JonyandClaude Opus 4.6
69279c654d
- 新增 Grok Image 节点及客户端 - 新增 save_image_format 节点 - 新增前端 JS 扩展：画笔工具、点阵网格、侧边栏隐藏、资源切换、重命名等 - 重构 nano-banana 节点，移除 pro 版本 - 移除 multi_res_preview 节点 - 新增 http_error 工具模块 - 各客户端和节点优化改进 Co-Authored-By: Claude Opus 4.6 <[email protected]>
636 lines
24 KiB
Python
636 lines
24 KiB
Python
"""
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Nano Banana 节点 (V3)
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ComfyUI 自定义节点，用于调用生图模型（OpenAI 兼容接口）
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使用 V3 DynamicCombo 实现模型-宽高比-分辨率动态联动
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"""
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import io as _io
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import re
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import json
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import time
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import math
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import base64
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import random
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import asyncio
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import aiohttp
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from concurrent.futures import ThreadPoolExecutor
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from typing import List, Optional
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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 comfy_api.latest import io
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from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts, encode_image_to_base64, encode_image_to_base64_limited
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from ..utils.config import (
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NETWORK_ROUTE_OPTIONS,
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get_base_url_by_route,
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get_api_key_or_raise,
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)
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from ..utils.http_error import RETRYABLE_STATUS_CODES, HTTP_ERROR_MESSAGES, _compute_delay, DEFAULT_MAX_RETRIES, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR
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from ..clients.gemini_client import GeminiAPIClient
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try:
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import folder_paths
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FOLDER_PATHS_AVAILABLE = True
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except ImportError:
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FOLDER_PATHS_AVAILABLE = False
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try:
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from comfy.utils import ProgressBar
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PROGRESS_BAR_AVAILABLE = True
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except ImportError:
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PROGRESS_BAR_AVAILABLE = False
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try:
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import psutil
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MEMORY_MONITOR_AVAILABLE = True
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except ImportError:
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MEMORY_MONITOR_AVAILABLE = False
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DEBUG_LOG_ENABLED = True
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REQUEST_LOG_ENABLED = False
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_NODE = "Nano Banana"
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_ENDPOINT = "/v1/chat/completions"
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_client_instance = None
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def _get_client():
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global _client_instance
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if _client_instance is None:
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_client_instance = GeminiAPIClient()
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return _client_instance
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def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.Tensor:
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if not images:
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placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
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return pil_to_tensor([placeholder])
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base_size = max(images, key=lambda img: img.size[0] * img.size[1]).size
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matched = [img for img in images if img.size == base_size]
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skipped = [img for img in images if img.size != base_size]
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if skipped:
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sizes_str = ", ".join(f"{img.size[0]}x{img.size[1]}" for img in skipped)
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print(
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f"{node_label}: 丢弃 {len(skipped)} 张较小尺寸的图 ({sizes_str})，"
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f"仅输出最大尺寸 {base_size[0]}x{base_size[1]} 的 {len(matched)} 张"
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)
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return pil_to_tensor(matched)
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MODEL_ID_MAP = {
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"Nano Banana Pro": "nano-banana-pro",
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"Nano Banana 2": "nano-banana-2",
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"Nano Banana": "nano-banana",
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}
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RESOLUTION_KEY_MAP = {
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"512px": "0.5k",
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"1K": "1k",
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"2K": "2k",
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"4K": "4k",
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}
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BILLING_SPECIAL_ONLY = {"nano-banana"}
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def _build_model_id(model_name: str, resolution: str, billing: str) -> str:
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base = MODEL_ID_MAP.get(model_name, "nano-banana-pro")
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if base == "nano-banana":
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if billing == "官方":
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raise ValueError(f"模型 \"{model_name}\" 仅支持特价计费")
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return "nano-banana"
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res_key = RESOLUTION_KEY_MAP.get(resolution, "2k")
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is_official = (billing == "官方")
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if base == "nano-banana-pro" and res_key == "1k" and not is_official:
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return "nano-banana-pro"
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if base == "nano-banana-2" and res_key == "0.5k":
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if is_official:
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raise ValueError("Nano Banana 2 的 512px 分辨率仅支持特价计费")
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return "nano-banana-2-0.5k"
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model_id = f"{base}-{res_key}"
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if is_official:
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model_id += "-official"
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return model_id
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_IMAGE_RE = re.compile(r"!\[.*?\]\(data:image/(\w+);base64,([A-Za-z0-9+/=]+)\)")
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def _get_headers(api_key: str) -> dict:
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return {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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"X-Accel-Buffering": "no",
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"Cache-Control": "no-cache, no-transform",
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}
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def _build_request_body(
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prompt: str,
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model: str,
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aspect_ratio: str,
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resolution: str,
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images: Optional[List[Image.Image]] = None,
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enable_grounding: bool = False,
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thinking_level: Optional[str] = None,
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) -> dict:
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content_parts = [{"type": "text", "text": prompt}]
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if images:
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for img in images:
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b64 = encode_image_to_base64_limited(img, format="PNG")
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content_parts.append({
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"type": "image_url",
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"image_url": {"url": f"data:image/png;base64,{b64}"}
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})
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body = {
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"model": model,
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"stream": True,
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"messages": [{"role": "user", "content": content_parts}],
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}
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google_config = {
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"image_config": {
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"image_size": resolution,
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}
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}
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if aspect_ratio and aspect_ratio != "智能":
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google_config["image_config"]["aspect_ratio"] = aspect_ratio
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if thinking_level:
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google_config["thinking_config"] = {
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"thinking_level": thinking_level.lower(),
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"include_thoughts": True,
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}
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body["extra_body"] = {"google": google_config}
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if enable_grounding:
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body["extra_body"]["google_search"] = True
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return body
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async def _generate_single(
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session: aiohttp.ClientSession,
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base_url: str,
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api_key: str,
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prompt: str,
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model: str,
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resolution: str,
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aspect_ratio: str,
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images: Optional[List[Image.Image]] = None,
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enable_grounding: bool = False,
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thinking_level: Optional[str] = None,
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) -> List[Image.Image]:
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url = f"{base_url}{_ENDPOINT}"
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headers = _get_headers(api_key)
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body = _build_request_body(
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prompt=prompt,
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model=model,
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aspect_ratio=aspect_ratio,
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resolution=resolution,
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images=images,
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enable_grounding=enable_grounding,
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thinking_level=thinking_level,
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)
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if REQUEST_LOG_ENABLED:
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extra = json.dumps(body.get("extra_body", {}), ensure_ascii=False)
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print(f"[请求] POST {url} | model={model} | extra_body={extra}")
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last_status = None
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for attempt in range(DEFAULT_MAX_RETRIES + 1):
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resp = await session.post(url, headers=headers, json=body)
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if resp.status == 200:
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break
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last_status = resp.status
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if resp.status in RETRYABLE_STATUS_CODES and attempt < DEFAULT_MAX_RETRIES:
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friendly = HTTP_ERROR_MESSAGES.get(resp.status, f"请求失败 ({resp.status})")
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delay = _compute_delay(attempt, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY, DEFAULT_BACKOFF_FACTOR)
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print(f"Nano Banana: {friendly} {delay:.1f}s 后重试 ({attempt+1}/{DEFAULT_MAX_RETRIES})...")
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resp.close()
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await asyncio.sleep(delay)
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continue
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error_text = await resp.text()
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resp.close()
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if resp.status in HTTP_ERROR_MESSAGES:
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raise RuntimeError(HTTP_ERROR_MESSAGES[resp.status])
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try:
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err_json = json.loads(error_text)
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msg = err_json.get("error", {}).get("message", error_text[:200])
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except Exception:
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msg = error_text[:200]
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raise RuntimeError(f"API 错误 ({resp.status}): {msg}")
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else:
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if last_status and last_status in HTTP_ERROR_MESSAGES:
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raise RuntimeError(HTTP_ERROR_MESSAGES[last_status])
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raise RuntimeError(f"API 错误: 重试 {DEFAULT_MAX_RETRIES} 次后仍然失败")
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full_content = ""
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buffer = ""
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t_request = time.time()
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t_first_token = None
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async for raw_chunk in resp.content.iter_any():
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if t_first_token is None:
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t_first_token = time.time()
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buffer += raw_chunk.decode("utf-8")
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while "\n" in buffer:
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line_str, buffer = buffer.split("\n", 1)
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line_str = line_str.strip()
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if not line_str or not line_str.startswith("data:"):
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continue
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data_str = line_str[5:].strip()
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if data_str == "[DONE]":
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break
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try:
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chunk = json.loads(data_str)
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delta = chunk.get("choices", [{}])[0].get("delta", {})
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if "content" in delta:
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full_content += delta["content"]
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except (json.JSONDecodeError, IndexError):
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continue
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t_done = time.time()
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resp.close()
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if not full_content:
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raise RuntimeError("API 未返回有效内容")
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# 思考模型可能输出多张临时图片，最终图片始终是最后一张
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matches = list(_IMAGE_RE.finditer(full_content))
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if not matches:
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raise RuntimeError(f"响应中未找到图片: {full_content[:100]}")
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last_match = matches[-1]
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img_data = base64.b64decode(last_match.group(2))
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final_image = Image.open(_io.BytesIO(img_data)).convert("RGB")
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first_token_ms = (t_first_token - t_request) * 1000 if t_first_token else 0
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download_ms = (t_done - t_first_token) * 1000 if t_first_token else 0
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return [final_image], first_token_ms, download_ms
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async def _generate_single_task(
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session: aiohttp.ClientSession,
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base_url: str,
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api_key: str,
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prompt: str,
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model: str,
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resolution: str,
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aspect_ratio: str,
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images: Optional[List[Image.Image]],
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global_task_index: int,
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enable_grounding: bool = False,
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thinking_level: Optional[str] = None,
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) -> dict:
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result = {
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"global_task_index": global_task_index,
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"prompt": prompt,
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"success": False,
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"generated_count": 0,
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"output_images": [],
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"error": None,
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}
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try:
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gen_images, first_token_ms, download_ms = await _generate_single(
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session=session,
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base_url=base_url,
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api_key=api_key,
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prompt=prompt,
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model=model,
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resolution=resolution,
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aspect_ratio=aspect_ratio,
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images=images if images else None,
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enable_grounding=enable_grounding,
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thinking_level=thinking_level,
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)
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result["output_images"] = gen_images
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result["success"] = True
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result["generated_count"] = len(gen_images)
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except Exception as e:
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result["error"] = str(e)
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return result
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async def _process_batch_async(
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base_url: str,
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api_key: str,
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prompts: List[str],
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model: str,
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resolution: str,
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aspect_ratio: str,
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images_per_prompt: int,
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input_images: Optional[List[Image.Image]],
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pbar=None,
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enable_grounding: bool = False,
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thinking_level: Optional[str] = None,
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) -> List[dict]:
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tasks_def = []
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for p_idx, prompt in enumerate(prompts):
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for sub_idx in range(images_per_prompt):
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tasks_def.append((p_idx, sub_idx, prompt))
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total_tasks = len(tasks_def)
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max_concurrent = 50
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num_batches = math.ceil(total_tasks / max_concurrent)
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all_results = []
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completed = 0
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success_count = 0
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fail_count = 0
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connector = aiohttp.TCPConnector(ssl=False, limit=0, limit_per_host=0)
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async with aiohttp.ClientSession(connector=connector) as session:
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for batch_idx in range(num_batches):
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start_idx = batch_idx * max_concurrent
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end_idx = min(start_idx + max_concurrent, total_tasks)
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tasks = []
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for i in range(start_idx, end_idx):
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_, _, prompt = tasks_def[i]
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task = asyncio.create_task(
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_generate_single_task(
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session=session,
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base_url=base_url,
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api_key=api_key,
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prompt=prompt,
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model=model,
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resolution=resolution,
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aspect_ratio=aspect_ratio,
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images=input_images,
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global_task_index=i,
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enable_grounding=enable_grounding,
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thinking_level=thinking_level,
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)
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)
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tasks.append(task)
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batch_results = []
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for coro in asyncio.as_completed(tasks):
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result_data = None
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try:
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result = await coro
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if isinstance(result, Exception):
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result_data = {"success": False, "error": str(result), "generated_count": 0, "output_images": [], "prompt": ""}
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else:
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result_data = result
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except Exception as e:
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result_data = {"success": False, "error": str(e), "generated_count": 0, "output_images": [], "prompt": ""}
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batch_results.append(result_data)
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completed += 1
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prompt_snippet = (result_data.get("prompt", "") or "")[:30]
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if result_data and result_data.get("success", False):
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success_count += 1
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count = result_data.get("generated_count", 1)
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print(f"Nano Banana: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✓成功({count}张)")
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else:
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fail_count += 1
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error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
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print(f"Nano Banana: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✗失败: {error_msg}")
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if pbar is not None:
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pbar.update(1)
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all_results.extend(batch_results)
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import gc; gc.collect()
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await asyncio.sleep(0.1)
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return all_results
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class NanoBanana(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="NanoBanana",
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display_name="Nano Banana",
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category="image/generation",
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inputs=[
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io.String.Input(
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"prompt",
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default="一个中国女子的OOTD",
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multiline=True,
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),
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io.DynamicCombo.Input("模型", options=[
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io.DynamicCombo.Option("Nano Banana Pro", [
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io.Combo.Input("宽高比", options=[
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"智能", "1:1", "2:3", "3:2", "3:4", "4:3",
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"4:5", "5:4", "9:16", "16:9", "21:9",
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], default="智能"),
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io.Combo.Input("分辨率", options=["1K", "2K", "4K"], default="2K"),
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]),
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io.DynamicCombo.Option("Nano Banana 2", [
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io.Combo.Input("宽高比", options=[
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"智能", "1:1", "1:4", "1:8", "2:3", "3:2", "3:4",
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"4:1", "4:3", "4:5", "5:4", "8:1",
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"9:16", "16:9", "21:9",
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], default="智能"),
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io.Combo.Input("分辨率", options=["512px", "1K", "2K", "4K"], default="2K"),
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io.Combo.Input("思考深度", options=["高", "低"], default="高"),
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]),
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io.DynamicCombo.Option("Nano Banana", [
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io.Combo.Input("宽高比", options=[
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"智能", "1:1", "2:3", "3:2", "3:4", "4:3",
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"4:5", "5:4", "9:16", "16:9", "21:9",
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], default="智能"),
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io.Combo.Input("分辨率", options=["1K"], default="1K"),
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]),
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]),
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io.Int.Input("生图数量", default=1, min=1, max=1000, step=1),
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io.Combo.Input("网络", options=NETWORK_ROUTE_OPTIONS, default="全球加速"),
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io.Combo.Input("计费", options=["特价", "官方"], default="特价"),
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io.Combo.Input("谷歌搜索", options=["关闭", "打开"], default="关闭"),
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io.Int.Input("seed", default=0, min=0, max=0xFFFFFFFFFFFFFFFF),
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io.Image.Input("参考图1", optional=True),
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io.Image.Input("参考图2", optional=True),
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io.Image.Input("参考图3", optional=True),
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io.Image.Input("参考图4", optional=True),
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io.Image.Input("参考图5", optional=True),
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io.Image.Input("参考图6", optional=True),
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io.Image.Input("参考图7", optional=True),
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io.Image.Input("参考图8", optional=True),
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io.Image.Input("参考图9", optional=True),
|
||
],
|
||
outputs=[
|
||
io.Image.Output(display_name="输出图像"),
|
||
],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, prompt, 模型, 生图数量, 计费, 网络, 谷歌搜索, seed, **kwargs) -> io.NodeOutput:
|
||
start_time = time.time()
|
||
|
||
model_name = 模型["模型"]
|
||
宽高比 = 模型["宽高比"]
|
||
分辨率 = 模型["分辨率"]
|
||
思考深度 = 模型.get("思考深度")
|
||
|
||
enable_grounding = (谷歌搜索 == "打开")
|
||
|
||
thinking_level = None
|
||
if model_name == "Nano Banana 2" and 思考深度:
|
||
thinking_level = "High" if 思考深度 == "高" else "Low"
|
||
|
||
actual_model = _build_model_id(model_name, 分辨率, 计费)
|
||
|
||
api_key = get_api_key_or_raise("O1KEY_API_KEY")
|
||
base_url = get_base_url_by_route(网络)
|
||
|
||
pbar = ProgressBar(生图数量) if PROGRESS_BAR_AVAILABLE else None
|
||
|
||
try:
|
||
random.seed(seed)
|
||
np.random.seed(seed % (2**32))
|
||
|
||
input_images = []
|
||
for i in range(1, 10):
|
||
key = f"参考图{i}"
|
||
if key in kwargs and kwargs[key] is not None:
|
||
pil_imgs = tensor_to_pil(kwargs[key])
|
||
input_images.extend(pil_imgs)
|
||
|
||
if input_images and len(input_images) > 14:
|
||
raise ValueError(f"输入图像数量 {len(input_images)} 超过限制 14 张")
|
||
|
||
batch_prompts = parse_batch_prompts(prompt)
|
||
|
||
grounding_str = " | 谷歌搜索接地" if enable_grounding else ""
|
||
thinking_str = f" | 思考:{thinking_level}" if thinking_level else ""
|
||
if batch_prompts:
|
||
num_prompts = len(batch_prompts)
|
||
total_images = num_prompts * 生图数量
|
||
mode_str = f"批量提示词模式 ({num_prompts}个提示词)"
|
||
if input_images:
|
||
mode_str += f" (输入{len(input_images)}张)"
|
||
print(f"Nano Banana: {mode_str} | {分辨率} {宽高比} | 共{total_images}张{grounding_str}{thinking_str}")
|
||
else:
|
||
mode_str = f"图生图模式 (输入{len(input_images)}张)" if input_images else "文生图模式"
|
||
print(f"Nano Banana: {mode_str} | {分辨率} {宽高比} | {生图数量}张{grounding_str}{thinking_str}")
|
||
|
||
if batch_prompts or 生图数量 > 1:
|
||
prompts = batch_prompts if batch_prompts else [prompt]
|
||
images_per_prompt = 生图数量
|
||
total_tasks = len(prompts) * images_per_prompt
|
||
|
||
if pbar is not None:
|
||
pbar = ProgressBar(total_tasks)
|
||
|
||
def run_async_in_thread():
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
try:
|
||
return loop.run_until_complete(
|
||
_process_batch_async(
|
||
base_url=base_url,
|
||
api_key=api_key,
|
||
prompts=prompts,
|
||
model=actual_model,
|
||
resolution=分辨率,
|
||
aspect_ratio=宽高比,
|
||
images_per_prompt=images_per_prompt,
|
||
input_images=input_images,
|
||
pbar=pbar,
|
||
enable_grounding=enable_grounding,
|
||
thinking_level=thinking_level,
|
||
)
|
||
)
|
||
finally:
|
||
loop.close()
|
||
|
||
with ThreadPoolExecutor(max_workers=1) as executor:
|
||
future = executor.submit(run_async_in_thread)
|
||
try:
|
||
results = future.result(timeout=900)
|
||
except TimeoutError:
|
||
raise RuntimeError("任务执行超时（900秒）")
|
||
|
||
success_count = sum(1 for r in results if r.get("success", False))
|
||
fail_count = len(results) - success_count
|
||
elapsed = time.time() - start_time
|
||
time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
|
||
print(f"完成！总耗时 {time_str} | 成功: {success_count}/{total_tasks} | 失败: {fail_count}")
|
||
|
||
output_images = []
|
||
for r in results:
|
||
output_images.extend(r.get("output_images", []))
|
||
if not output_images:
|
||
placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
|
||
output_images = [placeholder]
|
||
|
||
output_tensor = _images_to_tensor_safe(output_images, _NODE)
|
||
import gc; gc.collect()
|
||
return io.NodeOutput(output_tensor)
|
||
|
||
else:
|
||
def run_single():
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
try:
|
||
async def _do():
|
||
connector = aiohttp.TCPConnector(ssl=False)
|
||
async with aiohttp.ClientSession(connector=connector) as session:
|
||
return await _generate_single(
|
||
session=session,
|
||
base_url=base_url,
|
||
api_key=api_key,
|
||
prompt=prompt,
|
||
model=actual_model,
|
||
resolution=分辨率,
|
||
aspect_ratio=宽高比,
|
||
images=input_images if input_images else None,
|
||
enable_grounding=enable_grounding,
|
||
thinking_level=thinking_level,
|
||
)
|
||
return loop.run_until_complete(_do())
|
||
finally:
|
||
loop.close()
|
||
|
||
with ThreadPoolExecutor(max_workers=1) as executor:
|
||
future = executor.submit(run_single)
|
||
generated_images, first_token_ms, download_ms = future.result(timeout=900)
|
||
|
||
if pbar is not None:
|
||
pbar.update(1)
|
||
|
||
output_tensor = _images_to_tensor_safe(generated_images, _NODE)
|
||
elapsed = time.time() - start_time
|
||
time_str = f"{elapsed:.3f}s" if elapsed < 1 else f"{elapsed:.2f}s"
|
||
ft_str = f"{first_token_ms/1000:.2f}s"
|
||
dl_str = f"{download_ms/1000:.2f}s"
|
||
print(f"完成！总耗时 {time_str} | 首字 {ft_str} | 下载 {dl_str} | 成功 {len(generated_images)}张")
|
||
|
||
import gc; gc.collect()
|
||
return io.NodeOutput(output_tensor)
|
||
|
||
except ValueError as e:
|
||
if str(e) == "未授权！":
|
||
print("请联系作者授权后方可使用！")
|
||
raise ValueError("未授权！") from None
|
||
raise ValueError(str(e)) from None
|
||
except RuntimeError as e:
|
||
raise RuntimeError(str(e)) from None
|
||
except Exception as e:
|
||
raise type(e)(str(e)) from None
|
||
finally:
|
||
try:
|
||
client = _get_client()
|
||
client.base_url = base_url
|
||
balance_data = client.query_balance_sync()
|
||
balance_info = client.format_balance_info(balance_data)
|
||
print(f"Nano Banana: {balance_info}")
|
||
except Exception:
|
||
pass
|
||
import gc; gc.collect() |