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[Bugfix][API] - Fix API response for colab users

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Stephen 2022-10-24 11:16:07 -04:00 committed by AUTOMATIC1111
parent cbb857b675
commit db9ab1a46b
2 changed files with 19 additions and 8 deletions
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17
modules/api/api.py
View file

@ -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

10
modules/api/models.py
View file

@ -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()