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from datetime import datetime
from pydantic import BaseModel
class Prompt(BaseModel):
id: int
prompt: str
created_at: datetime
user_id: int
class Config:
from_attributes = True
class UserBase(BaseModel):
pass
class UserCreate(UserBase):
username: str
password: str
class UserUpdate(UserBase):
user_id: int
is_active: bool = True
is_superuser: bool = False
class User(UserBase):
user_id: int
username: str
is_active: bool
is_superuser: bool
created_at: datetime
updated_at: datetime
prompts: list[Prompt] = []
class Config:
from_attributes = True
class Generate(BaseModel):
seed: int | None = None
negative_prompt : str = "ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, bad anatomy, watermark, signature, cut off, low contrast, underexposed, overexposed, bad art, beginner, amateur, distorted face"
medium: str | None = ""
style: str | None = ""
artist: str | None = ""
website: str | None = ""
resolution: str | None = ""
additional_details: str | None = ""
color: str | None = ""
lightning: str | None = ""
class TextImage(Generate):
prompt: str
num_inference_steps: int = 4
guidance_scale: float = 2.0
class ImageImage(Generate):
prompt: str
image: str
num_inference_steps: int = 50
guidance_scale: float = 7.5
image_guidance_scale: float = 1.5
class BackgroundRemoval(BaseModel):
image: str
class ImageVariations(Generate):
image: str
num_samples: int = 4
num_inference_steps: int = 50
prompt: str | None = ""
scale: float = 0.5
guidance_scale: float = 7.5
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