import inspect from pydantic import BaseModel, Field, create_model from typing import Any, Optional from typing_extensions import Literal from inflection import underscore from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img from modules.shared import sd_upscalers, opts, parser from typing import Dict, List API_NOT_ALLOWED = [ "self", "kwargs", "sd_model", "outpath_samples", "outpath_grids", "sampler_index", # "do_not_save_samples", # "do_not_save_grid", "extra_generation_params", "overlay_images", "do_not_reload_embeddings", "seed_enable_extras", "prompt_for_display", "sampler_noise_scheduler_override", "ddim_discretize" ] class ModelDef(BaseModel): """Assistance Class for Pydantic Dynamic Model Generation""" field: str field_alias: str field_type: Any field_value: Any field_exclude: bool = False class PydanticModelGenerator: """ Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about: source_data is a snapshot of the default values produced by the class params are the names of the actual keys required by __init__ """ def __init__( self, model_name: str = None, class_instance = None, additional_fields = None, ): def field_type_generator(k, v): # field_type = str if not overrides.get(k) else overrides[k]["type"] # print(k, v.annotation, v.default) field_type = v.annotation return Optional[field_type] def merge_class_params(class_): all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_))) parameters = {} for classes in all_classes: parameters = {**parameters, **inspect.signature(classes.__init__).parameters} return parameters self._model_name = model_name self._class_data = merge_class_params(class_instance) self._model_def = [ ModelDef( field=underscore(k), field_alias=k, field_type=field_type_generator(k, v), field_value=v.default ) for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED ] for fields in additional_fields: self._model_def.append(ModelDef( field=underscore(fields["key"]), field_alias=fields["key"], field_type=fields["type"], field_value=fields["default"], field_exclude=fields["exclude"] if "exclude" in fields else False)) def generate_model(self): """ Creates a pydantic BaseModel from the json and overrides provided at initialization """ fields = { d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def } DynamicModel = create_model(self._model_name, **fields) DynamicModel.__config__.allow_population_by_field_name = True DynamicModel.__config__.allow_mutation = True return DynamicModel StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img, [ {"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}, {"key": "send_images", "type": bool, "default": True}, {"key": "save_images", "type": bool, "default": False}, {"key": "alwayson_scripts", "type": dict, "default": {}}, ] ).generate_model() StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingImg2Img", StableDiffusionProcessingImg2Img, [ {"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": []}, {"key": "send_images", "type": bool, "default": True}, {"key": "save_images", "type": bool, "default": False}, {"key": "alwayson_scripts", "type": dict, "default": {}}, ] ).generate_model() class TextToImageResponse(BaseModel): images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: dict info: str class ImageToImageResponse(BaseModel): images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: dict info: str class ExtrasBaseRequest(BaseModel): 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.") show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?") 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.") 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.") 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.") 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.") 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.") 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.") upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?") 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])}") 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])}") 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.") upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?") class ExtraBaseResponse(BaseModel): html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.") class ExtrasSingleImageRequest(ExtrasBaseRequest): image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.") class ExtrasSingleImageResponse(ExtraBaseResponse): image: str = Field(default=None, title="Image", description="The generated image in base64 format.") class FileData(BaseModel): data: str = Field(title="File data", description="Base64 representation of the file") name: str = Field(title="File name") class ExtrasBatchImagesRequest(ExtrasBaseRequest): imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") class ExtrasBatchImagesResponse(ExtraBaseResponse): images: List[str] = Field(title="Images", description="The generated images in base64 format.") class PNGInfoRequest(BaseModel): image: str = Field(title="Image", description="The base64 encoded PNG image") class PNGInfoResponse(BaseModel): info: str = Field(title="Image info", description="A string with the parameters used to generate the image") items: dict = Field(title="Items", description="An object containing all the info the image had") class ProgressRequest(BaseModel): skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization") class ProgressResponse(BaseModel): progress: float = Field(title="Progress", description="The progress with a range of 0 to 1") eta_relative: float = Field(title="ETA in secs") state: dict = Field(title="State", description="The current state snapshot") 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.") textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.") class InterrogateRequest(BaseModel): image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.") model: str = Field(default="clip", title="Model", description="The interrogate model used.") class InterrogateResponse(BaseModel): caption: str = Field(default=None, title="Caption", description="The generated caption for the image.") class TrainResponse(BaseModel): info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.") class CreateResponse(BaseModel): info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.") class PreprocessResponse(BaseModel): info: str = Field(title="Preprocess info", description="Response string from preprocessing task.") fields = {} for key, metadata in opts.data_labels.items(): value = opts.data.get(key) optType = opts.typemap.get(type(metadata.default), type(value)) if (metadata is not None): fields.update({key: (Optional[optType], Field( default=metadata.default ,description=metadata.label))}) else: fields.update({key: (Optional[optType], Field())}) OptionsModel = create_model("Options", **fields) flags = {} _options = vars(parser)['_option_string_actions'] for key in _options: if(_options[key].dest != 'help'): flag = _options[key] _type = str if _options[key].default is not None: _type = type(_options[key].default) flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))}) FlagsModel = create_model("Flags", **flags) class SamplerItem(BaseModel): name: str = Field(title="Name") aliases: List[str] = Field(title="Aliases") options: Dict[str, str] = Field(title="Options") class UpscalerItem(BaseModel): name: str = Field(title="Name") model_name: Optional[str] = Field(title="Model Name") model_path: Optional[str] = Field(title="Path") model_url: Optional[str] = Field(title="URL") scale: Optional[float] = Field(title="Scale") class LatentUpscalerModeItem(BaseModel): name: str = Field(title="Name") class SDModelItem(BaseModel): title: str = Field(title="Title") model_name: str = Field(title="Model Name") hash: Optional[str] = Field(title="Short hash") sha256: Optional[str] = Field(title="sha256 hash") filename: str = Field(title="Filename") config: Optional[str] = Field(title="Config file") class SDVaeItem(BaseModel): model_name: str = Field(title="Model Name") filename: str = Field(title="Filename") class HypernetworkItem(BaseModel): name: str = Field(title="Name") path: Optional[str] = Field(title="Path") class FaceRestorerItem(BaseModel): name: str = Field(title="Name") cmd_dir: Optional[str] = Field(title="Path") class RealesrganItem(BaseModel): name: str = Field(title="Name") path: Optional[str] = Field(title="Path") scale: Optional[int] = Field(title="Scale") class PromptStyleItem(BaseModel): name: str = Field(title="Name") prompt: Optional[str] = Field(title="Prompt") negative_prompt: Optional[str] = Field(title="Negative Prompt") class EmbeddingItem(BaseModel): step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available") sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available") 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") shape: int = Field(title="Shape", description="The length of each individual vector in the embedding") vectors: int = Field(title="Vectors", description="The number of vectors in the embedding") class EmbeddingsResponse(BaseModel): loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model") skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)") class MemoryResponse(BaseModel): ram: dict = Field(title="RAM", description="System memory stats") cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats") class ScriptsList(BaseModel): txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)") img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)") class ScriptArg(BaseModel): label: str = Field(default=None, title="Label", description="Name of the argument in UI") value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument") minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI") maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI") step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI") choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument") class ScriptInfo(BaseModel): name: str = Field(default=None, title="Name", description="Script name") is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script") is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script") args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")