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from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import argparse
from dataclasses import dataclass
from typing import Any, Callable
import torch
from PIL import Image
@dataclass
class State:
skipped: bool = False
interrupted: bool = False
job: str = ""
job_no: int = 0
job_count: int = 0
processing_has_refined_job_count: bool = False
job_timestamp: str = "0"
sampling_step: int = 0
sampling_steps: int = 0
current_latent: torch.Tensor | None = None
current_image: Image.Image | None = None
current_image_sampling_step: int = 0
id_live_preview: int = 0
textinfo: str | None = None
time_start: float | None = None
need_restart: bool = False
server_start: float | None = None
@dataclass
class OptionInfo:
default: Any = None
label: str = ""
component: Any = None
component_args: Callable[[], dict] | dict[str, Any] | None = None
onchange: Callable[[], None] | None = None
section: tuple[str, str] | None = None
refresh: Callable[[], None] | None = None
class Option:
data_labels: dict[str, OptionInfo]
def __init__(self):
self.data: dict[str, Any] = {}
def add_option(self, key: str, info: OptionInfo):
pass
def __getattr__(self, item: str):
if self.data is not None and item in self.data:
return self.data[item]
if item in self.data_labels:
return self.data_labels[item].default
return super().__getattribute__(item)
opts = Option()
cmd_opts = argparse.Namespace()
state = State()
else:
from modules.shared import OptionInfo, cmd_opts, opts, state
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