138 lines
6.6 KiB
Python
138 lines
6.6 KiB
Python
import os
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import json
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import tempfile
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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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class GoogleImagenGenerateVertex:
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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": "A majestic lion in the savanna"}),
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"project_id": ("STRING", {"multiline": False, "default": ""}),
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"location": (["global", "us-central1", "us-east1", "us-east4", "us-east5", "us-south1", "us-west1", "us-west2", "us-west3", "us-west4", "northamerica-northeast1", "northamerica-northeast2", "southamerica-east1", "southamerica-west1", "africa-south1", "europe-west1", "europe-north1", "europe-west2", "europe-west3", "europe-west4", "europe-west6", "europe-west8", "europe-west9", "europe-west12", "europe-southwest1", "europe-central2", "asia-east1", "asia-east2", "asia-northeast1", "asia-northeast2", "asia-northeast3", "asia-south1", "asia-south2", "asia-southeast1", "asia-southeast2", "australia-southeast1", "australia-southeast2", "me-central1", "me-central2", "me-west1"], {"default": "us-central1"}),
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"service_account": ("STRING", {"multiline": True, "default": ""}),
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"model": (["imagen-4.0-ultra-generate-001", "imagen-4.0-generate-001", "imagen-4.0-fast-generate-001", "imagen-3.0-generate-002"], {"default": "imagen-4.0-generate-001"}),
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"number_of_images": ("INT", {"default": 1, "min": 1, "max": 4, "step": 1}),
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"aspect_ratio": (["1:1", "9:16", "16:9", "4:3", "3:4"], {"default": "1:1"}),
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"image_size": (["1K", "2K"], {"default": "1K"}),
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"seed": ("INT", {"default": 69, "min": 1, "max": 2147483646, "step": 1}),
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"guidance_scale": ("FLOAT", {"default": 7.5, "min": 1.0, "max": 20.0, "step": 0.1}),
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},
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"optional": {
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"negative_prompt": ("STRING", {"multiline": True, "default": ""}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "generate_images"
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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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"""Setup Vertex AI client with service account JSON content"""
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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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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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temp_file = tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False)
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temp_file.write(service_account_json.strip())
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temp_file.close()
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os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = temp_file.name
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return genai.Client(vertexai=True, project=project_id.strip(), location=location.strip())
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def pil_to_tensor(self, images):
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if not isinstance(images, list):
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images = [images]
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tensors = []
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for image in images:
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if image.mode != 'RGB':
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image = image.convert('RGB')
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array = np.array(image).astype(np.float32) / 255.0
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tensors.append(torch.from_numpy(array))
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return torch.stack(tensors)
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def generate_images(self, prompt, project_id, location, service_account, model, number_of_images, aspect_ratio, image_size, seed, guidance_scale, negative_prompt=""):
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try:
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client = self.setup_client(service_account, project_id, location)
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config_params = {
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"number_of_images": number_of_images,
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"aspect_ratio": aspect_ratio,
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"add_watermark": False,
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"seed": seed,
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"guidance_scale": guidance_scale,
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}
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if negative_prompt and negative_prompt.strip():
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config_params["negative_prompt"] = negative_prompt
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config = types.GenerateImagesConfig(**config_params)
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if model in ["imagen-4.0-ultra-generate-001", "imagen-4.0-generate-001"]:
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config.image_size = image_size
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response = client.models.generate_images(
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model=model,
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prompt=prompt,
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config=config
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)
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if not response.generated_images:
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raise ValueError("No images generated by the API")
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pil_images = []
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for generated_image in response.generated_images:
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image_data = generated_image.image
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if hasattr(image_data, 'mode') and hasattr(image_data, 'size'):
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pil_images.append(image_data)
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elif hasattr(image_data, '_pil_image'):
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pil_images.append(image_data._pil_image)
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elif hasattr(image_data, 'show'):
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try:
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from io import BytesIO
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buffer = BytesIO()
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image_data.save(buffer, format='PNG')
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buffer.seek(0)
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pil_images.append(Image.open(buffer))
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except:
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pil_images.append(Image.new('RGB', (512, 512), color='gray'))
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elif hasattr(image_data, 'read') or isinstance(image_data, bytes):
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from io import BytesIO
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image_bytes = image_data.read() if hasattr(image_data, 'read') else image_data
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pil_images.append(Image.open(BytesIO(image_bytes)))
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else:
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try:
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pil_images.append(Image.open(image_data))
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except:
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pil_images.append(Image.new('RGB', (512, 512), color='gray'))
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return (self.pil_to_tensor(pil_images),)
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except Exception as e:
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print(f"Google Imagen Generate Error: {str(e)}")
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error_image = Image.new('RGB', (512, 512), color='black')
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return (self.pil_to_tensor([error_image]),)
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return f"{kwargs.get('prompt', '')}-{kwargs.get('model', '')}-{kwargs.get('number_of_images', 1)}-{kwargs.get('aspect_ratio', '1:1')}-{kwargs.get('image_size', '1K')}"
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NODE_CLASS_MAPPINGS = {"GoogleImagenGenerateVertex": GoogleImagenGenerateVertex}
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NODE_DISPLAY_NAME_MAPPINGS = {"GoogleImagenGenerateVertex": "Imagen Generate (Vertex AI)"} |