imaginAIry/imaginairy/http/stablestudio/models.py

124 lines
3.5 KiB
Python

from datetime import datetime
from typing import List, Optional
from pydantic import BaseModel, Extra, Field, HttpUrl, validator
from imaginairy.http.utils import Base64Bytes
from imaginairy.schema import ImaginePrompt
class StableStudioPrompt(BaseModel):
text: Optional[str] = None
weight: Optional[float] = Field(None, ge=-1, le=1)
class StableStudioModel(BaseModel):
id: str
name: Optional[str] = None
description: Optional[str] = None
image: Optional[HttpUrl] = None
class StableStudioStyle(BaseModel):
id: str
name: Optional[str] = None
description: Optional[str] = None
image: Optional[HttpUrl] = None
class StableStudioSampler(BaseModel):
id: str
name: Optional[str] = None
class StableStudioInputImage(BaseModel):
blob: Optional[Base64Bytes] = None
weight: Optional[float] = Field(None, ge=0, le=1)
class StableStudioImage(BaseModel):
id: str
created_at: Optional[datetime] = None
input: Optional["StableStudioInput"] = None
blob: Optional[Base64Bytes] = None
class StableStudioImages(BaseModel):
id: str
exclusive_start_image_id: Optional[str] = None
images: Optional[List[StableStudioImage]] = None
class StableStudioInput(BaseModel, extra=Extra.forbid):
prompts: Optional[List[StableStudioPrompt]] = None
model: Optional[str] = None
style: Optional[str] = None
width: Optional[int] = None
height: Optional[int] = None
sampler: Optional[StableStudioSampler] = None
cfg_scale: Optional[float] = Field(None, alias="cfgScale")
steps: Optional[int] = None
seed: Optional[int] = None
mask_image: Optional[StableStudioInputImage] = Field(None, alias="maskImage")
initial_image: Optional[StableStudioInputImage] = Field(None, alias="initialImage")
@validator("seed")
def validate_seed(cls, v): # noqa
if v == 0:
return None
return v
def to_imagine_prompt(self):
"""Converts this StableStudioInput to an ImaginePrompt."""
from io import BytesIO
from PIL import Image
if self.prompts:
positive_prompt = self.prompts[0].text
else:
positive_prompt = None
if self.prompts and len(self.prompts) > 1:
negative_prompt = self.prompts[1].text if len(self.prompts) > 1 else None
else:
negative_prompt = None
init_image = None
init_image_strength = None
if self.initial_image:
init_image = self.initial_image.blob
init_image_strength = self.initial_image.weight
mask_image = self.mask_image.blob if self.mask_image else None
sampler_type = self.sampler.id if self.sampler else None
return ImaginePrompt(
prompt=positive_prompt,
prompt_strength=self.cfg_scale,
negative_prompt=negative_prompt,
model=self.model,
sampler_type=sampler_type,
seed=self.seed,
steps=self.steps,
height=self.height,
width=self.width,
init_image=Image.open(BytesIO(init_image)) if init_image else None,
init_image_strength=init_image_strength,
mask_image=Image.open(BytesIO(mask_image)) if mask_image else None,
mask_mode="keep",
)
class StableStudioBatchRequest(BaseModel):
input: StableStudioInput
count: int = 1
class StableStudioBatchResponse(BaseModel):
images: List[StableStudioImage]
StableStudioInput.model_rebuild()
StableStudioImage.model_rebuild()