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https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
resolve conflict - first revert
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aria1th
parent
1764ac3c8b
commit
0abb39f461
@ -21,7 +21,6 @@ from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_norm
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from collections import defaultdict, deque
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from statistics import stdev, mean
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optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
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class HypernetworkModule(torch.nn.Module):
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multiplier = 1.0
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@ -34,9 +33,12 @@ class HypernetworkModule(torch.nn.Module):
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"tanh": torch.nn.Tanh,
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"sigmoid": torch.nn.Sigmoid,
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}
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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'})
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activation_dict.update(
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{cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if
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inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
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def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
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def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
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add_layer_norm=False, use_dropout=False):
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super().__init__()
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assert layer_structure is not None, "layer_structure must not be None"
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@ -47,7 +49,7 @@ class HypernetworkModule(torch.nn.Module):
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for i in range(len(layer_structure) - 1):
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# Add a fully-connected layer
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linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
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linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i + 1])))
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# Add an activation func
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if activation_func == "linear" or activation_func is None:
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@ -59,7 +61,7 @@ class HypernetworkModule(torch.nn.Module):
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# Add layer normalization
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if add_layer_norm:
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linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
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linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i + 1])))
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# Add dropout expect last layer
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if use_dropout and i < len(layer_structure) - 3:
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@ -128,7 +130,8 @@ class Hypernetwork:
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filename = None
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name = None
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def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
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def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None,
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add_layer_norm=False, use_dropout=False):
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self.filename = None
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self.name = name
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self.layers = {}
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@ -140,13 +143,13 @@ class Hypernetwork:
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self.weight_init = weight_init
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self.add_layer_norm = add_layer_norm
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self.use_dropout = use_dropout
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self.optimizer_name = None
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self.optimizer_state_dict = None
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for size in enable_sizes or []:
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self.layers[size] = (
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HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
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HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
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HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
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self.add_layer_norm, self.use_dropout),
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HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
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self.add_layer_norm, self.use_dropout),
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)
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def weights(self):
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@ -161,7 +164,6 @@ class Hypernetwork:
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def save(self, filename):
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state_dict = {}
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optimizer_saved_dict = {}
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for k, v in self.layers.items():
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state_dict[k] = (v[0].state_dict(), v[1].state_dict())
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@ -175,14 +177,8 @@ class Hypernetwork:
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state_dict['use_dropout'] = self.use_dropout
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state_dict['sd_checkpoint'] = self.sd_checkpoint
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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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def load(self, filename):
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self.filename = filename
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@ -202,23 +198,13 @@ class Hypernetwork:
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self.use_dropout = state_dict.get('use_dropout', False)
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print(f"Dropout usage is set to {self.use_dropout}")
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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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print("No saved optimizer exists in checkpoint")
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for size, sd in state_dict.items():
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if type(size) == int:
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self.layers[size] = (
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HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
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HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
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HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
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self.add_layer_norm, self.use_dropout),
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HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
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self.add_layer_norm, self.use_dropout),
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)
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self.name = state_dict.get('name', self.name)
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@ -233,7 +219,7 @@ 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 + f"({sd_models.model_hash(filename)})"] = filename
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res[name] = filename
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return res
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@ -330,7 +316,7 @@ def statistics(data):
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std = 0
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else:
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std = stdev(data)
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total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
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total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std / (len(data) ** 0.5):.3f})"
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recent_data = data[-32:]
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if len(recent_data) < 2:
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std = 0
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@ -340,7 +326,7 @@ def statistics(data):
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return total_information, recent_information
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def report_statistics(loss_info:dict):
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def report_statistics(loss_info: dict):
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keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x]))
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for key in keys:
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try:
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@ -352,14 +338,18 @@ def report_statistics(loss_info:dict):
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print(e)
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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):
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def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width,
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training_height, steps, create_image_every, save_hypernetwork_every, template_file,
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preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps,
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preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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# images allows training previews to have infotext. Importing it at the top causes a circular import problem.
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from modules import images
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save_hypernetwork_every = save_hypernetwork_every or 0
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create_image_every = create_image_every or 0
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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")
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textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps,
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save_hypernetwork_every, create_image_every, log_directory,
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name="hypernetwork")
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path = shared.hypernetworks.get(hypernetwork_name, None)
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shared.loaded_hypernetwork = Hypernetwork()
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@ -379,7 +369,6 @@ 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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@ -395,39 +384,34 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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return hypernetwork, filename
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scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
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# dataset loading may take a while, so input validations and early returns should be done before this
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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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)
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width,
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height=training_height,
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repeats=shared.opts.training_image_repeats_per_epoch,
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placeholder_token=hypernetwork_name,
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model=shared.sd_model, device=devices.device,
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template_file=template_file, include_cond=True,
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batch_size=batch_size)
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if unload:
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shared.sd_model.cond_stage_model.to(devices.cpu)
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shared.sd_model.first_stage_model.to(devices.cpu)
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size = len(ds.indexes)
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loss_dict = defaultdict(lambda : deque(maxlen = 1024))
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loss_dict = defaultdict(lambda: deque(maxlen=1024))
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losses = torch.zeros((size,))
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previous_mean_losses = [0]
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previous_mean_loss = 0
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print("Mean loss of {} elements".format(size))
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weights = hypernetwork.weights()
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for weight in weights:
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weight.requires_grad = True
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# Here we use optimizer from saved HN, or we can specify as UI option.
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if (optimizer_name := hypernetwork.optimizer_name) in optimizer_dict:
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optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
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else:
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print(f"Optimizer type {optimizer_name} is not defined!")
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optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
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optimizer_name = 'AdamW'
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if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer.
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try:
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optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
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except RuntimeError as e:
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print("Cannot resume from saved optimizer!")
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print(e)
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# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
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optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
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steps_without_grad = 0
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@ -441,7 +425,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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if len(loss_dict) > 0:
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previous_mean_losses = [i[-1] for i in loss_dict.values()]
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previous_mean_loss = mean(previous_mean_losses)
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scheduler.apply(optimizer, hypernetwork.step)
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if scheduler.finished:
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break
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@ -460,7 +444,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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losses[hypernetwork.step % losses.shape[0]] = loss.item()
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for entry in entries:
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loss_dict[entry.filename].append(loss.item())
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optimizer.zero_grad()
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weights[0].grad = None
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loss.backward()
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@ -475,9 +459,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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steps_done = hypernetwork.step + 1
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if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
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if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
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raise RuntimeError("Loss diverged.")
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if len(previous_mean_losses) > 1:
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std = stdev(previous_mean_losses)
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else:
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@ -489,11 +473,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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# Before saving, change name to match current checkpoint.
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hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
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last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
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hypernetwork.optimizer_name = optimizer_name
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if shared.opts.save_optimizer_state:
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hypernetwork.optimizer_state_dict = optimizer.state_dict()
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save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
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hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
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textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
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"loss": f"{previous_mean_loss:.7f}",
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"learn_rate": scheduler.learn_rate
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@ -529,7 +510,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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preview_text = p.prompt
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processed = processing.process_images(p)
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image = processed.images[0] if len(processed.images)>0 else None
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image = processed.images[0] if len(processed.images) > 0 else None
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if unload:
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shared.sd_model.cond_stage_model.to(devices.cpu)
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@ -537,7 +518,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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if image is not None:
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shared.state.current_image = image
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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)
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last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt,
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shared.opts.samples_format, processed.infotexts[0],
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p=p, forced_filename=forced_filename,
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save_to_dirs=False)
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last_saved_image += f", prompt: {preview_text}"
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shared.state.job_no = hypernetwork.step
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@ -551,15 +535,12 @@ Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
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Last saved image: {html.escape(last_saved_image)}<br/>
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</p>
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"""
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report_statistics(loss_dict)
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filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
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hypernetwork.optimizer_name = optimizer_name
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if shared.opts.save_optimizer_state:
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hypernetwork.optimizer_state_dict = optimizer.state_dict()
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save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
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del optimizer
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hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
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return hypernetwork, filename
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@ -576,4 +557,4 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
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hypernetwork.sd_checkpoint = old_sd_checkpoint
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hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
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hypernetwork.name = old_hypernetwork_name
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raise
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raise
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