feat: 新增AI生图（批量版）节点，模型改名，超时优化与友好报错

- 新增 BatchAsyncImageGenerator 节点（全并发+即时落盘，不怕中途失败丢图） - 原版 AsyncImageGenerator 移除批量提示词功能，单节点只处理单提示词 - 模型改名：限时特价→次卡，gemini→nano-banana-官方 - 异步节点过滤官方计费渠道，仅保留次卡和官方模型 - 单任务超时提升至900s，批量超时改为动态计算（批次数×900s） - No available channel for model 错误转化为中文友好提示 - 新增 base_async_provider / gemini_async_provider 异步客户端基类 Co-Authored-By: Claude Opus 4.6 <[email protected]>
2026-04-29 18:24:55 +08:00
parent b6de4e49ab
commit 2e93a34434
12 changed files with 1405 additions and 741 deletions
@@ -12,8 +12,8 @@ Comfyui_o1key - ComfyUI 自定义节点集合

 import ssl

-from .nodes import NanoBananaPro, NanoBananaProAsync, BatchNanoBananaPro, GoogleGemini, LoadFile, ImageStitchPro, SaveCleanImage, BatchCleanMetadata, VideoPreview, GoogleVeo, FluxImageEdit, UniversalLLMChat, KlingVideo, KlingFirstLastFrame, KlingMotionControlTest, AspectRatioPreset, MultiResPreview, BatchImagesO1key, Seedance, SeedanceMultiModal, StreamPreview, DoubaoImage, O1keyGPTImage, KVideo
-from .nodes import K3Video, K3VideoFirstLast, K3MotionControl, K3MotionVideoCheck
+from .nodes import NanoBananaPro, BatchNanoBananaPro, GoogleGemini, LoadFile, ImageStitchPro, SaveCleanImage, BatchCleanMetadata, VideoPreview, GoogleVeo, FluxImageEdit, UniversalLLMChat, KlingVideo, KlingFirstLastFrame, KlingMotionControlTest, AspectRatioPreset, MultiResPreview, BatchImagesO1key, Seedance, SeedanceMultiModal, StreamPreview, DoubaoImage, O1keyGPTImage, KVideo
+from .nodes import K3Video, K3VideoFirstLast, K3MotionControl, K3MotionVideoCheck, AsyncImageGenerator, BatchAsyncImageGenerator

 # 报错弹框友好文案（不修改原节点代码，仅在外层统一处理）
 _MSG_TIMEOUT = "API 请求超时，请稍后重试或检查网络。"
@@ -46,13 +46,13 @@ def _wrap_generate_for_error_display(cls, attr="generate"):
    setattr(cls, attr, wrapped)

 _wrap_generate_for_error_display(NanoBananaPro)
-_wrap_generate_for_error_display(NanoBananaProAsync)
 _wrap_generate_for_error_display(BatchNanoBananaPro)
+_wrap_generate_for_error_display(AsyncImageGenerator)
+_wrap_generate_for_error_display(BatchAsyncImageGenerator)

 # ComfyUI 节点注册
 NODE_CLASS_MAPPINGS = {
    "NanoBananaPro": NanoBananaPro,
-    "NanoBananaProAsync": NanoBananaProAsync,
    "BatchNanoBananaPro": BatchNanoBananaPro,
    "GoogleGemini": GoogleGemini,
    "LoadFile": LoadFile,
@@ -79,11 +79,12 @@ NODE_CLASS_MAPPINGS = {
    "K3VideoFirstLast": K3VideoFirstLast,
    "K3MotionControl": K3MotionControl,
    "K3MotionVideoCheck": K3MotionVideoCheck,
+    "AsyncImageGenerator": AsyncImageGenerator,
+    "BatchAsyncImageGenerator": BatchAsyncImageGenerator,
 }

 NODE_DISPLAY_NAME_MAPPINGS = {
    "NanoBananaPro": "Nano Banana",
-    "NanoBananaProAsync": "Nano Banana（异步）",
    "BatchNanoBananaPro": "批量 Nano Banana",
    "GoogleGemini": "Google Gemini",
    "LoadFile": "加载文件",
@@ -110,6 +111,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "K3VideoFirstLast": "首尾帧 K3 自研",
    "K3MotionControl": "动作控制 K3 自研",
    "K3MotionVideoCheck": "视频时长检测 K3",
+    "AsyncImageGenerator": "AI生图",
+    "BatchAsyncImageGenerator": "AI生图（批量版）",
 }

 WEB_DIRECTORY = "./web"
@@ -0,0 +1,183 @@
+"""
+异步生图 Provider 抽象基类
+定义异步提交+轮询模式的统一接口，支持多种生图模型后端
+
+每个 Provider 封装一种 API 后端的通信协议：
+  - 如何提交任务（端点、请求体格式）
+  - 如何轮询状态（端点、状态字段语义）
+  - 如何解析结果（响应格式、图片提取方式）
+
+新增第三方生图模型时，只需实现此接口即可接入异步节点。
+"""
+
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional
+from PIL import Image
+
+
+class BaseAsyncImageProvider(ABC):
+    """异步生图 Provider 抽象基类"""
+
+    def __init__(self, api_key: str, proxy_url: Optional[str] = None):
+        self.api_key = api_key
+        self.proxy_url = proxy_url
+
+    # ========================================================================
+    # 必须实现的抽象方法
+    # ========================================================================
+
+    @property
+    @abstractmethod
+    def api_base_url(self) -> str:
+        """异步 API 的基础 URL，如 https://cf-api.o1key.com"""
+        ...
+
+    @abstractmethod
+    def get_submit_endpoint(self, model: str, resolution: str) -> str:
+        """获取提交任务的 API 端点路径（不含 base_url）"""
+        ...
+
+    @abstractmethod
+    def build_submit_body(
+        self,
+        prompt: str,
+        model: str,
+        resolution: str,
+        aspect_ratio: str,
+        images: Optional[List[Image.Image]] = None,
+        **kwargs
+    ) -> dict:
+        """构建提交任务的请求体"""
+        ...
+
+    @abstractmethod
+    def extract_task_id(self, response: dict) -> str:
+        """从提交响应中提取 task_id"""
+        ...
+
+    @abstractmethod
+    def extract_status(self, response: dict) -> str:
+        """从轮询响应中提取任务状态（如 SUBMITTED / IN_PROGRESS / SUCCESS / FAILURE）"""
+        ...
+
+    @abstractmethod
+    async def parse_result(
+        self,
+        result_data: dict,
+        session
+    ) -> List[Image.Image]:
+        """从任务完成后的 result data 中解析生成的图像列表"""
+        ...
+
+    @abstractmethod
+    def get_models(self) -> List[str]:
+        """获取此 Provider 支持的模型 ID 列表"""
+        ...
+
+    @abstractmethod
+    def get_model_aspect_ratios(self, model_id: str) -> List[str]:
+        """获取指定模型支持的宽高比"""
+        ...
+
+    @abstractmethod
+    def get_model_resolutions(self, model_id: str) -> List[str]:
+        """获取指定模型支持的分辨率"""
+        ...
+
+    # ========================================================================
+    # 可选的覆盖方法
+    # ========================================================================
+
+    def get_poll_endpoint(self, task_id: str) -> str:
+        """获取轮询任务状态的 API 端点路径（默认实现适用于 o1key 异步 API）"""
+        return f"/async/v1/tasks/{task_id}"
+
+    def get_headers(self) -> dict:
+        """获取 HTTP 请求头"""
+        return {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+
+    def get_all_aspect_ratios(self) -> List[str]:
+        """获取所有模型支持的宽高比（去重合并）"""
+        seen = set()
+        result = []
+        for model_id in self.get_models():
+            for ratio in self.get_model_aspect_ratios(model_id):
+                if ratio not in seen:
+                    seen.add(ratio)
+                    result.append(ratio)
+        return result
+
+    def get_all_resolutions(self) -> List[str]:
+        """获取所有模型支持的分辨率（去重，按固定顺序排列）"""
+        _ORDER = ["512px", "1K", "2K", "4K"]
+        seen = set()
+        for model_id in self.get_models():
+            for res in self.get_model_resolutions(model_id):
+                seen.add(res)
+        return [r for r in _ORDER if r in seen]
+
+    def get_extra_inputs(self) -> dict:
+        """
+        返回此 Provider 特有的额外 ComfyUI 输入参数。
+        子类重写以声明 Provider 专有的选项（如 Google Search Grounding）。
+
+        Returns:
+            dict，格式与 ComfyUI INPUT_TYPES 的 optional 字段一致
+        """
+        return {}
+
+    def get_extra_kwargs(self, **kwargs) -> dict:
+        """
+        从 ComfyUI kwargs 中提取此 Provider 特有的参数，
+        转换为 build_submit_body 可接收的 kwargs。
+
+        子类重写以处理 Provider 专有参数。
+        """
+        return {}
+
+    def extract_progress(self, response: dict) -> Optional[float]:
+        """
+        从轮询响应中提取生成进度。
+
+        Args:
+            response: 轮询接口返回的完整响应字典
+
+        Returns:
+            0.0-1.0 之间的进度值，或 None 表示该响应不含进度信息
+        """
+        return None
+
+    def query_balance_sync(self) -> Optional[dict]:
+        """
+        同步查询账户余额（可选）。
+        返回 None 表示不支持。
+        """
+        return None
+
+    def format_balance_info(self, balance_data: dict) -> str:
+        """格式化余额信息为展示文本"""
+        return ""
+
+    # ========================================================================
+    # 工具方法
+    # ========================================================================
+
+    @staticmethod
+    def build_proxy_url(port: str) -> Optional[str]:
+        """
+        将端口号字符串转为 aiohttp 可用的 HTTP 代理 URL。
+        兼容 v2rayN (10808)、Clash Verge (7897) 等。
+
+        Args:
+            port: 用户填写的端口号，如 "7897"，空字符串返回 None
+
+        Returns:
+            代理 URL 或 None
+        """
+        port = (port or "").strip()
+        if not port or not port.isdigit():
+            return None
+        return f"http://127.0.0.1:{port}"
@@ -0,0 +1,149 @@
+"""
+Gemini 异步生图 Provider
+通过 cf-api.o1key.com 的异步提交+轮询接口调用 Gemini 图像生成模型
+
+协议说明：
+  - 提交：POST {base}/async{gemini_endpoint}?image_format=url
+  - 轮询：GET  {base}/async/v1/tasks/{task_id}
+  - 结果：可能直接返回 image_url，也可能返回 Gemini 标准 candidates 格式
+"""
+
+from io import BytesIO
+from typing import Dict, List, Optional
+from PIL import Image
+
+from .base_async_provider import BaseAsyncImageProvider
+from .gemini_client import GeminiAPIClient
+from ..utils.config import get_async_api_base_url, get_api_key_or_raise
+from ..models_config import (
+    get_enabled_models,
+    get_model_supported_aspect_ratios,
+    get_model_supported_resolutions,
+)
+
+
+class GeminiAsyncImageProvider(BaseAsyncImageProvider):
+    """
+    Gemini 异步生图 Provider
+
+    委托 GeminiAPIClient 处理：
+      - 端点构造（get_endpoint）
+      - 请求体构建（build_request_body）
+      - 响应解析（parse_response_async）
+    """
+
+    def __init__(self, api_key: str = None, proxy_url: str = None):
+        if api_key is None:
+            api_key = get_api_key_or_raise("O1KEY_API_KEY")
+        super().__init__(api_key=api_key, proxy_url=proxy_url)
+        self._client = GeminiAPIClient(api_key=api_key)
+
+    # ========================================================================
+    # 抽象方法实现
+    # ========================================================================
+
+    @property
+    def api_base_url(self) -> str:
+        return get_async_api_base_url()
+
+    def get_submit_endpoint(self, model: str, resolution: str) -> str:
+        gemini_endpoint = self._client.get_endpoint(
+            model=model, resolution=resolution, image_format="url"
+        )
+        base = gemini_endpoint.split("?")[0]
+        async_endpoint = f"/async{base}"
+        if "?" in gemini_endpoint:
+            async_endpoint += "?" + gemini_endpoint.split("?", 1)[1]
+        return async_endpoint
+
+    def build_submit_body(
+        self,
+        prompt: str,
+        model: str,
+        resolution: str,
+        aspect_ratio: str,
+        images: Optional[List[Image.Image]] = None,
+        **kwargs
+    ) -> dict:
+        return self._client.build_request_body(
+            prompt=prompt,
+            images=images,
+            aspect_ratio=aspect_ratio,
+            resolution=resolution,
+            enable_grounding=kwargs.get("enable_grounding", False),
+            enable_image_search=kwargs.get("enable_image_search", False),
+        )
+
+    def extract_task_id(self, response: dict) -> str:
+        task_id = response.get("task_id")
+        if not task_id:
+            raise RuntimeError(f"提交响应中未找到 task_id: {response}")
+        return task_id
+
+    def extract_status(self, response: dict) -> str:
+        return response.get("status", "UNKNOWN")
+
+    async def parse_result(self, result_data: dict, session) -> List[Image.Image]:
+        # 异步接口可能直接返回 image_url
+        image_url = result_data.get("image_url", "") if isinstance(result_data, dict) else ""
+        if image_url:
+            async with session.get(image_url) as img_resp:
+                if img_resp.status == 200:
+                    img_bytes = await img_resp.read()
+                    return [Image.open(BytesIO(img_bytes))]
+                raise RuntimeError(f"下载图片失败 ({img_resp.status}): {image_url}")
+
+        # 否则按 Gemini 标准格式解析
+        images_list, _ = await self._client.parse_response_async(result_data, session=session)
+        return images_list
+
+    def get_models(self) -> List[str]:
+        return get_enabled_models()
+
+    def get_model_aspect_ratios(self, model_id: str) -> List[str]:
+        return get_model_supported_aspect_ratios(model_id)
+
+    def get_model_resolutions(self, model_id: str) -> List[str]:
+        return get_model_supported_resolutions(model_id)
+
+    # ========================================================================
+    # 可选方法覆盖
+    # ========================================================================
+
+    def get_extra_inputs(self) -> dict:
+        """Gemini 专有：Google Search Grounding"""
+        return {
+            "联网功能": (["关闭", "打开"], {"default": "关闭"}),
+        }
+
+    def get_extra_kwargs(self, **kwargs) -> dict:
+        return {
+            "enable_grounding": kwargs.pop("联网功能", "关闭") == "打开",
+        }
+
+    def extract_progress(self, response: dict) -> Optional[float]:
+        """从轮询响应中提取进度（0.0-1.0）"""
+        # 直接字段：progress / percentage
+        for field in ("progress", "percentage"):
+            val = response.get(field)
+            if val is not None and isinstance(val, (int, float)):
+                return val / 100.0 if val > 1 else float(val)
+
+        # 嵌套字段：progressInfo / progress_info
+        progress_info = response.get("progressInfo") or response.get("progress_info")
+        if isinstance(progress_info, dict):
+            for field in ("progress", "percentage"):
+                val = progress_info.get(field)
+                if val is not None and isinstance(val, (int, float)):
+                    return val / 100.0 if val > 1 else float(val)
+
+        return None
+
+    def query_balance_sync(self) -> Optional[dict]:
+        try:
+            return self._client.query_balance_sync()
+        except Exception:
+            return None
+
+    def format_balance_info(self, balance_data: dict) -> str:
+        return self._client.format_balance_info(balance_data)
@@ -70,7 +70,7 @@ class GeminiAPIClient(BaseAPIClient):
        from ..models_config import get_model_endpoint

        # 特殊处理：动态端点模型（根据分辨率选择）
-        if model == "nano-banana-pro-限时特价":
+        if model == "nano-banana-pro-次卡":
            if resolution == "1K":
                endpoint = "/v1beta/models/nano-banana-pro:generateContent"
            elif resolution == "2K":
@@ -80,7 +80,7 @@ class GeminiAPIClient(BaseAPIClient):
            else:
                endpoint = "/v1beta/models/nano-banana-pro-2k:generateContent"

-        elif model == "nano-banana-2-限时特价":
+        elif model == "nano-banana-2-次卡":
            if resolution == "512px":
                endpoint = "/v1beta/models/nano-banana-2-0.5k:generateContent"
            elif resolution == "1K":
@@ -33,8 +33,7 @@ _ENDPOINT_EDITS       = "/v1/images/edits/"

 # ── 模型名映射（UI 显示名 → API 实际参数名）─────────────────────────────────
 _MODEL_NAME_MAP = {
-    "gpt-image-1.5-特价": "gpt-image-1.5-special",
-    "gpt-image-2-特价":   "gpt-image-2-special",
+    "gpt-image-2-次卡": "gpt-image-2-special",
 }

 # ── 超时 ──────────────────────────────────────────────────────────────────────
@@ -46,7 +45,7 @@ class GptImageClient:
    GPT Image API 客户端

    接口说明：
-      generations：JSON body，支持 background / quality / size / n / model
+      generations：JSON body，支持 quality / size / n / model
      edits：multipart/form-data，必须包含 image（PNG），可选 mask（PNG）

    两个接口的响应格式相同：
@@ -227,7 +226,6 @@ class GptImageClient:
        prompt: str,
        model: str,
        quality: str,
-        background: str,
        size: str,
        n: int,
        seed: int,
@@ -244,7 +242,6 @@ class GptImageClient:
            "model":      api_model,
            "prompt":     prompt,
            "quality":    quality,
-            "background": background,
            "n":          n,
            "moderation": "low",
        }
@@ -278,7 +275,7 @@ class GptImageClient:

        url = f"{self.base_url}{_ENDPOINT_GENERATIONS}"
        print(f"[o1key GPT Image] {mode} | 模型={model} | quality={quality} | "
-              f"background={background} | size={size} | n={n}")
+              f"size={size} | n={n}")

        connector = aiohttp.TCPConnector(ssl=False, force_close=True)
        timeout   = aiohttp.ClientTimeout(total=_REQUEST_TIMEOUT)
@@ -311,15 +308,12 @@ class GptImageClient:
            return await self._parse_response(resp_json, session)

    # ── 图像编辑（edits 接口，multipart/form-data）──────────────────────────
-    # 注意：o1key 中转服务的 edits 接口暂不支持 quality / background / moderation 参数，
-    # 这些字段暂时不传递，待服务方更新后可恢复。

    async def _edit_async(
        self,
        prompt: str,
        model: str,
        quality: str,
-        background: str,
        size: str,
        n: int,
        seed: int,
@@ -328,8 +322,6 @@ class GptImageClient:
    ) -> List[Image.Image]:
        """
        调用 /v1/images/edits/ 接口（multipart/form-data）。
-        当前仅传递 model / prompt / n / size / image / mask，
-        quality / background / moderation 暂不支持（o1key 服务端限制）。
        """
        # 模型名映射：UI 显示名 → API 参数名
        api_model = _MODEL_NAME_MAP.get(model, model)
@@ -342,13 +334,11 @@ class GptImageClient:
            normalized_tensors.append(t)
        num_images = len(normalized_tensors)

-        # o1key 中转服务的 edits 接口暂不支持 background / moderation / seed，
-        # 待服务方更新后可重新加入。
        form = aiohttp.FormData()
-        form.add_field("model",   api_model)
-        form.add_field("prompt",  prompt)
-        form.add_field("n",       str(n))
-        form.add_field("quality", quality)
+        form.add_field("model",      api_model)
+        form.add_field("prompt",     prompt)
+        form.add_field("n",          str(n))
+        form.add_field("quality",    quality)

        form.add_field("size", size if size else "auto")

@@ -387,7 +377,8 @@ class GptImageClient:
            mode = "图像编辑（无蒙版）"

        url = f"{self.base_url}{_ENDPOINT_EDITS}"
-        print(f"[o1key GPT Image] {mode} | 模型={model} | 参考图={num_images}张 | size={size} | n={n}")
+        print(f"[o1key GPT Image] {mode} | 模型={model} | 参考图={num_images}张 | "
+              f"quality={quality} | size={size} | n={n}")

        connector = aiohttp.TCPConnector(ssl=False, force_close=True)
        timeout   = aiohttp.ClientTimeout(total=_REQUEST_TIMEOUT)
@@ -430,7 +421,6 @@ class GptImageClient:
        prompt: str,
        model: str,
        quality: str,
-        background: str,
        size: str,
        n: int,
        seed: int,
@@ -443,21 +433,19 @@ class GptImageClient:
        路由逻辑：
          - 无 image_tensor  → generations 接口（文生图，JSON body）
          - 有 image_tensor  → edits 接口（图生图/编辑，multipart/form-data）
-            所有模型统一走 multipart，quality/background 通过表单字段传递，
-            new-api 开启"透传请求体"后原样转发给上游。
        """
        use_edits = (image_tensor is not None)

        if use_edits:
            coro = self._edit_async(
                prompt=prompt, model=model, quality=quality,
-                background=background, size=size, n=n, seed=seed,
+                size=size, n=n, seed=seed,
                image_list=image_tensor, mask_tensor=mask_tensor,
            )
        else:
            coro = self._generate_async(
                prompt=prompt, model=model, quality=quality,
-                background=background, size=size, n=n, seed=seed,
+                size=size, n=n, seed=seed,
                image_list=image_tensor,
            )

@@ -56,14 +56,14 @@ class OpenAIAPIClient(BaseAPIClient):
        只是把拼在 URL 路径里的模型段提取出来单独返回。

        Args:
-            model:      节点下拉框中的模型 ID，如 "nano-banana-pro-限时特价"
+            model:      节点下拉框中的模型 ID，如 "nano-banana-pro-次卡"
            resolution: 分辨率字符串，如 "1K" / "2K" / "4K" / "512"

        Returns:
            实际模型名，如 "nano-banana-pro-2k"
        """
        # ── 动态端点模型 ──────────────────────────────────────────────────
-        if model == "nano-banana-pro-限时特价":
+        if model == "nano-banana-pro-次卡":
            if resolution == "1K":
                return "nano-banana-pro"
            elif resolution == "4K":
@@ -44,9 +44,10 @@ from typing import List, Dict, Optional, Tuple

 GEMINI_MODELS = [
    {
-        "id": "nano-banana-pro-限时特价",
-        "description": "Nano Banana Pro 限时特价,根据分辨率自动选择端点 (1K/2K/4K),高性能图像生成模型",
+        "id": "nano-banana-pro-次卡",
+        "description": "Nano Banana Pro 次卡,根据分辨率自动选择端点 (1K/2K/4K),高性能图像生成模型",
        "enabled": True,
+        "provider": "gemini_async",
        "endpoint_type": "dynamic",
        "endpoint": None,  # 动态端点，由代码根据分辨率选择
        "supported_aspect_ratios": [
@@ -58,6 +59,7 @@ GEMINI_MODELS = [
        "id": "nano-banana-pro-官方计费",
        "description": "Nano Banana Pro 官方计费，按分辨率路由 (1K/2K/4K)，使用官方计费通道",
        "enabled": True,
+        "provider": "gemini_async",
        "endpoint_type": "dynamic",
        "endpoint": None,  # 动态端点，由代码根据分辨率选择
        "supported_aspect_ratios": [
@@ -66,9 +68,10 @@ GEMINI_MODELS = [
        "supported_resolutions": ["1K", "2K", "4K"]
    },
    {
-        "id": "nano-banana-2-限时特价",
-        "description": "Nano Banana 2 限时特价，根据分辨率自动选择端点 (512px/1K/2K/4K)，图像生成模型",
+        "id": "nano-banana-2-次卡",
+        "description": "Nano Banana 2 次卡，根据分辨率自动选择端点 (512px/1K/2K/4K)，图像生成模型",
        "enabled": True,
+        "provider": "gemini_async",
        "endpoint_type": "dynamic",
        "endpoint": None,  # 动态端点，由代码根据分辨率选择
        "supported_aspect_ratios": [
@@ -81,6 +84,7 @@ GEMINI_MODELS = [
        "id": "nano-banana-2-官方计费",
        "description": "Nano Banana 2 官方计费，按分辨率路由 (512/1K/2K/4K)，使用官方计费通道",
        "enabled": True,
+        "provider": "gemini_async",
        "endpoint_type": "dynamic",
        "endpoint": None,  # 动态端点，由代码根据分辨率选择
        "supported_aspect_ratios": [
@@ -90,9 +94,10 @@ GEMINI_MODELS = [
        "supported_resolutions": ["512px", "1K", "2K", "4K"]
    },
    {
-        "id": "gemini-3-pro-image-preview",
-        "description": "标准模式,固定端点,适用于常规图像生成",
-        "enabled": False,
+        "id": "nano-banana-pro-官方",
+        "description": "Nano Banana Pro 官方，固定端点，适用于常规图像生成",
+        "enabled": True,
+        "provider": "gemini_async",
        "endpoint_type": "standard",
        "endpoint": "/v1beta/models/gemini-3-pro-image-preview:generateContent",
        "supported_aspect_ratios": [
@@ -101,9 +106,10 @@ GEMINI_MODELS = [
        "supported_resolutions": ["1K", "2K", "4K"]
    },
    {
-        "id": "gemini-3.1-flash-image-preview",
-        "description": "Gemini 3.1 Flash 图像生成,固定端点,快速图像生成模型",
-        "enabled": False,
+        "id": "nano-banana-2-官方",
+        "description": "Nano Banana 2 官方，固定端点，快速图像生成模型",
+        "enabled": True,
+        "provider": "gemini_async",
        "endpoint_type": "standard",
        "endpoint": "/v1beta/models/gemini-3.1-flash-image-preview:generateContent",
        "supported_aspect_ratios": [
@@ -341,6 +347,35 @@ def get_all_supported_resolutions() -> List[str]:
    return [res for res in _ORDER if res in seen]


+def get_model_provider(model_id: str) -> Optional[str]:
+    """
+    获取模型的异步 Provider 名称
+
+    Args:
+        model_id: 模型 ID
+
+    Returns:
+        Provider 名称（如 "gemini_async"），如果模型未配置 provider 则返回 None
+    """
+    config = get_model_config(model_id)
+    if config is None:
+        return None
+    return config.get("provider")
+
+
+def get_enabled_async_models() -> List[str]:
+    """
+    获取所有启用的、支持异步模式的模型 ID 列表
+
+    Returns:
+        模型 ID 列表（仅包含配置了 provider 且 enabled 的模型）
+    """
+    return [
+        model["id"] for model in GEMINI_MODELS
+        if model.get("enabled", False) and model.get("provider")
+    ]
+
+
 def get_endpoint_type(model_id: str) -> Optional[str]:
    """
    获取模型的端点类型
@@ -5,7 +5,6 @@

 from .stream_preview import StreamPreview
 from .nano_banana_pro import NanoBananaPro
-from .nano_banana_pro_async import NanoBananaProAsync
 from .batch_nano_banana_pro import BatchNanoBananaPro
 from .google_gemini import GoogleGemini
 from .load_file import LoadFile
@@ -19,6 +18,7 @@ from .universal_llm import UniversalLLMChat
 from .multi_res_preview import MultiResPreview
 from .batch_images_o1key import BatchImagesO1key
 from .seedance_video import Seedance, SeedanceMultiModal
+from .async_image_generation import AsyncImageGenerator, BatchAsyncImageGenerator
 from .doubao_image import DoubaoImage
 from .gpt_image import O1keyGPTImage
 from .K_video import KVideo
@@ -26,4 +26,4 @@ from .K3_video import K3Video
 from .K3_video_firstlast import K3VideoFirstLast
 from .K3_motion_control import K3MotionControl, K3MotionVideoCheck

-__all__ = ['NanoBananaPro', 'NanoBananaProAsync', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'SaveCleanImage', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'MultiResPreview', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'KVideo', 'K3Video', 'K3VideoFirstLast', 'K3MotionControl', 'K3MotionVideoCheck']
+__all__ = ['NanoBananaPro', 'BatchNanoBananaPro', 'GoogleGemini', 'LoadFile', 'ImageStitchPro', 'SaveCleanImage', 'BatchCleanMetadata', 'VideoPreview', 'KlingVideo', 'KlingFirstLastFrame', 'KlingMotionControlTest', 'AspectRatioPreset', 'GoogleVeo', 'FluxImageEdit', 'UniversalLLMChat', 'MultiResPreview', 'BatchImagesO1key', 'Seedance', 'SeedanceMultiModal', 'StreamPreview', 'DoubaoImage', 'O1keyGPTImage', 'KVideo', 'K3Video', 'K3VideoFirstLast', 'K3MotionControl', 'K3MotionVideoCheck', 'AsyncImageGenerator', 'BatchAsyncImageGenerator']
@@ -0,0 +1,992 @@
+"""
+异步生图节点（通用）
+ComfyUI 自定义节点，通过异步提交+轮询模式调用多种生图模型
+
+架构：
+  - 节点层（本文件）：批量调度、进度条、ComfyUI 集成，不关心具体 API 协议
+  - Provider 层：封装每种 API 后端的通信协议（端点、请求体格式、响应解析）
+
+新增第三方生图模型时，只需实现 BaseAsyncImageProvider 并注册即可。
+"""
+
+import os
+import time
+import math
+import random
+import asyncio
+import aiohttp
+from concurrent.futures import ThreadPoolExecutor
+from typing import Dict, List, Optional, Tuple
+
+try:
+    from comfy.model_management import processing_interrupted, InterruptProcessingException
+    INTERRUPT_AVAILABLE = True
+except ImportError:
+    INTERRUPT_AVAILABLE = False
+    InterruptProcessingException = RuntimeError  # fallback
+
+import torch
+import numpy as np
+from PIL import Image
+
+from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts
+from ..utils.config import get_api_key_or_raise
+from ..models_config import (
+    get_enabled_async_models,
+    get_model_provider,
+    get_model_supported_aspect_ratios,
+    get_all_supported_aspect_ratios,
+    get_model_supported_resolutions,
+    get_all_supported_resolutions,
+)
+from ..clients.base_async_provider import BaseAsyncImageProvider
+
+try:
+    import folder_paths  # noqa: F401
+    FOLDER_PATHS_AVAILABLE = True
+except ImportError:
+    FOLDER_PATHS_AVAILABLE = False
+
+try:
+    from comfy.utils import ProgressBar
+    PROGRESS_BAR_AVAILABLE = True
+except ImportError:
+    PROGRESS_BAR_AVAILABLE = False
+
+try:
+    import psutil
+    MEMORY_MONITOR_AVAILABLE = True
+except ImportError:
+    MEMORY_MONITOR_AVAILABLE = False
+
+DEBUG_LOG_ENABLED = False
+REQUEST_LOG_ENABLED = True
+
+_POLL_INTERVAL = 2      # 轮询间隔（秒）
+_MAX_WAIT_TIME = 900     # 单任务最大等待时间（秒）
+_MAX_CONCURRENT = 50     # 最大并发提交数
+
+
+def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.Tensor:
+    """将图像列表转为 tensor，过滤不同尺寸的图"""
+    if not images:
+        placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
+        return pil_to_tensor([placeholder])
+
+    base_size = max(images, key=lambda img: img.size[0] * img.size[1]).size
+    matched = [img for img in images if img.size == base_size]
+    skipped = [img for img in images if img.size != base_size]
+
+    if skipped:
+        sizes_str = ", ".join(f"{img.size[0]}x{img.size[1]}" for img in skipped)
+        print(
+            f"{node_label}: 丢弃 {len(skipped)} 张较小尺寸的图 ({sizes_str})，"
+            f"仅输出最大尺寸 {base_size[0]}x{base_size[1]} 的 {len(matched)} 张"
+        )
+
+    return pil_to_tensor(matched)
+
+
+class AsyncImageGenerator:
+    """
+    异步生图节点（通用）
+
+    功能：
+      - 异步提交 + 轮询模式，避免 ComfyUI 主线程阻塞
+      - 支持多种生图模型后端（通过 Provider 扩展）
+      - 支持批量提示词、多参考图、代理端口
+    """
+
+    NODE_LABEL = "AI生图"
+
+    # Provider 注册表：provider 名称 → 类路径
+    PROVIDER_CLASSES = {
+        "gemini_async": "..clients.gemini_async_provider.GeminiAsyncImageProvider",
+    }
+
+    # Provider 专有输入参数声明（用于 INPUT_TYPES 合并）
+    _PROVIDER_EXTRA_INPUTS: Dict[str, dict] = {
+        "gemini_async": {
+            "联网功能": (["关闭", "打开"], {"default": "关闭"}),
+        },
+    }
+
+    def __init__(self):
+        self._provider: Optional[BaseAsyncImageProvider] = None
+        self._provider_name: Optional[str] = None
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        # 模型列表（排除官方计费渠道）
+        models = [m for m in get_enabled_async_models() if "官方计费" not in m]
+        if not models:
+            models = ["请在 models_config.py 中启用至少一个异步模型"]
+
+        # 宽高比 / 分辨率（取所有模型的并集，运行时验证）
+        all_aspect_ratios = get_all_supported_aspect_ratios()
+        if not all_aspect_ratios:
+            all_aspect_ratios = [
+                "1:1", "4:3", "3:4", "16:9", "9:16",
+                "2:3", "3:2", "4:5", "5:4", "21:9",
+                "1:4", "4:1", "1:8", "8:1"
+            ]
+        all_resolutions = get_all_supported_resolutions()
+        if not all_resolutions:
+            all_resolutions = ["512px", "1K", "2K", "4K"]
+
+        # 可选输入（按展示顺序）
+        optional = {}
+
+        # Provider 专有参数（紧接 required 参数下方）
+        for provider_extra in cls._PROVIDER_EXTRA_INPUTS.values():
+            optional.update(provider_extra)
+
+        for i in range(1, 10):
+            optional[f"参考图{i}"] = ("IMAGE",)
+
+        optional["seed"] = ("INT", {
+            "default": 0,
+            "min": 0,
+            "max": 0xffffffffffffffff
+        })
+
+        optional["代理端口"] = ("STRING", {
+            "default": "",
+            "multiline": False,
+            "placeholder": "本地代理端口，如 7897（Clash Verge）或 10808（v2rayN），留空不使用"
+        })
+
+        return {
+            "required": {
+                "prompt": ("STRING", {
+                    "default": "一个中国女子的OOTD",
+                    "multiline": True
+                }),
+                "模型": (models, {"default": models[0]}),
+                "宽高比": (all_aspect_ratios, {"default": "1:1"}),
+                "分辨率": (all_resolutions, {"default": "2K"}),
+                "生图数量": ("INT", {
+                    "default": 1,
+                    "min": 1,
+                    "max": 1000,
+                    "step": 1
+                })
+            },
+            "optional": optional
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("输出图像",)
+    FUNCTION = "generate"
+    CATEGORY = "image/generation"
+    OUTPUT_NODE = True
+
+    # ========================================================================
+    # Provider 工厂
+    # ========================================================================
+
+    def _get_provider(self, model_id: str, proxy_url: Optional[str] = None) -> BaseAsyncImageProvider:
+        """根据模型 ID 获取或创建对应的 Provider 实例"""
+        provider_name = get_model_provider(model_id)
+        if not provider_name:
+            raise ValueError(f"模型 \"{model_id}\" 不支持异步模式")
+
+        # 同类型 Provider 复用，只更新代理
+        if self._provider is not None and self._provider_name == provider_name:
+            self._provider.proxy_url = proxy_url
+            return self._provider
+
+        # 创建新 Provider
+        class_path = self.PROVIDER_CLASSES.get(provider_name)
+        if not class_path:
+            raise ValueError(f"未注册的 Provider: {provider_name}")
+
+        module_path, class_name = class_path.rsplit(".", 1)
+        if module_path.startswith(".."):
+            import importlib
+            module = importlib.import_module(module_path, package=__package__)
+        else:
+            import importlib
+            module = importlib.import_module(module_path)
+        provider_class = getattr(module, class_name)
+
+        api_key = get_api_key_or_raise("O1KEY_API_KEY")
+        self._provider = provider_class(api_key=api_key, proxy_url=proxy_url)
+        self._provider_name = provider_name
+        return self._provider
+
+    # ========================================================================
+    # 工具方法
+    # ========================================================================
+
+    @staticmethod
+    def _friendly_error(error_msg: str) -> str:
+        """将上游错误转化为用户友好的提示"""
+        if "No available channel for model" in error_msg:
+            return (
+                "当前分组下模型不可用，请检查分组是否正确。"
+                "若是正常出图过程中遇到该报错，说明该错误只是暂时的，稍后重试即可。或切换其他模型。"
+                f"\n（原始错误: {error_msg}）"
+            )
+        return error_msg
+
+    @staticmethod
+    def _check_interrupt():
+        """检查 ComfyUI 是否点击了取消按钮，是则抛出 InterruptProcessingException"""
+        if INTERRUPT_AVAILABLE and processing_interrupted():
+            raise InterruptProcessingException()
+
+    # ========================================================================
+    # 核心异步逻辑
+    # ========================================================================
+
+    async def _submit_one(
+        self,
+        session: aiohttp.ClientSession,
+        provider: BaseAsyncImageProvider,
+        prompt: str,
+        model: str,
+        resolution: str,
+        aspect_ratio: str,
+        input_images: List[Image.Image],
+        **extra_kwargs,
+    ) -> str:
+        """提交单个异步任务，返回 task_id"""
+        endpoint = provider.get_submit_endpoint(model, resolution)
+        request_body = provider.build_submit_body(
+            prompt=prompt,
+            model=model,
+            resolution=resolution,
+            aspect_ratio=aspect_ratio,
+            images=input_images if input_images else None,
+            **extra_kwargs,
+        )
+
+        url = f"{provider.api_base_url}{endpoint}"
+        headers = provider.get_headers()
+
+        if REQUEST_LOG_ENABLED:
+            import json
+            _log_body = {k: v for k, v in request_body.items()}
+            print(f"[异步提交] URL: {url}")
+            print(f"[异步提交] 请求体: {json.dumps(_log_body, ensure_ascii=False)[:500]}")
+
+        async with session.post(url, json=request_body, headers=headers, proxy=provider.proxy_url) as response:
+            if response.status != 200:
+                error_text = await response.text()
+                raise RuntimeError(f"提交任务失败 ({response.status}): {error_text}")
+            data = await response.json()
+
+        if DEBUG_LOG_ENABLED:
+            import json
+            print(f"[异步提交] 响应: {json.dumps(data, ensure_ascii=False)[:500]}")
+
+        return provider.extract_task_id(data)
+
+    async def _poll_one(
+        self,
+        session: aiohttp.ClientSession,
+        provider: BaseAsyncImageProvider,
+        task_id: str,
+        on_progress=None,
+    ) -> dict:
+        """轮询单个任务直到完成，返回 result data；on_progress(delta) 可选，用于驱动进度条"""
+        poll_endpoint = provider.get_poll_endpoint(task_id)
+        url = f"{provider.api_base_url}{poll_endpoint}"
+        headers = provider.get_headers()
+
+        start_time = time.time()
+        poll_count = 0
+        last_progress = 0.0
+
+        while True:
+            self._check_interrupt()
+
+            elapsed = time.time() - start_time
+            if elapsed > _MAX_WAIT_TIME:
+                raise RuntimeError(f"任务 {task_id} 超时（{_MAX_WAIT_TIME}秒）")
+
+            poll_count += 1
+
+            async with session.get(url, headers=headers, proxy=provider.proxy_url) as response:
+                if response.status != 200:
+                    error_text = await response.text()
+                    raise RuntimeError(f"查询任务失败 ({response.status}): {error_text}")
+
+                result = await response.json()
+                status = provider.extract_status(result)
+
+                # 提取进度并回调（封顶 1.0 防止异常值导致进度条溢出）
+                if on_progress and status in ("SUBMITTED", "IN_PROGRESS"):
+                    p = provider.extract_progress(result)
+                    if p is not None:
+                        p = min(p, 1.0)
+                        if p > last_progress:
+                            on_progress(p - last_progress)
+                            last_progress = p
+                            print(f"{self.NODE_LABEL}: 任务{task_id[:8]}... 进度 {p * 100:.0f}%")
+
+                if DEBUG_LOG_ENABLED:
+                    import json
+                    print(f"[轮询 #{poll_count}] {task_id}: status={status}")
+
+                if status == "SUCCESS":
+                    # 补足剩余进度
+                    if on_progress and last_progress < 1.0:
+                        on_progress(1.0 - last_progress)
+                    return result.get("data", {})
+                elif status == "FAILURE":
+                    error_msg = result.get("error", "未知错误")
+                    friendly_msg = self._friendly_error(error_msg)
+                    raise RuntimeError(f"任务失败: {friendly_msg}")
+                elif status in ("SUBMITTED", "IN_PROGRESS"):
+                    await asyncio.sleep(_POLL_INTERVAL)
+                else:
+                    raise RuntimeError(f"未知任务状态: {status}")
+
+    async def _execute_one(
+        self,
+        session: aiohttp.ClientSession,
+        provider: BaseAsyncImageProvider,
+        prompt: str,
+        model: str,
+        resolution: str,
+        aspect_ratio: str,
+        input_images: List[Image.Image],
+        global_task_index: int,
+        on_progress=None,
+        **extra_kwargs,
+    ) -> dict:
+        """执行单个异步生成任务（提交 + 轮询 + 解析）；on_progress(delta) 可选"""
+        result = {
+            "global_task_index": global_task_index,
+            "prompt": prompt,
+            "success": False,
+            "generated_count": 0,
+            "output_images": [],
+            "error": None,
+        }
+
+        contributed = [0.0]  # mutable container，追踪本任务已贡献的 pbar 进度
+
+        def _track_progress(delta):
+            contributed[0] += delta
+            if on_progress:
+                on_progress(delta)
+
+        try:
+            task_id = await self._submit_one(
+                session, provider, prompt, model,
+                resolution, aspect_ratio, input_images,
+                **extra_kwargs,
+            )
+            response_data = await self._poll_one(
+                session, provider, task_id, on_progress=_track_progress,
+            )
+            images_list = await provider.parse_result(response_data, session)
+
+            result["success"] = True
+            result["generated_count"] = len(images_list)
+            result["output_images"] = images_list
+        except InterruptProcessingException:
+            # 用户取消：补齐进度后向上传播，不吞掉
+            if contributed[0] < 1.0 and on_progress:
+                on_progress(1.0 - contributed[0])
+            raise
+        except Exception as e:
+            result["error"] = str(e)
+            # 失败也补齐 1.0 进度，保证进度条总数正确
+            if contributed[0] < 1.0 and on_progress:
+                on_progress(1.0 - contributed[0])
+
+        return result
+
+    async def _process_batch(
+        self,
+        provider: BaseAsyncImageProvider,
+        prompts: List[str],
+        model: str,
+        resolution: str,
+        aspect_ratio: str,
+        images_per_prompt: int,
+        input_images: List[Image.Image],
+        pbar=None,
+        **extra_kwargs,
+    ) -> List[dict]:
+        """异步批量处理"""
+        # 构建任务定义
+        tasks_def = []
+        for p_idx, prompt in enumerate(prompts):
+            for sub_idx in range(images_per_prompt):
+                tasks_def.append((p_idx, sub_idx, prompt))
+
+        total_tasks = len(tasks_def)
+        num_batches = math.ceil(total_tasks / _MAX_CONCURRENT)
+        all_results: List[dict] = []
+        completed = 0
+
+        # 所有任务共享同一个进度回调，驱动同一个进度条
+        _on_progress = (lambda delta: pbar.update(delta)) if pbar is not None else None
+
+        connector = aiohttp.TCPConnector(ssl=False, limit=0, limit_per_host=0)
+
+        async with aiohttp.ClientSession(connector=connector) as session:
+            for batch_idx in range(num_batches):
+                self._check_interrupt()
+
+                start_idx = batch_idx * _MAX_CONCURRENT
+                end_idx = min(start_idx + _MAX_CONCURRENT, total_tasks)
+
+                batch_tasks = []
+                for i in range(start_idx, end_idx):
+                    _, _, prompt = tasks_def[i]
+
+                    task = asyncio.create_task(
+                        self._execute_one(
+                            session=session,
+                            provider=provider,
+                            prompt=prompt,
+                            model=model,
+                            resolution=resolution,
+                            aspect_ratio=aspect_ratio,
+                            input_images=input_images,
+                            global_task_index=i,
+                            on_progress=_on_progress,
+                            **extra_kwargs,
+                        )
+                    )
+                    batch_tasks.append(task)
+
+                batch_results = []
+                for coro in asyncio.as_completed(batch_tasks):
+                    result_data = None
+                    try:
+                        result_data = await coro
+                    except InterruptProcessingException:
+                        # 用户取消：终止所有未完成任务
+                        for t in batch_tasks:
+                            t.cancel()
+                        raise
+                    except Exception as e2:
+                        # 意外错误（不应发生，_execute_one 内部已捕获常规异常）
+                        result_data = {
+                            "success": False,
+                            "error": str(e2),
+                            "generated_count": 0,
+                            "output_images": [],
+                            "prompt": "",
+                        }
+
+                    batch_results.append(result_data)
+                    completed += 1
+
+                    prompt_snippet = (result_data.get("prompt", "") or "")[:30]
+                    if result_data and result_data.get("success"):
+                        count = result_data.get("generated_count", 1)
+                        print(f"{self.NODE_LABEL}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> OK({count}张)")
+                    else:
+                        error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
+                        print(f"{self.NODE_LABEL}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> FAIL: {error_msg}")
+
+                all_results.extend(batch_results)
+
+                import gc
+                gc.collect()
+                await asyncio.sleep(0.1)
+
+        return all_results
+
+    # ========================================================================
+    # ComfyUI 入口
+    # ========================================================================
+
+    def generate(
+        self,
+        prompt: str,
+        模型: str,
+        宽高比: str,
+        分辨率: str,
+        生图数量: int,
+        **kwargs
+    ) -> Tuple[torch.Tensor]:
+        """生成图像（异步模式）"""
+        start_time = time.time()
+
+        # 提取通用可选参数
+        seed: int = kwargs.pop("seed", 0)
+        proxy_port: str = kwargs.pop("代理端口", "")
+
+        # 初始化 Provider
+        proxy_url = BaseAsyncImageProvider.build_proxy_url(proxy_port)
+        provider = self._get_provider(模型, proxy_url=proxy_url)
+
+        if proxy_url:
+            print(f"{self.NODE_LABEL}: 已启用代理加速 -> {proxy_url}")
+
+        # 提取 Provider 专有参数
+        extra_kwargs = provider.get_extra_kwargs(**kwargs)
+
+        # 进度条
+        pbar = None
+        if PROGRESS_BAR_AVAILABLE:
+            pbar = ProgressBar(生图数量)
+
+        try:
+            # 初始化随机种子
+            random.seed(seed)
+            np.random.seed(seed % (2**32))
+
+            # 内存监控
+            if MEMORY_MONITOR_AVAILABLE and 生图数量 > 50:
+                process = psutil.Process()
+                initial_memory = process.memory_info().rss / 1024 / 1024
+                print(f"{self.NODE_LABEL}: 初始内存使用: {initial_memory:.1f} MB")
+
+            # 运行时验证分辨率
+            supported_resolutions = provider.get_model_resolutions(模型)
+            if supported_resolutions and 分辨率 not in supported_resolutions:
+                raise ValueError(
+                    f"分辨率 \"{分辨率}\" 与模型 \"{模型}\" 不兼容！\n"
+                    f"该模型支持的分辨率：{', '.join(supported_resolutions)}"
+                )
+
+            # 运行时验证宽高比
+            supported_ratios = provider.get_model_aspect_ratios(模型)
+            if supported_ratios and 宽高比 not in supported_ratios:
+                raise ValueError(
+                    f"宽高比 \"{宽高比}\" 与模型 \"{模型}\" 不兼容！\n"
+                    f"该模型支持的宽高比：{', '.join(supported_ratios)}"
+                )
+
+            # 收集参考图
+            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"
+                )
+
+            # 单提示词模式
+            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"{self.NODE_LABEL}: {mode_str} | {分辨率} {宽高比} | {生图数量}张")
+            prompts_list = [prompt]
+            images_per_prompt = 生图数量
+            total_tasks = 生图数量
+
+            if total_tasks > 100:
+                print(f"  {self.NODE_LABEL}: 警告！批量生成 {total_tasks} 张图片，内存占用可能较高")
+
+            if pbar is not None:
+                pbar = ProgressBar(total_tasks)
+
+            # 在独立线程中运行异步批量处理
+            def run_async():
+                loop = asyncio.new_event_loop()
+                asyncio.set_event_loop(loop)
+                try:
+                    return loop.run_until_complete(
+                        self._process_batch(
+                            provider=provider,
+                            prompts=prompts_list,
+                            model=模型,
+                            resolution=分辨率,
+                            aspect_ratio=宽高比,
+                            images_per_prompt=images_per_prompt,
+                            input_images=input_images,
+                            pbar=pbar,
+                            **extra_kwargs,
+                        )
+                    )
+                finally:
+                    loop.close()
+
+            with ThreadPoolExecutor(max_workers=1) as executor:
+                future = executor.submit(run_async)
+                # 总超时 = 批次数 × 单任务超时，保证每批都有完整的时间窗口
+                num_batches = math.ceil(total_tasks / _MAX_CONCURRENT)
+                batch_timeout = num_batches * _MAX_WAIT_TIME
+                try:
+                    results = future.result(timeout=batch_timeout)
+                except TimeoutError:
+                    raise RuntimeError(f"任务执行超时（{batch_timeout}秒），请减少数量或检查网络")
+
+            # 统计结果
+            success_count = sum(1 for r in results if r.get("success"))
+            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"{self.NODE_LABEL}: 完成！总耗时 {time_str} | 成功: {success_count}/{total_tasks} | 失败: {fail_count}")
+
+            # 打印失败详情
+            failed = [r for r in results if not r.get("success")]
+            if failed:
+                for fr in failed:
+                    idx = fr.get("global_task_index", -1) + 1
+                    prompt_snippet = (fr.get("prompt", "") or "")[:30]
+                    error_msg = fr.get("error", "未知错误")
+                    print(f"  FAIL #{idx}: {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> {error_msg}")
+
+            # 收集输出图像
+            output_images = []
+            for r in results:
+                output_images.extend(r.get("output_images", []))
+
+            if not output_images:
+                # 收集所有错误原因
+                error_details = "\n".join(
+                    f"  - {r.get('prompt', '未知提示词')[:40]}: {r.get('error', '未知错误')}"
+                    for r in results if not r.get("success")
+                )
+                raise RuntimeError(
+                    f"所有任务均失败 ({fail_count}/{total_tasks})：\n{error_details}"
+                )
+
+            output_tensor = _images_to_tensor_safe(output_images, self.NODE_LABEL)
+
+            import gc
+            gc.collect()
+            return (output_tensor,)
+
+        except InterruptProcessingException:
+            print(f"{self.NODE_LABEL}: 用户取消")
+            raise
+        except ValueError as e:
+            if str(e) == "未授权！":
+                print(f"{self.NODE_LABEL}: 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:
+            # 查询并打印余额
+            try:
+                balance_data = provider.query_balance_sync()
+                if balance_data:
+                    print(f"{self.NODE_LABEL}: {provider.format_balance_info(balance_data)}")
+            except Exception:
+                pass
+
+            import gc
+            gc.collect()
+
+
+class BatchAsyncImageGenerator(AsyncImageGenerator):
+    """
+    AI生图（批量版）- 全并发提交 + 即时落盘
+
+    与原版区别：
+      - 所有任务一次性全并发提交，不分批次
+      - 每完成一个任务立即将图像保存到磁盘，不会因中途失败丢失已完成图片
+      - 最终从磁盘加载所有已保存的图像输出
+    """
+
+    NODE_LABEL = "AI生图（批量版）"
+    RETURN_NAMES = ("输出图像",)
+
+    def __init__(self):
+        super().__init__()
+        self._output_file_paths: List[str] = []
+        self._output_dir: str = ""
+
+    def _get_output_dir(self) -> str:
+        """获取本次运行的输出目录（带时间戳）"""
+        if FOLDER_PATHS_AVAILABLE:
+            base = folder_paths.get_output_directory()
+        else:
+            base = os.path.join(os.path.dirname(__file__), "..", "output")
+        run_id = time.strftime("%Y%m%d_%H%M%S")
+        run_dir = os.path.join(base, f"batch_{run_id}")
+        os.makedirs(run_dir, exist_ok=True)
+        return run_dir
+
+    async def _process_batch(
+        self,
+        provider: BaseAsyncImageProvider,
+        prompts: List[str],
+        model: str,
+        resolution: str,
+        aspect_ratio: str,
+        images_per_prompt: int,
+        input_images: List[Image.Image],
+        pbar=None,
+        **extra_kwargs,
+    ) -> List[dict]:
+        """全并发处理：所有任务一次性提交，谁先完成谁先落盘"""
+        # 构建任务定义
+        tasks_def = []
+        for p_idx, prompt in enumerate(prompts):
+            for sub_idx in range(images_per_prompt):
+                tasks_def.append((p_idx, sub_idx, prompt))
+
+        total_tasks = len(tasks_def)
+        all_results: List[dict] = []
+        completed = 0
+
+        # 初始化输出目录
+        self._output_file_paths = []
+        self._output_dir = self._get_output_dir()
+        print(f"{self.NODE_LABEL}: 输出目录: {self._output_dir}")
+
+        _on_progress = (lambda delta: pbar.update(delta)) if pbar is not None else None
+
+        connector = aiohttp.TCPConnector(ssl=False, limit=0, limit_per_host=0)
+
+        async with aiohttp.ClientSession(connector=connector) as session:
+            # 一次性提交所有任务（全并发）
+            batch_tasks = []
+            for i, (_, _, prompt) in enumerate(tasks_def):
+                task = asyncio.create_task(
+                    self._execute_one(
+                        session=session,
+                        provider=provider,
+                        prompt=prompt,
+                        model=model,
+                        resolution=resolution,
+                        aspect_ratio=aspect_ratio,
+                        input_images=input_images,
+                        global_task_index=i,
+                        on_progress=_on_progress,
+                        **extra_kwargs,
+                    )
+                )
+                batch_tasks.append(task)
+
+            # 谁先完成先处理谁
+            for coro in asyncio.as_completed(batch_tasks):
+                result_data = None
+                try:
+                    result_data = await coro
+                except InterruptProcessingException:
+                    for t in batch_tasks:
+                        t.cancel()
+                    raise
+                except Exception as e2:
+                    result_data = {
+                        "success": False,
+                        "error": str(e2),
+                        "generated_count": 0,
+                        "output_images": [],
+                        "prompt": "",
+                    }
+
+                # 即时落盘
+                if result_data and result_data.get("success"):
+                    for img_idx, img in enumerate(result_data.get("output_images", [])):
+                        filepath = os.path.join(
+                            self._output_dir,
+                            f"task_{completed:04d}_{img_idx:02d}.png"
+                        )
+                        img.save(filepath)
+                        self._output_file_paths.append(filepath)
+                    # 释放内存中的图像对象
+                    result_data["saved_count"] = len(result_data.get("output_images", []))
+                    result_data["output_images"] = []
+
+                all_results.append(result_data)
+                completed += 1
+
+                prompt_snippet = (result_data.get("prompt", "") or "")[:30]
+                if result_data and result_data.get("success"):
+                    count = result_data.get("saved_count", result_data.get("generated_count", 1))
+                    print(f"{self.NODE_LABEL}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> OK({count}张) [已落盘]")
+                else:
+                    error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
+                    print(f"{self.NODE_LABEL}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> FAIL: {error_msg}")
+
+            import gc
+            gc.collect()
+
+        return all_results
+
+    def generate(
+        self,
+        prompt: str,
+        模型: str,
+        宽高比: str,
+        分辨率: str,
+        生图数量: int,
+        **kwargs
+    ) -> Tuple[torch.Tensor]:
+        """生成图像（异步模式 - 批量版：全并发 + 即时落盘）"""
+        start_time = time.time()
+
+        seed: int = kwargs.pop("seed", 0)
+        proxy_port: str = kwargs.pop("代理端口", "")
+
+        proxy_url = BaseAsyncImageProvider.build_proxy_url(proxy_port)
+        provider = self._get_provider(模型, proxy_url=proxy_url)
+
+        if proxy_url:
+            print(f"{self.NODE_LABEL}: 已启用代理加速 -> {proxy_url}")
+
+        extra_kwargs = provider.get_extra_kwargs(**kwargs)
+
+        pbar = None
+        if PROGRESS_BAR_AVAILABLE:
+            pbar = ProgressBar(生图数量)
+
+        try:
+            random.seed(seed)
+            np.random.seed(seed % (2**32))
+
+            if MEMORY_MONITOR_AVAILABLE and 生图数量 > 50:
+                process = psutil.Process()
+                initial_memory = process.memory_info().rss / 1024 / 1024
+                print(f"{self.NODE_LABEL}: 初始内存使用: {initial_memory:.1f} MB")
+
+            supported_resolutions = provider.get_model_resolutions(模型)
+            if supported_resolutions and 分辨率 not in supported_resolutions:
+                raise ValueError(
+                    f"分辨率 \"{分辨率}\" 与模型 \"{模型}\" 不兼容！\n"
+                    f"该模型支持的分辨率：{', '.join(supported_resolutions)}"
+                )
+
+            supported_ratios = provider.get_model_aspect_ratios(模型)
+            if supported_ratios and 宽高比 not in supported_ratios:
+                raise ValueError(
+                    f"宽高比 \"{宽高比}\" 与模型 \"{模型}\" 不兼容！\n"
+                    f"该模型支持的宽高比：{', '.join(supported_ratios)}"
+                )
+
+            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)
+
+            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"{self.NODE_LABEL}: {mode_str} | {分辨率} {宽高比} | 共{total_images}张")
+                prompts_list = batch_prompts
+                images_per_prompt = 生图数量
+                total_tasks = total_images
+            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"{self.NODE_LABEL}: {mode_str} | {分辨率} {宽高比} | {生图数量}张")
+                prompts_list = [prompt]
+                images_per_prompt = 生图数量
+                total_tasks = 生图数量
+
+            if total_tasks > 100:
+                print(f"  {self.NODE_LABEL}: 全并发模式，{total_tasks} 张图片将同时提交")
+
+            if pbar is not None:
+                pbar = ProgressBar(total_tasks)
+
+            def run_async():
+                loop = asyncio.new_event_loop()
+                asyncio.set_event_loop(loop)
+                try:
+                    return loop.run_until_complete(
+                        self._process_batch(
+                            provider=provider,
+                            prompts=prompts_list,
+                            model=模型,
+                            resolution=分辨率,
+                            aspect_ratio=宽高比,
+                            images_per_prompt=images_per_prompt,
+                            input_images=input_images,
+                            pbar=pbar,
+                            **extra_kwargs,
+                        )
+                    )
+                finally:
+                    loop.close()
+
+            with ThreadPoolExecutor(max_workers=1) as executor:
+                future = executor.submit(run_async)
+                # 全并发：所有任务并行，总超时 = 单任务超时
+                try:
+                    results = future.result(timeout=_MAX_WAIT_TIME)
+                except TimeoutError:
+                    raise RuntimeError(f"任务执行超时（{_MAX_WAIT_TIME}秒），请减少数量或检查网络")
+
+            success_count = sum(1 for r in results if r.get("success"))
+            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"{self.NODE_LABEL}: 完成！总耗时 {time_str} | 成功: {success_count}/{total_tasks} | 失败: {fail_count} | 已落盘: {len(self._output_file_paths)} 张")
+
+            failed = [r for r in results if not r.get("success")]
+            if failed:
+                for fr in failed:
+                    idx = fr.get("global_task_index", -1) + 1
+                    prompt_snippet = (fr.get("prompt", "") or "")[:30]
+                    error_msg = fr.get("error", "未知错误")
+                    print(f"  FAIL #{idx}: {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} -> {error_msg}")
+
+            # 从磁盘加载已保存的图像
+            output_images = []
+            for fp in self._output_file_paths:
+                try:
+                    img = Image.open(fp)
+                    output_images.append(img)
+                except Exception as e:
+                    print(f"{self.NODE_LABEL}: 加载图像失败 {fp}: {e}")
+
+            if not output_images:
+                error_details = "\n".join(
+                    f"  - {r.get('prompt', '未知提示词')[:40]}: {r.get('error', '未知错误')}"
+                    for r in results if not r.get("success")
+                )
+                raise RuntimeError(
+                    f"所有任务均失败 ({fail_count}/{total_tasks})：\n{error_details}"
+                )
+
+            output_tensor = _images_to_tensor_safe(output_images, self.NODE_LABEL)
+
+            import gc
+            gc.collect()
+            return (output_tensor,)
+
+        except InterruptProcessingException:
+            # 即使被取消，已落盘的图片路径仍然保留
+            if self._output_file_paths:
+                print(f"{self.NODE_LABEL}: 用户取消，但 {len(self._output_file_paths)} 张已完成的图片已保存至: {self._output_dir}")
+            else:
+                print(f"{self.NODE_LABEL}: 用户取消")
+            raise
+        except ValueError as e:
+            if str(e) == "未授权！":
+                print(f"{self.NODE_LABEL}: 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:
+                balance_data = provider.query_balance_sync()
+                if balance_data:
+                    print(f"{self.NODE_LABEL}: {provider.format_balance_info(balance_data)}")
+            except Exception:
+                pass
+
+            import gc
+            gc.collect()
@@ -23,8 +23,8 @@ class O1keyGPTImage:
      - 模型     : 模型选择
      - 分辨率   : 图像尺寸（auto 让 API 自动决定）
      - 生图数量 : 每条提示词生成数量 1-8
-      - seed     : 随机种子（0 表示不指定）
      - 质量     : 生成质量
+      - seed     : 随机种子（0 表示不指定）
      - 图片     : 可选参考图（用于图生图或编辑）
      - 遮罩     : 可选蒙版（白色区域将被替换）
    """
@@ -39,12 +39,10 @@ class O1keyGPTImage:
            })

        optional_inputs["模型"] = ([
-            "gpt-image-2",
-            "gpt-image-1.5",
-            "gpt-image-2-特价",
-            "gpt-image-1.5-特价",
+            "gpt-image-2-按量",
+            "gpt-image-2-次卡",
        ], {
-            "default": "gpt-image-2",
+            "default": "gpt-image-2-次卡",
        })
        optional_inputs["分辨率"] = ([
            "auto（默认）",
@@ -53,6 +51,7 @@ class O1keyGPTImage:
            "1024x1536（肖像）",
            "2048x2048（2K 平方）",
            "2048x1152（2K 横屏）",
+            "1152x2048（2K 竖屏）",
            "3840x2160（4K 横屏）",
            "2160x3840（4K 竖屏）",
        ], {
@@ -67,6 +66,10 @@ class O1keyGPTImage:
            "display": "number",
            "tooltip": "How many images to generate per prompt",
        })
+        optional_inputs["质量"] = (["高", "中", "低", "自动"], {
+            "default": "自动",
+            "tooltip": "Image quality: 高=high, 中=medium, 低=low, 自动=auto",
+        })
        optional_inputs["seed"] = ("INT", {
            "default": 0,
            "min": 0,
@@ -76,10 +79,6 @@ class O1keyGPTImage:
            "control_after_generate": True,
            "tooltip": "Random seed (0 = not specified)",
        })
-        optional_inputs["质量"] = (["高", "中", "低", "自动"], {
-            "default": "自动",
-            "tooltip": "Image quality: 高=high, 中=medium, 低=low, 自动=auto",
-        })
        optional_inputs["遮罩"] = ("MASK", {
            "tooltip": "Optional mask for inpainting (white areas will be replaced)",
        })
@@ -104,7 +103,7 @@ class O1keyGPTImage:
    def generate(
        self,
        prompt: str,
-        模型: str = "gpt-image-2",
+        模型: str = "gpt-image-2-次卡",
        分辨率: str = "auto",
        质量: str = "自动",
        生图数量: int = 1,
@@ -169,7 +168,6 @@ class O1keyGPTImage:
                            prompt=p,
                            model=模型,
                            quality=quality,
-                            background="auto",
                            size=size,
                            n=生图数量,
                            seed=seed,
@@ -192,7 +190,6 @@ class O1keyGPTImage:
                        prompt=prompt,
                        model=模型,
                        quality=quality,
-                        background="auto",
                        size=size,
                        n=生图数量,
                        seed=seed,
@@ -1,683 +0,0 @@
-"""
-Nano Banana Pro（异步）节点
-ComfyUI 自定义节点，用于调用 Gemini 模型生成图像（异步提交+轮询模式）
-"""
-
-import time
-import math
-import random
-import asyncio
-import aiohttp
-from concurrent.futures import ThreadPoolExecutor
-from typing import Optional, Tuple, List
-
-import torch
-import numpy as np
-from PIL import Image
-
-from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts
-from ..utils.file_utils import ImageInfo, generate_timestamp_filename, save_image
-from ..utils.config import get_async_api_base_url
-from ..clients.gemini_client import GeminiAPIClient
-from ..models_config import (
-    get_enabled_models, get_model_description,
-    get_model_supported_aspect_ratios, get_all_supported_aspect_ratios,
-    get_model_supported_resolutions, get_all_supported_resolutions
-)
-
-try:
-    import folder_paths
-    FOLDER_PATHS_AVAILABLE = True
-except ImportError:
-    FOLDER_PATHS_AVAILABLE = False
-
-try:
-    from comfy.utils import ProgressBar
-    PROGRESS_BAR_AVAILABLE = True
-except ImportError:
-    PROGRESS_BAR_AVAILABLE = False
-    print("⚠️ NanoBananaProAsync: comfy.utils.ProgressBar 不可用，将只使用终端进度显示")
-
-try:
-    import psutil
-    MEMORY_MONITOR_AVAILABLE = True
-except ImportError:
-    MEMORY_MONITOR_AVAILABLE = False
-    print("⚠️ NanoBananaProAsync: psutil 不可用，内存监控功能禁用")
-
-DEBUG_LOG_ENABLED = False
-REQUEST_LOG_ENABLED = False
-
-_NODE = "Nano Banana Pro（异步）"
-_POLL_INTERVAL = 2  # 轮询间隔（秒）
-_MAX_WAIT_TIME = 300  # 最大等待时间（秒）
-
-
-def _images_to_tensor_safe(images: List[Image.Image], node_label: str) -> torch.Tensor:
-    if not images:
-        placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
-        return pil_to_tensor([placeholder])
-
-    base_size = max(images, key=lambda img: img.size[0] * img.size[1]).size
-    matched = [img for img in images if img.size == base_size]
-    skipped = [img for img in images if img.size != base_size]
-
-    if skipped:
-        sizes_str = ", ".join(f"{img.size[0]}×{img.size[1]}" for img in skipped)
-        print(
-            f"{node_label}: 丢弃 {len(skipped)} 张较小尺寸的图 ({sizes_str})，"
-            f"仅输出最大尺寸 {base_size[0]}×{base_size[1]} 的 {len(matched)} 张"
-        )
-
-    return pil_to_tensor(matched)
-
-
-class NanoBananaProAsync:
-    """
-    Nano Banana Pro（异步）节点
-
-    功能：
-    - 异步提交任务到 cf-api.o1key.com
-    - 轮询任务状态直到完成
-    - 支持批量并发生成
-    """
-
-    MODELS = None
-    ASPECT_RATIOS = [
-        "1:1", "4:3", "3:4", "16:9", "9:16",
-        "2:3", "3:2", "4:5", "5:4", "21:9",
-        "1:4", "4:1", "1:8", "8:1"
-    ]
-    RESOLUTIONS = ["512px", "1K", "2K", "4K"]
-
-    def __init__(self):
-        self.client = None
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        enabled_models = get_enabled_models()
-        if not enabled_models:
-            enabled_models = ["请在 models_config.py 中启用至少一个模型"]
-
-        all_aspect_ratios = get_all_supported_aspect_ratios()
-        if not all_aspect_ratios:
-            all_aspect_ratios = cls.ASPECT_RATIOS
-
-        all_resolutions = get_all_supported_resolutions()
-        if not all_resolutions:
-            all_resolutions = cls.RESOLUTIONS
-
-        optional_inputs = {}
-        for i in range(1, 10):
-            optional_inputs[f"参考图{i}"] = ("IMAGE",)
-
-        optional_inputs["代理端口"] = ("STRING", {
-            "default": "",
-            "multiline": False,
-            "placeholder": "本地代理端口，如 7897（Clash Verge）或 10808（v2rayN），留空不使用"
-        })
-
-        return {
-            "required": {
-                "prompt": ("STRING", {
-                    "default": "一个中国女子的OOTD",
-                    "multiline": True
-                }),
-                "模型": (enabled_models, {
-                    "default": enabled_models[0]
-                }),
-                "宽高比": (all_aspect_ratios, {
-                    "default": "1:1"
-                }),
-                "分辨率": (all_resolutions, {
-                    "default": "2K"
-                }),
-                "生图数量": ("INT", {
-                    "default": 1,
-                    "min": 1,
-                    "max": 1000,
-                    "step": 1
-                }),
-                "联网功能": (["关闭", "打开"], {
-                    "default": "关闭"
-                }),
-                "seed": ("INT", {
-                    "default": 0,
-                    "min": 0,
-                    "max": 0xffffffffffffffff
-                })
-            },
-            "optional": optional_inputs
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("输出图像",)
-
-    try:
-        import folder_paths
-        FOLDER_PATHS_AVAILABLE = True
-    except ImportError:
-        FOLDER_PATHS_AVAILABLE = False
-
-    FUNCTION = "generate"
-    CATEGORY = "image/generation"
-
-    async def _submit_task_async(
-        self,
-        session: aiohttp.ClientSession,
-        prompt: str,
-        model: str,
-        resolution: str,
-        aspect_ratio: str,
-        images: List[Image.Image],
-        enable_grounding: bool = False,
-    ) -> str:
-        """提交异步任务，返回 task_id"""
-        endpoint = self.client.get_endpoint(model=model, resolution=resolution, image_format="url")
-        async_endpoint = f"/async{endpoint.split('?')[0]}"
-        if "?" in endpoint:
-            async_endpoint += "?" + endpoint.split("?")[1]
-
-        request_body = self.client.build_request_body(
-            prompt=prompt,
-            images=images if images else None,
-            aspect_ratio=aspect_ratio,
-            resolution=resolution,
-            enable_grounding=enable_grounding,
-            enable_image_search=False,
-        )
-
-        url = f"{get_async_api_base_url()}{async_endpoint}"
-        headers = {
-            "Authorization": f"Bearer {self.client.api_key}",
-            "Content-Type": "application/json"
-        }
-
-        if REQUEST_LOG_ENABLED:
-            import json
-            import copy
-            debug_body = copy.deepcopy(request_body)
-            for content in debug_body.get("contents", []):
-                for part in content.get("parts", []):
-                    if "inline_data" in part and "data" in part["inline_data"]:
-                        data_str = part["inline_data"]["data"]
-                        part["inline_data"]["data"] = f"{data_str[:50]}...[截断]" if len(data_str) > 50 else data_str
-            print(f"\n{'='*60}")
-            print(f"[异步提交] URL: {url}")
-            print(f"[异步提交] 请求体:\n{json.dumps(debug_body, indent=2, ensure_ascii=False)}")
-            print(f"{'='*60}\n")
-
-        async with session.post(url, json=request_body, headers=headers, proxy=self.client.proxy_url) as response:
-            if response.status != 200:
-                error_text = await response.text()
-                raise RuntimeError(f"提交任务失败 ({response.status}): {error_text}")
-
-            data = await response.json()
-
-            if DEBUG_LOG_ENABLED:
-                import json
-                print(f"\n{'='*60}")
-                print(f"[异步提交] 响应:\n{json.dumps(data, indent=2, ensure_ascii=False)}")
-                print(f"{'='*60}\n")
-
-            task_id = data.get("task_id")
-            if not task_id:
-                raise RuntimeError(f"提交响应中未找到 task_id: {data}")
-
-            return task_id
-
-    async def _poll_task_async(
-        self,
-        session: aiohttp.ClientSession,
-        task_id: str,
-    ) -> dict:
-        """轮询任务状态直到完成"""
-        url = f"{get_async_api_base_url()}/async/v1/tasks/{task_id}"
-        headers = {
-            "Authorization": f"Bearer {self.client.api_key}",
-            "Content-Type": "application/json"
-        }
-
-        start_time = time.time()
-        poll_count = 0
-
-        while True:
-            elapsed = time.time() - start_time
-            if elapsed > _MAX_WAIT_TIME:
-                raise RuntimeError(f"任务 {task_id} 超时（{_MAX_WAIT_TIME}秒），请稍后手动查询")
-
-            poll_count += 1
-
-            async with session.get(url, headers=headers, proxy=self.client.proxy_url) as response:
-                if response.status != 200:
-                    error_text = await response.text()
-                    raise RuntimeError(f"查询任务失败 ({response.status}): {error_text}")
-
-                result = await response.json()
-                status = result.get("status")
-
-                if DEBUG_LOG_ENABLED:
-                    import json
-                    print(f"\n{'='*60}")
-                    print(f"[轮询 #{poll_count}] task_id: {task_id}")
-                    print(f"[轮询 #{poll_count}] 响应:\n{json.dumps(result, indent=2, ensure_ascii=False)}")
-                    print(f"{'='*60}\n")
-
-                if status == "SUCCESS":
-                    return result.get("data", {})
-                elif status == "FAILURE":
-                    error_msg = result.get("error", "未知错误")
-                    raise RuntimeError(f"任务失败: {error_msg}")
-                elif status in ["SUBMITTED", "IN_PROGRESS"]:
-                    await asyncio.sleep(_POLL_INTERVAL)
-                else:
-                    raise RuntimeError(f"未知任务状态: {status}")
-
-    async def _generate_single_task(
-        self,
-        session: aiohttp.ClientSession,
-        prompt: str,
-        model: str,
-        resolution: str,
-        aspect_ratio: str,
-        images: List[Image.Image],
-        output_folder: str,
-        global_task_index: int,
-        enable_grounding: bool = False,
-        save_to_disk: bool = True,
-    ) -> dict:
-        """执行单个异步生成任务"""
-        result = {
-            "global_task_index": global_task_index,
-            "prompt": prompt,
-            "success": False,
-            "generated_count": 0,
-            "saved_files": [],
-            "output_images": [],
-            "error": None
-        }
-
-        try:
-            task_id = await self._submit_task_async(
-                session=session,
-                prompt=prompt,
-                model=model,
-                resolution=resolution,
-                aspect_ratio=aspect_ratio,
-                images=images,
-                enable_grounding=enable_grounding,
-            )
-
-            response_data = await self._poll_task_async(session=session, task_id=task_id)
-
-            # 兼容两种响应格式：
-            # 1. 异步接口直接返回 image_url：{"image_url": "https://..."}
-            # 2. Gemini 标准格式：{"candidates": [...]}
-            image_url = response_data.get("image_url", "") if isinstance(response_data, dict) else ""
-            if image_url:
-                from io import BytesIO
-                async with session.get(image_url) as img_resp:
-                    if img_resp.status == 200:
-                        img_bytes = await img_resp.read()
-                        img = Image.open(BytesIO(img_bytes))
-                        images_list = [img]
-                    else:
-                        raise RuntimeError(f"下载图片失败 ({img_resp.status}): {image_url}")
-            else:
-                images_list, _ = await self.client.parse_response_async(response_data, session=session)
-
-            if save_to_disk:
-                for gen_img in images_list:
-                    output_path = generate_timestamp_filename(
-                        output_folder=output_folder,
-                        extension=".png"
-                    )
-                    save_image(gen_img, output_path)
-                    result["saved_files"].append(output_path)
-                    gen_img = None
-            else:
-                result["output_images"] = images_list
-
-            result["success"] = True
-            result["generated_count"] = len(images_list)
-        except Exception as e:
-            result["error"] = str(e)
-
-        return result
-
-    async def _process_batch_async(
-        self,
-        prompts: List[str],
-        model: str,
-        resolution: str,
-        aspect_ratio: str,
-        images_per_prompt: int,
-        input_images: List[Image.Image],
-        output_folder: str,
-        pbar=None,
-        enable_grounding: bool = False,
-        save_to_disk: bool = True,
-    ) -> List[dict]:
-        """异步批量处理"""
-        tasks_def = []
-        for p_idx, prompt in enumerate(prompts):
-            for sub_idx in range(images_per_prompt):
-                tasks_def.append((p_idx, sub_idx, prompt))
-
-        total_tasks = len(tasks_def)
-        max_concurrent = 50
-        num_batches = math.ceil(total_tasks / max_concurrent)
-
-        all_results = []
-        completed = 0
-        success_count = 0
-        fail_count = 0
-
-        connector = aiohttp.TCPConnector(ssl=False, limit=0, limit_per_host=0)
-
-        async with aiohttp.ClientSession(connector=connector) as session:
-            for batch_idx in range(num_batches):
-                start_idx = batch_idx * max_concurrent
-                end_idx = min(start_idx + max_concurrent, total_tasks)
-
-                tasks = []
-                for i in range(start_idx, end_idx):
-                    _, _, prompt = tasks_def[i]
-                    task = asyncio.create_task(
-                        self._generate_single_task(
-                            session=session,
-                            prompt=prompt,
-                            model=model,
-                            resolution=resolution,
-                            aspect_ratio=aspect_ratio,
-                            images=input_images,
-                            output_folder=output_folder,
-                            global_task_index=i,
-                            enable_grounding=enable_grounding,
-                            save_to_disk=save_to_disk,
-                        )
-                    )
-                    tasks.append(task)
-
-                batch_results = []
-                for coro in asyncio.as_completed(tasks):
-                    result_data = None
-                    try:
-                        result = await coro
-                        if isinstance(result, Exception):
-                            result_data = {"success": False, "error": str(result), "generated_count": 0, "saved_files": [], "prompt": ""}
-                        else:
-                            result_data = result
-                            batch_results.append(result_data)
-                    except Exception as e:
-                        result_data = {"success": False, "error": str(e), "generated_count": 0, "saved_files": [], "prompt": ""}
-                        batch_results.append(result_data)
-
-                    completed += 1
-                    prompt_snippet = (result_data.get("prompt", "") or "")[:30]
-
-                    if result_data and result_data.get("success", False):
-                        success_count += 1
-                        count = result_data.get("generated_count", 1)
-                        print(f"{_NODE}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✓成功({count}张)")
-                    else:
-                        fail_count += 1
-                        error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
-                        print(f"{_NODE}: [{completed}/{total_tasks}] {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → ✗失败: {error_msg}")
-
-                    if pbar is not None:
-                        pbar.update(1)
-
-                all_results.extend(batch_results)
-
-                import gc
-                gc.collect()
-
-                await asyncio.sleep(0.1)
-
-        return all_results
-
-    def generate(
-        self,
-        prompt: str,
-        模型: str,
-        宽高比: str,
-        分辨率: str,
-        生图数量: int,
-        seed: int,
-        **kwargs
-    ) -> Tuple[torch.Tensor]:
-        """生成图像（异步模式）"""
-        start_time = time.time()
-
-        enable_grounding: bool = (kwargs.pop("联网功能", "关闭") == "打开")
-        proxy_port: str = kwargs.pop("代理端口", "")
-
-        pbar = None
-        if PROGRESS_BAR_AVAILABLE:
-            pbar = ProgressBar(生图数量)
-
-        try:
-            random.seed(seed)
-            np.random.seed(seed % (2**32))
-
-            if MEMORY_MONITOR_AVAILABLE and 生图数量 > 50:
-                import psutil
-                process = psutil.Process()
-                initial_memory = process.memory_info().rss / 1024 / 1024
-                print(f"{_NODE}: 初始内存使用: {initial_memory:.1f} MB")
-
-            if self.client is None:
-                try:
-                    self.client = GeminiAPIClient()
-                except ValueError as e:
-                    raise ValueError(f"初始化失败: {str(e)}")
-
-            self.client.proxy_url = GeminiAPIClient.build_proxy_url(proxy_port)
-            if self.client.proxy_url:
-                print(f"{_NODE}: 已启用代理加速 → {self.client.proxy_url}")
-
-            supported_resolutions = get_model_supported_resolutions(模型)
-            if supported_resolutions and 分辨率 not in supported_resolutions:
-                raise ValueError(
-                    f"分辨率 \"{分辨率}\" 与模型 \"{模型}\" 不兼容！\n"
-                    f"该模型支持的分辨率：{', '.join(supported_resolutions)}"
-                )
-
-            supported_ratios = get_model_supported_aspect_ratios(模型)
-            if supported_ratios and 宽高比 not in supported_ratios:
-                raise ValueError(
-                    f"宽高比 \"{宽高比}\" 与模型 \"{模型}\" 不兼容！\n"
-                    f"该模型支持的宽高比：{', '.join(supported_ratios)}"
-                )
-
-            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:
-                if len(input_images) > 14:
-                    raise ValueError(
-                        f"输入图像数量 {len(input_images)} 超过限制 14 张，请减少输入图像数量"
-                    )
-
-            batch_prompts = parse_batch_prompts(prompt)
-
-            grounding_str = ""
-            if enable_grounding:
-                grounding_str = " | 谷歌搜索接地"
-
-            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"{_NODE}: {mode_str} | {分辨率} {宽高比} | 共{total_images}张{grounding_str}")
-
-                if total_images > 100:
-                    print(f"⚠️ {_NODE}: 警告！批量生成 {total_images} 张图片，内存占用可能较高")
-                    print(f"⚠️ 建议：分批执行或减少生图数量")
-            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"{_NODE}: {mode_str} | {分辨率} {宽高比} | {生图数量}张{grounding_str}")
-
-                if 生图数量 > 100:
-                    print(f"⚠️ {_NODE}: 警告！批量生成 {生图数量} 张图片，内存占用可能较高")
-                    print(f"⚠️ 建议：分批执行或减少生图数量")
-
-            success_count = 0
-            fail_count = 0
-
-            if batch_prompts:
-                num_prompts = len(batch_prompts)
-                total_images = num_prompts * 生图数量
-
-                if pbar is not None:
-                    pbar = ProgressBar(total_images)
-
-                def run_async_in_thread():
-                    loop = asyncio.new_event_loop()
-                    asyncio.set_event_loop(loop)
-                    try:
-                        return loop.run_until_complete(
-                            self._process_batch_async(
-                                prompts=batch_prompts,
-                                model=模型,
-                                resolution=分辨率,
-                                aspect_ratio=宽高比,
-                                images_per_prompt=生图数量,
-                                input_images=input_images,
-                                output_folder="",
-                                pbar=pbar,
-                                enable_grounding=enable_grounding,
-                                save_to_disk=False,
-                            )
-                        )
-                    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），请减少提示词数量或检查网络连接")
-
-                success_count = sum(1 for r in results if r.get("success", False))
-                fail_count = len(results) - success_count
-                total_generated = sum(r.get("generated_count", 0) for r in results)
-
-                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_images} | 失败: {fail_count}")
-
-                failed_results = [r for r in results if not r.get("success", False)]
-                if failed_results:
-                    for fr in failed_results:
-                        idx = fr.get("global_task_index", -1) + 1
-                        prompt_snippet = (fr.get("prompt", "") or "")[:30]
-                        error_msg = fr.get("error", "未知错误")
-                        print(f"  失败 #{idx}: {prompt_snippet}{'...' if len(prompt_snippet) >= 30 else ''} → {error_msg}")
-
-                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 (output_tensor,)
-            else:
-                if pbar is not None:
-                    pbar = ProgressBar(生图数量)
-
-                def run_async_in_thread():
-                    loop = asyncio.new_event_loop()
-                    asyncio.set_event_loop(loop)
-                    try:
-                        return loop.run_until_complete(
-                            self._process_batch_async(
-                                prompts=[prompt],
-                                model=模型,
-                                resolution=分辨率,
-                                aspect_ratio=宽高比,
-                                images_per_prompt=生图数量,
-                                input_images=input_images,
-                                output_folder="",
-                                pbar=pbar,
-                                enable_grounding=enable_grounding,
-                                save_to_disk=False,
-                            )
-                        )
-                    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），请减少生图数量或检查网络连接")
-
-                success_count = sum(1 for r in results if r.get("success", False))
-                fail_count = len(results) - success_count
-                total_generated = sum(r.get("generated_count", 0) for r in results)
-
-                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}/{生图数量} | 失败: {fail_count}")
-
-                failed_results = [r for r in results if not r.get("success", False)]
-                if failed_results:
-                    for fr in failed_results:
-                        idx = fr.get("global_task_index", -1) + 1
-                        error_msg = fr.get("error", "未知错误")
-                        print(f"  失败 #{idx}: {prompt[:30]}{'...' if len(prompt) >= 30 else ''} → {error_msg}")
-
-                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 (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:
-            if self.client is not None:
-                try:
-                    balance_data = self.client.query_balance_sync()
-                    balance_info = self.client.format_balance_info(balance_data)
-                    print(f"{_NODE}: {balance_info}")
-                except Exception:
-                    pass
-
-            import gc
-            gc.collect()
@@ -25,10 +25,10 @@ from ..utils.file_types import FileList
 # ============================================================================

 SUPPORTED_MODELS = [
+    "gpt-5.5",
    "gpt-5.4",
    "gemini-3.1-flash-lite-preview",
    "gemini-3.1-pro-preview",
-    "deepseek-v3.2",
    "deepseek-v4-pro",
    "doubao-seed-2-0-pro-260215",
 ]