mirror of https://github.com/vladmandic/automatic
572 lines
34 KiB
Python
572 lines
34 KiB
Python
import re
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import inspect
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from typing import Any, Optional, Union
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from collections.abc import Callable
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from pydantic import BaseModel, Field, create_model
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from pydantic import VERSION
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PYDANTIC_V2 = VERSION.startswith("2.")
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try:
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from pydantic import ConfigDict
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except ImportError:
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ConfigDict = None
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try:
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from pydantic import BaseConfig
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except ImportError:
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BaseConfig = object
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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import modules.shared as shared
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API_NOT_ALLOWED = [
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"self",
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"kwargs",
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"sd_model",
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"outpath_samples",
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"outpath_grids",
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]
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class ModelDef(BaseModel):
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field: str
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field_alias: str
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field_type: Any
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field_value: Any
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field_exclude: bool = False
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class DummyConfig(BaseConfig):
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dummy_value = None
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if not hasattr(BaseModel, "__config__"):
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BaseModel.__config__ = DummyConfig
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class PydanticConfig(BaseConfig):
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arbitrary_types_allowed = True
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orm_mode = True
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allow_population_by_field_name = True
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def underscore(name: str) -> str: # Convert CamelCase or PascalCase string to underscore_case (snake_case).
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# use instead of inflection.underscore
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s1 = re.sub('([a-z0-9])([A-Z])', r'\1_\2', name)
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s2 = re.sub('([A-Z]+)([A-Z][a-z])', r'\1_\2', s1)
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return s2.lower()
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class PydanticModelGenerator:
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def __init__(
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self,
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model_name: str,
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class_instance,
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additional_fields: list,
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exclude_fields: list | None = None,
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):
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if exclude_fields is None:
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exclude_fields = []
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def field_type_generator(_k, v):
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field_type = v.annotation
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return Optional[field_type]
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def merge_class_params(class_):
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all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
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parameters = {}
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for classes in all_classes:
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parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
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return parameters
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self._model_name = model_name
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self._class_data = merge_class_params(class_instance)
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self._model_def = [
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ModelDef(
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field=underscore(k),
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field_alias=k,
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field_type=field_type_generator(k, v),
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field_value=v.default
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)
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for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
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]
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for fld in additional_fields:
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self._model_def.append(ModelDef(
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field=underscore(fld["key"]),
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field_alias=fld["key"],
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field_type=fld["type"],
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field_value=fld["default"],
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field_exclude=fld["exclude"] if "exclude" in fld else False))
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for fld in exclude_fields:
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self._model_def = [x for x in self._model_def if x.field != fld]
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def generate_model(self):
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model_fields: dict[str, Any] = { d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def }
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if PYDANTIC_V2:
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config = ConfigDict(arbitrary_types_allowed=True, from_attributes=True, populate_by_name=True)
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else:
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config = PydanticConfig
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DynamicModel = create_model(self._model_name, __config__=config, **model_fields)
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return DynamicModel
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### item classes
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class ItemSampler(BaseModel):
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name: str = Field(title="Name")
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options: dict = Field(title="Options")
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class ItemVae(BaseModel):
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model_name: str = Field(title="Model Name", description="VAE model display name")
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filename: str = Field(title="Filename", description="Path to the VAE file")
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class ItemUpscaler(BaseModel):
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name: str = Field(title="Name", description="Upscaler display name")
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model_name: str | None = Field(title="Model Name", description="Underlying model name")
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model_path: str | None = Field(title="Path", description="Path to the model file")
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model_url: str | None = Field(title="URL", description="Download URL for the model")
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scale: float | None = Field(title="Scale", description="Default upscale factor")
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class ItemModel(BaseModel):
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title: str = Field(title="Title", description="Full model title including hash")
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model_name: str = Field(title="Model Name", description="Model display name")
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filename: str = Field(title="Filename", description="Path to the model file")
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type: str = Field(title="Model type", description="Model architecture type (e.g., SD, SDXL, Flux)")
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sha256: str | None = Field(title="SHA256 hash", description="Full SHA256 hash of the model file")
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hash: str | None = Field(title="Short hash", description="Short hash identifier for the model")
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config: str | None = Field(title="Config file", description="Path to the model configuration file")
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class ItemDetailer(BaseModel):
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name: str = Field(title="Name", description="Detailer model name")
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path: str | None = Field(title="Path", description="Path to the detailer model file")
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class ItemStyle(BaseModel):
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name: str = Field(title="Name", description="Style name")
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prompt: str | None = Field(title="Prompt", description="Prompt template text")
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negative_prompt: str | None = Field(title="Negative Prompt", description="Negative prompt template text")
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extra: str | None = Field(title="Extra", description="Additional style data")
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filename: str | None = Field(title="Filename", description="Path to the styles file")
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preview: str | None = Field(title="Preview", description="URL to the style preview image")
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class ItemExtraNetwork(BaseModel):
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name: str = Field(title="Name", description="Network short name")
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type: str = Field(title="Type", description="Network type (lora, checkpoint, embedding, etc.)")
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title: str | None = Field(title="Title", description="Display title")
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fullname: str | None = Field(title="Fullname", description="Fully qualified network name")
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filename: str | None = Field(title="Filename", description="Path to the network file")
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hash: str | None = Field(title="Hash", description="Short hash identifier")
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preview: str | None = Field(title="Preview image URL", description="URL to the preview thumbnail")
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version: str | None = Field(default=None, title="Model version or class", description="Model version string or architecture class")
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tags: str | None = Field(default=None, title="Tags", description="Pipe-separated tag list")
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class ItemExtraNetworkDetail(BaseModel):
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name: str = Field(title="Name", description="Network short name")
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type: str = Field(title="Type", description="Network type (lora, checkpoint, embedding, etc.)")
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title: str | None = Field(default=None, title="Title", description="Display title")
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filename: str | None = Field(default=None, title="Filename", description="Path to the network file")
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hash: str | None = Field(default=None, title="Hash", description="Short hash identifier")
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alias: str | None = Field(default=None, title="Alias", description="Short alias for prompt use")
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size: int | None = Field(default=None, title="File size in bytes", description="File size in bytes on disk")
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mtime: str | None = Field(default=None, title="Last modified ISO timestamp", description="Last modification time in ISO 8601 format")
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version: str | None = Field(default=None, title="Model version or class", description="Model version string or architecture class")
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tags: str | None = Field(default=None, title="Tags", description="Pipe-separated tag list")
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description: str | None = Field(default=None, title="Description", description="Human-readable model description")
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info: dict | None = Field(default=None, title="Sidecar JSON info", description="Metadata from sidecar JSON file")
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class ItemExtraNetworkFull(BaseModel):
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name: str = Field(title="Name", description="Network short name")
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type: str = Field(title="Type", description="Network type (lora, checkpoint, embedding, etc.)")
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title: str | None = Field(default=None, title="Title", description="Display title")
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fullname: str | None = Field(default=None, title="Fullname", description="Fully qualified network name")
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filename: str | None = Field(default=None, title="Filename", description="Path to the network file")
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hash: str | None = Field(default=None, title="Hash", description="Short hash identifier")
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preview: str | None = Field(default=None, title="Preview image URL", description="URL to the preview thumbnail")
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alias: str | None = Field(default=None, title="Alias", description="Short alias for prompt use")
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size: int | None = Field(default=None, title="File size in bytes", description="File size in bytes on disk")
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mtime: str | None = Field(default=None, title="Last modified ISO timestamp", description="Last modification time in ISO 8601 format")
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version: str | None = Field(default=None, title="Model version or class", description="Model version string or architecture class")
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tags: str | None = Field(default=None, title="Tags", description="Pipe-separated tag list")
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description: str | None = Field(default=None, title="Description", description="Human-readable model description")
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info: dict | None = Field(default=None, title="Sidecar JSON info", description="Metadata from sidecar JSON file")
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class ResExtraNetworkDetails(BaseModel):
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items: list[ItemExtraNetworkFull] = Field(title="Items", description="List of extra network items with full detail")
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total: int = Field(title="Total", description="Total number of matching items before pagination")
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offset: int = Field(title="Offset", description="Number of items skipped")
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limit: int = Field(title="Limit", description="Maximum items returned per page")
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class ItemEmbedding(BaseModel):
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step: int | None = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
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sd_checkpoint: str | None = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
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sd_checkpoint_name: str | None = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
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shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
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vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
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class ItemIPAdapter(BaseModel):
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adapter: str = Field(title="Adapter", default="Base", description="IP adapter name")
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images: list[str] = Field(title="Image", default=[], description="IP adapter input images")
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masks: list[str] | None = Field(title="Mask", default=[], description="IP adapter mask images")
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scale: float = Field(title="Scale", default=0.5, ge=0, le=1, description="IP adapter scale")
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start: float = Field(title="Start", default=0.0, ge=0, le=1, description="IP adapter start step")
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end: float = Field(title="End", default=1.0, gt=0, le=1, description="IP adapter end step")
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crop: bool = Field(title="Crop", default=False, description="IP adapter crop face from input")
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class ItemFace(BaseModel):
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mode: str = Field(title="Mode", default="FaceID", description="The mode to use (available values: FaceID, FaceSwap, PhotoMaker, InstantID).")
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source_images: list[str] = Field(title="Source Images", description="Source face images, must be base64 encoded containing the image's data.")
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ip_model: str = Field(title="IPAdapter Model", default="FaceID Base", description="The IPAdapter model to use.")
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ip_override_sampler: bool = Field(title="IPAdapter Override Sampler", default=True, description="Should the sampler be overriden?")
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ip_cache_model: bool = Field(title="IPAdapter Cache", default=True, description="Should the IPAdapter model be cached?")
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ip_strength: float = Field(title="IPAdapter Strength", default=1, ge=0, le=2, description="IPAdapter strength of the source images, must be between 0.0 and 2.0.")
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ip_structure: float = Field(title="IPAdapter Structure", default=1, ge=0, le=1, description="IPAdapter structure to use, must be between 0.0 and 1.0.")
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id_strength: float = Field(title="InstantID Strength", default=1, ge=0, le=2, description="InstantID Strength of the source images, must be between 0.0 and 2.0.")
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id_conditioning: float = Field(title="InstantID Condition", default=0.5, ge=0, le=2, description="InstantID control amount, must be between 0.0 and 2.0.")
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id_cache: bool = Field(title="InstantID Cache", default=True, description="Should the InstantID model be cached?")
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pm_trigger: str = Field(title="PhotoMaker Trigger", default="person", description="PhotoMaker trigger word to use.")
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pm_strength: float = Field(title="PhotoMaker Strength", default=1, ge=0, le=2, description="PhotoMaker strength to use, must be between 0.0 and 2.0.")
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pm_start: float = Field(title="PhotoMaker Start", default=0.5, ge=0, le=1, description="PhotoMaker start value, must be between 0.0 and 1.0.")
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fs_cache: bool = Field(title="FaceSwap Cache", default=True, description="Should the FaceSwap model be cached?")
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class ItemControlUnit(BaseModel):
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process: str = Field(title="Preprocessor", default="", description="Preprocessor name (e.g. 'Canny', 'OpenPose')")
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model: str = Field(title="Control model", default="", description="Control model filename or path")
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strength: float = Field(title="Strength", default=1.0, description="Control influence strength (0.0-2.0)")
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start: float = Field(title="Start", default=0.0, description="Step fraction at which control begins (0.0-1.0)")
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end: float = Field(title="End", default=1.0, description="Step fraction at which control ends (0.0-1.0)")
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override: str | None = Field(title="Override image", default=None, description="Base64-encoded pre-processed control image")
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unit_type: str | None = Field(title="Unit type", default=None, description="Control unit type: 'controlnet', 't2i adapter', 'xs', 'lite', 'reference', or 'ip'")
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image: str | None = Field(title="Image", default=None, description="Base64-encoded control input image")
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class ScriptArg(BaseModel):
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label: str = Field(default=None, title="Label", description="Name of the argument in UI")
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value: Any | None = Field(default=None, title="Value", description="Default value of the argument")
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minimum: Any | None = Field(default=None, title="Minimum", description="Minimum allowed value for the argument in UI")
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maximum: Any | None = Field(default=None, title="Maximum", description="Maximum allowed value for the argument in UI")
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step: Any | None = Field(default=None, title="Step", description="Step for changing value of the argument in UI")
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choices: Any | None = Field(default=None, title="Choices", description="Possible values for the argument")
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class ItemScript(BaseModel):
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name: str = Field(default=None, title="Name", description="Script name")
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is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
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is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
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args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
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class ItemExtension(BaseModel):
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name: str = Field(title="Name", description="Extension name")
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remote: str = Field(title="Remote", description="Extension Repository URL")
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branch: str = Field(default="unknown", title="Branch", description="Extension Repository Branch")
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commit_hash: str = Field(title="Commit Hash", description="Extension Repository Commit Hash")
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version: str = Field(title="Version", description="Extension Version")
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commit_date: str | int = Field(title="Commit Date", description="Extension Repository Commit Date")
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enabled: bool = Field(title="Enabled", description="Flag specifying whether this extension is enabled")
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class ItemScheduler(BaseModel):
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name: str = Field(title="Name", description="Scheduler name")
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cls: str = Field(title="Class", description="Scheduler class name")
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options: dict[str, Any] = Field(title="Options", description="Dictionary of scheduler options")
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### request/response classes
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ReqTxt2Img = PydanticModelGenerator(
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"StableDiffusionProcessingTxt2Img",
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StableDiffusionProcessingTxt2Img,
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[
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{"key": "sampler_index", "type": Union[int, str], "default": 0},
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{"key": "sampler_name", "type": str, "default": "Default"},
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{"key": "hr_sampler_name", "type": str, "default": "Same as primary"},
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{"key": "script_name", "type": Optional[str], "default": ""},
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{"key": "script_args", "type": list, "default": []},
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{"key": "send_images", "type": bool, "default": True},
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{"key": "save_images", "type": bool, "default": False},
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{"key": "alwayson_scripts", "type": dict, "default": {}},
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{"key": "ip_adapter", "type": Optional[list[ItemIPAdapter]], "default": None, "exclude": True},
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{"key": "control_units", "type": Optional[list[ItemControlUnit]], "default": None, "exclude": True},
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{"key": "init_control", "type": Optional[list], "default": None, "exclude": True},
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{"key": "face", "type": Optional[ItemFace], "default": None, "exclude": True},
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{"key": "extra", "type": Optional[dict], "default": {}, "exclude": True},
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]
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).generate_model()
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if not hasattr(ReqTxt2Img, "__config__"):
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ReqTxt2Img.__config__ = DummyConfig
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StableDiffusionTxt2ImgProcessingAPI = ReqTxt2Img
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class ResTxt2Img(BaseModel):
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images: list[str] = Field(default=None, title="Image", description="The generated images in base64 format.")
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parameters: dict = Field(title="Parameters", description="The request parameters echoed back.")
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info: str = Field(title="Info", description="Generation info string with all parameters used.")
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ReqImg2Img = PydanticModelGenerator(
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"StableDiffusionProcessingImg2Img",
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StableDiffusionProcessingImg2Img,
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[
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{"key": "sampler_index", "type": Union[int, str], "default": 0},
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{"key": "sampler_name", "type": str, "default": "UniPC"},
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{"key": "hr_sampler_name", "type": str, "default": "Same as primary"},
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{"key": "init_images", "type": list, "default": None},
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{"key": "denoising_strength", "type": float, "default": 0.5},
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{"key": "mask", "type": Optional[str], "default": None},
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{"key": "include_init_images", "type": bool, "default": False, "exclude": True},
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{"key": "script_name", "type": Optional[str], "default": ""},
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{"key": "script_args", "type": list, "default": []},
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{"key": "send_images", "type": bool, "default": True},
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{"key": "save_images", "type": bool, "default": False},
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{"key": "alwayson_scripts", "type": dict, "default": {}},
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{"key": "ip_adapter", "type": Optional[list[ItemIPAdapter]], "default": None, "exclude": True},
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{"key": "control_units", "type": Optional[list[ItemControlUnit]], "default": None, "exclude": True},
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{"key": "init_control", "type": Optional[list], "default": None, "exclude": True},
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{"key": "face_id", "type": Optional[ItemFace], "default": None, "exclude": True},
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{"key": "extra", "type": Optional[dict], "default": {}, "exclude": True},
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]
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).generate_model()
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if not hasattr(ReqImg2Img, "__config__"):
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ReqImg2Img.__config__ = DummyConfig
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StableDiffusionImg2ImgProcessingAPI = ReqImg2Img
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class ResImg2Img(BaseModel):
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images: list[str] = Field(default=None, title="Image", description="The generated images in base64 format.")
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parameters: dict = Field(title="Parameters", description="The request parameters echoed back.")
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info: str = Field(title="Info", description="Generation info string with all parameters used.")
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class FileData(BaseModel):
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data: str = Field(title="File data", description="Base64 representation of the file")
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name: str = Field(title="File name")
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class ReqProcess(BaseModel):
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resize_mode: float = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
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show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
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upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=8, description="By how much to upscale the image, only used when resize_mode=0.")
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upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
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upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
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upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
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upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in shared.sd_upscalers])}")
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upscaler_2: str = Field(default="None", title="Refine upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in shared.sd_upscalers])}")
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extras_upscaler_2_visibility: float = Field(default=0, title="Refine upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
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class ResProcess(BaseModel):
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html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
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class ReqPromptEnhance(BaseModel):
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prompt: str = Field(title="Prompt", description="Prompt to enhance")
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type: str = Field(title="Type", default='text', description="Type of enhancement: text, image, video")
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model: str | None = Field(title="Model", default=None, description="Model to use for enhancement")
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system_prompt: str | None = Field(title="System prompt", default=None, description="Model system prompt")
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image: str | None = Field(title="Image", default=None, description="Image to work on, must be a Base64 string containing the image's data.")
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seed: int = Field(title="Seed", default=-1, description="Seed used to generate the prompt")
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nsfw: bool = Field(title="NSFW", default=True, description="Should NSFW content be allowed?")
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prefix: Optional[str] = Field(title="Prefix", default=None, description="Text prepended to enhanced prompt")
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suffix: Optional[str] = Field(title="Suffix", default=None, description="Text appended to enhanced prompt")
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do_sample: Optional[bool] = Field(title="Sample", default=None, description="Enable sampling")
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max_tokens: Optional[int] = Field(title="Max tokens", default=None, description="Max generation tokens")
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temperature: Optional[float] = Field(title="Temperature", default=None, description="Controls randomness in token selection (0=deterministic, higher=more creative)")
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repetition_penalty: Optional[float] = Field(title="Repetition penalty", default=None, description="Penalizes repeated tokens to reduce repetition (1.0=no penalty)")
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top_k: Optional[int] = Field(title="Top K", default=None, description="Limits token selection to the K most likely candidates")
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top_p: Optional[float] = Field(title="Top P", default=None, description="Nucleus sampling threshold (0-1)")
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thinking: bool = Field(title="Thinking", default=False, description="Enable thinking/reasoning mode")
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keep_thinking: bool = Field(title="Keep thinking", default=False, description="Keep thinking tokens in output")
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use_vision: bool = Field(title="Use vision", default=True, description="Use vision if model supports it")
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prefill: Optional[str] = Field(title="Prefill", default=None, description="Text to prefill the model response with")
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keep_prefill: bool = Field(title="Keep prefill", default=False, description="Keep prefill text in the output")
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class ResPromptEnhance(BaseModel):
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prompt: str = Field(title="Prompt", description="Enhanced prompt")
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seed: int = Field(title="Seed", description="Seed used to generate the prompt")
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class ReqProcessImage(ReqProcess):
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image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
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class ResProcessImage(ResProcess):
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image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
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class ReqProcessBatch(ReqProcess):
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imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
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class ResProcessBatch(ResProcess):
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images: list[str] = Field(title="Images", description="The generated images in base64 format.")
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class ReqImageInfo(BaseModel):
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image: str = Field(title="Image", description="The base64 encoded image")
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class ResImageInfo(BaseModel):
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info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
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items: dict = Field(title="Items", description="A dictionary containing all the other fields the image had")
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parameters: dict = Field(title="Parameters", description="A dictionary with parsed generation info fields")
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class ReqGetLog(BaseModel):
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lines: int = Field(default=100, title="Lines", description="How many lines to return")
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clear: bool = Field(default=False, title="Clear", description="Should the log be cleared after returning the lines?")
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class ReqPostLog(BaseModel):
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message: str | None = Field(default=None, title="Message", description="The info message to log")
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debug: str | None = Field(default=None, title="Debug message", description="The debug message to log")
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error: str | None = Field(default=None, title="Error message", description="The error message to log")
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class ReqHistory(BaseModel):
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id: int | str | None = Field(default=None, title="Task ID", description="Task ID")
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class ReqProgress(BaseModel):
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skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
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class ResProgress(BaseModel):
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id: int | str | None = Field(title="TaskID", description="Task ID")
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progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
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eta_relative: float = Field(title="ETA in secs")
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state: dict = Field(title="State", description="The current state snapshot")
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current_image: str | None = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
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textinfo: str | None = Field(default=None, title="Info text", description="Info text used by WebUI.")
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class ResHistory(BaseModel):
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id: int | str | None = Field(title="ID", description="Task ID")
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job: str = Field(title="Job", description="Job name")
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op: str = Field(title="Operation", description="Job state")
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timestamp: float | None = Field(title="Timestamp", description="Job timestamp")
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duration: float | None = Field(title="Duration", description="Job duration")
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outputs: list[str] = Field(title="Outputs", description="List of filenames")
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class ResStatus(BaseModel):
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status: str = Field(title="Status", description="Current status")
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task: str = Field(title="Task", description="Current job")
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timestamp: str | None = Field(title="Timestamp", description="Timestamp of the current job")
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current: str = Field(title="Task", description="Current job")
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id: int | str | None = Field(title="ID", description="ID of the current task")
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job: int = Field(title="Job", description="Current job")
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jobs: int = Field(title="Jobs", description="Total jobs")
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total: int = Field(title="Total Jobs", description="Total jobs")
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step: int = Field(title="Step", description="Current step")
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steps: int = Field(title="Steps", description="Total steps")
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queued: int = Field(title="Queued", description="Number of queued tasks")
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uptime: int = Field(title="Uptime", description="Uptime of the server")
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elapsed: float | None = Field(default=None, title="Elapsed time")
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eta: float | None = Field(default=None, title="ETA in secs")
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progress: float | None = Field(default=None, title="Progress", description="The progress with a range of 0 to 1")
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class ReqLatentHistory(BaseModel):
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name: str = Field(title="Name", description="Name of the history item to select")
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class ResPreprocess(BaseModel):
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info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
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fields = {}
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for key, metadata in shared.opts.data_labels.items():
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value = shared.opts.data.get(key) or shared.opts.data_labels[key].default
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optType = shared.opts.typemap.get(type(metadata.default), type(value))
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if metadata is not None:
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fields.update({key: (Optional[optType], Field(
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default=metadata.default, description=metadata.label))})
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else:
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fields.update({key: (Optional[optType], Field())})
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if PYDANTIC_V2:
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pydantic_config = ConfigDict(arbitrary_types_allowed=True, from_attributes=True, populate_by_name=True)
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else:
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pydantic_config = PydanticConfig
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OptionsModel = create_model("Options", __config__=pydantic_config, **fields)
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flags = {}
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_options = vars(shared.parser)['_option_string_actions']
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for key in _options:
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if _options[key].dest != 'help':
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flag = _options[key]
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_type = Optional[str]
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if _options[key].default is not None:
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_type = type(_options[key].default)
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flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
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if PYDANTIC_V2:
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pydantic_config = ConfigDict(arbitrary_types_allowed=True, from_attributes=True, populate_by_name=True)
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else:
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pydantic_config = PydanticConfig
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FlagsModel = create_model("Flags", __config__=pydantic_config, **flags)
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class ResEmbeddings(BaseModel):
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loaded: list = Field(default=None, title="loaded", description="List of loaded embeddings")
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skipped: list = Field(default=None, title="skipped", description="List of skipped embeddings")
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class ResMemory(BaseModel):
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ram: dict = Field(title="RAM", description="System memory stats")
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cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
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class ResScripts(BaseModel):
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txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
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img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
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control: list = Field(default=None, title="Control", description="Titles of scripts (control)")
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class ResGPU(BaseModel): # definition of http response
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name: str = Field(title="GPU Name", description="GPU device name")
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data: dict = Field(title="Name/Value data", description="Key-value pairs of GPU metrics (utilization, temperature, clocks, memory, etc.)")
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chart: list[float, float] = Field(title="Exactly two items to place on chart", description="Two numeric values for chart display (e.g., GPU utilization %, VRAM usage %)")
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class ItemLoadedModel(BaseModel):
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name: str = Field(title="Model Name", description="Model or component name")
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category: str = Field(title="Category", description="Model category (pipeline, component, controlnet, lora, ipadapter, upscaler, detailer, caption, enhance)")
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device: Optional[str] = Field(default=None, title="Device", description="Device where the model is loaded (e.g., cuda:0, cpu)")
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size_bytes: Optional[int] = Field(default=None, title="Size (bytes)", description="Total parameter memory footprint in bytes")
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dtype: Optional[str] = Field(default=None, title="Dtype", description="Effective data type (e.g., float16, nf4)")
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extra: Optional[dict] = Field(default=None, title="Extra metadata", description="Additional metadata (role, class, quantization method, etc.)")
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# helper function
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def create_model_from_signature(func: Callable, model_name: str, base_model: type[BaseModel] = BaseModel, additional_fields: list | None = None, exclude_fields: list[str] | None = None) -> type[BaseModel]:
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from PIL import Image
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if exclude_fields is None:
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exclude_fields = []
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if additional_fields is None:
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additional_fields = []
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class Config:
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extra = 'allow'
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args, _, varkw, defaults, kwonlyargs, kwonlydefaults, annotations = inspect.getfullargspec(func)
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config = Config if varkw else None # Allow extra params if there is a **kwargs parameter in the function signature
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defaults = defaults or []
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args = args or []
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for arg in exclude_fields:
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if arg in args:
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args.remove(arg)
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non_default_args = len(args) - len(defaults)
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defaults = (...,) * non_default_args + defaults
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kw_defaults = kwonlydefaults or {}
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keyword_only_params = {param: (annotations.get(param, Any), kw_defaults.get(param, ...)) for param in kwonlyargs}
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for k, v in annotations.items():
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if v == list[Image.Image]:
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annotations[k] = list[str]
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elif v == Image.Image:
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annotations[k] = str
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elif str(v) == 'typing.List[modules.control.unit.Unit]':
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annotations[k] = list[str]
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model_fields = {param: (annotations.get(param, Any), default) for param, default in zip(args, defaults, strict=False)}
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for fld in additional_fields:
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model_def = ModelDef(
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field=underscore(fld["key"]),
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field_alias=fld["key"],
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field_type=fld["type"],
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field_value=fld["default"],
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field_exclude=fld["exclude"] if "exclude" in fld else False)
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model_fields[model_def.field] = (model_def.field_type, Field(default=model_def.field_value, alias=model_def.field_alias, exclude=model_def.field_exclude))
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for fld in exclude_fields:
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if fld in model_fields:
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del model_fields[fld]
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if PYDANTIC_V2:
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config = ConfigDict(arbitrary_types_allowed=True, from_attributes=True, populate_by_name=True, extra='allow' if varkw else 'ignore')
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create_model_args = {'__base__': base_model, '__config__': config}
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else:
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class CustomConfig(PydanticConfig):
|
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extra = 'allow' if varkw else 'ignore'
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config = CustomConfig
|
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if base_model == BaseModel:
|
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create_model_args = {'__config__': config}
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else:
|
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create_model_args = {'__base__': base_model}
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|
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model = create_model(
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model_name,
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**create_model_args,
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**model_fields,
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**keyword_only_params,
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)
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return model
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