feat: 新增启动欢迎通知、流式预览节点及多项功能更新
- 新增启动弹窗通知（绿色主题，支持关闭） - 新增 StreamPreview 流式文本预览节点 - 新增 fileUpload、updateNotifier 前端 JS 模块 - 重构多个 client，统一错误处理 - 删除废弃节点 batch_nano_banana_v2、quan_neng_sheng_tu 等 - 将 .config 纳入版本控制（已清空密钥）
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Jony
@@ -236,18 +236,6 @@ class BatchNanoBananaPro:
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"分辨率": (all_resolutions, {
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"default": "2K"
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}),
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"像素缩放": ("BOOLEAN", {
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"default": False,
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"label_on": "打开",
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"label_off": "关闭"
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}),
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"分辨率像素": ("FLOAT", {
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"default": 1.0,
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"min": 0.1,
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"max": 100.0,
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"step": 0.1,
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"display": "number"
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}),
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"谷歌搜索（联网）": (["关闭", "打开"], {
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"default": "关闭"
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}),
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@@ -298,11 +286,6 @@ class BatchNanoBananaPro:
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"保存路径": ("STRING", {
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"default": "",
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"multiline": False
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}),
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"跳过错误": ("BOOLEAN", {
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"default": False,
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"label_on": "打开",
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"label_off": "关闭"
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})
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},
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"optional": optional_inputs
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@@ -324,8 +307,6 @@ class BatchNanoBananaPro:
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folder2: Optional[str],
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folder3: Optional[str],
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folder4: Optional[str],
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enable_scaling: bool,
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target_megapixels: float,
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folder5: Optional[str] = None,
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folder6: Optional[str] = None,
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folder7: Optional[str] = None,
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@@ -334,48 +315,25 @@ class BatchNanoBananaPro:
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) -> List[List[ImageInfo]]:
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"""
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加载所有文件夹中的图片
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Args:
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folder1-9: 文件夹路径
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enable_scaling: 是否启用像素缩放
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target_megapixels: 目标像素数（百万像素）
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Returns:
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图片列表的列表
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"""
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folders = [folder1, folder2, folder3, folder4, folder5, folder6, folder7, folder8, folder9]
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all_images = []
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for i, folder in enumerate(folders, 1):
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if folder and folder.strip():
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try:
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images = load_images_from_folder(folder)
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if images:
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# 应用像素缩放
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if enable_scaling:
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scaled_images = []
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for img_info in images:
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scaled_img = self.resize_to_megapixels(
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img_info.image,
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target_megapixels
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)
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# 创建新的 ImageInfo，保留其他元数据
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scaled_info = ImageInfo(
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image=scaled_img,
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filename=img_info.filename,
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extension=img_info.extension,
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source_path=img_info.source_path
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)
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scaled_images.append(scaled_info)
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images = scaled_images
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all_images.append(images)
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else:
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# 空文件夹，静默跳过
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pass
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except ValueError as e:
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print(f"BatchNanoBananaPro: 文件夹{i} 加载失败 - {e}")
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return all_images
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def _create_pairs(
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@@ -596,8 +554,7 @@ class BatchNanoBananaPro:
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total_tasks = len(pairs)
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# 保持并发数为10不变（按用户要求）
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max_concurrent = 10
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max_concurrent = 50
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# 分批保存的批次大小（与并发数一致）
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save_batch_size = 10
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@@ -627,7 +584,7 @@ class BatchNanoBananaPro:
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if num_batches > 1:
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print(f"BatchNanoBananaPro: 任务数 {total_tasks} 超过并发上限 {max_concurrent}，将分 {num_batches} 批执行")
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connector = aiohttp.TCPConnector(limit=0, limit_per_host=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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# 分批处理：每批最多10个任务
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@@ -761,14 +718,11 @@ class BatchNanoBananaPro:
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文件夹7: str,
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文件夹8: str,
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文件夹9: str,
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像素缩放: bool,
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分辨率像素: float,
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seed: int,
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图片配对模式: str,
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模型: str,
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宽高比: str,
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分辨率: str,
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跳过错误: bool = False,
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保存路径: str = "",
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**kwargs
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) -> Tuple[torch.Tensor]:
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@@ -778,8 +732,6 @@ class BatchNanoBananaPro:
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Args:
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prompt: 提示词
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文件夹1-9: 图片文件夹路径
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像素缩放: 是否启用像素缩放
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分辨率像素: 目标像素数（百万像素）
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seed: 随机种子
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保存路径: 输出保存路径
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图片配对模式: 1:1 或 1*N
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@@ -840,7 +792,6 @@ class BatchNanoBananaPro:
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print("BatchNanoBananaPro: 开始加载图片...")
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image_lists = self._load_folders(
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文件夹1, 文件夹2, 文件夹3, 文件夹4,
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像素缩放, 分辨率像素,
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文件夹5, 文件夹6, 文件夹7, 文件夹8, 文件夹9
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)
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@@ -856,10 +807,6 @@ class BatchNanoBananaPro:
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if key in kwargs and kwargs[key] is not None:
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pil_images = tensor_to_pil(kwargs[key])
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for j, img in enumerate(pil_images):
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# 如果启用像素缩放，也对参考图进行缩放
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if 像素缩放:
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img = self.resize_to_megapixels(img, 分辨率像素)
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manual_images.append(
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ImageInfo(
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image=img,
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@@ -976,9 +923,9 @@ class BatchNanoBananaPro:
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with ThreadPoolExecutor(max_workers=1) as executor:
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future = executor.submit(run_async_in_thread)
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try:
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results = future.result(timeout=3600) # 1小时超时
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results = future.result(timeout=900) # 900秒超时
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except TimeoutError:
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print("BatchNanoBananaPro: 任务执行超时（1小时）")
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print("BatchNanoBananaPro: 任务执行超时（900秒）")
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raise RuntimeError("任务执行超时，请减少任务数量或检查网络连接")
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except Exception as e:
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# 即使失败，也尝试返回部分结果
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@@ -1078,24 +1025,12 @@ class BatchNanoBananaPro:
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if str(e) == "未授权！":
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print("请联系作者授权后方可使用！")
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raise ValueError("未授权！") from None
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if 跳过错误:
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print("BatchNanoBananaPro: ⚠️ 跳过错误已开启，返回占位图继续执行队列")
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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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raise ValueError(str(e)) from None
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except RuntimeError as e:
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if 跳过错误:
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print("BatchNanoBananaPro: ⚠️ 跳过错误已开启，返回占位图继续执行队列")
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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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raise RuntimeError(str(e)) from None
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except Exception as e:
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if 跳过错误:
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print("BatchNanoBananaPro: ⚠️ 跳过错误已开启，返回占位图继续执行队列")
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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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raise type(e)(str(e)) from None
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finally:
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