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Move processing's models into models.py

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It didn't make sense to have two differente files for the same and
"models" is a more descriptive name.
This commit is contained in:
Bruno Seoane 2022-10-23 15:35:49 -03:00
parent e0ca4dfbc1
commit 866b36d705
3 changed files with 120 additions and 157 deletions
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59
modules/api/api.py
View file

@ -1,16 +1,11 @@
from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI import uvicorn
from gradio import processing_utils
from fastapi import APIRouter, HTTPException
import modules.shared as shared
from modules.api.models import *
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.sd_samplers import all_samplers from modules.sd_samplers import all_samplers
import modules.shared as shared
import uvicorn
from fastapi import APIRouter, HTTPException
import json
import io
import base64
from modules.api.models import *
from PIL import Image
from modules.extras import run_extras from modules.extras import run_extras
from gradio import processing_utils
def upscaler_to_index(name: str): def upscaler_to_index(name: str):
try: try:
@ -20,29 +15,6 @@ def upscaler_to_index(name: str):
sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
# def img_to_base64(img: str):
# buffer = io.BytesIO()
# img.save(buffer, format="png")
# return base64.b64encode(buffer.getvalue())
# def base64_to_bytes(base64Img: str):
# if "," in base64Img:
# base64Img = base64Img.split(",")[1]
# return io.BytesIO(base64.b64decode(base64Img))
# def base64_to_images(base64Imgs: list[str]):
# imgs = []
# for img in base64Imgs:
# img = Image.open(base64_to_bytes(img))
# imgs.append(img)
# return imgs
class ImageToImageResponse(BaseModel):
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: dict
info: str
class Api: class Api:
def __init__(self, app, queue_lock): def __init__(self, app, queue_lock):
self.router = APIRouter() self.router = APIRouter()
@ -51,15 +23,7 @@ class Api:
self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse) self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse) self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse) self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
self.app.add_api_route("/sdapi/v1/extra-batch-image", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse) self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
# def __base64_to_image(self, base64_string):
# # if has a comma, deal with prefix
# if "," in base64_string:
# base64_string = base64_string.split(",")[1]
# imgdata = base64.b64decode(base64_string)
# # convert base64 to PIL image
# return Image.open(io.BytesIO(imgdata))
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
sampler_index = sampler_to_index(txt2imgreq.sampler_index) sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@ -81,7 +45,7 @@ class Api:
b64images = list(map(processing_utils.encode_pil_to_base64, processed.images)) b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.info) return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.info)
def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
sampler_index = sampler_to_index(img2imgreq.sampler_index) sampler_index = sampler_to_index(img2imgreq.sampler_index)
@ -120,10 +84,7 @@ class Api:
processed = process_images(p) processed = process_images(p)
b64images = list(map(processing_utils.encode_pil_to_base64, processed.images)) b64images = list(map(processing_utils.encode_pil_to_base64, processed.images))
# for i in processed.images:
# buffer = io.BytesIO()
# i.save(buffer, format="png")
# b64images.append(base64.b64encode(buffer.getvalue()))
return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.info) return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.info)
def extras_single_image_api(self, req: ExtrasSingleImageRequest): def extras_single_image_api(self, req: ExtrasSingleImageRequest):
@ -134,12 +95,12 @@ class Api:
reqDict.pop('upscaler_1') reqDict.pop('upscaler_1')
reqDict.pop('upscaler_2') reqDict.pop('upscaler_2')
reqDict['image'] = processing_utils.decode_base64_to_file(reqDict['image']) reqDict['image'] = processing_utils.decode_base64_to_image(reqDict['image'])
with self.queue_lock: with self.queue_lock:
result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="") result = run_extras(**reqDict, extras_upscaler_1=upscaler1Index, extras_upscaler_2=upscaler2Index, extras_mode=0, image_folder="", input_dir="", output_dir="")
return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0]), html_info_x=result[1], html_info=result[2]) return ExtrasSingleImageResponse(image=processing_utils.encode_pil_to_base64(result[0][0]), html_info_x=result[1], html_info=result[2])
def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
upscaler1Index = upscaler_to_index(req.upscaler_1) upscaler1Index = upscaler_to_index(req.upscaler_1)

112
modules/api/models.py
View file

@ -1,10 +1,118 @@
from pydantic import BaseModel, Field, Json import inspect
from pydantic import BaseModel, Field, Json, create_model
from typing import Any, Optional
from typing_extensions import Literal from typing_extensions import Literal
from inflection import underscore
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
from modules.shared import sd_upscalers from modules.shared import sd_upscalers
API_NOT_ALLOWED = [
"self",
"kwargs",
"sd_model",
"outpath_samples",
"outpath_grids",
"sampler_index",
"do_not_save_samples",
"do_not_save_grid",
"extra_generation_params",
"overlay_images",
"do_not_reload_embeddings",
"seed_enable_extras",
"prompt_for_display",
"sampler_noise_scheduler_override",
"ddim_discretize"
]
class ModelDef(BaseModel):
"""Assistance Class for Pydantic Dynamic Model Generation"""
field: str
field_alias: str
field_type: Any
field_value: Any
class PydanticModelGenerator:
"""
Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
source_data is a snapshot of the default values produced by the class
params are the names of the actual keys required by __init__
"""
def __init__(
self,
model_name: str = 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"]
# 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 = [
ModelDef(
field=underscore(k),
field_alias=k,
field_type=field_type_generator(k, v),
field_value=v.default
)
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
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
}
DynamicModel = create_model(self._model_name, **fields)
DynamicModel.__config__.allow_population_by_field_name = True
DynamicModel.__config__.allow_mutation = True
return DynamicModel
StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
"StableDiffusionProcessingTxt2Img",
StableDiffusionProcessingTxt2Img,
[{"key": "sampler_index", "type": str, "default": "Euler"}]
).generate_model()
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}]
).generate_model()
class TextToImageResponse(BaseModel): 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: str 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 info: str
class ExtrasBaseRequest(BaseModel): class ExtrasBaseRequest(BaseModel):

106
modules/api/processing.py
View file

@ -1,106 +0,0 @@
from array import array
from inflection import underscore
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field, create_model
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
import inspect
API_NOT_ALLOWED = [
"self",
"kwargs",
"sd_model",
"outpath_samples",
"outpath_grids",
"sampler_index",
"do_not_save_samples",
"do_not_save_grid",
"extra_generation_params",
"overlay_images",
"do_not_reload_embeddings",
"seed_enable_extras",
"prompt_for_display",
"sampler_noise_scheduler_override",
"ddim_discretize"
]
class ModelDef(BaseModel):
"""Assistance Class for Pydantic Dynamic Model Generation"""
field: str
field_alias: str
field_type: Any
field_value: Any
class PydanticModelGenerator:
"""
Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
source_data is a snapshot of the default values produced by the class
params are the names of the actual keys required by __init__
"""
def __init__(
self,
model_name: str = 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"]
# 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 = [
ModelDef(
field=underscore(k),
field_alias=k,
field_type=field_type_generator(k, v),
field_value=v.default
)
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
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
}
DynamicModel = create_model(self._model_name, **fields)
DynamicModel.__config__.allow_population_by_field_name = True
DynamicModel.__config__.allow_mutation = True
return DynamicModel
StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
"StableDiffusionProcessingTxt2Img",
StableDiffusionProcessingTxt2Img,
[{"key": "sampler_index", "type": str, "default": "Euler"}]
).generate_model()
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}]
).generate_model()