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Add BFL Kontext API Nodes. (#8333)
* Added initial Flux.1 Kontext Pro Image node - recreated branch to save myself sanity from rebase crap after master got rebased * Add safety filter to Kontext. * Make safety = 2 and input image is optional. * Add BFL kontext API nodes. --------- Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
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Robin Huang
GitHub
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f1c9ca816a
@ -108,6 +108,40 @@ class BFLFluxProGenerateRequest(BaseModel):
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# )
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class BFLFluxKontextProGenerateRequest(BaseModel):
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prompt: str = Field(..., description='The text prompt for what you wannt to edit.')
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input_image: Optional[str] = Field(None, description='Image to edit in base64 format')
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seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
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guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process')
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steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process')
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safety_tolerance: Optional[conint(ge=0, le=2)] = Field(
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2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.'
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)
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output_format: Optional[BFLOutputFormat] = Field(
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BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
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)
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aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.')
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prompt_upsampling: Optional[bool] = Field(
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None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
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)
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class BFLFluxKontextMaxGenerateRequest(BaseModel):
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prompt: str = Field(..., description='The text prompt for what you wannt to edit.')
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input_image: Optional[str] = Field(None, description='Image to edit in base64 format')
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seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
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guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process')
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steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process')
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safety_tolerance: Optional[conint(ge=0, le=2)] = Field(
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2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.'
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)
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output_format: Optional[BFLOutputFormat] = Field(
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BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png']
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)
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aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.')
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prompt_upsampling: Optional[bool] = Field(
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None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.'
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)
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class BFLFluxProUltraGenerateRequest(BaseModel):
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prompt: str = Field(..., description='The text prompt for image generation.')
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prompt_upsampling: Optional[bool] = Field(
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@ -1,6 +1,6 @@
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import io
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from inspect import cleandoc
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from typing import Union
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from typing import Union, Optional
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from comfy.comfy_types.node_typing import IO, ComfyNodeABC
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from comfy_api_nodes.apis.bfl_api import (
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BFLStatus,
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@ -9,6 +9,7 @@ from comfy_api_nodes.apis.bfl_api import (
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BFLFluxCannyImageRequest,
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BFLFluxDepthImageRequest,
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BFLFluxProGenerateRequest,
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BFLFluxKontextProGenerateRequest,
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BFLFluxProUltraGenerateRequest,
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BFLFluxProGenerateResponse,
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)
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@ -269,6 +270,287 @@ class FluxProUltraImageNode(ComfyNodeABC):
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return (output_image,)
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class FluxKontextProImageNode(ComfyNodeABC):
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"""
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Edits images using Flux.1 Kontext Pro via api based on prompt and resolution.
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"""
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MINIMUM_RATIO = 1 / 4
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MAXIMUM_RATIO = 4 / 1
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MINIMUM_RATIO_STR = "1:4"
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MAXIMUM_RATIO_STR = "4:1"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"prompt": (
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IO.STRING,
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{
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"multiline": True,
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"default": "",
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"tooltip": "Prompt for the image generation - specify what and how to edit.",
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},
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),
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"aspect_ratio": (
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IO.STRING,
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{
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"default": "16:9",
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"tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
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},
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),
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"guidance": (
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IO.FLOAT,
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{
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"default": 3.0,
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"min": 0.1,
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"max": 99.0,
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"step": 0.1,
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"tooltip": "Guidance strength for the image generation process"
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},
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),
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"steps": (
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IO.INT,
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{
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"default": 50,
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"min": 1,
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"max": 150,
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"tooltip": "Number of steps for the image generation process"
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},
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),
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"seed": (
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IO.INT,
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{
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"default": 0,
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"min": 0,
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"max": 0xFFFFFFFFFFFFFFFF,
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"control_after_generate": True,
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"tooltip": "The random seed used for creating the noise.",
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},
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),
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"prompt_upsampling": (
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IO.BOOLEAN,
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{
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"default": False,
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"tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
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},
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),
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},
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"optional": {
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"input_image": (IO.IMAGE,),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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"comfy_api_key": "API_KEY_COMFY_ORG",
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"unique_id": "UNIQUE_ID",
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},
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}
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@classmethod
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def VALIDATE_INPUTS(cls, aspect_ratio: str):
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try:
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validate_aspect_ratio(
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aspect_ratio,
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minimum_ratio=cls.MINIMUM_RATIO,
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maximum_ratio=cls.MAXIMUM_RATIO,
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minimum_ratio_str=cls.MINIMUM_RATIO_STR,
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maximum_ratio_str=cls.MAXIMUM_RATIO_STR,
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)
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except Exception as e:
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return str(e)
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return True
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RETURN_TYPES = (IO.IMAGE,)
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DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
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FUNCTION = "api_call"
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API_NODE = True
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CATEGORY = "api node/image/BFL"
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def api_call(
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self,
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prompt: str,
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aspect_ratio: str,
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guidance: float,
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steps: int,
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input_image: Optional[torch.Tensor]=None,
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seed=0,
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prompt_upsampling=False,
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unique_id: Union[str, None] = None,
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**kwargs,
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):
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if input_image is None:
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validate_string(prompt, strip_whitespace=False)
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/proxy/bfl/flux-kontext-pro/generate",
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method=HttpMethod.POST,
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request_model=BFLFluxKontextProGenerateRequest,
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response_model=BFLFluxProGenerateResponse,
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),
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request=BFLFluxKontextProGenerateRequest(
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prompt=prompt,
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prompt_upsampling=prompt_upsampling,
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guidance=round(guidance, 1),
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steps=steps,
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seed=seed,
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aspect_ratio=validate_aspect_ratio(
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aspect_ratio,
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minimum_ratio=self.MINIMUM_RATIO,
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maximum_ratio=self.MAXIMUM_RATIO,
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minimum_ratio_str=self.MINIMUM_RATIO_STR,
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maximum_ratio_str=self.MAXIMUM_RATIO_STR,
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),
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input_image=(
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input_image
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if input_image is None
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else convert_image_to_base64(input_image)
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)
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),
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auth_kwargs=kwargs,
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)
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output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
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return (output_image,)
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class FluxKontextMaxImageNode(ComfyNodeABC):
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"""
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Edits images using Flux.1 Kontext Max via api based on prompt and resolution.
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"""
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MINIMUM_RATIO = 1 / 4
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MAXIMUM_RATIO = 4 / 1
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MINIMUM_RATIO_STR = "1:4"
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MAXIMUM_RATIO_STR = "4:1"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"prompt": (
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IO.STRING,
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{
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"multiline": True,
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"default": "",
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"tooltip": "Prompt for the image generation - specify what and how to edit.",
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},
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),
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"aspect_ratio": (
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IO.STRING,
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{
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"default": "16:9",
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"tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
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},
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),
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"guidance": (
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IO.FLOAT,
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{
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"default": 3.0,
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"min": 0.1,
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"max": 99.0,
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"step": 0.1,
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"tooltip": "Guidance strength for the image generation process"
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},
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),
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"steps": (
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IO.INT,
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{
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"default": 50,
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"min": 1,
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"max": 150,
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"tooltip": "Number of steps for the image generation process"
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},
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),
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"seed": (
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IO.INT,
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{
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"default": 0,
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"min": 0,
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"max": 0xFFFFFFFFFFFFFFFF,
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"control_after_generate": True,
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"tooltip": "The random seed used for creating the noise.",
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},
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),
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"prompt_upsampling": (
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IO.BOOLEAN,
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{
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"default": False,
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"tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
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},
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),
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},
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"optional": {
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"input_image": (IO.IMAGE,),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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"comfy_api_key": "API_KEY_COMFY_ORG",
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"unique_id": "UNIQUE_ID",
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},
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}
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@classmethod
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def VALIDATE_INPUTS(cls, aspect_ratio: str):
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try:
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validate_aspect_ratio(
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aspect_ratio,
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minimum_ratio=cls.MINIMUM_RATIO,
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maximum_ratio=cls.MAXIMUM_RATIO,
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minimum_ratio_str=cls.MINIMUM_RATIO_STR,
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maximum_ratio_str=cls.MAXIMUM_RATIO_STR,
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)
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except Exception as e:
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return str(e)
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return True
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RETURN_TYPES = (IO.IMAGE,)
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DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
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FUNCTION = "api_call"
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API_NODE = True
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CATEGORY = "api node/image/BFL"
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def api_call(
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self,
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prompt: str,
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aspect_ratio: str,
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guidance: float,
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steps: int,
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input_image: Optional[torch.Tensor]=None,
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seed=0,
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prompt_upsampling=False,
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unique_id: Union[str, None] = None,
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**kwargs,
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):
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if input_image is None:
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validate_string(prompt, strip_whitespace=False)
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/proxy/bfl/flux-kontext-max/generate",
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method=HttpMethod.POST,
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request_model=BFLFluxKontextProGenerateRequest,
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response_model=BFLFluxProGenerateResponse,
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),
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request=BFLFluxKontextProGenerateRequest(
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prompt=prompt,
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prompt_upsampling=prompt_upsampling,
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guidance=round(guidance, 1),
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steps=steps,
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seed=seed,
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aspect_ratio=validate_aspect_ratio(
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aspect_ratio,
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minimum_ratio=self.MINIMUM_RATIO,
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maximum_ratio=self.MAXIMUM_RATIO,
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minimum_ratio_str=self.MINIMUM_RATIO_STR,
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maximum_ratio_str=self.MAXIMUM_RATIO_STR,
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),
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input_image=(
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input_image
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if input_image is None
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else convert_image_to_base64(input_image)
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)
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),
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auth_kwargs=kwargs,
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)
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output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
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return (output_image,)
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class FluxProImageNode(ComfyNodeABC):
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"""
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@ -914,6 +1196,8 @@ class FluxProDepthNode(ComfyNodeABC):
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NODE_CLASS_MAPPINGS = {
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"FluxProUltraImageNode": FluxProUltraImageNode,
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# "FluxProImageNode": FluxProImageNode,
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"FluxKontextProImageNode": FluxKontextProImageNode,
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"FluxKontextMaxImageNode": FluxKontextMaxImageNode,
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"FluxProExpandNode": FluxProExpandNode,
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"FluxProFillNode": FluxProFillNode,
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"FluxProCannyNode": FluxProCannyNode,
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@ -924,6 +1208,8 @@ NODE_CLASS_MAPPINGS = {
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NODE_DISPLAY_NAME_MAPPINGS = {
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"FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image",
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# "FluxProImageNode": "Flux 1.1 [pro] Image",
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"FluxKontextProImageNode": "Flux.1 Kontext Pro Image",
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"FluxKontextMaxImageNode": "Flux.1 Kontext Max Image",
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"FluxProExpandNode": "Flux.1 Expand Image",
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"FluxProFillNode": "Flux.1 Fill Image",
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"FluxProCannyNode": "Flux.1 Canny Control Image",
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