File size: 6,334 Bytes
34097e9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
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,
    )