import datetime import glob import html import os import inspect from contextlib import closing import modules.textual_inversion.dataset import torch import tqdm from einops import rearrange, repeat from ldm.util import default from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors from modules.textual_inversion import textual_inversion, logging from modules.textual_inversion.learn_schedule import LearnRateScheduler from torch import einsum from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_ from collections import 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): activation_dict = { "linear": torch.nn.Identity, "relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU, "swish": torch.nn.Hardswish, "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'}) def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, activate_output=False, dropout_structure=None): super().__init__() self.multiplier = 1.0 assert layer_structure is not None, "layer_structure must not be None" assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" linears = [] 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]))) # Add an activation func except last layer if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output): pass elif activation_func in self.activation_dict: linears.append(self.activation_dict[activation_func]()) else: raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}') # Add layer normalization if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) # Everything should be now parsed into dropout structure, and applied here. # Since we only have dropouts after layers, dropout structure should start with 0 and end with 0. if dropout_structure is not None and dropout_structure[i+1] > 0: assert 0 < dropout_structure[i+1] < 1, "Dropout probability should be 0 or float between 0 and 1!" linears.append(torch.nn.Dropout(p=dropout_structure[i+1])) # Code explanation : [1, 2, 1] -> dropout is missing when last_layer_dropout is false. [1, 2, 2, 1] -> [0, 0.3, 0, 0], when its True, [0, 0.3, 0.3, 0]. self.linear = torch.nn.Sequential(*linears) if state_dict is not None: self.fix_old_state_dict(state_dict) self.load_state_dict(state_dict) else: for layer in self.linear: if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm: w, b = layer.weight.data, layer.bias.data if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm: normal_(w, mean=0.0, std=0.01) normal_(b, mean=0.0, std=0) elif weight_init == 'XavierUniform': xavier_uniform_(w) zeros_(b) elif weight_init == 'XavierNormal': xavier_normal_(w) zeros_(b) elif weight_init == 'KaimingUniform': kaiming_uniform_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu') zeros_(b) elif weight_init == 'KaimingNormal': kaiming_normal_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu') zeros_(b) else: raise KeyError(f"Key {weight_init} is not defined as initialization!") self.to(devices.device) def fix_old_state_dict(self, state_dict): changes = { 'linear1.bias': 'linear.0.bias', 'linear1.weight': 'linear.0.weight', 'linear2.bias': 'linear.1.bias', 'linear2.weight': 'linear.1.weight', } for fr, to in changes.items(): x = state_dict.get(fr, None) if x is None: continue del state_dict[fr] state_dict[to] = x def forward(self, x): return x + self.linear(x) * (self.multiplier if not self.training else 1) def trainables(self): layer_structure = [] for layer in self.linear: if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm: layer_structure += [layer.weight, layer.bias] return layer_structure #param layer_structure : sequence used for length, use_dropout : controlling boolean, last_layer_dropout : for compatibility check. def parse_dropout_structure(layer_structure, use_dropout, last_layer_dropout): if layer_structure is None: layer_structure = [1, 2, 1] if not use_dropout: return [0] * len(layer_structure) dropout_values = [0] dropout_values.extend([0.3] * (len(layer_structure) - 3)) if last_layer_dropout: dropout_values.append(0.3) else: dropout_values.append(0) dropout_values.append(0) return dropout_values 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, activate_output=False, **kwargs): self.filename = None self.name = name self.layers = {} self.step = 0 self.sd_checkpoint = None self.sd_checkpoint_name = None self.layer_structure = layer_structure self.activation_func = activation_func self.weight_init = weight_init self.add_layer_norm = add_layer_norm self.use_dropout = use_dropout self.activate_output = activate_output self.last_layer_dropout = kwargs.get('last_layer_dropout', True) self.dropout_structure = kwargs.get('dropout_structure', None) if self.dropout_structure is None: self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout) self.optimizer_name = None self.optimizer_state_dict = None self.optional_info = None 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.activate_output, dropout_structure=self.dropout_structure), HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.activate_output, dropout_structure=self.dropout_structure), ) self.eval() def weights(self): res = [] for layers in self.layers.values(): for layer in layers: res += layer.parameters() return res def train(self, mode=True): for layers in self.layers.values(): for layer in layers: layer.train(mode=mode) for param in layer.parameters(): param.requires_grad = mode def to(self, device): for layers in self.layers.values(): for layer in layers: layer.to(device) return self def set_multiplier(self, multiplier): for layers in self.layers.values(): for layer in layers: layer.multiplier = multiplier return self def eval(self): for layers in self.layers.values(): for layer in layers: layer.eval() for param in layer.parameters(): param.requires_grad = False 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()) state_dict['step'] = self.step state_dict['name'] = self.name state_dict['layer_structure'] = self.layer_structure state_dict['activation_func'] = self.activation_func state_dict['is_layer_norm'] = self.add_layer_norm state_dict['weight_initialization'] = self.weight_init state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name state_dict['activate_output'] = self.activate_output state_dict['use_dropout'] = self.use_dropout state_dict['dropout_structure'] = self.dropout_structure state_dict['last_layer_dropout'] = (self.dropout_structure[-2] != 0) if self.dropout_structure is not None else self.last_layer_dropout state_dict['optional_info'] = self.optional_info if self.optional_info else None 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'] = self.shorthash() optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict torch.save(optimizer_saved_dict, filename + '.optim') def load(self, filename): self.filename = filename if self.name is None: self.name = os.path.splitext(os.path.basename(filename))[0] state_dict = torch.load(filename, map_location='cpu') self.layer_structure = state_dict.get('layer_structure', [1, 2, 1]) self.optional_info = state_dict.get('optional_info', None) self.activation_func = state_dict.get('activation_func', None) self.weight_init = state_dict.get('weight_initialization', 'Normal') self.add_layer_norm = state_dict.get('is_layer_norm', False) self.dropout_structure = state_dict.get('dropout_structure', None) self.use_dropout = True if self.dropout_structure is not None and any(self.dropout_structure) else state_dict.get('use_dropout', False) self.activate_output = state_dict.get('activate_output', True) self.last_layer_dropout = state_dict.get('last_layer_dropout', False) # Dropout structure should have same length as layer structure, Every digits should be in [0,1), and last digit must be 0. if self.dropout_structure is None: self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout) if shared.opts.print_hypernet_extra: if self.optional_info is not None: print(f" INFO:\n {self.optional_info}\n") print(f" Layer structure: {self.layer_structure}") print(f" Activation function: {self.activation_func}") print(f" Weight initialization: {self.weight_init}") print(f" Layer norm: {self.add_layer_norm}") print(f" Dropout usage: {self.use_dropout}" ) print(f" Activate last layer: {self.activate_output}") print(f" Dropout structure: {self.dropout_structure}") optimizer_saved_dict = torch.load(self.filename + '.optim', map_location='cpu') if os.path.exists(self.filename + '.optim') else {} if self.shorthash() == 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: self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') if shared.opts.print_hypernet_extra: print("Loaded existing optimizer from checkpoint") print(f"Optimizer name is {self.optimizer_name}") else: self.optimizer_name = "AdamW" if shared.opts.print_hypernet_extra: print("No saved optimizer exists in checkpoint") 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.activate_output, self.dropout_structure), HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.activate_output, self.dropout_structure), ) self.name = state_dict.get('name', self.name) self.step = state_dict.get('step', 0) self.sd_checkpoint = state_dict.get('sd_checkpoint', None) self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) self.eval() def shorthash(self): sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}') return sha256[0:10] if sha256 else None def list_hypernetworks(path): res = {} for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower): name = os.path.splitext(os.path.basename(filename))[0] # Prevent a hypothetical "None.pt" from being listed. if name != "None": res[name] = filename return res def load_hypernetwork(name): path = shared.hypernetworks.get(name, None) if path is None: return None try: hypernetwork = Hypernetwork() hypernetwork.load(path) return hypernetwork except Exception: errors.report(f"Error loading hypernetwork {path}", exc_info=True) return None def load_hypernetworks(names, multipliers=None): already_loaded = {} for hypernetwork in shared.loaded_hypernetworks: if hypernetwork.name in names: already_loaded[hypernetwork.name] = hypernetwork shared.loaded_hypernetworks.clear() for i, name in enumerate(names): hypernetwork = already_loaded.get(name, None) if hypernetwork is None: hypernetwork = load_hypernetwork(name) if hypernetwork is None: continue hypernetwork.set_multiplier(multipliers[i] if multipliers else 1.0) shared.loaded_hypernetworks.append(hypernetwork) def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None): hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None) if hypernetwork_layers is None: return context_k, context_v if layer is not None: layer.hyper_k = hypernetwork_layers[0] layer.hyper_v = hypernetwork_layers[1] context_k = devices.cond_cast_unet(hypernetwork_layers[0](devices.cond_cast_float(context_k))) context_v = devices.cond_cast_unet(hypernetwork_layers[1](devices.cond_cast_float(context_v))) return context_k, context_v def apply_hypernetworks(hypernetworks, context, layer=None): context_k = context context_v = context for hypernetwork in hypernetworks: context_k, context_v = apply_single_hypernetwork(hypernetwork, context_k, context_v, layer) return context_k, context_v def attention_CrossAttention_forward(self, x, context=None, mask=None, **kwargs): h = self.heads q = self.to_q(x) context = default(context, x) context_k, context_v = apply_hypernetworks(shared.loaded_hypernetworks, context, self) k = self.to_k(context_k) v = self.to_v(context_v) q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v)) sim = einsum('b i d, b j d -> b i j', q, k) * self.scale if mask is not None: mask = rearrange(mask, 'b ... -> b (...)') max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, 'b j -> (b h) () j', h=h) sim.masked_fill_(~mask, max_neg_value) # attention, what we cannot get enough of attn = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', attn, v) out = rearrange(out, '(b h) n d -> b n (h d)', h=h) return self.to_out(out) def stack_conds(conds): if len(conds) == 1: return torch.stack(conds) # same as in reconstruct_multicond_batch token_count = max([x.shape[0] for x in conds]) for i in range(len(conds)): if conds[i].shape[0] != token_count: last_vector = conds[i][-1:] last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1]) conds[i] = torch.vstack([conds[i], last_vector_repeated]) return torch.stack(conds) def statistics(data): if len(data) < 2: std = 0 else: std = stdev(data) 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 else: std = stdev(recent_data) recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + u"\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})" return total_information, recent_information def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None): # Remove illegal characters from name. name = "".join( x for x in name if (x.isalnum() or x in "._- ")) assert name, "Name cannot be empty!" fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") if not overwrite_old: assert not os.path.exists(fn), f"file {fn} already exists" if type(layer_structure) == str: layer_structure = [float(x.strip()) for x in layer_structure.split(",")] if use_dropout and dropout_structure and type(dropout_structure) == str: dropout_structure = [float(x.strip()) for x in dropout_structure.split(",")] else: dropout_structure = [0] * len(layer_structure) hypernet = modules.hypernetworks.hypernetwork.Hypernetwork( name=name, enable_sizes=[int(x) for x in enable_sizes], layer_structure=layer_structure, activation_func=activation_func, weight_init=weight_init, add_layer_norm=add_layer_norm, use_dropout=use_dropout, dropout_structure=dropout_structure ) hypernet.save(fn) shared.reload_hypernetworks() def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, 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 template_file = textual_inversion.textual_inversion_templates.get(template_filename, None) textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") template_file = template_file.path path = shared.hypernetworks.get(hypernetwork_name, None) hypernetwork = Hypernetwork() hypernetwork.load(path) shared.loaded_hypernetworks = [hypernetwork] shared.state.job = "train-hypernetwork" 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) unload = shared.opts.unload_models_when_training if save_hypernetwork_every > 0: hypernetwork_dir = os.path.join(log_directory, "hypernetworks") os.makedirs(hypernetwork_dir, exist_ok=True) else: hypernetwork_dir = None if create_image_every > 0: images_dir = os.path.join(log_directory, "images") os.makedirs(images_dir, exist_ok=True) else: images_dir = None checkpoint = sd_models.select_checkpoint() initial_step = hypernetwork.step or 0 if initial_step >= steps: shared.state.textinfo = "Model has already been trained beyond specified max steps" return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, initial_step) clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None if clip_grad: clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False) if shared.opts.training_enable_tensorboard: tensorboard_writer = textual_inversion.tensorboard_setup(log_directory) # 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)}..." pin_memory = shared.opts.pin_memory 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, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight) if shared.opts.save_training_settings_to_txt: saved_params = dict( model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]} ) logging.save_settings_to_file(log_directory, {**saved_params, **locals()}) latent_sampling_method = ds.latent_sampling_method dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory) old_parallel_processing_allowed = shared.parallel_processing_allowed if unload: shared.parallel_processing_allowed = False shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) weights = hypernetwork.weights() hypernetwork.train() # Here we use optimizer from saved HN, or we can specify as UI option. if hypernetwork.optimizer_name in optimizer_dict: optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) optimizer_name = hypernetwork.optimizer_name else: print(f"Optimizer type {hypernetwork.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) scaler = torch.cuda.amp.GradScaler() batch_size = ds.batch_size gradient_step = ds.gradient_step # n steps = batch_size * gradient_step * n image processed steps_per_epoch = len(ds) // batch_size // gradient_step max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step loss_step = 0 _loss_step = 0 #internal # size = len(ds.indexes) # loss_dict = defaultdict(lambda : deque(maxlen = 1024)) loss_logging = deque(maxlen=len(ds) * 3) # this should be configurable parameter, this is 3 * epoch(dataset size) # losses = torch.zeros((size,)) # previous_mean_losses = [0] # previous_mean_loss = 0 # print("Mean loss of {} elements".format(size)) steps_without_grad = 0 last_saved_file = "" last_saved_image = "" forced_filename = "" pbar = tqdm.tqdm(total=steps - initial_step) try: sd_hijack_checkpoint.add() for _ in range((steps-initial_step) * gradient_step): if scheduler.finished: break if shared.state.interrupted: break for j, batch in enumerate(dl): # works as a drop_last=True for gradient accumulation if j == max_steps_per_epoch: break scheduler.apply(optimizer, hypernetwork.step) if scheduler.finished: break if shared.state.interrupted: break if clip_grad: clip_grad_sched.step(hypernetwork.step) with devices.autocast(): x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) if use_weight: w = batch.weight.to(devices.device, non_blocking=pin_memory) if tag_drop_out != 0 or shuffle_tags: shared.sd_model.cond_stage_model.to(devices.device) c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory) shared.sd_model.cond_stage_model.to(devices.cpu) else: c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory) if use_weight: loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step del w else: loss = shared.sd_model.forward(x, c)[0] / gradient_step del x del c _loss_step += loss.item() scaler.scale(loss).backward() # go back until we reach gradient accumulation steps if (j + 1) % gradient_step != 0: continue loss_logging.append(_loss_step) if clip_grad: clip_grad(weights, clip_grad_sched.learn_rate) scaler.step(optimizer) scaler.update() hypernetwork.step += 1 pbar.update() optimizer.zero_grad(set_to_none=True) loss_step = _loss_step _loss_step = 0 steps_done = hypernetwork.step + 1 epoch_num = hypernetwork.step // steps_per_epoch epoch_step = hypernetwork.step % steps_per_epoch description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}" pbar.set_description(description) if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: # 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. if shared.opts.training_enable_tensorboard: epoch_num = hypernetwork.step // len(ds) epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1 mean_loss = sum(loss_logging) / len(loss_logging) textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num) textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, { "loss": f"{loss_step:.7f}", "learn_rate": scheduler.learn_rate }) if images_dir is not None and steps_done % create_image_every == 0: forced_filename = f'{hypernetwork_name}-{steps_done}' last_saved_image = os.path.join(images_dir, forced_filename) hypernetwork.eval() rng_state = torch.get_rng_state() cuda_rng_state = None if torch.cuda.is_available(): cuda_rng_state = torch.cuda.get_rng_state_all() shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device) p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, do_not_save_grid=True, do_not_save_samples=True, ) p.disable_extra_networks = True if preview_from_txt2img: p.prompt = preview_prompt p.negative_prompt = preview_negative_prompt p.steps = preview_steps p.sampler_name = sd_samplers.samplers[preview_sampler_index].name p.cfg_scale = preview_cfg_scale p.seed = preview_seed p.width = preview_width p.height = preview_height else: p.prompt = batch.cond_text[0] p.steps = 20 p.width = training_width p.height = training_height preview_text = p.prompt with closing(p): processed = processing.process_images(p) image = processed.images[0] if len(processed.images) > 0 else None if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) torch.set_rng_state(rng_state) if torch.cuda.is_available(): torch.cuda.set_rng_state_all(cuda_rng_state) hypernetwork.train() if image is not None: shared.state.assign_current_image(image) if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images: textual_inversion.tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, hypernetwork.step) 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 shared.state.textinfo = f"""

Loss: {loss_step:.7f}
Step: {steps_done}
Last prompt: {html.escape(batch.cond_text[0])}
Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" except Exception: errors.report("Exception in training hypernetwork", exc_info=True) finally: pbar.leave = False pbar.close() hypernetwork.eval() sd_hijack_checkpoint.remove() 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. shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device) shared.parallel_processing_allowed = old_parallel_processing_allowed 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 old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None try: hypernetwork.sd_checkpoint = checkpoint.shorthash hypernetwork.sd_checkpoint_name = checkpoint.model_name hypernetwork.name = hypernetwork_name hypernetwork.save(filename) except: hypernetwork.sd_checkpoint = old_sd_checkpoint hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name hypernetwork.name = old_hypernetwork_name raise