feat(api-nodes): add Nano Banana Pro (#10814)
* feat(api-nodes): add Nano Banana Pro * frontend bump to 1.28.9
This commit is contained in:
Alexander Piskun
GitHub
@@ -29,11 +29,13 @@ from comfy_api_nodes.apis.gemini_api import (
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GeminiMimeType,
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GeminiPart,
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GeminiRole,
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Modality,
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)
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from comfy_api_nodes.util import (
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ApiEndpoint,
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audio_to_base64_string,
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bytesio_to_image_tensor,
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get_number_of_images,
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sync_op,
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tensor_to_base64_string,
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validate_string,
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@@ -147,6 +149,49 @@ def get_image_from_response(response: GeminiGenerateContentResponse) -> torch.Te
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return torch.cat(image_tensors, dim=0)
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def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | None:
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if not response.modelVersion:
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return None
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# Define prices (Cost per 1,000,000 tokens), see https://cloud.google.com/vertex-ai/generative-ai/pricing
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if response.modelVersion in ("gemini-2.5-pro-preview-05-06", "gemini-2.5-pro"):
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input_tokens_price = 1.25
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output_text_tokens_price = 10.0
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output_image_tokens_price = 0.0
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elif response.modelVersion in (
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"gemini-2.5-flash-preview-04-17",
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"gemini-2.5-flash",
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):
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input_tokens_price = 0.30
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output_text_tokens_price = 2.50
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output_image_tokens_price = 0.0
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elif response.modelVersion in (
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"gemini-2.5-flash-image-preview",
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"gemini-2.5-flash-image",
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):
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input_tokens_price = 0.30
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output_text_tokens_price = 2.50
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output_image_tokens_price = 30.0
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elif response.modelVersion == "gemini-3-pro-preview":
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input_tokens_price = 2
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output_text_tokens_price = 12.0
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output_image_tokens_price = 0.0
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elif response.modelVersion == "gemini-3-pro-image-preview":
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input_tokens_price = 2
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output_text_tokens_price = 12.0
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output_image_tokens_price = 120.0
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else:
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return None
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final_price = response.usageMetadata.promptTokenCount * input_tokens_price
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for i in response.usageMetadata.candidatesTokensDetails:
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if i.modality == Modality.IMAGE:
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final_price += output_image_tokens_price * i.tokenCount # for Nano Banana models
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else:
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final_price += output_text_tokens_price * i.tokenCount
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if response.usageMetadata.thoughtsTokenCount:
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final_price += output_text_tokens_price * response.usageMetadata.thoughtsTokenCount
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return final_price / 1_000_000.0
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class GeminiNode(IO.ComfyNode):
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"""
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Node to generate text responses from a Gemini model.
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@@ -314,6 +359,7 @@ class GeminiNode(IO.ComfyNode):
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]
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),
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response_model=GeminiGenerateContentResponse,
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price_extractor=calculate_tokens_price,
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)
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output_text = get_text_from_response(response)
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@@ -476,6 +522,13 @@ class GeminiImage(IO.ComfyNode):
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"or otherwise generates 1:1 squares.",
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optional=True,
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),
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IO.Combo.Input(
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"response_modalities",
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options=["IMAGE+TEXT", "IMAGE"],
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tooltip="Choose 'IMAGE' for image-only output, or "
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"'IMAGE+TEXT' to return both the generated image and a text response.",
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optional=True,
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),
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],
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outputs=[
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IO.Image.Output(),
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@@ -498,6 +551,7 @@ class GeminiImage(IO.ComfyNode):
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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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response_modalities: str = "IMAGE+TEXT",
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) -> IO.NodeOutput:
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validate_string(prompt, strip_whitespace=True, min_length=1)
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parts: list[GeminiPart] = [GeminiPart(text=prompt)]
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@@ -520,17 +574,16 @@ class GeminiImage(IO.ComfyNode):
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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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responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
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imageConfig=None if aspect_ratio == "auto" else image_config,
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),
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),
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response_model=GeminiGenerateContentResponse,
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price_extractor=calculate_tokens_price,
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)
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output_image = get_image_from_response(response)
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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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render_spec = {
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"node_id": cls.hidden.unique_id,
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"component": "ChatHistoryWidget",
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@@ -551,9 +604,150 @@ class GeminiImage(IO.ComfyNode):
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"display_component",
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render_spec,
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)
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return IO.NodeOutput(get_image_from_response(response), output_text)
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output_text = output_text or "Empty response from Gemini model..."
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return IO.NodeOutput(output_image, output_text)
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class GeminiImage2(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="GeminiImage2Node",
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display_name="Nano Banana Pro (Google Gemini Image)",
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category="api node/image/Gemini",
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description="Generate or edit images synchronously via Google Vertex API.",
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inputs=[
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IO.String.Input(
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"prompt",
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multiline=True,
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tooltip="Text prompt describing the image to generate or the edits to apply. "
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"Include any constraints, styles, or details the model should follow.",
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default="",
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),
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IO.Combo.Input(
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"model",
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options=["gemini-3-pro-image-preview"],
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),
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IO.Int.Input(
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"seed",
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default=42,
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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="When the seed is fixed to a specific value, the model makes a best effort to provide "
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"the same response for repeated requests. Deterministic output isn't guaranteed. "
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"Also, changing the model or parameter settings, such as the temperature, "
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"can cause variations in the response even when you use the same seed value. "
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"By default, a random seed value is used.",
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),
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IO.Combo.Input(
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"aspect_ratio",
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options=["auto", "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"],
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default="auto",
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tooltip="If set to 'auto', matches your input image's aspect ratio; "
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"if no image is provided, generates a 1:1 square.",
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),
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IO.Combo.Input(
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"resolution",
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options=["1K", "2K", "4K"],
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tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
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),
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IO.Combo.Input(
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"response_modalities",
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options=["IMAGE+TEXT", "IMAGE"],
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tooltip="Choose 'IMAGE' for image-only output, or "
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"'IMAGE+TEXT' to return both the generated image and a text response.",
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),
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IO.Image.Input(
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"images",
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optional=True,
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tooltip="Optional reference image(s). "
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"To include multiple images, use the Batch Images node (up to 14).",
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),
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IO.Custom("GEMINI_INPUT_FILES").Input(
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"files",
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optional=True,
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tooltip="Optional file(s) to use as context for the model. "
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"Accepts inputs from the Gemini Generate Content Input Files node.",
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),
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],
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outputs=[
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IO.Image.Output(),
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IO.String.Output(),
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],
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hidden=[
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IO.Hidden.auth_token_comfy_org,
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IO.Hidden.api_key_comfy_org,
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IO.Hidden.unique_id,
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],
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is_api_node=True,
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)
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@classmethod
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async def execute(
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cls,
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prompt: str,
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model: str,
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seed: int,
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aspect_ratio: str,
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resolution: str,
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response_modalities: str,
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images: torch.Tensor | 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=True, min_length=1)
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parts: list[GeminiPart] = [GeminiPart(text=prompt)]
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if images is not None:
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if get_number_of_images(images) > 14:
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raise ValueError("The current maximum number of supported images is 14.")
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parts.extend(create_image_parts(images))
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if files is not None:
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parts.extend(files)
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image_config = GeminiImageConfig(imageSize=resolution)
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if aspect_ratio != "auto":
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image_config.aspectRatio = aspect_ratio
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response = await sync_op(
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cls,
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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=GeminiRole.user, parts=parts),
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],
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generationConfig=GeminiImageGenerationConfig(
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responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
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imageConfig=image_config,
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),
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),
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response_model=GeminiGenerateContentResponse,
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price_extractor=calculate_tokens_price,
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)
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output_text = get_text_from_response(response)
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if output_text:
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render_spec = {
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"node_id": cls.hidden.unique_id,
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"component": "ChatHistoryWidget",
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"props": {
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"history": json.dumps(
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[
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{
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"prompt": prompt,
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"response": output_text,
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"response_id": str(uuid.uuid4()),
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"timestamp": time.time(),
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}
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]
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),
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},
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}
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PromptServer.instance.send_sync(
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"display_component",
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render_spec,
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)
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return IO.NodeOutput(get_image_from_response(response), output_text)
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class GeminiExtension(ComfyExtension):
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@@ -562,6 +756,7 @@ class GeminiExtension(ComfyExtension):
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return [
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GeminiNode,
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GeminiImage,
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GeminiImage2,
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GeminiInputFiles,
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]
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