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from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from dataclasses import dataclass, field
from typing import Any, Callable
import numpy as np
import torch
from PIL import Image
def _image():
return Image.new("L", (512, 512))
@dataclass
class StableDiffusionProcessing:
sd_model: torch.nn.Module = field(default_factory=lambda: torch.nn.Linear(1, 1))
outpath_samples: str = ""
outpath_grids: str = ""
prompt: str = ""
prompt_for_display: str = ""
negative_prompt: str = ""
styles: list[str] = field(default_factory=list)
seed: int = -1
subseed: int = -1
subseed_strength: float = 0.0
seed_resize_from_h: int = -1
seed_resize_from_w: int = -1
sampler_name: str | None = None
batch_size: int = 1
n_iter: int = 1
steps: int = 50
cfg_scale: float = 7.0
width: int = 512
height: int = 512
restore_faces: bool = False
tiling: bool = False
do_not_save_samples: bool = False
do_not_save_grid: bool = False
extra_generation_params: dict[str, Any] = field(default_factory=dict)
overlay_images: list[Image.Image] = field(default_factory=list)
eta: float = 0.0
do_not_reload_embeddings: bool = False
paste_to: tuple[int | float, ...] = (0, 0, 0, 0)
color_corrections: list[np.ndarray] = field(default_factory=list)
denoising_strength: float = 0.0
sampler_noise_scheduler_override: Callable | None = None
ddim_discretize: str = ""
s_min_uncond: float = 0.0
s_churn: float = 0.0
s_tmin: float = 0.0
s_tmax: float = 0.0
s_noise: float = 0.0
override_settings: dict[str, Any] = field(default_factory=dict)
override_settings_restore_afterwards: bool = False
is_using_inpainting_conditioning: bool = False
disable_extra_networks: bool = False
scripts: Any = None
script_args: list[Any] = field(default_factory=list)
all_prompts: list[str] = field(default_factory=list)
all_negative_prompts: list[str] = field(default_factory=list)
all_seeds: list[int] = field(default_factory=list)
all_subseeds: list[int] = field(default_factory=list)
iteration: int = 1
is_hr_pass: bool = False
@dataclass
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler: Callable | None = None
enable_hr: bool = False
denoising_strength: float = 0.75
hr_scale: float = 2.0
hr_upscaler: str = ""
hr_second_pass_steps: int = 0
hr_resize_x: int = 0
hr_resize_y: int = 0
hr_upscale_to_x: int = 0
hr_upscale_to_y: int = 0
width: int = 512
height: int = 512
truncate_x: int = 512
truncate_y: int = 512
applied_old_hires_behavior_to: tuple[int, int] = (512, 512)
@dataclass
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler: Callable | None = None
init_images: list[Image.Image] = field(default_factory=list)
resize_mode: int = 0
denoising_strength: float = 0.75
image_cfg_scale: float | None = None
init_latent: torch.Tensor | None = None
image_mask: Image.Image = field(default_factory=_image)
latent_mask: Image.Image = field(default_factory=_image)
mask_for_overlay: Image.Image = field(default_factory=_image)
mask_blur: int = 4
inpainting_fill: int = 0
inpaint_full_res: bool = True
inpaint_full_res_padding: int = 0
inpainting_mask_invert: int | bool = 0
initial_noise_multiplier: float = 1.0
mask: torch.Tensor | None = None
nmask: torch.Tensor | None = None
image_conditioning: torch.Tensor | None = None
@dataclass
class Processed:
images: list[Image.Image] = field(default_factory=list)
prompt: list[str] = field(default_factory=list)
negative_prompt: list[str] = field(default_factory=list)
seed: list[int] = field(default_factory=list)
subseed: list[int] = field(default_factory=list)
subseed_strength: float = 0.0
info: str = ""
comments: str = ""
width: int = 512
height: int = 512
sampler_name: str = ""
cfg_scale: float = 7.0
image_cfg_scale: float | None = None
steps: int = 50
batch_size: int = 1
restore_faces: bool = False
face_restoration_model: str | None = None
sd_model_hash: str = ""
seed_resize_from_w: int = -1
seed_resize_from_h: int = -1
denoising_strength: float = 0.0
extra_generation_params: dict[str, Any] = field(default_factory=dict)
index_of_first_image: int = 0
styles: list[str] = field(default_factory=list)
job_timestamp: str = ""
clip_skip: int = 1
eta: float = 0.0
ddim_discretize: str = ""
s_churn: float = 0.0
s_tmin: float = 0.0
s_tmax: float = 0.0
s_noise: float = 0.0
sampler_noise_scheduler_override: Callable | None = None
is_using_inpainting_conditioning: bool = False
all_prompts: list[str] = field(default_factory=list)
all_negative_prompts: list[str] = field(default_factory=list)
all_seeds: list[int] = field(default_factory=list)
all_subseeds: list[int] = field(default_factory=list)
infotexts: list[str] = field(default_factory=list)
def create_infotext(
p: StableDiffusionProcessingTxt2Img | StableDiffusionProcessingImg2Img,
all_prompts: list[str],
all_seeds: list[int],
all_subseeds: list[int],
comments: Any,
iteration: int = 0,
position_in_batch: int = 0,
) -> str:
pass
def process_images(
p: StableDiffusionProcessingTxt2Img | StableDiffusionProcessingImg2Img,
) -> Processed:
pass
else:
from modules.processing import (
StableDiffusionProcessing,
StableDiffusionProcessingImg2Img,
StableDiffusionProcessingTxt2Img,
create_infotext,
process_images,
)
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