diff options
Diffstat (limited to 'modules/hypernetworks/hypernetwork.py')
| -rw-r--r-- | modules/hypernetworks/hypernetwork.py | 635 |
1 files changed, 635 insertions, 0 deletions
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py new file mode 100644 index 00000000..8466887f --- /dev/null +++ b/modules/hypernetworks/hypernetwork.py @@ -0,0 +1,635 @@ +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
+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(f"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 train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, 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.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 = f"Model has already been trained beyond specified max steps"
+ return hypernetwork, filename
+
+ scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
+
+ # 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)
+
+ 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)
+
+ if unload:
+ 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 = "<none>"
+ last_saved_image = "<none>"
+ forced_filename = "<none>"
+
+ 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
+
+ with torch.autocast("cuda"):
+ 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
+ # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.7f}")
+ # scaler.unscale_(optimizer)
+ # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
+ # torch.nn.utils.clip_grad_norm_(weights, max_norm=1.0)
+ # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
+ 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"""
+<p>
+Loss: {loss_step:.7f}<br/>
+Step: {steps_done}<br/>
+Last prompt: {html.escape(batch.cond_text[0])}<br/>
+Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+ 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)
+
+ 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
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