provide sampler by name

from modules.api.processing import StableDiffusionProcessingAPI

from modules.processing import StableDiffusionProcessingTxt2Img, process_images

from modules.sd_samplers import samplers_k_diffusion

import modules.shared as shared

import uvicorn

from fastapi import Body, APIRouter

from fastapi import Body, APIRouter, HTTPException

from fastapi.responses import JSONResponse

from pydantic import BaseModel, Field, Json

import json

import io

import base64

sampler_to_index = lambda name: next(filter(lambda row: name in row[1][2], enumerate(samplers_k_diffusion)), None)

class TextToImageResponse(BaseModel):

    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")

    parameters: Json

        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])

    def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):

        sampler_index = sampler_to_index(txt2imgreq.sampler_index)

        if sampler_index is None:

            raise HTTPException(status_code=404, detail="Sampler not found") 

        populate = txt2imgreq.copy(update={ # Override __init__ params

            "sd_model": shared.sd_model, 

            "sampler_index": 0,

            "sampler_index": sampler_index[0],

            "do_not_save_samples": True,

            "do_not_save_grid": True

            }

    def __init__(

        self,

        model_name: str = None,

        class_instance = 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"]

            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"]))

    def generate_model(self):

        """

        Creates a pydantic BaseModel

        DynamicModel.__config__.allow_mutation = True

        return DynamicModel

StableDiffusionProcessingAPI = PydanticModelGenerator("StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img).generate_model()

StableDiffusionProcessingAPI = PydanticModelGenerator(

    "StableDiffusionProcessingTxt2Img", 

    StableDiffusionProcessingTxt2Img,

    [{"key": "sampler_index", "type": str, "default": "k_euler_a"}]

).generate_model()