150 lines
6.2 KiB
Python
150 lines
6.2 KiB
Python
import io
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import json
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import torch
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import numpy as np
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from PIL import Image
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from google import genai
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from google.genai import types
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from google.oauth2 import service_account
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class NanoBananaVertexNode:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"prompt": ("STRING", {"multiline": True, "default": ""}),
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"project_id": ("STRING", {"multiline": False, "default": ""}),
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"location": ([
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"global", "us-central1", "us-east1", "us-east4", "us-east5", "us-south1",
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"us-west1", "us-west2", "us-west3", "us-west4",
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"northamerica-northeast1", "northamerica-northeast2",
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"southamerica-east1", "southamerica-west1", "africa-south1",
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"europe-west1", "europe-north1", "europe-west2", "europe-west3",
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"europe-west4", "europe-west6", "europe-west8", "europe-west9",
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"europe-west12", "europe-southwest1", "europe-central2",
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"asia-east1", "asia-east2", "asia-northeast1", "asia-northeast2",
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"asia-northeast3", "asia-south1", "asia-south2", "asia-southeast1",
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"asia-southeast2", "australia-southeast1", "australia-southeast2",
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"me-central1", "me-central2", "me-west1"
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], {"default": "us-central1"}),
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"service_account": ("STRING", {"multiline": True, "default": ""}),
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"model": (["gemini-3-pro-image", "gemini-2.5-flash-image", "gemini-3.1-flash-image", "gemini-3.1-flash-lite-image"],),
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"aspect_ratio": (["1:1", "2:3", "3:2", "3:4", "4:3", "9:16", "16:9", "21:9"],),
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"resolution": (["1K", "2K", "4K"], {"default": "1K"}),
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"temperature": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"top_p": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.01}),
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"google_search": ("BOOLEAN", {"default": False}),
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"seed": ("INT", {"default": 69, "min": -1, "max": 2147483646, "step": 1}),
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},
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"optional": {
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"system_instruction": ("STRING", {"multiline": True, "default": ""}),
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"image_1": ("IMAGE",),
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"image_2": ("IMAGE",),
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"image_3": ("IMAGE",),
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"image_4": ("IMAGE",),
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"image_5": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "generate"
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CATEGORY = "image/generation"
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def setup_client(self, service_account_json, project_id, location):
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if not service_account_json.strip():
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raise ValueError("Service account JSON content is required.")
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if not project_id.strip():
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raise ValueError("Project ID is required.")
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try:
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sa_info = json.loads(service_account_json)
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except json.JSONDecodeError as e:
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raise ValueError(f"Invalid JSON content: {str(e)}")
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credentials = service_account.Credentials.from_service_account_info(
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sa_info,
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scopes=["https://www.googleapis.com/auth/cloud-platform"]
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)
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return genai.Client(
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vertexai=True,
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project=project_id.strip(),
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location=location.strip(),
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credentials=credentials,
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http_options=types.HttpOptions(
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retry_options=types.HttpRetryOptions(attempts=10, jitter=10)
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)
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)
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def _convert_tensor_to_bytes(self, tensor):
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if tensor.dim() == 4:
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tensor = tensor[0]
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arr = (tensor.cpu().numpy() * 255).astype(np.uint8)
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buf = io.BytesIO()
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Image.fromarray(arr).save(buf, format='PNG')
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return buf.getvalue()
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def generate(self, project_id, location, service_account, model, aspect_ratio, resolution,
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temperature, top_p, google_search, seed,
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prompt="", system_instruction="", **kwargs):
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client = self.setup_client(service_account, project_id, location)
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parts = []
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for i in range(1, 6):
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img = kwargs.get(f"image_{i}")
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if img is not None:
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parts.append(types.Part.from_bytes(mime_type="image/png", data=self._convert_tensor_to_bytes(img)))
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if prompt.strip():
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parts.append(types.Part.from_text(text=prompt))
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if not parts:
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raise ValueError("At least one image or prompt must be provided.")
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tools = None
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if google_search:
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if "gemini-2.5" in model or "gemini-3.1-flash-lite-image" in model:
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print(f"Ignoring google_search: {model} does not support it.")
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else:
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tools = [types.Tool(googleSearch=types.GoogleSearch())]
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img_config_params = {"aspect_ratio": aspect_ratio}
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if "gemini-3-pro" in model:
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img_config_params["image_size"] = resolution
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config = types.GenerateContentConfig(
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temperature=temperature,
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seed=seed,
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top_p=top_p,
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response_modalities=["IMAGE"],
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image_config=types.ImageConfig(**img_config_params),
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system_instruction=system_instruction.strip() if system_instruction.strip() else None,
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tools=tools
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)
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try:
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response = client.models.generate_content(
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model=model,
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contents=[types.Content(role="user", parts=parts)],
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config=config,
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)
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except Exception as e:
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raise RuntimeError(f"Gemini API Error: {str(e)}")
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try:
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img_data = response.candidates[0].content.parts[0].inline_data.data
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result_pil = Image.open(io.BytesIO(img_data)).convert("RGB")
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return (torch.from_numpy(np.array(result_pil).astype(np.float32) / 255.0).unsqueeze(0),)
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except (AttributeError, IndexError, TypeError):
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raise ValueError("API returned a response, but no valid image data was found.")
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@classmethod
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def IS_CHANGED(cls, seed, **kwargs):
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return seed
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NODE_CLASS_MAPPINGS = {"NanoBananaVertexNode": NanoBananaVertexNode}
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NODE_DISPLAY_NAME_MAPPINGS = {"NanoBananaVertexNode": "Nano Banana (Vertex AI)"} |