267 lines
13 KiB
Python
267 lines
13 KiB
Python
import inspect
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from pydantic import BaseModel, Field, create_model
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from typing import Any, Optional
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from typing_extensions import Literal
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from inflection import underscore
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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from modules.shared import sd_upscalers, opts, parser
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from typing import Dict, List
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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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"sampler_index",
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"do_not_save_samples",
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"do_not_save_grid",
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"extra_generation_params",
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"overlay_images",
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"do_not_reload_embeddings",
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"seed_enable_extras",
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"prompt_for_display",
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"sampler_noise_scheduler_override",
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"ddim_discretize"
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]
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class ModelDef(BaseModel):
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"""Assistance Class for Pydantic Dynamic Model Generation"""
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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 PydanticModelGenerator:
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"""
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Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
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source_data is a snapshot of the default values produced by the class
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params are the names of the actual keys required by __init__
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"""
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def __init__(
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self,
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model_name: str = None,
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class_instance = None,
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additional_fields = None,
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):
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def field_type_generator(k, v):
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# field_type = str if not overrides.get(k) else overrides[k]["type"]
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# print(k, v.annotation, v.default)
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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 fields in additional_fields:
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self._model_def.append(ModelDef(
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field=underscore(fields["key"]),
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field_alias=fields["key"],
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field_type=fields["type"],
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field_value=fields["default"],
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field_exclude=fields["exclude"] if "exclude" in fields else False))
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def generate_model(self):
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"""
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Creates a pydantic BaseModel
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from the json and overrides provided at initialization
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"""
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fields = {
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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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}
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DynamicModel = create_model(self._model_name, **fields)
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DynamicModel.__config__.allow_population_by_field_name = True
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DynamicModel.__config__.allow_mutation = True
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return DynamicModel
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StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
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"StableDiffusionProcessingTxt2Img",
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StableDiffusionProcessingTxt2Img,
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
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).generate_model()
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StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
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"StableDiffusionProcessingImg2Img",
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StableDiffusionProcessingImg2Img,
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[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
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).generate_model()
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class TextToImageResponse(BaseModel):
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images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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class ImageToImageResponse(BaseModel):
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images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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class ExtrasBaseRequest(BaseModel):
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resize_mode: Literal[0, 1] = 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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gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
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codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
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codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
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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 sd_upscalers])}")
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upscaler_2: str = Field(default="None", title="Secondary 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 sd_upscalers])}")
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extras_upscaler_2_visibility: float = Field(default=0, title="Secondary 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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upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
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class ExtraBaseResponse(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 ExtrasSingleImageRequest(ExtrasBaseRequest):
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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 ExtrasSingleImageResponse(ExtraBaseResponse):
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image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
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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 ExtrasBatchImagesRequest(ExtrasBaseRequest):
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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 ExtrasBatchImagesResponse(ExtraBaseResponse):
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images: List[str] = Field(title="Images", description="The generated images in base64 format.")
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class PNGInfoRequest(BaseModel):
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image: str = Field(title="Image", description="The base64 encoded PNG image")
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class PNGInfoResponse(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="An object containing all the info the image had")
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class ProgressRequest(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 ProgressResponse(BaseModel):
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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 = 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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class InterrogateRequest(BaseModel):
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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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model: str = Field(default="clip", title="Model", description="The interrogate model used.")
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class InterrogateResponse(BaseModel):
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caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
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class TrainResponse(BaseModel):
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info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
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class CreateResponse(BaseModel):
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info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
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class PreprocessResponse(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 opts.data_labels.items():
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value = opts.data.get(key)
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optType = 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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OptionsModel = create_model("Options", **fields)
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flags = {}
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_options = vars(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 = str
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if _options[key].default is not None: _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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FlagsModel = create_model("Flags", **flags)
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class SamplerItem(BaseModel):
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name: str = Field(title="Name")
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aliases: List[str] = Field(title="Aliases")
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options: Dict[str, str] = Field(title="Options")
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class UpscalerItem(BaseModel):
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name: str = Field(title="Name")
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model_name: Optional[str] = Field(title="Model Name")
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model_path: Optional[str] = Field(title="Path")
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model_url: Optional[str] = Field(title="URL")
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class SDModelItem(BaseModel):
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title: str = Field(title="Title")
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model_name: str = Field(title="Model Name")
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hash: str = Field(title="Hash")
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filename: str = Field(title="Filename")
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config: str = Field(title="Config file")
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class HypernetworkItem(BaseModel):
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name: str = Field(title="Name")
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path: Optional[str] = Field(title="Path")
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class FaceRestorerItem(BaseModel):
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name: str = Field(title="Name")
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cmd_dir: Optional[str] = Field(title="Path")
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class RealesrganItem(BaseModel):
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name: str = Field(title="Name")
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path: Optional[str] = Field(title="Path")
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scale: Optional[int] = Field(title="Scale")
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class PromptStyleItem(BaseModel):
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name: str = Field(title="Name")
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prompt: Optional[str] = Field(title="Prompt")
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negative_prompt: Optional[str] = Field(title="Negative Prompt")
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class ArtistItem(BaseModel):
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name: str = Field(title="Name")
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score: float = Field(title="Score")
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category: str = Field(title="Category")
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class EmbeddingItem(BaseModel):
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step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
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sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
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sd_checkpoint_name: Optional[str] = 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 EmbeddingsResponse(BaseModel):
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loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
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skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
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class MemoryResponse(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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