import csv import datetime import glob import html import os import sys import traceback import inspect 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 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 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 = { "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, use_dropout=False, activate_output=False, last_layer_dropout=False): super().__init__() 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]))) # Add dropout except last layer if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2): linears.append(torch.nn.Dropout(p=0.3)) 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 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 def apply_strength(value=None): HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength 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['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] = ( HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), ) self.eval_mode() def weights(self): res = [] for k, layers in self.layers.items(): for layer in layers: res += layer.parameters() return res def train_mode(self): for k, layers in self.layers.items(): for layer in layers: layer.train() for param in layer.parameters(): param.requires_grad = True def eval_mode(self): for k, layers in self.layers.items(): 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['use_dropout'] = self.use_dropout 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['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 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]) print(self.layer_structure) self.activation_func = state_dict.get('activation_func', None) print(f"Activation function is {self.activation_func}") self.weight_init = state_dict.get('weight_initialization', 'Normal') print(f"Weight initialization is {self.weight_init}") 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}" ) self.activate_output = state_dict.get('activate_output', True) 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] = ( HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout), ) 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) def list_hypernetworks(path): res = {} for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)): name = os.path.splitext(os.path.basename(filename))[0] # Prevent a hypothetical "None.pt" from being listed. if name != "None": res[name + f"({sd_models.model_hash(filename)})"] = filename return res def load_hypernetwork(filename): path = shared.hypernetworks.get(filename, None) # Prevent any file named "None.pt" from being loaded. if path is not None and filename != "None": print(f"Loading hypernetwork {filename}") try: shared.loaded_hypernetwork = Hypernetwork() shared.loaded_hypernetwork.load(path) except Exception: print(f"Error loading hypernetwork {path}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) else: if shared.loaded_hypernetwork is not None: print("Unloading hypernetwork") shared.loaded_hypernetwork = None def find_closest_hypernetwork_name(search: str): if not search: return None search = search.lower() applicable = [name for name in shared.hypernetworks if search in name.lower()] if not applicable: return None applicable = sorted(applicable, key=lambda name: len(name)) return applicable[0] def apply_hypernetwork(hypernetwork, context, layer=None): hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) if hypernetwork_layers is None: return context, context if layer is not None: layer.hyper_k = hypernetwork_layers[0] layer.hyper_v = hypernetwork_layers[1] context_k = hypernetwork_layers[0](context) context_v = hypernetwork_layers[1](context) return context_k, context_v def attention_CrossAttention_forward(self, x, context=None, mask=None): h = self.heads q = self.to_q(x) context = default(context, x) context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self) k = self.to_k(context_k) v = self.to_v(context_v) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (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 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: print("Loss statistics for file " + key) info, recent = statistics(list(loss_info[key])) print(info) print(recent) except Exception as e: print(e) 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. name = "".join( x for x in name if (x.isalnum() or x in "._- ")) 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(",")] 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, ) hypernet.save(fn) shared.reload_hypernetworks() def train_hypernetwork(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, 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, gradient_step, 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() shared.loaded_hypernetwork.load(path) 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 hypernetwork = shared.loaded_hypernetwork 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) # 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) if shared.opts.save_training_settings_to_txt: saved_params = dict( model_name=checkpoint.model_name, model_hash=checkpoint.hash, 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_mode() # 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)) # 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: for i 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 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) loss = shared.sd_model(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 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 pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}") 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. 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_mode() 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, ) 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 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) hypernetwork.train_mode() 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 += 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: print(traceback.format_exc(), file=sys.stderr) finally: pbar.leave = False pbar.close() hypernetwork.eval_mode() #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. 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.hash 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