feat(api-nodes): network client v2: async ops, cancellation, downloads, refactor (#10390)
* feat(api-nodes): implement new API client for V3 nodes * feat(api-nodes): implement new API client for V3 nodes * feat(api-nodes): implement new API client for V3 nodes * converted WAN nodes to use new client; polishing * fix(auth): do not leak authentification for the absolute urls * convert BFL API nodes to use new API client; remove deprecated BFL nodes * converted Google Veo nodes * fix(Veo3.1 model): take into account "generate_audio" parameter
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Alexander Piskun
GitHub
@@ -50,44 +50,6 @@ class BFLFluxFillImageRequest(BaseModel):
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mask: str = Field(None, description='A Base64-encoded string representing the mask of the areas you with to modify.')
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class BFLFluxCannyImageRequest(BaseModel):
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prompt: str = Field(..., description='Text prompt for image generation')
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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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canny_low_threshold: Optional[int] = Field(None, description='Low threshold for Canny edge detection')
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canny_high_threshold: Optional[int] = Field(None, description='High threshold for Canny edge detection')
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seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
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steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process')
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guidance: confloat(ge=1, le=100) = Field(..., description='Guidance strength for the image generation process')
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safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
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6, description='Tolerance level for input and output moderation. Between 0 and 6, 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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control_image: Optional[str] = Field(None, description='Base64 encoded image to use as control input if no preprocessed image is provided')
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preprocessed_image: Optional[str] = Field(None, description='Optional pre-processed image that will bypass the control preprocessing step')
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class BFLFluxDepthImageRequest(BaseModel):
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prompt: str = Field(..., description='Text prompt for image generation')
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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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seed: Optional[int] = Field(None, description='The seed value for reproducibility.')
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steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process')
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guidance: confloat(ge=1, le=100) = Field(..., description='Guidance strength for the image generation process')
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safety_tolerance: Optional[conint(ge=0, le=6)] = Field(
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6, description='Tolerance level for input and output moderation. Between 0 and 6, 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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control_image: Optional[str] = Field(None, description='Base64 encoded image to use as control input if no preprocessed image is provided')
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preprocessed_image: Optional[str] = Field(None, description='Optional pre-processed image that will bypass the control preprocessing step')
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class BFLFluxProGenerateRequest(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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@@ -160,15 +122,8 @@ class BFLStatus(str, Enum):
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error = "Error"
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class BFLFluxProStatusResponse(BaseModel):
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class BFLFluxStatusResponse(BaseModel):
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id: str = Field(..., description="The unique identifier for the generation task.")
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status: BFLStatus = Field(..., description="The status of the task.")
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result: Optional[Dict[str, Any]] = Field(
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None, description="The result of the task (null if not completed)."
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)
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progress: confloat(ge=0.0, le=1.0) = Field(
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..., description="The progress of the task (0.0 to 1.0)."
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)
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details: Optional[Dict[str, Any]] = Field(
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None, description="Additional details about the task (null if not available)."
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)
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result: Optional[Dict[str, Any]] = Field(None, description="The result of the task (null if not completed).")
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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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111
comfy_api_nodes/apis/veo_api.py
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111
comfy_api_nodes/apis/veo_api.py
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@@ -0,0 +1,111 @@
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from typing import Optional, Union
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from enum import Enum
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from pydantic import BaseModel, Field
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class Image2(BaseModel):
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bytesBase64Encoded: str
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gcsUri: Optional[str] = None
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mimeType: Optional[str] = None
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class Image3(BaseModel):
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bytesBase64Encoded: Optional[str] = None
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gcsUri: str
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mimeType: Optional[str] = None
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class Instance1(BaseModel):
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image: Optional[Union[Image2, Image3]] = Field(
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None, description='Optional image to guide video generation'
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)
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prompt: str = Field(..., description='Text description of the video')
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class PersonGeneration1(str, Enum):
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ALLOW = 'ALLOW'
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BLOCK = 'BLOCK'
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class Parameters1(BaseModel):
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aspectRatio: Optional[str] = Field(None, examples=['16:9'])
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durationSeconds: Optional[int] = None
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enhancePrompt: Optional[bool] = None
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generateAudio: Optional[bool] = Field(
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None,
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description='Generate audio for the video. Only supported by veo 3 models.',
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)
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negativePrompt: Optional[str] = None
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personGeneration: Optional[PersonGeneration1] = None
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sampleCount: Optional[int] = None
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seed: Optional[int] = None
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storageUri: Optional[str] = Field(
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None, description='Optional Cloud Storage URI to upload the video'
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)
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class VeoGenVidRequest(BaseModel):
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instances: Optional[list[Instance1]] = None
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parameters: Optional[Parameters1] = None
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class VeoGenVidResponse(BaseModel):
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name: str = Field(
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...,
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description='Operation resource name',
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examples=[
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'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8'
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],
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)
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class VeoGenVidPollRequest(BaseModel):
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operationName: str = Field(
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...,
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description='Full operation name (from predict response)',
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examples=[
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'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID'
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],
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)
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class Video(BaseModel):
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bytesBase64Encoded: Optional[str] = Field(
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None, description='Base64-encoded video content'
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)
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gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video')
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mimeType: Optional[str] = Field(None, description='Video MIME type')
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class Error1(BaseModel):
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code: Optional[int] = Field(None, description='Error code')
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message: Optional[str] = Field(None, description='Error message')
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class Response1(BaseModel):
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field_type: Optional[str] = Field(
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None,
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alias='@type',
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examples=[
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'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse'
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],
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)
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raiMediaFilteredCount: Optional[int] = Field(
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None, description='Count of media filtered by responsible AI policies'
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)
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raiMediaFilteredReasons: Optional[list[str]] = Field(
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None, description='Reasons why media was filtered by responsible AI policies'
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)
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videos: Optional[list[Video]] = None
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class VeoGenVidPollResponse(BaseModel):
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done: Optional[bool] = None
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error: Optional[Error1] = Field(
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None, description='Error details if operation failed'
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)
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name: Optional[str] = None
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response: Optional[Response1] = Field(
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None, description='The actual prediction response if done is true'
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)
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