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from __future__ import annotations
from enum import Enum
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field, confloat, conint
class BFLOutputFormat(str, Enum):
png = 'png'
jpeg = 'jpeg'
class BFLFluxExpandImageRequest(BaseModel):
prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.')
prompt_upsampling: Optional[bool] = Field(
None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
)
seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
top: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the top of the image')
bottom: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the bottom of the image')
left: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the left side of the image')
right: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the right side of the image')
steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process')
guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process')
safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.'
)
output_format: Optional[BFLOutputFormat] = Field(
BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
)
image: str = Field(None, description='A Base64-encoded string representing the image you wish to expand')
class BFLFluxFillImageRequest(BaseModel):
prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.')
prompt_upsampling: Optional[bool] = Field(
None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
)
seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process')
guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process')
safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.'
)
output_format: Optional[BFLOutputFormat] = Field(
BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
)
image: str = Field(None, description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.')
mask: str = Field(None, description='A Base64-encoded string representing the mask of the areas you with to modify.')
class BFLFluxProGenerateRequest(BaseModel):
prompt: str = Field(..., description='The text prompt for image generation.')
prompt_upsampling: Optional[bool] = Field(
None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
)
seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
width: conint(ge=256, le=1440) = Field(1024, description='Width of the generated image in pixels. Must be a multiple of 32.')
height: conint(ge=256, le=1440) = Field(768, description='Height of the generated image in pixels. Must be a multiple of 32.')
safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.'
)
output_format: Optional[BFLOutputFormat] = Field(
BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
)
image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format')
# image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field(
# None, description='Blend between the prompt and the image prompt.'
# )
class BFLFluxKontextProGenerateRequest(BaseModel):
prompt: str = Field(..., description='The text prompt for what you wannt to edit.')
input_image: Optional[str] = Field(None, description='Image to edit in base64 format')
seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process')
steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process')
safety_tolerance: Optional[conint(ge=0, le=2)] = Field(
2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.'
)
output_format: Optional[BFLOutputFormat] = Field(
BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
)
aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.')
prompt_upsampling: Optional[bool] = Field(
None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
)
class BFLFluxProUltraGenerateRequest(BaseModel):
prompt: str = Field(..., description='The text prompt for image generation.')
prompt_upsampling: Optional[bool] = Field(
None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
)
seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.')
safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.'
)
output_format: Optional[BFLOutputFormat] = Field(
BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
)
raw: Optional[bool] = Field(None, description='Generate less processed, more natural-looking images.')
image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format')
image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field(
None, description='Blend between the prompt and the image prompt.'
)
class BFLFluxProGenerateResponse(BaseModel):
id: str = Field(..., description='The unique identifier for the generation task.')
polling_url: str = Field(..., description='URL to poll for the generation result.')
class BFLStatus(str, Enum):
task_not_found = "Task not found"
pending = "Pending"
request_moderated = "Request Moderated"
content_moderated = "Content Moderated"
ready = "Ready"
error = "Error"
class BFLFluxStatusResponse(BaseModel):
id: str = Field(..., description="The unique identifier for the generation task.")
status: BFLStatus = Field(..., description="The status of the task.")
result: Optional[Dict[str, Any]] = Field(None, description="The result of the task (null if not completed).")
progress: Optional[float] = Field(None, description="The progress of the task (0.0 to 1.0).", ge=0.0, le=1.0)
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