p, forced_filename=forced_filename,
|
||||
save_to_dirs=False)
|
||||
last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
|
||||
last_saved_image += f", prompt: {preview_text}"
|
||||
|
||||
shared.state.job_no = hypernetwork.step
|
||||
|
|
@ -543,7 +522,6 @@ Last saved image: {html.escape(last_saved_image)}<br/>
|
|||
|
||||
return hypernetwork, filename
|
||||
|
||||
|
||||
def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
|
||||
old_hypernetwork_name = hypernetwork.name
|
||||
old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
|
||||
|
|
|
|||
Loading…
Reference in a new issue