diff options
Diffstat (limited to 'modules/api/models.py')
| -rw-r--r-- | modules/api/models.py | 92 |
1 files changed, 76 insertions, 16 deletions
diff --git a/modules/api/models.py b/modules/api/models.py index 079e33d9..68fb45c6 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -1,10 +1,11 @@ -from array import array -from inflection import underscore -from typing import Any, Dict, Optional +import inspect +from click import prompt 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 -import inspect - +from modules.shared import sd_upscalers API_NOT_ALLOWED = [ "self", @@ -51,17 +52,17 @@ class PydanticModelGenerator: # 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 = [ @@ -73,11 +74,11 @@ class PydanticModelGenerator: ) 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=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)) @@ -94,15 +95,74 @@ class PydanticModelGenerator: DynamicModel.__config__.allow_population_by_field_name = True DynamicModel.__config__.allow_mutation = True return DynamicModel - + StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( - "StableDiffusionProcessingTxt2Img", + "StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img, [{"key": "sampler_index", "type": str, "default": "Euler"}] ).generate_model() StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( - "StableDiffusionProcessingImg2Img", + "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}] -).generate_model()
\ No newline at end of file +).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=4, 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 choosen 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 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.") |