| 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 |
|
|
| def close(self) -> None: |
| pass |
|
|
| @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, |
| use_main_prompt: bool = False, |
| index: int | None = None, |
| all_negative_prompts: list[str] | None = None, |
| ) -> str: |
| pass |
|
|
| def process_images( |
| p: StableDiffusionProcessingTxt2Img | StableDiffusionProcessingImg2Img, |
| ) -> Processed: |
| pass |
|
|
| else: |
| from modules.processing import ( |
| Processed, |
| StableDiffusionProcessing, |
| StableDiffusionProcessingImg2Img, |
| StableDiffusionProcessingTxt2Img, |
| create_infotext, |
| process_images, |
| ) |
|
|
