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5de606a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | from enum import Enum
from typing import Any
from pydantic import BaseModel, ConfigDict, Field
class BFLFluxExpandImageRequest(BaseModel):
prompt: str = Field(...)
prompt_upsampling: bool | None = Field(None)
seed: int | None = Field(None)
top: int = Field(...)
bottom: int = Field(...)
left: int = Field(...)
right: int = Field(...)
steps: int = Field(...)
guidance: float = Field(...)
safety_tolerance: int = Field(6)
output_format: str = Field("png")
image: str = Field(None, description="A Base64-encoded string representing the image you wish to expand")
class BFLFluxFillImageRequest(BaseModel):
prompt: str = Field(...)
prompt_upsampling: bool | None = Field(None)
seed: int | None = Field(None)
steps: int = Field(...)
guidance: float = Field(...)
safety_tolerance: int = Field(6)
output_format: str = Field("png")
image: str = Field(
None, description="Base64-encoded string representing the image to modify. Can contain alpha mask if desired.",
)
mask: str = Field(
None, description="Base64-encoded string representing the mask of the areas you wish to modify."
)
class BFLFluxEraseRequest(BaseModel):
image: str = Field(..., description="A Base64-encoded string representing the image to erase from.")
mask: str = Field(
...,
description="A Base64-encoded black/white mask matching the input dimensions; "
"white (255) marks areas to remove, black (0) marks areas to preserve.",
)
dilate_pixels: int = Field(10)
seed: int | None = Field(None)
output_format: str = Field("png")
class BFLFluxVTORequest(BaseModel):
prompt: str = Field(
..., description="Natural-language styling instruction. Required field, but may be an empty string."
)
person: str = Field(..., description="A Base64-encoded string representing the person image.")
garment: str = Field(..., description="A Base64-encoded string representing the garment reference image.")
seed: int | None = Field(None)
safety_tolerance: int = Field(5)
output_format: str = Field("png")
class BFLFluxProGenerateRequest(BaseModel):
prompt: str = Field(...)
prompt_upsampling: bool | None = Field(None)
seed: int | None = Field(None)
width: int = Field(1024, description="Must be a multiple of 32.")
height: int = Field(768, description="Must be a multiple of 32.")
safety_tolerance: int = Field(6)
output_format: str = Field("png")
image_prompt: str | None = Field(None, description="Optional image to remix in base64 format")
class Flux2ProGenerateRequest(BaseModel):
prompt: str = Field(...)
width: int = Field(1024, description="Must be a multiple of 32.")
height: int = Field(768, description="Must be a multiple of 32.")
seed: int | None = Field(None)
prompt_upsampling: bool | None = Field(None)
input_image: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_2: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_3: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_4: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_5: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_6: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_7: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_8: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
input_image_9: str | None = Field(None, description="Base64 encoded image for image-to-image generation")
safety_tolerance: int = Field(5)
output_format: str = Field("png")
class BFLFluxKontextProGenerateRequest(BaseModel):
prompt: str = Field(...)
input_image: str | None = Field(None, description="Image to edit in base64 format")
seed: int | None = Field(None)
guidance: float = Field(...)
steps: int = Field(...)
safety_tolerance: int = Field(2)
output_format: str = Field("png")
aspect_ratio: str | None = Field(None)
prompt_upsampling: bool | None = Field(None)
class BFLFluxProUltraGenerateRequest(BaseModel):
prompt: str = Field(...)
prompt_upsampling: bool | None = Field(None)
seed: int | None = Field(None)
aspect_ratio: str | None = Field(None)
safety_tolerance: int = Field(6)
output_format: str = Field("png")
raw: bool | None = Field(None)
image_prompt: str | None = Field(None, description="Optional image to remix in base64 format")
image_prompt_strength: float | None = Field(None)
class BFLFluxProGenerateResponse(BaseModel):
id: str = Field(...)
polling_url: str = Field(...)
cost: float | None = Field(None, description="Price in cents")
class BFLStatus(str, Enum):
task_not_found = "Task not found"
pending = "Pending"
reasoning = "Reasoning"
generating = "Generating"
request_moderated = "Request Moderated"
content_moderated = "Content Moderated"
ready = "Ready"
error = "Error"
class BFLFluxStatusResponse(BaseModel):
id: str = Field(...)
status: BFLStatus = Field(...)
result: dict[str, Any] | None = Field(None)
progress: float | None = Field(None, ge=0.0, le=1.0)
class Flux3VideoRequest(BaseModel):
"""Fields shared by every generation mode of /v1/flux-3-video."""
model_config = ConfigDict(extra="forbid")
prompt: str = Field(...)
aspect_ratio: str = Field("auto")
duration: int | str = Field("auto", description="Whole seconds, or 'auto'.")
resolution: str = Field("hd", description="'hd' is the 720p class, 'fhd' the 1080p class.")
generate_audio: bool = Field(True)
safety_tolerance: int = Field(2, description="0 is the strictest; conditioned modes cap at 2.")
class Flux3TextToVideoRequest(Flux3VideoRequest):
mode: str = Field("t2v")
class Flux3ImageToVideoRequest(Flux3VideoRequest):
mode: str = Field("i2v")
keyframes: list[str] | list[tuple[float, str]] = Field(
...,
description="Images (URL or base64), or [seconds, image] pairs pinning each to a time.",
)
class Flux3VideoContinuationRequest(Flux3VideoRequest):
mode: str = Field("v2v")
start_video: str = Field(
..., description="MP4 (URL or base64); the new clip carries on from its final frames."
)
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