File size: 8,629 Bytes
c31821c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | from abc import ABC, abstractmethod
from typing import List, ClassVar, Dict, Optional, Set, NamedTuple, Any
from dataclasses import dataclass, field
import numpy as np
import torch
from modules import shared, devices
from scripts.enums import ControlNetUnionControlType
from scripts.logging import logger
from scripts.utils import ndarray_lru_cache
CACHE_SIZE = getattr(shared.cmd_opts, "controlnet_preprocessor_cache_size", 0)
@dataclass
class PreprocessorParameter:
"""
Class representing a parameter for a preprocessor.
Attributes:
label (str): The label for the parameter.
minimum (float): The minimum value of the parameter. Default is 0.0.
maximum (float): The maximum value of the parameter. Default is 1.0.
step (float): The step size for the parameter. Default is 0.01.
value (float): The initial value of the parameter. Default is 0.5.
visible (bool): Whether the parameter is visible or not. Default is False.
"""
label: str = "EMPTY_LABEL"
minimum: float = 0.0
maximum: float = 1.0
step: float = 0.01
value: float = 0.5
visible: bool = True
@property
def gradio_update_kwargs(self) -> dict:
return dict(
minimum=self.minimum,
maximum=self.maximum,
step=self.step,
label=self.label,
value=self.value,
visible=self.visible,
)
@property
def api_json(self) -> dict:
return dict(
name=self.label,
value=self.value,
min=self.minimum,
max=self.maximum,
step=self.step,
)
@dataclass
class Preprocessor(ABC):
"""
Class representing a preprocessor.
Attributes:
name (str): The name of the preprocessor.
tags (List[str]): The tags associated with the preprocessor.
slider_resolution (PreprocessorParameter): The parameter representing the resolution of the slider.
slider_1 (PreprocessorParameter): The first parameter of the slider.
slider_2 (PreprocessorParameter): The second parameter of the slider.
slider_3 (PreprocessorParameter): The third parameter of the slider.
show_control_mode (bool): Whether to show the control mode or not.
do_not_need_model (bool): Whether the preprocessor needs a model or not.
sorting_priority (int): The sorting priority of the preprocessor.
corp_image_with_a1111_mask_when_in_img2img_inpaint_tab (bool): Whether to crop the image with a1111 mask when in img2img inpaint tab or not.
fill_mask_with_one_when_resize_and_fill (bool): Whether to fill the mask with one when resizing and filling or not.
use_soft_projection_in_hr_fix (bool): Whether to use soft projection in hr fix or not.
expand_mask_when_resize_and_fill (bool): Whether to expand the mask when resizing and filling or not.
"""
name: str
_label: str = None
tags: List[str] = field(default_factory=list)
slider_resolution = PreprocessorParameter(
label="Resolution",
minimum=64,
maximum=2048,
value=512,
step=8,
visible=True,
)
slider_1 = PreprocessorParameter(visible=False)
slider_2 = PreprocessorParameter(visible=False)
slider_3 = PreprocessorParameter(visible=False)
returns_image: bool = True
show_control_mode = True
do_not_need_model = False
sorting_priority = 0 # higher goes to top in the list
accepts_mask: bool = False
requires_mask: bool = False
corp_image_with_a1111_mask_when_in_img2img_inpaint_tab = True
fill_mask_with_one_when_resize_and_fill = False
use_soft_projection_in_hr_fix = False
expand_mask_when_resize_and_fill = False
model: Optional[torch.nn.Module] = None
device = devices.get_device_for("controlnet")
preprocessor_deps: List[str] = field(default_factory=list)
all_processors: ClassVar[Dict[str, "Preprocessor"]] = {}
all_processors_by_name: ClassVar[Dict[str, "Preprocessor"]] = {}
@property
def label(self) -> str:
"""Display name on UI."""
return self._label if self._label is not None else self.name
@classmethod
def add_supported_preprocessor(cls, p: "Preprocessor"):
assert p.label not in cls.all_processors, f"{p.label} already registered!"
cls.all_processors[p.label] = p
assert p.name not in cls.all_processors_by_name, f"{p.name} already registered!"
cls.all_processors_by_name[p.name] = p
logger.debug(
f"{p.name} registered. Total preprocessors ({len(cls.all_processors)})."
)
@classmethod
def get_preprocessor(cls, name: str) -> Optional["Preprocessor"]:
return cls.all_processors.get(name, cls.all_processors_by_name.get(name, None))
@classmethod
def get_sorted_preprocessors(cls) -> List["Preprocessor"]:
preprocessors = [p for k, p in cls.all_processors.items() if k != "none"]
return [cls.all_processors["none"]] + sorted(
preprocessors,
key=lambda x: str(x.sorting_priority).zfill(8) + x.label,
reverse=True,
)
@classmethod
def get_all_preprocessor_tags(cls):
tags = set()
for _, p in cls.all_processors.items():
tags.update(set(p.tags))
return ["All"] + sorted(list(tags))
@classmethod
def get_filtered_preprocessors(cls, tag: str) -> List["Preprocessor"]:
if tag == "All":
return cls.all_processors
return [
p
for p in cls.get_sorted_preprocessors()
if tag in p.tags or p.label == "none"
]
@classmethod
def get_default_preprocessor(cls, tag: str) -> "Preprocessor":
ps = cls.get_filtered_preprocessors(tag)
assert len(ps) > 0
return ps[0] if len(ps) == 1 else ps[1]
@classmethod
def tag_to_filters(cls, tag: str) -> Set[str]:
filters_aliases = {
"instructp2p": ["ip2p"],
"segmentation": ["seg"],
"normalmap": ["normal"],
"t2i-adapter": ["t2i_adapter", "t2iadapter", "t2ia"],
"ip-adapter": ["ip_adapter", "ipadapter"],
"openpose": ["openpose", "densepose"],
"instant-id": ["instant_id", "instantid"],
"scribble": ["sketch"],
"tile": ["blur"],
}
tag = tag.lower()
union_tags = ["union"] if tag in ControlNetUnionControlType.all_tags() else []
return set([tag] + filters_aliases.get(tag, []) + union_tags)
@classmethod
def unload_unused(cls, active_processors: Set["Preprocessor"]):
logger.debug(
f"Unload unused preprocessors. Active: {[p.name for p in active_processors]}"
)
for p in cls.all_processors.values():
if p not in active_processors:
success = p.unload()
if success:
logger.debug(f"Unload unused preprocessor {p.name}")
class Result(NamedTuple):
value: Any
# The display images shown on UI.
display_images: List[np.ndarray]
def cached_call(self, input_image, *args, **kwargs) -> "Preprocessor.Result":
"""The function exposed that also returns an image for display."""
result = self._cached_call(input_image, *args, **kwargs)
if isinstance(result, Preprocessor.Result):
return result
else:
return Preprocessor.Result(
value=result,
display_images=[result if self.returns_image else input_image],
)
@ndarray_lru_cache(max_size=CACHE_SIZE)
def _cached_call(self, *args, **kwargs):
"""The actual cached function."""
logger.debug(f"Calling preprocessor {self.name} outside of cache.")
return self(*args, **kwargs)
def __hash__(self):
return hash(self.name)
def __eq__(self, other):
return self.__hash__() == other.__hash__()
@abstractmethod
def __call__(
self,
input_image,
resolution,
slider_1=None,
slider_2=None,
slider_3=None,
input_mask=None,
**kwargs,
):
pass
def unload(self):
if self.model is not None:
if hasattr(self.model, "unload_model"):
self.model.unload_model()
return True
if hasattr(self.model, "to"):
self.model.to("cpu")
return True
raise Exception(f"Unable to unload model {self.model}")
return False
|