Merge branch 'master' into feat/allow-origins
This commit is contained in:
AUTOMATIC1111
GitHub
commit
37ba0070ec
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@ -218,6 +218,10 @@ class Api:
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return options
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def set_config(self, req: OptionsModel):
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# currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
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# overwrite all options with default values.
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raise RuntimeError('Setting options via API is not supported')
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reqDict = vars(req)
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for o in reqDict:
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setattr(shared.opts, o, reqDict[o])
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@ -1,6 +1,6 @@
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import inspect
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from pydantic import BaseModel, Field, create_model
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from typing import Any, Optional, Union
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from typing import Any, Optional
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from typing_extensions import Literal
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from inflection import underscore
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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@ -185,22 +185,22 @@ _options = vars(parser)['_option_string_actions']
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for key in _options:
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if(_options[key].dest != 'help'):
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flag = _options[key]
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_type = str
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if(_options[key].default != None): _type = type(_options[key].default)
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_type = str
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if _options[key].default is not None: _type = type(_options[key].default)
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flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
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FlagsModel = create_model("Flags", **flags)
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class SamplerItem(BaseModel):
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name: str = Field(title="Name")
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aliases: list[str] = Field(title="Aliases")
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aliases: list[str] = Field(title="Aliases")
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options: dict[str, str] = Field(title="Options")
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class UpscalerItem(BaseModel):
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name: str = Field(title="Name")
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model_name: str | None = Field(title="Model Name")
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model_path: str | None = Field(title="Path")
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model_url: str | None = Field(title="URL")
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model_name: Optional[str] = Field(title="Model Name")
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model_path: Optional[str] = Field(title="Path")
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model_url: Optional[str] = Field(title="URL")
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class SDModelItem(BaseModel):
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title: str = Field(title="Title")
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@ -211,21 +211,21 @@ class SDModelItem(BaseModel):
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class HypernetworkItem(BaseModel):
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name: str = Field(title="Name")
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path: str | None = Field(title="Path")
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path: Optional[str] = Field(title="Path")
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class FaceRestorerItem(BaseModel):
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name: str = Field(title="Name")
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cmd_dir: str | None = Field(title="Path")
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cmd_dir: Optional[str] = Field(title="Path")
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class RealesrganItem(BaseModel):
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name: str = Field(title="Name")
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path: str | None = Field(title="Path")
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scale: int | None = Field(title="Scale")
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path: Optional[str] = Field(title="Path")
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scale: Optional[int] = Field(title="Scale")
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class PromptStyleItem(BaseModel):
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name: str = Field(title="Name")
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prompt: str | None = Field(title="Prompt")
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negative_prompt: str | None = Field(title="Negative Prompt")
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prompt: Optional[str] = Field(title="Prompt")
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negative_prompt: Optional[str] = Field(title="Negative Prompt")
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class ArtistItem(BaseModel):
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name: str = Field(title="Name")
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@ -34,8 +34,11 @@ class Extension:
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if repo is None or repo.bare:
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self.remote = None
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else:
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self.remote = next(repo.remote().urls, None)
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self.status = 'unknown'
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try:
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self.remote = next(repo.remote().urls, None)
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self.status = 'unknown'
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except Exception:
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self.remote = None
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def list_files(self, subdir, extension):
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from modules import scripts
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@ -22,6 +22,8 @@ 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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activation_dict = {
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@ -142,6 +144,8 @@ class Hypernetwork:
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self.use_dropout = use_dropout
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self.activate_output = activate_output
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self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
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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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@ -163,6 +167,7 @@ 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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@ -178,8 +183,15 @@ class Hypernetwork:
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state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
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state_dict['activate_output'] = self.activate_output
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state_dict['last_layer_dropout'] = self.last_layer_dropout
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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 shared.opts.save_optimizer_state and 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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@ -202,6 +214,18 @@ class Hypernetwork:
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print(f"Activate last layer is set to {self.activate_output}")
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self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
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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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@ -219,11 +243,11 @@ class Hypernetwork:
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def list_hypernetworks(path):
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res = {}
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for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
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for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
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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] = filename
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res[name + f"({sd_models.model_hash(filename)})"] = filename
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return res
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@ -358,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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shared.state.textinfo = "Initializing hypernetwork training..."
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shared.state.job_count = steps
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hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
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filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
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log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
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@ -404,8 +429,19 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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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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# 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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# 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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steps_without_grad = 0
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@ -467,7 +503,11 @@ 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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@ -530,8 +570,12 @@ Last saved image: {html.escape(last_saved_image)}<br/>
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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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def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
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@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
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from modules.hypernetworks import hypernetwork
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not_available = ["hardswish", "multiheadattention"]
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keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
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keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
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def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
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# Remove illegal characters from name.
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@ -87,6 +87,9 @@ parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load mod
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parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
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parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
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parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)
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parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
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parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
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parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
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cmd_opts = parser.parse_args()
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restricted_opts = {
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@ -318,6 +321,7 @@ options_templates.update(options_section(('system', "System"), {
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options_templates.update(options_section(('training', "Training"), {
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"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
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"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
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"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
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"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
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"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
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@ -407,7 +411,8 @@ class Options:
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if key in self.data or key in self.data_labels:
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assert not cmd_opts.freeze_settings, "changing settings is disabled"
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comp_args = opts.data_labels[key].component_args
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info = opts.data_labels.get(key, None)
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comp_args = info.component_args if info else None
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if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
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raise RuntimeError(f"not possible to set {key} because it is restricted")
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@ -188,7 +188,7 @@ def refresh_available_extensions_from_data():
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code += f"""
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<tr>
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<td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
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<td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a></td>
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<td>{html.escape(description)}</td>
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<td>{install_code}</td>
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</tr>
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@ -57,10 +57,18 @@ class Upscaler:
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self.scale = scale
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dest_w = img.width * scale
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dest_h = img.height * scale
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for i in range(3):
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if img.width > dest_w and img.height > dest_h:
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break
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shape = (img.width, img.height)
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img = self.do_upscale(img, selected_model)
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if shape == (img.width, img.height):
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break
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if img.width >= dest_w and img.height >= dest_h:
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break
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if img.width != dest_w or img.height != dest_h:
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img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)
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20
webui.py
20
webui.py
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@ -35,7 +35,7 @@ from modules.shared import cmd_opts
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import modules.hypernetworks.hypernetwork
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queue_lock = threading.Lock()
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server_name = "0.0.0.0" if cmd_opts.listen else cmd_opts.server_name
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def wrap_queued_call(func):
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def f(*args, **kwargs):
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@ -86,6 +86,20 @@ def initialize():
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shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
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shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
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if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
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try:
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if not os.path.exists(cmd_opts.tls_keyfile):
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print("Invalid path to TLS keyfile given")
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if not os.path.exists(cmd_opts.tls_certfile):
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print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
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except TypeError:
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cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
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print("TLS setup invalid, running webui without TLS")
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else:
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print("Running with TLS")
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# make the program just exit at ctrl+c without waiting for anything
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def sigint_handler(sig, frame):
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print(f'Interrupted with signal {sig} in {frame}')
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@ -138,8 +152,10 @@ def webui():
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app, local_url, share_url = demo.launch(
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share=cmd_opts.share,
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server_name="0.0.0.0" if cmd_opts.listen else None,
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server_name=server_name,
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server_port=cmd_opts.port,
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ssl_keyfile=cmd_opts.tls_keyfile,
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ssl_certfile=cmd_opts.tls_certfile,
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debug=cmd_opts.gradio_debug,
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auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
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inbrowser=cmd_opts.autolaunch,
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