565 lines
25 KiB
Python
565 lines
25 KiB
Python
import requests
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import json
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import time
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import base64
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import io
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import os
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import numpy as np
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import torch
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import tiktoken
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from PIL import Image
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import hashlib
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from dotenv import load_dotenv
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from .chat_manager import ChatSessionManager
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# Load environment variables from .env file
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load_dotenv()
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class NanoBananaNode:
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"""
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A node for interacting with Vertex AI's generateContent API.
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Supports text and images as input.
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Returns three outputs:
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1) "Output": the text response from the LLM
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2) "image": an image tensor if generated
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3) "Stats": a string detailing tokens per second, input tokens, and output tokens
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"""
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models_cache = None
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last_fetch_time = 0
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cache_duration = 3600 # Cache duration in seconds (1 hour)
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def __init__(self):
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self.chat_manager = ChatSessionManager()
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@classmethod
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def INPUT_TYPES(cls):
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"""
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Defines the input specification for this node.
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Includes optional inputs for image data.
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"""
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return {
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"required": {
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"system_prompt": ("STRING", {
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"multiline": True,
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"default": "You are a master artist and expert at digital art."
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}),
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"user_message_box": ("STRING", {
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"multiline": True,
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"default": "Combine all the input images with a background of your envisioning in the year 2050."
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}),
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"model": (cls.fetch_nano_banana_models(), {"default": "gemini-3-pro-image-preview"}),
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"image_generation": ("BOOLEAN", {"default": True}),
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"resolution": (["2K", "4K"], {"default": "4K"}),
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"aspect_ratio": (["1:1", "4:3", "3:4", "16:9", "9:16", "3:2", "2:3", "5:4", "4:5", "21:9"], {"default": "1:1"}),
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"temperature": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.1,
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"display": "slider",
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"round": 0.1,
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}),
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"chat_mode": ("BOOLEAN", {"default": False}),
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}
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}
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RETURN_TYPES = ("STRING", "IMAGE", "STRING",)
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RETURN_NAMES = ("STRING (Text Output)", "IMAGE", "Stats")
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FUNCTION = "generate_response"
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CATEGORY = "LLM"
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@classmethod
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def fetch_nano_banana_models(cls):
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"""
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Fetches a list of Gemini model IDs for Vertex AI, caching them.
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Note: This requires proper authentication and project/location configuration.
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"""
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current_time = time.time()
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if (cls.models_cache is None) or (current_time - cls.last_fetch_time > cls.cache_duration):
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# Get model list from environment variable or use defaults
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models_env = os.getenv("VERTEX_AI_MODELS", "gemini-3-pro-image-preview,gemini-2.5-flash-image")
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model_list = [m.strip() for m in models_env.split(",") if m.strip()]
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cls.models_cache = sorted(model_list)
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cls.last_fetch_time = current_time
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return cls.models_cache if cls.models_cache else ["gemini-3-pro-image-preview"] # Ensure it's never empty
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def validate_temperature(self, temperature):
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"""
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Validates and converts temperature value to float within acceptable range.
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"""
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try:
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temp = float(temperature)
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return max(0.0, min(2.0, temp)) # Clamp between 0.0 and 2.0
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except (ValueError, TypeError):
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return 1.0 # Return default if conversion fails
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def generate_response(self, system_prompt, user_message_box, model,
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temperature, chat_mode, image_generation=False,
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resolution="4K", aspect_ratio="1:1", **kwargs):
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"""
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Sends a completion request to the Vertex AI generateContent endpoint.
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Handles text and optional image inputs.
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Returns three outputs:
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(1) Output: the LLM's text response
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(2) image: an image tensor if the response contains an image, else empty tensor
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(3) Stats: a string with tokens per second, prompt tokens, completion tokens
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"""
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# Create empty placeholder image
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placeholder_image = torch.zeros((1, 1, 1, 3), dtype=torch.float32)
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# Get API key from environment variable
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api_key = os.getenv("VERTEX_AI_API_KEY", "")
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if not api_key:
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return ("Error: VERTEX_AI_API_KEY not found in environment variables.", placeholder_image, "Stats N/A")
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# Get endpoint configuration from environment variables
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endpoint_base = "https://aiplatform.googleapis.com/v1"
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use_simple_endpoint = os.getenv("VERTEX_AI_USE_SIMPLE_ENDPOINT", "false").lower() == "true"
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if use_simple_endpoint:
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if not os.getenv("VERTEX_AI_ENDPOINT"):
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return ("Error: VERTEX_AI_ENDPOINT must be set when using simple endpoint format.", placeholder_image, "Stats N/A")
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# Simple endpoint format for Vertex AI: {endpoint}/google/{model}:generateContent
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endpoint_base = os.getenv("VERTEX_AI_ENDPOINT")
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url = f"{endpoint_base}/google/{model}:generateContent"
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else:
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# Standard Vertex AI format (for direct API access)
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project = os.getenv("VERTEX_AI_PROJECT", "project")
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location = os.getenv("VERTEX_AI_LOCATION", "location")
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url = f"{endpoint_base}/projects/{project}/locations/{location}/publishers/*/models/{model}:generateContent"
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headers = {
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"api-key": f"{api_key}",
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"Content-Type": "application/json",
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}
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# Validate and convert temperature
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validated_temp = self.validate_temperature(temperature)
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# Use user_message_box directly
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user_text = user_message_box
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# Initialize session_path
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session_path = None
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# Handle chat mode
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if chat_mode:
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# Get or create a chat session
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session_path, messages = self.chat_manager.get_or_create_session(user_text, system_prompt)
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# Check if we need to update the system prompt (for existing sessions)
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if messages and messages[0]["role"] == "system" and messages[0]["content"] != system_prompt:
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# Update system prompt if it has changed
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messages[0]["content"] = system_prompt
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else:
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# Non-chat mode: Build the messages array, starting with a system prompt.
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messages = [
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{"role": "system", "content": system_prompt},
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]
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# --- Build the user message content ---
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user_content_blocks = []
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# 1. Add Text part (always present)
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user_content_blocks.append({
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"type": "text",
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"text": user_text
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})
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# 2. Add Image parts (optional) - support multiple images from kwargs
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# Process all image_N inputs from kwargs
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image_keys = sorted([k for k in kwargs.keys() if k.startswith('image_')],
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key=lambda x: int(x.split('_')[1]))
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for image_key in image_keys:
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if kwargs[image_key] is not None:
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try:
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img_str = self.image_to_base64(kwargs[image_key])
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user_content_blocks.append({
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"type": "image_url",
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"image_url": {
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"url": f"data:image/png;base64,{img_str}"
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}
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})
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except Exception as e:
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print(f"Error processing {image_key}: {e}")
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return (f"Error processing {image_key}: {e}", placeholder_image, "Stats N/A")
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# Determine message format based on content type
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# Use simple string format for text-only requests to ensure compatibility
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# Use structured format only when we have multimodal content
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has_multimodal_content = len(user_content_blocks) > 1 or any(block.get("type") != "text" for block in user_content_blocks)
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if has_multimodal_content:
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# Use structured format for multimodal content
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new_user_message = {
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"role": "user",
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"content": user_content_blocks
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}
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else:
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# Use simple string format for text-only requests
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new_user_message = {
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"role": "user",
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"content": user_text
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}
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if chat_mode:
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# In chat mode, append to existing conversation (but don't save yet - wait for response)
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messages.append(new_user_message)
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else:
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# In non-chat mode, messages array already has system prompt, just append user message
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messages.append(new_user_message)
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# --- Construct the final payload ---
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# Convert messages to Vertex AI contents format
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contents = []
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for msg in messages:
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role = "user" if msg["role"] == "user" else "model"
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if msg["role"] == "system":
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# System messages become systemInstruction in Vertex AI
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continue
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parts = []
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if isinstance(msg["content"], str):
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parts.append({"text": msg["content"]})
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elif isinstance(msg["content"], list):
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for block in msg["content"]:
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if block.get("type") == "text":
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parts.append({"text": block.get("text", "")})
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elif block.get("type") == "image_url":
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parts.append({"inlineData": {"mimeType": "image/jpeg", "data": block["image_url"]["url"].split(",")[1]}})
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contents.append({"role": role, "parts": parts})
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data = {
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"contents": contents,
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"generationConfig": {
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"temperature": validated_temp
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}
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}
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# Add system instruction if present
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for msg in messages:
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if msg["role"] == "system":
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data["systemInstruction"] = {
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"parts": [{"text": msg["content"]}]
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}
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break
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# Only add modalities parameter if explicitly requested by user
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# This prevents "Multi-modal output is not supported" errors on text-only models
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if image_generation:
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data["generationConfig"]["candidateCount"] = 1
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# Add aspect_ratio and resolution for Nano Banana Pro 4K Generation
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data["generationConfig"]["image_config"] = {
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"aspect_ratio": aspect_ratio,
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"image_size": resolution
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}
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# Request image output modality
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data["generationConfig"]["responseModalities"] = ["TEXT", "IMAGE"]
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print(f"[NanoBanana] Image generation enabled", flush=True)
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print(f"[NanoBanana] Resolution: {resolution}", flush=True)
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print(f"[NanoBanana] Aspect Ratio: {aspect_ratio}", flush=True)
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print(f"[NanoBanana] URL: {url}", flush=True)
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# --- Pre-calculate text input tokens (rough estimate) ---
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# Note: Actual token count depends on the model and includes image data.
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# Rely on the API response for accurate usage stats.
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text_token_estimate = 0
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try:
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text_token_estimate = self.count_tokens(system_prompt, model) + self.count_tokens(user_text, model)
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except Exception as e:
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print(f"Warning: Token counting failed - {e}")
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# --- Make API Call and Process Response (Debugging disabled) ---
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try:
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print(f"[NanoBanana] Sending request to API...", flush=True)
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# print(f"[NanoBanana] Request payload (first 1000 chars): {json.dumps(data, indent=2)[:1000]}...", flush=True)
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start_time = time.time()
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response = requests.post(url, headers=headers, json=data, timeout=120)
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end_time = time.time()
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print(f"[NanoBanana] Response received in {end_time - start_time:.2f}s", flush=True)
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print(f"[NanoBanana] Response status: {response.status_code}", flush=True)
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# Log raw response for debugging
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# response_text = response.text
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# print(f"[NanoBanana] Raw response (first 500 chars): {response_text[:500]}", flush=True)
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response.raise_for_status() # Raises HTTPError for bad responses (4xx or 5xx)
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result = response.json()
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print(f"[NanoBanana] Response keys: {list(result.keys())}", flush=True)
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# --- Extract results and calculate stats ---
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# Vertex AI returns "candidates" instead of "choices"
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if not result.get("candidates") or not result["candidates"][0].get("content"):
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raise ValueError("Invalid response format from API: 'candidates' or 'content' missing.")
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# Parse response for text and image content
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candidate = result["candidates"][0]
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content = candidate.get("content", {})
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parts = content.get("parts", [])
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# print(f"[NanoBanana] Number of parts in response: {len(parts)}", flush=True)
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# for i, part in enumerate(parts):
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# print(f"[NanoBanana] Part {i} keys: {list(part.keys())}", flush=True)
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text_output = ""
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image_tensor = placeholder_image
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# Extract text and image from parts
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for part in parts:
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if "text" in part:
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text_output += part["text"]
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print(f"[NanoBanana] Found text part: {len(part['text'])} chars", flush=True)
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elif "inlineData" in part:
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# Handle inline image data
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inline_data = part["inlineData"]
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mime_type = inline_data.get("mimeType", "")
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print(f"[NanoBanana] Found inlineData with mimeType: {mime_type}", flush=True)
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if mime_type.startswith("image"):
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base64_str = inline_data.get("data", "")
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print(f"[NanoBanana] Image base64 length: {len(base64_str)} chars", flush=True)
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try:
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image_tensor = self.base64_to_image(base64_str)
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print(f"[NanoBanana] Successfully decoded image, shape: {image_tensor.shape}", flush=True)
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except Exception as e:
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print(f"[NanoBanana] Error decoding image: {e}", flush=True)
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elif "fileData" in part:
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# Handle file data (alternative format)
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file_data = part["fileData"]
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print(f"[NanoBanana] Found fileData: {list(file_data.keys())}", flush=True)
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else:
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print(f"[NanoBanana] Unknown part type: {list(part.keys())}", flush=True)
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# Vertex AI returns usage metadata differently
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usage_metadata = result.get("usageMetadata", {})
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prompt_tokens = usage_metadata.get("promptTokenCount", text_token_estimate)
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completion_tokens = usage_metadata.get("candidatesTokenCount", 0)
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if completion_tokens == 0 and text_output: # Estimate completion tokens if API doesn't provide them
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try:
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completion_tokens = self.count_tokens(text_output, model)
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except Exception as e:
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print(f"Warning: Completion token counting failed - {e}")
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# Vertex AI doesn't provide response_ms, use client-side timing
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response_ms = None
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# Calculate tokens per second (TPS)
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tps = 0
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elapsed_time = end_time - start_time
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if response_ms is not None:
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server_elapsed_time = response_ms / 1000.0
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if server_elapsed_time > 0:
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tps = completion_tokens / server_elapsed_time
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elif elapsed_time > 0:
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# Use client-side timing as fallback, less accurate due to network latency
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tps = completion_tokens / elapsed_time
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# Optional: apply a heuristic correction factor if needed, but server time is better
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# correction_factor = 1.28 # Example factor, might need tuning
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# tps *= correction_factor
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stats_text = (
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f"TPS: {tps:.2f}, "
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f"Prompt Tokens: {prompt_tokens}, "
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f"Completion Tokens: {completion_tokens}, "
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f"Temp: {validated_temp:.1f}, "
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f"Model: {model}"
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)
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# Save conversation in chat mode
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if chat_mode and session_path:
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# Append assistant's response to the conversation
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assistant_message = {
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"role": "assistant",
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"content": text_output
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}
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messages.append(assistant_message)
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# Save the updated conversation
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self.chat_manager.save_conversation(session_path, messages)
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return (text_output, image_tensor, stats_text)
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except requests.exceptions.RequestException as e:
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error_message = f"API Request Error: {str(e)}"
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print(f"[NanoBanana] RequestException: {e}", flush=True)
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if hasattr(e, 'response') and e.response is not None:
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print(f"[NanoBanana] Response status: {e.response.status_code}", flush=True)
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print(f"[NanoBanana] Response text: {e.response.text[:500]}", flush=True)
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try:
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error_detail = e.response.json() # Try to get JSON error detail
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error_message += f" | Details: {error_detail}"
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except json.JSONDecodeError:
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error_message += f" | Status: {e.response.status_code} | Response: {e.response.text[:500]}" # Show raw text if not JSON
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else:
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error_message += " (Network or connection issue)" # Generic network error
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return (error_message, placeholder_image, "Stats N/A due to error")
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except Exception as e: # Catch other potential errors (e.g., JSON parsing, value errors)
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import traceback
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print(f"[NanoBanana] Exception: {e}", flush=True)
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print(f"[NanoBanana] Traceback: {traceback.format_exc()}", flush=True)
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return (f"Node Error: {str(e)}", placeholder_image, "Stats N/A due to error")
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@staticmethod
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def image_to_base64(image):
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"""
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Converts a ComfyUI IMAGE (torch.Tensor, BHWC, float 0-1)
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into a base64-encoded PNG string.
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"""
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if not isinstance(image, torch.Tensor):
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raise TypeError("Input 'image' is not a torch.Tensor")
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# Remove batch dimension if present
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if image.ndim == 4:
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if image.shape[0] != 1:
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print(f"Warning: Image batch size is {image.shape[0]}, using only the first image.")
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image = image.squeeze(0) # Shape HWC
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if image.ndim != 3:
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raise ValueError(f"Unexpected image dimensions: {image.shape}. Expected HWC.")
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# Convert float tensor (0-1) to numpy array (0-255, uint8)
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image_np = image.cpu().numpy()
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if image_np.dtype != np.uint8:
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if image_np.min() < 0 or image_np.max() > 1:
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print("Warning: Image tensor values outside [0, 1] range. Clamping.")
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image_np = np.clip(image_np, 0, 1)
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image_np = (image_np * 255).astype(np.uint8)
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# Convert numpy array to PIL Image
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pil_image = Image.fromarray(image_np, 'RGB') # Assuming RGB, adjust if needed
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# Save PIL Image to a bytes buffer as PNG
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buffered = io.BytesIO()
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pil_image.save(buffered, format="PNG")
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# Encode the bytes buffer to base64 string
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return base64.b64encode(buffered.getvalue()).decode('utf-8')
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@staticmethod
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def base64_to_image(base64_str: str) -> torch.Tensor:
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"""
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Converts a base64 image string to a ComfyUI image tensor
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Returns tensor in [1, H, W, 3] format with values in [0, 1]
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"""
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try:
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# Decode base64 string to image
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img_data = base64.b64decode(base64_str)
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img = Image.open(io.BytesIO(img_data))
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img = img.convert("RGB")
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# Convert to numpy array and normalize to [0, 1]
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img_array = np.array(img).astype(np.float32) / 255.0
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# Add batch dimension: [1, H, W, 3]
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img_tensor = torch.from_numpy(img_array).unsqueeze(0)
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print(f"Successfully converted base64 to image tensor: {img_tensor.shape}")
|
|
return img_tensor
|
|
|
|
except Exception as e:
|
|
print(f"Error in base64_to_image: {e}")
|
|
# Return a small placeholder image instead of failing
|
|
return torch.zeros((1, 64, 64, 3), dtype=torch.float32)
|
|
|
|
@staticmethod
|
|
def count_tokens(text, model):
|
|
"""
|
|
Count tokens for a given text using tiktoken.
|
|
Uses model-specific encodings where possible, falls back to cl100k_base.
|
|
Handles potential errors during encoding.
|
|
"""
|
|
if not text or not isinstance(text, str):
|
|
return 0
|
|
|
|
# Strip any model modifiers like :floor, :nitro, :online
|
|
base_model = model.split(':')[0] if ':' in model else model
|
|
|
|
# Simplified mapping, cl100k_base is common for many recent models
|
|
encoding_name = "cl100k_base"
|
|
try:
|
|
# List known models/prefixes that definitely use cl100k_base
|
|
# Add others if known, but cl100k_base is a safe default for many
|
|
cl100k_models = [
|
|
"openai/gpt-4", "openai/gpt-3.5", "openai/gpt-4o",
|
|
"anthropic/claude",
|
|
"google/gemini",
|
|
"meta-llama/llama-2", "meta-llama/llama-3",
|
|
"mistralai/mistral", "mistralai/mixtral",
|
|
]
|
|
# Check if the base_model or its prefix matches known cl100k models
|
|
is_cl100k = any(base_model.startswith(prefix) for prefix in cl100k_models)
|
|
|
|
if is_cl100k:
|
|
encoding_name = "cl100k_base"
|
|
# else: # Add logic for other encodings if needed, e.g., p50k_base for older models
|
|
# pass # Stick with cl100k_base as default for now
|
|
|
|
encoding = tiktoken.get_encoding(encoding_name)
|
|
token_count = len(encoding.encode(text, disallowed_special=())) # Allow special tokens
|
|
return token_count
|
|
|
|
except Exception as e:
|
|
print(f"Warning: Tiktoken error for model '{model}' (base: '{base_model}', encoding: '{encoding_name}'): {e}. Falling back to estimation.")
|
|
# Fallback: Estimate tokens based on characters (rough approximation)
|
|
# Average ~4 chars per token is a common heuristic
|
|
return max(1, round(len(text) / 4))
|
|
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, system_prompt, user_message_box, model,
|
|
temperature, chat_mode, image_generation=False, **kwargs):
|
|
"""
|
|
Check if any input that affects the output has changed.
|
|
Includes hashing image data.
|
|
"""
|
|
# Hash image data if present - handle multiple images from kwargs
|
|
image_hashes = []
|
|
image_keys = sorted([k for k in kwargs.keys() if k.startswith('image_')],
|
|
key=lambda x: int(x.split('_')[1]))
|
|
|
|
for image_key in image_keys:
|
|
if kwargs[image_key] is not None:
|
|
image = kwargs[image_key]
|
|
if isinstance(image, torch.Tensor):
|
|
try:
|
|
hasher = hashlib.sha256()
|
|
hasher.update(image.cpu().numpy().tobytes())
|
|
image_hashes.append(hasher.hexdigest())
|
|
except Exception as e:
|
|
print(f"Warning: Could not hash {image_key} data for IS_CHANGED: {e}")
|
|
image_hashes.append(f"{image_key}_hashing_error")
|
|
else:
|
|
image_hashes.append(None)
|
|
|
|
# Ensure temperature is consistently represented (e.g., as float)
|
|
try:
|
|
temp_float = float(temperature) if isinstance(temperature, (str, int, float)) else 1.0
|
|
temp_float = max(0.0, min(2.0, temp_float))
|
|
except (ValueError, TypeError):
|
|
temp_float = 1.0
|
|
|
|
|
|
# Combine all relevant inputs into a tuple for comparison
|
|
# Use primitive types where possible for reliable hashing/comparison
|
|
return (system_prompt, user_message_box, model,
|
|
temp_float, chat_mode, image_generation,
|
|
tuple(image_hashes))
|
|
|
|
# Node class mappings
|
|
NODE_CLASS_MAPPINGS = {
|
|
"NanoBananaNode": NanoBananaNode
|
|
}
|
|
|
|
# Node display name mappings
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"NanoBananaNode": "Nano Banana (Pro) Node"
|
|
}
|