mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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108 lines
3.8 KiB
Python
108 lines
3.8 KiB
Python
from array import array
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from inflection import underscore
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, Field, create_model
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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import inspect
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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"}]
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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}]
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).generate_model() |