diff options
| -rw-r--r-- | modules/api/api.py | 17 | ||||
| -rw-r--r-- | modules/api/models.py | 10 |
2 files changed, 19 insertions, 8 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index a860a964..ba890243 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -7,6 +7,7 @@ import uvicorn from fastapi import Body, APIRouter, HTTPException from fastapi.responses import JSONResponse from pydantic import BaseModel, Field, Json +from typing import List import json import io import base64 @@ -15,12 +16,12 @@ from PIL import Image sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) class TextToImageResponse(BaseModel): - images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: Json info: Json class ImageToImageResponse(BaseModel): - images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: Json info: Json @@ -41,6 +42,9 @@ class Api: # convert base64 to PIL image return Image.open(io.BytesIO(imgdata)) + def __processed_info_to_json(self, processed): + return json.dumps(processed.info) + def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): sampler_index = sampler_to_index(txt2imgreq.sampler_index) @@ -65,7 +69,7 @@ class Api: i.save(buffer, format="png") b64images.append(base64.b64encode(buffer.getvalue())) - return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info)) + return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js()) @@ -111,7 +115,12 @@ class Api: i.save(buffer, format="png") b64images.append(base64.b64encode(buffer.getvalue())) - return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info)) + if (not img2imgreq.include_init_images): + # remove img2imgreq.init_images and img2imgreq.mask + img2imgreq.init_images = None + img2imgreq.mask = None + + return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js()) def extrasapi(self): raise NotImplementedError diff --git a/modules/api/models.py b/modules/api/models.py index f551fa35..c6d43606 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -31,6 +31,7 @@ class ModelDef(BaseModel): field_alias: str field_type: Any field_value: Any + field_exclude: bool = False class PydanticModelGenerator: @@ -68,7 +69,7 @@ class PydanticModelGenerator: field=underscore(k), field_alias=k, field_type=field_type_generator(k, v), - field_value=v.default + field_value=v.default, ) for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED ] @@ -78,7 +79,8 @@ class PydanticModelGenerator: field=underscore(fields["key"]), field_alias=fields["key"], field_type=fields["type"], - field_value=fields["default"])) + field_value=fields["default"], + field_exclude=fields["exclude"] if "exclude" in fields else False)) def generate_model(self): """ @@ -86,7 +88,7 @@ class PydanticModelGenerator: from the json and overrides provided at initialization """ fields = { - d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def + 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 @@ -102,5 +104,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( 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": "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()
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