263 lines
12 KiB
Python
263 lines
12 KiB
Python
import inspect
|
|
from pydantic import BaseModel, Field, create_model
|
|
from typing import Any, Optional
|
|
from typing_extensions import Literal
|
|
from inflection import underscore
|
|
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
|
|
from modules.shared import sd_upscalers, opts, parser
|
|
from typing import Dict, List
|
|
|
|
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
|
|
field_exclude: bool = False
|
|
|
|
|
|
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"],
|
|
field_exclude=fields["exclude"] if "exclude" in fields else False))
|
|
|
|
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, 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
|
|
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}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
|
|
).generate_model()
|
|
|
|
class TextToImageResponse(BaseModel):
|
|
images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
|
|
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
|
|
|
|
class ExtrasBaseRequest(BaseModel):
|
|
resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
|
|
show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
|
|
gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
|
|
codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
|
|
codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
|
|
upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=8, description="By how much to upscale the image, only used when resize_mode=0.")
|
|
upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
|
|
upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
|
|
upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
|
|
upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
|
|
upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
|
|
extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
|
|
upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
|
|
|
|
class ExtraBaseResponse(BaseModel):
|
|
html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
|
|
|
|
class ExtrasSingleImageRequest(ExtrasBaseRequest):
|
|
image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
|
|
|
|
class ExtrasSingleImageResponse(ExtraBaseResponse):
|
|
image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
|
|
|
|
class FileData(BaseModel):
|
|
data: str = Field(title="File data", description="Base64 representation of the file")
|
|
name: str = Field(title="File name")
|
|
|
|
class ExtrasBatchImagesRequest(ExtrasBaseRequest):
|
|
imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
|
|
|
|
class ExtrasBatchImagesResponse(ExtraBaseResponse):
|
|
images: List[str] = Field(title="Images", description="The generated images in base64 format.")
|
|
|
|
class PNGInfoRequest(BaseModel):
|
|
image: str = Field(title="Image", description="The base64 encoded PNG image")
|
|
|
|
class PNGInfoResponse(BaseModel):
|
|
info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
|
|
items: dict = Field(title="Items", description="An object containing all the info the image had")
|
|
|
|
class ProgressRequest(BaseModel):
|
|
skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
|
|
|
|
class ProgressResponse(BaseModel):
|
|
progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
|
|
eta_relative: float = Field(title="ETA in secs")
|
|
state: dict = Field(title="State", description="The current state snapshot")
|
|
current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
|
|
|
|
class InterrogateRequest(BaseModel):
|
|
image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
|
|
model: str = Field(default="clip", title="Model", description="The interrogate model used.")
|
|
|
|
class InterrogateResponse(BaseModel):
|
|
caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
|
|
|
|
class TrainResponse(BaseModel):
|
|
info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
|
|
|
|
class CreateResponse(BaseModel):
|
|
info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
|
|
|
|
class PreprocessResponse(BaseModel):
|
|
info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
|
|
|
|
fields = {}
|
|
for key, metadata in opts.data_labels.items():
|
|
value = opts.data.get(key)
|
|
optType = opts.typemap.get(type(metadata.default), type(value))
|
|
|
|
if (metadata is not None):
|
|
fields.update({key: (Optional[optType], Field(
|
|
default=metadata.default ,description=metadata.label))})
|
|
else:
|
|
fields.update({key: (Optional[optType], Field())})
|
|
|
|
OptionsModel = create_model("Options", **fields)
|
|
|
|
flags = {}
|
|
_options = vars(parser)['_option_string_actions']
|
|
for key in _options:
|
|
if(_options[key].dest != 'help'):
|
|
flag = _options[key]
|
|
_type = str
|
|
if _options[key].default is not None: _type = type(_options[key].default)
|
|
flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
|
|
|
|
FlagsModel = create_model("Flags", **flags)
|
|
|
|
class SamplerItem(BaseModel):
|
|
name: str = Field(title="Name")
|
|
aliases: List[str] = Field(title="Aliases")
|
|
options: Dict[str, str] = Field(title="Options")
|
|
|
|
class UpscalerItem(BaseModel):
|
|
name: str = Field(title="Name")
|
|
model_name: Optional[str] = Field(title="Model Name")
|
|
model_path: Optional[str] = Field(title="Path")
|
|
model_url: Optional[str] = Field(title="URL")
|
|
|
|
class SDModelItem(BaseModel):
|
|
title: str = Field(title="Title")
|
|
model_name: str = Field(title="Model Name")
|
|
hash: str = Field(title="Hash")
|
|
filename: str = Field(title="Filename")
|
|
config: str = Field(title="Config file")
|
|
|
|
class HypernetworkItem(BaseModel):
|
|
name: str = Field(title="Name")
|
|
path: Optional[str] = Field(title="Path")
|
|
|
|
class FaceRestorerItem(BaseModel):
|
|
name: str = Field(title="Name")
|
|
cmd_dir: Optional[str] = Field(title="Path")
|
|
|
|
class RealesrganItem(BaseModel):
|
|
name: str = Field(title="Name")
|
|
path: Optional[str] = Field(title="Path")
|
|
scale: Optional[int] = Field(title="Scale")
|
|
|
|
class PromptStyleItem(BaseModel):
|
|
name: str = Field(title="Name")
|
|
prompt: Optional[str] = Field(title="Prompt")
|
|
negative_prompt: Optional[str] = Field(title="Negative Prompt")
|
|
|
|
class ArtistItem(BaseModel):
|
|
name: str = Field(title="Name")
|
|
score: float = Field(title="Score")
|
|
category: str = Field(title="Category")
|
|
|
|
class EmbeddingItem(BaseModel):
|
|
step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
|
|
sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
|
|
sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
|
|
shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
|
|
vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
|
|
|
|
class EmbeddingsResponse(BaseModel):
|
|
loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
|
|
skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
|