| 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 |
|
|
