feat: 新增 Grok 图像节点、前端 UI 增强、重构 nano-banana 系列
- 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]>
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Jony
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-185
@@ -1,79 +1,18 @@
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"""
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图像元数据去除节点
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替代 ComfyUI 原生"保存图像"节点，保存时不写入提示词、工作流等 AI 元数据
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提供两种节点：
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1. SaveCleanImage - 接收 IMAGE 张量，去除元数据后直接保存到 output 目录
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2. BatchCleanMetadata - 指定文件夹路径，批量去除已有图片中的元数据
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提供批量去除已有图片中元数据的功能
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"""
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import os
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from datetime import datetime
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import random
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from typing import List
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import numpy as np
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import torch
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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from ..utils.image_utils import tensor_to_pil
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from ..utils.file_utils import _get_port_suffix
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# 尝试导入 ComfyUI 的 folder_paths
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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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# 支持的图片格式
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SUPPORTED_EXTENSIONS = {'.png', '.jpg', '.jpeg', '.webp', '.bmp', '.tiff', '.tif'}
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def _get_output_dir() -> str:
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"""
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获取 ComfyUI output 目录
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Returns:
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output 目录的绝对路径
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"""
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if FOLDER_PATHS_AVAILABLE:
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return folder_paths.get_output_directory()
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# fallback: 相对于插件目录推断
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plugin_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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return os.path.join(os.path.dirname(os.path.dirname(plugin_dir)), "output")
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def _get_next_counter(directory: str, prefix: str) -> int:
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"""
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扫描目录，获取下一个可用的文件计数器
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Args:
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directory: 目标目录
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prefix: 文件名前缀
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Returns:
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下一个计数器值
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"""
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if not os.path.exists(directory):
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return 1
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if prefix:
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pattern = re.compile(rf'^{re.escape(prefix)}_(\d+)')
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else:
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pattern = re.compile(rf'^(\d+)\.')
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max_counter = 0
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for f in os.listdir(directory):
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m = pattern.match(f)
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if m:
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counter = int(m.group(1))
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max_counter = max(max_counter, counter)
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return max_counter + 1
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def _save_image_clean(image: Image.Image, path: str, fmt: str = None, quality: int = 95) -> None:
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"""
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保存图像，不包含任何元数据
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@@ -127,129 +66,7 @@ def _save_image_clean(image: Image.Image, path: str, fmt: str = None, quality: i
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# ============================================================================
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# 节点 1：保存干净图像
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# ============================================================================
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class SaveCleanImage:
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"""
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保存干净图像节点（不含元数据）
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功能：
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- 接收 IMAGE 张量（支持单图和批次）
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- 去除所有元数据后保存到 ComfyUI/output 目录
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- 文件名自动添加 nometa 标识，方便辨认
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- 支持 PNG/JPEG/WEBP 格式
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- 作为终端节点，替代 ComfyUI 原生"保存图像"节点
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使用场景：
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- 生图完成后，直接保存不含 AI 元数据的干净图像
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- 分享图像时不暴露提示词和工作流
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"""
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SAVE_FORMATS = ["PNG", "JPEG", "WEBP"]
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@classmethod
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def INPUT_TYPES(cls):
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"""
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定义输入参数
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Returns:
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输入参数配置字典
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"""
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return {
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"required": {
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"图像": ("IMAGE",),
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"文件名前缀": ("STRING", {"default": "ComfyUI_nometa"}),
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"保存格式": (cls.SAVE_FORMATS, {"default": "PNG"}),
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},
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"optional": {
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"JPEG/WEBP质量": ("INT", {
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"default": 95,
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"min": 1,
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"max": 100,
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"step": 1
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}),
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}
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}
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RETURN_TYPES = ()
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OUTPUT_NODE = True
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FUNCTION = "save_clean"
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CATEGORY = "image"
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DESCRIPTION = (
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"保存干净图像（不含元数据）。\n"
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"替代 ComfyUI 原生'保存图像'节点，保存时不写入提示词、工作流等 AI 元数据。\n"
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"文件保存到 ComfyUI/output 目录。"
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)
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def save_clean(
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self,
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图像: torch.Tensor,
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文件名前缀: str = "ComfyUI_nometa",
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保存格式: str = "PNG",
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**kwargs
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) -> dict:
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"""
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去除元数据并保存图像
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Args:
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图像: ComfyUI 图像张量 [B, H, W, C]
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文件名前缀: 保存文件名前缀
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保存格式: 图像格式（PNG/JPEG/WEBP）
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**kwargs: 可选参数（JPEG/WEBP质量）
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Returns:
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UI 结果字典，包含保存的图像信息用于前端预览
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"""
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quality = kwargs.get("JPEG/WEBP质量", 95)
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output_dir = _get_output_dir()
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port_suffix = _get_port_suffix()
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os.makedirs(output_dir, exist_ok=True)
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# 格式与扩展名映射
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ext_map = {"PNG": ".png", "JPEG": ".jpg", "WEBP": ".webp"}
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ext = ext_map.get(保存格式, ".png")
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# 转换为 PIL 图像
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pil_images = tensor_to_pil(图像)
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results = []
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saved_paths = []
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for img in pil_images:
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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ms = random.randint(0, 999)
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while True:
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if 文件名前缀:
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filename = f"{文件名前缀}_{ts}_{ms:03d}{port_suffix}{ext}"
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else:
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filename = f"{ts}_{ms:03d}{port_suffix}{ext}"
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filepath = os.path.join(output_dir, filename)
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if not os.path.exists(filepath):
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break
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ms = (ms + 1) % 1000
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_save_image_clean(img, filepath, fmt=保存格式, quality=quality)
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results.append({
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"filename": filename,
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"subfolder": "",
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"type": "output"
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})
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saved_paths.append(filepath)
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# 打印详细日志，方便用户定位保存的文件
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print(f"保存干净图像: 已保存 {len(pil_images)} 张无元数据图像 (格式: {保存格式})")
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for p in saved_paths:
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print(f" → {p}")
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return {"ui": {"images": results}}
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# ============================================================================
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# 节点 2：批量去除元数据
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# 批量去除元数据
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# ============================================================================
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class BatchCleanMetadata:
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