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feat(api-nodes): add new Gemini model (#10789)
This commit is contained in:
Alexander Piskun
GitHub
parent
d526974576
commit
24fdb92edf
@ -1,22 +1,229 @@
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from typing import Optional
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from datetime import date
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from enum import Enum
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from typing import Any
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from comfy_api_nodes.apis import GeminiGenerationConfig, GeminiContent, GeminiSafetySetting, GeminiSystemInstructionContent, GeminiTool, GeminiVideoMetadata
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from pydantic import BaseModel
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from pydantic import BaseModel, Field
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class GeminiSafetyCategory(str, Enum):
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HARM_CATEGORY_SEXUALLY_EXPLICIT = "HARM_CATEGORY_SEXUALLY_EXPLICIT"
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HARM_CATEGORY_HATE_SPEECH = "HARM_CATEGORY_HATE_SPEECH"
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HARM_CATEGORY_HARASSMENT = "HARM_CATEGORY_HARASSMENT"
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HARM_CATEGORY_DANGEROUS_CONTENT = "HARM_CATEGORY_DANGEROUS_CONTENT"
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class GeminiSafetyThreshold(str, Enum):
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OFF = "OFF"
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BLOCK_NONE = "BLOCK_NONE"
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BLOCK_LOW_AND_ABOVE = "BLOCK_LOW_AND_ABOVE"
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BLOCK_MEDIUM_AND_ABOVE = "BLOCK_MEDIUM_AND_ABOVE"
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BLOCK_ONLY_HIGH = "BLOCK_ONLY_HIGH"
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class GeminiSafetySetting(BaseModel):
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category: GeminiSafetyCategory
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threshold: GeminiSafetyThreshold
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class GeminiRole(str, Enum):
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user = "user"
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model = "model"
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class GeminiMimeType(str, Enum):
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application_pdf = "application/pdf"
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audio_mpeg = "audio/mpeg"
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audio_mp3 = "audio/mp3"
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audio_wav = "audio/wav"
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image_png = "image/png"
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image_jpeg = "image/jpeg"
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image_webp = "image/webp"
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text_plain = "text/plain"
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video_mov = "video/mov"
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video_mpeg = "video/mpeg"
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video_mp4 = "video/mp4"
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video_mpg = "video/mpg"
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video_avi = "video/avi"
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video_wmv = "video/wmv"
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video_mpegps = "video/mpegps"
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video_flv = "video/flv"
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class GeminiInlineData(BaseModel):
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data: str | None = Field(
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None,
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description="The base64 encoding of the image, PDF, or video to include inline in the prompt. "
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"When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB",
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)
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mimeType: GeminiMimeType | None = Field(None)
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class GeminiPart(BaseModel):
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inlineData: GeminiInlineData | None = Field(None)
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text: str | None = Field(None)
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class GeminiTextPart(BaseModel):
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text: str | None = Field(None)
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class GeminiContent(BaseModel):
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parts: list[GeminiPart] = Field(...)
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role: GeminiRole = Field(..., examples=["user"])
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class GeminiSystemInstructionContent(BaseModel):
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parts: list[GeminiTextPart] = Field(
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...,
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description="A list of ordered parts that make up a single message. "
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"Different parts may have different IANA MIME types.",
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)
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role: GeminiRole = Field(
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...,
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description="The identity of the entity that creates the message. "
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"The following values are supported: "
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"user: This indicates that the message is sent by a real person, typically a user-generated message. "
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"model: This indicates that the message is generated by the model. "
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"The model value is used to insert messages from model into the conversation during multi-turn conversations. "
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"For non-multi-turn conversations, this field can be left blank or unset.",
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)
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class GeminiFunctionDeclaration(BaseModel):
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description: str | None = Field(None)
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name: str = Field(...)
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parameters: dict[str, Any] = Field(..., description="JSON schema for the function parameters")
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class GeminiTool(BaseModel):
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functionDeclarations: list[GeminiFunctionDeclaration] | None = Field(None)
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class GeminiOffset(BaseModel):
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nanos: int | None = Field(None, ge=0, le=999999999)
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seconds: int | None = Field(None, ge=-315576000000, le=315576000000)
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class GeminiVideoMetadata(BaseModel):
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endOffset: GeminiOffset | None = Field(None)
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startOffset: GeminiOffset | None = Field(None)
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class GeminiGenerationConfig(BaseModel):
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maxOutputTokens: int | None = Field(None, ge=16, le=8192)
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seed: int | None = Field(None)
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stopSequences: list[str] | None = Field(None)
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temperature: float | None = Field(1, ge=0.0, le=2.0)
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topK: int | None = Field(40, ge=1)
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topP: float | None = Field(0.95, ge=0.0, le=1.0)
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class GeminiImageConfig(BaseModel):
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aspectRatio: Optional[str] = None
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aspectRatio: str | None = Field(None)
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resolution: str | None = Field(None)
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class GeminiImageGenerationConfig(GeminiGenerationConfig):
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responseModalities: Optional[list[str]] = None
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imageConfig: Optional[GeminiImageConfig] = None
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responseModalities: list[str] | None = Field(None)
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imageConfig: GeminiImageConfig | None = Field(None)
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class GeminiImageGenerateContentRequest(BaseModel):
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contents: list[GeminiContent]
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generationConfig: Optional[GeminiImageGenerationConfig] = None
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safetySettings: Optional[list[GeminiSafetySetting]] = None
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systemInstruction: Optional[GeminiSystemInstructionContent] = None
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tools: Optional[list[GeminiTool]] = None
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videoMetadata: Optional[GeminiVideoMetadata] = None
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contents: list[GeminiContent] = Field(...)
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generationConfig: GeminiImageGenerationConfig | None = Field(None)
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safetySettings: list[GeminiSafetySetting] | None = Field(None)
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systemInstruction: GeminiSystemInstructionContent | None = Field(None)
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tools: list[GeminiTool] | None = Field(None)
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videoMetadata: GeminiVideoMetadata | None = Field(None)
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class GeminiGenerateContentRequest(BaseModel):
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contents: list[GeminiContent] = Field(...)
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generationConfig: GeminiGenerationConfig | None = Field(None)
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safetySettings: list[GeminiSafetySetting] | None = Field(None)
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systemInstruction: GeminiSystemInstructionContent | None = Field(None)
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tools: list[GeminiTool] | None = Field(None)
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videoMetadata: GeminiVideoMetadata | None = Field(None)
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class Modality(str, Enum):
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MODALITY_UNSPECIFIED = "MODALITY_UNSPECIFIED"
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TEXT = "TEXT"
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IMAGE = "IMAGE"
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VIDEO = "VIDEO"
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AUDIO = "AUDIO"
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DOCUMENT = "DOCUMENT"
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class ModalityTokenCount(BaseModel):
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modality: Modality | None = None
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tokenCount: int | None = Field(None, description="Number of tokens for the given modality.")
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class Probability(str, Enum):
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NEGLIGIBLE = "NEGLIGIBLE"
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LOW = "LOW"
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MEDIUM = "MEDIUM"
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HIGH = "HIGH"
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UNKNOWN = "UNKNOWN"
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class GeminiSafetyRating(BaseModel):
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category: GeminiSafetyCategory | None = None
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probability: Probability | None = Field(
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None,
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description="The probability that the content violates the specified safety category",
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)
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class GeminiCitation(BaseModel):
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authors: list[str] | None = None
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endIndex: int | None = None
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license: str | None = None
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publicationDate: date | None = None
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startIndex: int | None = None
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title: str | None = None
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uri: str | None = None
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class GeminiCitationMetadata(BaseModel):
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citations: list[GeminiCitation] | None = None
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class GeminiCandidate(BaseModel):
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citationMetadata: GeminiCitationMetadata | None = None
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content: GeminiContent | None = None
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finishReason: str | None = None
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safetyRatings: list[GeminiSafetyRating] | None = None
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class GeminiPromptFeedback(BaseModel):
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blockReason: str | None = None
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blockReasonMessage: str | None = None
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safetyRatings: list[GeminiSafetyRating] | None = None
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class GeminiUsageMetadata(BaseModel):
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cachedContentTokenCount: int | None = Field(
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None,
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description="Output only. Number of tokens in the cached part in the input (the cached content).",
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)
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candidatesTokenCount: int | None = Field(None, description="Number of tokens in the response(s).")
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candidatesTokensDetails: list[ModalityTokenCount] | None = Field(
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None, description="Breakdown of candidate tokens by modality."
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)
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promptTokenCount: int | None = Field(
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None,
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description="Number of tokens in the request. When cachedContent is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.",
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)
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promptTokensDetails: list[ModalityTokenCount] | None = Field(
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None, description="Breakdown of prompt tokens by modality."
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)
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thoughtsTokenCount: int | None = Field(None, description="Number of tokens present in thoughts output.")
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toolUsePromptTokenCount: int | None = Field(None, description="Number of tokens present in tool-use prompt(s).")
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class GeminiGenerateContentResponse(BaseModel):
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candidates: list[GeminiCandidate] | None = Field(None)
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promptFeedback: GeminiPromptFeedback | None = Field(None)
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usageMetadata: GeminiUsageMetadata | None = Field(None)
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@ -3,8 +3,6 @@ API Nodes for Gemini Multimodal LLM Usage via Remote API
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See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
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"""
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from __future__ import annotations
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import base64
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import json
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import os
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@ -12,7 +10,7 @@ import time
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import uuid
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from enum import Enum
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from io import BytesIO
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from typing import Literal, Optional
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from typing import Literal
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import torch
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from typing_extensions import override
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@ -20,18 +18,17 @@ from typing_extensions import override
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import folder_paths
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from comfy_api.latest import IO, ComfyExtension, Input
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from comfy_api.util import VideoCodec, VideoContainer
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from comfy_api_nodes.apis import (
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from comfy_api_nodes.apis.gemini_api import (
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GeminiContent,
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GeminiGenerateContentRequest,
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GeminiGenerateContentResponse,
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GeminiInlineData,
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GeminiMimeType,
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GeminiPart,
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)
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from comfy_api_nodes.apis.gemini_api import (
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GeminiImageConfig,
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GeminiImageGenerateContentRequest,
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GeminiImageGenerationConfig,
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GeminiInlineData,
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GeminiMimeType,
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GeminiPart,
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GeminiRole,
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)
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from comfy_api_nodes.util import (
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ApiEndpoint,
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@ -57,6 +54,7 @@ class GeminiModel(str, Enum):
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gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17"
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gemini_2_5_pro = "gemini-2.5-pro"
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gemini_2_5_flash = "gemini-2.5-flash"
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gemini_3_0_pro = "gemini-3-pro-preview"
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class GeminiImageModel(str, Enum):
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@ -103,6 +101,16 @@ def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Litera
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Returns:
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List of response parts matching the requested type.
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"""
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if response.candidates is None:
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if response.promptFeedback.blockReason:
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feedback = response.promptFeedback
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raise ValueError(
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f"Gemini API blocked the request. Reason: {feedback.blockReason} ({feedback.blockReasonMessage})"
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)
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raise NotImplementedError(
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"Gemini returned no response candidates. "
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"Please report to ComfyUI repository with the example of workflow to reproduce this."
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)
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parts = []
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for part in response.candidates[0].content.parts:
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if part_type == "text" and hasattr(part, "text") and part.text:
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@ -272,10 +280,10 @@ class GeminiNode(IO.ComfyNode):
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prompt: str,
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model: str,
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seed: int,
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images: Optional[torch.Tensor] = None,
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audio: Optional[Input.Audio] = None,
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video: Optional[Input.Video] = None,
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files: Optional[list[GeminiPart]] = None,
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images: torch.Tensor | None = None,
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audio: Input.Audio | None = None,
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video: Input.Video | None = None,
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files: list[GeminiPart] | None = None,
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) -> IO.NodeOutput:
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validate_string(prompt, strip_whitespace=False)
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@ -300,7 +308,7 @@ class GeminiNode(IO.ComfyNode):
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data=GeminiGenerateContentRequest(
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contents=[
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GeminiContent(
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role="user",
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role=GeminiRole.user,
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parts=parts,
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)
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]
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@ -308,7 +316,6 @@ class GeminiNode(IO.ComfyNode):
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response_model=GeminiGenerateContentResponse,
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)
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# Get result output
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output_text = get_text_from_response(response)
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if output_text:
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# Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button.
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@ -406,7 +413,7 @@ class GeminiInputFiles(IO.ComfyNode):
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)
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@classmethod
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def execute(cls, file: str, GEMINI_INPUT_FILES: Optional[list[GeminiPart]] = None) -> IO.NodeOutput:
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def execute(cls, file: str, GEMINI_INPUT_FILES: list[GeminiPart] | None = None) -> IO.NodeOutput:
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"""Loads and formats input files for Gemini API."""
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if GEMINI_INPUT_FILES is None:
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GEMINI_INPUT_FILES = []
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@ -421,7 +428,7 @@ class GeminiImage(IO.ComfyNode):
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def define_schema(cls):
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return IO.Schema(
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node_id="GeminiImageNode",
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display_name="Google Gemini Image",
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display_name="Nano Banana (Google Gemini Image)",
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category="api node/image/Gemini",
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description="Edit images synchronously via Google API.",
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inputs=[
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@ -488,8 +495,8 @@ class GeminiImage(IO.ComfyNode):
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prompt: str,
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model: str,
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seed: int,
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images: Optional[torch.Tensor] = None,
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files: Optional[list[GeminiPart]] = None,
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images: torch.Tensor | None = None,
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files: list[GeminiPart] | None = None,
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aspect_ratio: str = "auto",
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) -> IO.NodeOutput:
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validate_string(prompt, strip_whitespace=True, min_length=1)
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@ -510,7 +517,7 @@ class GeminiImage(IO.ComfyNode):
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endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"),
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data=GeminiImageGenerateContentRequest(
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contents=[
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GeminiContent(role="user", parts=parts),
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GeminiContent(role=GeminiRole.user, parts=parts),
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],
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generationConfig=GeminiImageGenerationConfig(
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responseModalities=["TEXT", "IMAGE"],
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