diff --git a/README.md b/README.md
index facff5f..7dd1b11 100644
--- a/README.md
+++ b/README.md
@@ -24,6 +24,7 @@ Rui-Node🐶 是一个功能丰富的 ComfyUI 节点集合，提供图像处理
 ### 🤖 AI模型类
 - [千问编辑图像生成 / Qwen Edit Image Generation](#4-千问编辑图像生成--qwen-edit-image-generation)
 - [SDMatte 精细抠图 / SDMatte Interactive Matting](#17-sdmatte-精细抠图--sdmatte-interactive-matting)
+- [ZenMux API 连接 / ZenMux API Connector](#18-zenmux-api-连接--zenmux-api-connector)
 
 ### 📝 文本处理类
 - [镜头分词器 / Shot Splitter](#5-镜头分词器--shot-splitter)
@@ -710,6 +711,46 @@ ComfyUI-SDMatte 传 `aux_input="trimap"`，把模型推到了没训练过的输
 
 ---
 
+### 18. ZenMux API 连接 / ZenMux API Connector
+
+**分类**: `Rui-Node🐶/AI模型🤖`
+
+**功能描述**:  
+连接 [ZenMux](https://zenmux.ai) 聚合平台（OpenAI 兼容协议），一个节点即可调用其收录的**所有文本类模型**（Anthropic、OpenAI、Google、DeepSeek、Qwen 等 20 家厂商、130+ 模型）。支持文本生成与多模态图像理解（最多 6 张图）。
+
+**特色功能**:
+- **厂商 → 模型二级选择**: 先在 `vendor` 下拉选厂商，`model` 下拉会自动收窄为该厂商的模型（由前端脚本 `web/zenmux_cascade.js` 联动）
+- **价格直接标在选项上**: 每个模型后缀形如 `[入$0.2/M 出$1.25/M]`，即输入/输出每百万 token 的美元价格，选型时一目了然
+- **离线可用的模型清单**: 模型与价格来自随包分发的 `zenmux/models_snapshot.json`；价格有变动时运行 `python zenmux/build_snapshot.py` 即可重新拉取更新
+- **旧工作流兼容**: 价格快照更新后，旧工作流里保存的带旧价格标签仍能正确解析出模型 id，不会失效
+
+**输入参数**:
+- `api_key` (STRING): ZenMux 平台的 API Key（在 zenmux.ai 控制台获取）
+- `vendor` (选择): 厂商筛选，默认 `openai`
+- `model` (选择): 模型（带价格标注），默认 `openai/gpt-5.4-nano`
+- `system_prompt` (STRING): 系统提示词
+- `user_prompt` (STRING): 用户提示词
+- `seed` (INT): 随机种子
+- `temperature` (FLOAT, 可选): 采样温度，默认 0.7，范围 0.0 ~ 2.0
+- `top_p` (FLOAT, 可选): 核采样阈值，默认 1.0
+- `max_tokens` (INT, 可选): 最大输出 token 数，默认 1024
+- `image_1` ~ `image_6` (IMAGE, 可选): 多模态图像输入（所选模型需支持 image 输入）
+- `detail` (选择, 可选): 图像分析细节等级，auto/low/high
+- `image_max_size` (INT, 可选): 发送前图像最长边缩放上限，默认 1024
+- `base_url` (STRING, 可选): API 地址，默认 `zenmux.ai/api/v1`（无需写 `https://`，节点会自动补全）
+- `proxy_url` (STRING, 可选): HTTP/HTTPS 代理地址，如 `127.0.0.1:7890`
+
+**输出**:
+- `text` (STRING): 模型生成的文本内容
+- `model_id` (STRING): 实际调用的模型 id（如 `openai/gpt-5.4-nano`），便于下游记录
+
+**使用场景**:
+- 一个 Key 试遍多家厂商的模型，横向对比效果与成本
+- 按预算选型：价格就写在下拉列表里，直接挑便宜的
+- 调用 Claude / GPT / Gemini / DeepSeek 等做文本生成或图像理解
+
+---
+
 ## 🐕 关于 Rui-Node🐶
 
 Rui-Node🐶 致力于为 ComfyUI 用户提供实用、高效的节点工具集。🐶 是我们的项目标志，代表着忠诚、友好和可靠。
diff --git a/__init__.py b/__init__.py
index c3436db..b41ffcb 100644
--- a/__init__.py
+++ b/__init__.py
@@ -41,6 +41,9 @@ except Exception as _e:
     print("[Ruinode] 如需使用，请安装：pip install diffusers transformers safetensors scipy opencv-python")
     SDMATTE_NODE_CLASS_MAPPINGS = {}
     SDMATTE_NODE_DISPLAY_NAME_MAPPINGS = {}
+# 新增：ZenMux API 连接节点（聚合平台，厂商→模型二级选择，标签含价格）
+from .zenmux import NODE_CLASS_MAPPINGS as ZENMUX_NODE_CLASS_MAPPINGS
+from .zenmux import NODE_DISPLAY_NAME_MAPPINGS as ZENMUX_NODE_DISPLAY_NAME_MAPPINGS
 
 # 合并节点映射字典
 NODE_CLASS_MAPPINGS = {}
@@ -61,6 +64,7 @@ NODE_CLASS_MAPPINGS.update(OPENAI_NODE_CLASS_MAPPINGS)
 NODE_CLASS_MAPPINGS.update(COLORMATCHER_NODE_CLASS_MAPPINGS)
 NODE_CLASS_MAPPINGS.update(IMAGESPLITTER_NODE_CLASS_MAPPINGS)
 NODE_CLASS_MAPPINGS.update(SDMATTE_NODE_CLASS_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(ZENMUX_NODE_CLASS_MAPPINGS)
 
 # 合并节点显示名称映射
 NODE_DISPLAY_NAME_MAPPINGS = {}
@@ -81,5 +85,9 @@ NODE_DISPLAY_NAME_MAPPINGS.update(OPENAI_NODE_DISPLAY_NAME_MAPPINGS)
 NODE_DISPLAY_NAME_MAPPINGS.update(COLORMATCHER_NODE_DISPLAY_NAME_MAPPINGS)
 NODE_DISPLAY_NAME_MAPPINGS.update(IMAGESPLITTER_NODE_DISPLAY_NAME_MAPPINGS)
 NODE_DISPLAY_NAME_MAPPINGS.update(SDMATTE_NODE_DISPLAY_NAME_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(ZENMUX_NODE_DISPLAY_NAME_MAPPINGS)
 
-__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
+# 前端扩展目录（ZenMux 节点的「厂商→模型」级联脚本等）
+WEB_DIRECTORY = "./web"
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
diff --git a/web/zenmux_cascade.js b/web/zenmux_cascade.js
new file mode 100644
index 0000000..955af41
--- /dev/null
+++ b/web/zenmux_cascade.js
@@ -0,0 +1,60 @@
+// ZenMux 节点「厂商 → 模型」二级级联
+// ==================================
+// 后端把全量模型标签（含价格）都放进 model 下拉以通过校验；
+// 本扩展在前端按 vendor 收窄 model 的候选列表。
+// 约定：模型标签第一个空格前是 "vendor/model" 形式的 id，
+//       故 label.split("/")[0] 即厂商名，与 vendor 下拉的取值同源。
+import { app } from "../../scripts/app.js";
+
+app.registerExtension({
+    name: "Ruinode.ZenMuxCascade",
+    async beforeRegisterNodeDef(nodeType, nodeData, _app) {
+        if (nodeData.name !== "ZenMuxAPINode") return;
+
+        const onNodeCreated = nodeType.prototype.onNodeCreated;
+        nodeType.prototype.onNodeCreated = function () {
+            const r = onNodeCreated?.apply(this, arguments);
+
+            const vendorW = this.widgets?.find((w) => w.name === "vendor");
+            const modelW = this.widgets?.find((w) => w.name === "model");
+            if (!vendorW || !modelW) return r;
+
+            // 全量标签快照（此时 options.values 是后端给的完整列表）
+            const allLabels = (modelW.options.values || []).slice();
+            const vendorOf = (label) => String(label).split("/")[0];
+
+            // resetIfMissing：当前值不在收窄后的列表时是否重置为列表首项。
+            // 手动切厂商 → 重置；加载旧工作流 → 保留（旧价格标签后端能解析）。
+            const applyFilter = (vendor, resetIfMissing) => {
+                const filtered = allLabels.filter((l) => vendorOf(l) === vendor);
+                modelW.options.values = filtered.length ? filtered : allLabels.slice();
+                if (resetIfMissing && !modelW.options.values.includes(modelW.value)) {
+                    modelW.value = modelW.options.values[0];
+                    modelW.callback?.(modelW.value);
+                }
+                this.setDirtyCanvas?.(true, true);
+            };
+
+            const origCallback = vendorW.callback;
+            vendorW.callback = function (value) {
+                const r2 = origCallback?.apply(this, arguments);
+                applyFilter(value, true);
+                return r2;
+            };
+
+            // 新建节点：按默认厂商先收窄一次
+            applyFilter(vendorW.value, false);
+
+            // 加载已保存的工作流：configure 恢复 widget 值后再按真实厂商收窄，
+            // 但不动用户保存的 model 选择
+            const onConfigure = this.onConfigure;
+            this.onConfigure = function () {
+                const r3 = onConfigure?.apply(this, arguments);
+                applyFilter(vendorW.value, false);
+                return r3;
+            };
+
+            return r;
+        };
+    },
+});
diff --git a/zenmux/__init__.py b/zenmux/__init__.py
new file mode 100644
index 0000000..aa7b5a6
--- /dev/null
+++ b/zenmux/__init__.py
@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+"""ZenMux API 连接节点子包。"""
+from .zenmux_node import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
diff --git a/zenmux/build_snapshot.py b/zenmux/build_snapshot.py
new file mode 100644
index 0000000..3e90861
--- /dev/null
+++ b/zenmux/build_snapshot.py
@@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+"""
+从 ZenMux 拉取模型列表，提炼成节点需要的精简快照。
+
+运行：python build_snapshot.py
+产出：models_snapshot.json（随节点分发，保证离线可用）
+
+只保留文本类模型（输出模态含 text），并预先算好基础输入/输出价格，
+避免节点运行时每次都解析 ZenMux 那套分段定价结构。
+"""
+import json
+import os
+import sys
+
+import requests
+
+# Windows GBK 控制台打印中文/特殊字符会崩，强制 UTF-8
+if hasattr(sys.stdout, "reconfigure"):
+    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+
+API = "https://zenmux.ai/api/v1/models"
+HERE = os.path.dirname(os.path.abspath(__file__))
+OUT = os.path.join(HERE, "models_snapshot.json")
+
+
+def base_price(pricings, key):
+    """取某项价格的基础段（prompt_tokens 从 0 起的那一段），单位 USD / 百万 token。"""
+    seg = (pricings or {}).get(key)
+    if not seg:
+        return None
+    vals = [s for s in seg if isinstance(s, dict) and "value" in s]
+    if not vals:
+        return None
+    base = None
+    for s in vals:
+        cond = s.get("conditions", {}).get("prompt_tokens", {})
+        if cond.get("gte", 0) in (0, None):
+            base = s
+            break
+    base = base or vals[0]
+    try:
+        return float(base["value"])
+    except (TypeError, ValueError, KeyError):
+        return None
+
+
+def build(raw_models):
+    out = []
+    for m in raw_models:
+        out_mod = m.get("output_modalities", [])
+        if "text" not in out_mod:  # 只要文本类模型
+            continue
+        pr = m.get("pricings") or {}
+        out.append({
+            "id": m["id"],
+            "display_name": m.get("display_name", m["id"]),
+            # 厂商一律取模型 id 的前缀（"openai/gpt-5.4-nano" -> "openai"），
+            # 与前端级联 JS 的过滤规则（label.split("/")[0]）保持同一真源。
+            "vendor": m["id"].split("/")[0] if "/" in m["id"] else m.get("owned_by", "other"),
+            "input_price": base_price(pr, "prompt"),
+            "output_price": base_price(pr, "completion"),
+            "context_length": m.get("context_length"),
+            "input_modalities": m.get("input_modalities", []),
+        })
+    # 按厂商、再按 id 排序，方便前端级联展示
+    out.sort(key=lambda x: (x["vendor"].lower(), x["id"].lower()))
+    return out
+
+
+def main():
+    print(f"拉取 {API} ...")
+    r = requests.get(API, timeout=30)
+    r.raise_for_status()
+    data = r.json()["data"]
+    models = build(data)
+    payload = {
+        "source": API,
+        "count": len(models),
+        "models": models,
+    }
+    with open(OUT, "w", encoding="utf-8") as f:
+        json.dump(payload, f, ensure_ascii=False, indent=1)
+    print(f"写入 {OUT}：{len(models)} 个文本模型")
+    vendors = sorted(set(m["vendor"] for m in models))
+    print(f"厂商 {len(vendors)} 个：{', '.join(vendors)}")
+    assert any(m["id"] == "openai/gpt-5.4-nano" for m in models), "默认模型缺失！"
+    print("默认模型 openai/gpt-5.4-nano 存在 ✓")
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/zenmux/model_registry.py b/zenmux/model_registry.py
new file mode 100644
index 0000000..02a4126
--- /dev/null
+++ b/zenmux/model_registry.py
@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+"""
+ZenMux 模型注册表
+=================
+从随包分发的 models_snapshot.json 加载模型清单，为节点提供：
+
+- all_vendors()          厂商列表（模型 id 的前缀，如 "openai"）
+- all_model_labels()     全量「带价格」下拉标签，按厂商 → 模型排序
+- default_model_label()  默认模型 openai/gpt-5.4-nano 对应的标签
+- label_to_model_id()    从下拉标签解析真实 model id（对旧标签/纯 id 也兼容）
+
+标签格式（价格单位：USD / 百万 token）：
+    openai/gpt-5.4-nano [入$0.04/M 出$0.32/M]
+标签第一个空格前恒为 model id，前端级联 JS 与后端解析都依赖这一点。
+
+快照可用 build_snapshot.py 随时重新拉取更新。
+"""
+import json
+import os
+
+DEFAULT_MODEL_ID = "openai/gpt-5.4-nano"
+
+_SNAPSHOT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                              "models_snapshot.json")
+
+
+def _fmt_price(v):
+    """10.0 -> '10'，0.04 -> '0.04'，None -> '?'。"""
+    if v is None:
+        return "?"
+    try:
+        s = f"{float(v):.4f}".rstrip("0").rstrip(".")
+        return s if s else "0"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _make_label(m):
+    return (f"{m['id']} "
+            f"[入${_fmt_price(m.get('input_price'))}/M "
+            f"出${_fmt_price(m.get('output_price'))}/M]")
+
+
+def _load_models():
+    """读取快照；文件缺失/损坏时退化为只含默认模型的最小清单。"""
+    try:
+        with open(_SNAPSHOT_PATH, "r", encoding="utf-8") as f:
+            models = json.load(f)["models"]
+        if models:
+            return models
+    except Exception as e:
+        print(f"[Rui-Node] ZenMux 模型快照加载失败（{e}），退化为最小模型清单。"
+              f"可运行 zenmux/build_snapshot.py 重新生成。")
+    return [{
+        "id": DEFAULT_MODEL_ID,
+        "vendor": DEFAULT_MODEL_ID.split("/")[0],
+        "input_price": None,
+        "output_price": None,
+    }]
+
+
+# 模块加载时构建一次即可（ComfyUI 启动时构建节点定义）
+_MODELS = _load_models()
+_LABELS = [_make_label(m) for m in _MODELS]
+_LABEL_TO_ID = {lb: m["id"] for lb, m in zip(_LABELS, _MODELS)}
+_KNOWN_IDS = {m["id"] for m in _MODELS}
+_VENDORS = sorted({m.get("vendor") or m["id"].split("/")[0] for m in _MODELS},
+                  key=str.lower)
+
+
+def all_vendors():
+    return list(_VENDORS)
+
+
+def all_model_labels():
+    return list(_LABELS)
+
+
+def default_model_label():
+    for lb, m in zip(_LABELS, _MODELS):
+        if m["id"] == DEFAULT_MODEL_ID:
+            return lb
+    return _LABELS[0]
+
+
+def label_to_model_id(label):
+    """
+    从下拉标签解析 model id。三层兼容：
+    1. 当前标签精确命中；
+    2. 旧版工作流里存的标签（价格已变动）→ 取第一个空格前的 id 段；
+    3. 用户直接填了纯 model id → 原样返回。
+    解析不出（空串等）返回 None，由调用方兜底到默认模型。
+    """
+    s = (label or "").strip()
+    if not s:
+        return None
+    if s in _LABEL_TO_ID:
+        return _LABEL_TO_ID[s]
+    head = s.split(" ")[0].split("\t")[0]
+    if head in _KNOWN_IDS:
+        return head
+    # 未收录的 id（快照偏旧但模型真实存在）也放行，交给服务端判定
+    return head if head else None
diff --git a/zenmux/models_snapshot.json b/zenmux/models_snapshot.json
new file mode 100644
index 0000000..0955b90
--- /dev/null
+++ b/zenmux/models_snapshot.json
@@ -0,0 +1,1700 @@
+{
+ "source": "https://zenmux.ai/api/v1/models",
+ "count": 138,
+ "models": [
+  {
+   "id": "anthropic/claude-fable-5",
+   "display_name": "Anthropic: Claude Fable 5",
+   "vendor": "anthropic",
+   "input_price": 10.0,
+   "output_price": 50.0,
+   "context_length": 1000000,
+   "input_modalities": [
+    "text",
+    "image",
+    "file"
+   ]
+  },
+  {
+   "id": "anthropic/claude-haiku-4.5",
+   "display_name": "Anthropic: Claude Haiku 4.5",
+   "vendor": "anthropic",
+   "input_price": 1.0,
+   "output_price": 5.0,
+   "context_length": 200000,
+   "input_modalities": [
+    "image",
+    "text"
+   ]
+  },
+  {
+   "id": "anthropic/claude-opus-4",
+   "display_name": "Anthropic: Claude Opus 4",
+   "vendor": "anthropic",
+   "input_price": 15.0,
+   "output_price": 75.0,
+   "context_length": 200000,
+   "input_modalities": [
+    "image",
+    "text",
+    "file"
+   ]
+  },
+  {
+   "id": "anthropic/claude-opus-4.1",
+   "display_name": "Anthropic: Claude Opus 4.1",
+   "vendor": "anthropic",
+   "input_price": 15.0,
+   "output_price": 75.0,
+   "context_length": 200000,
+   "input_modalities": [
+    "image",
+    "text",
+    "file"
+   ]
+  },
+  {
+   "id": "anthropic/claude-opus-4.5",
+   "display_name": "Anthropic: Claude Opus 4.5",
+   "vendor": "anthropic",
+   "input_price": 5.0,
+   "output_price": 25.0,
+   "context_length": 200000,
+   "input_modalities": [
+    "file",
+    "image",
+    "text"
+   ]
+  },
+  {
+   "id": "anthropic/claude-opus-4.6",
+   "display_name": "Anthropic: Claude Opus 4.6",
+   "vendor": "anthropic",
+   "input_price": 5.0,
+   "output_price": 25.0,
+   "context_length": 1000000,
+   "input_modalities": [
+    "text",
+    "image",
+    "file"
+   ]
+  },
+  {
+   "id": "anthropic/claude-opus-4.7",
+   "display_name": "Anthropic: Claude Opus 4.7",
+   "vendor": "anthropic",
+   "input_price": 5.0,
+   "output_price": 25.0,
+   "context_length": 1000000,
+   "input_modalities": [
+    "text",
+    "image",
+    "file"
+   ]
+  },
+  {
+   "id": "anthropic/claude-opus-4.8",
+   "display_name": "Anthropic: Claude Opus 4.8",
+   "vendor": "anthropic",
+   "input_price": 5.0,
+   "output_price": 25.0,
+   "context_length": 1000000,
+   "input_modalities": [
+    "text",
+    "image",
+    "file"
+   ]
+  },
+  {
+   "id": "anthropic/claude-sonnet-4",
+   "display_name": "Anthropic: Claude Sonnet 4",
+   "vendor": "anthropic",
+   "input_price": 3.0,
+   "output_price": 15.0,
+   "context_length": 200000,
+   "input_modalities": [
+    "image",
+    "text",
+    "file"
+   ]
+  },
+  {
+   "id": "anthropic/claude-sonnet-4.5",
+   "display_name": "Anthropic: Claude Sonnet 4.5",
+   "vendor": "anthropic",
+   "input_price": 3.0,
+   "output_price": 15.0,
+   "context_length": 1000000,
+   "input_modalities": [
+    "text",
+    "image",
+    "file"
+   ]
+  },
+  {
+   "id": "anthropic/claude-sonnet-4.6",
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+   "input_modalities": [
+    "text"
+   ]
+  },
+  {
+   "id": "z-ai/glm-4.7-flashx",
+   "display_name": "Z.AI: GLM 4.7 FlashX",
+   "vendor": "z-ai",
+   "input_price": 0.0728,
+   "output_price": 0.4367,
+   "context_length": 200000,
+   "input_modalities": [
+    "text"
+   ]
+  },
+  {
+   "id": "z-ai/glm-5",
+   "display_name": "Z.AI: GLM 5",
+   "vendor": "z-ai",
+   "input_price": 0.58,
+   "output_price": 2.6,
+   "context_length": 200000,
+   "input_modalities": [
+    "text"
+   ]
+  },
+  {
+   "id": "z-ai/glm-5-turbo",
+   "display_name": "Z.AI: GLM 5 Turbo",
+   "vendor": "z-ai",
+   "input_price": 0.73,
+   "output_price": 3.19,
+   "context_length": 200000,
+   "input_modalities": [
+    "text"
+   ]
+  },
+  {
+   "id": "z-ai/glm-5.1",
+   "display_name": "Z.AI: GLM 5.1",
+   "vendor": "z-ai",
+   "input_price": 0.8781,
+   "output_price": 3.5126,
+   "context_length": 200000,
+   "input_modalities": [
+    "text"
+   ]
+  },
+  {
+   "id": "z-ai/glm-5.2",
+   "display_name": "Z.AI: GLM 5.2",
+   "vendor": "z-ai",
+   "input_price": 0.98,
+   "output_price": 3.08,
+   "context_length": 1000000,
+   "input_modalities": [
+    "text"
+   ]
+  },
+  {
+   "id": "z-ai/glm-5v-turbo",
+   "display_name": "Z.AI: GLM 5V Turbo",
+   "vendor": "z-ai",
+   "input_price": 0.726,
+   "output_price": 3.1946,
+   "context_length": 200000,
+   "input_modalities": [
+    "text",
+    "image",
+    "video",
+    "file"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/zenmux/zenmux_node.py b/zenmux/zenmux_node.py
new file mode 100644
index 0000000..68affd1
--- /dev/null
+++ b/zenmux/zenmux_node.py
@@ -0,0 +1,313 @@
+# -*- coding: utf-8 -*-
+"""
+ZenMux API 连接节点
+===================
+通过 ZenMux 聚合平台（https://zenmux.ai）调用其收录的所有文本类模型。
+
+特性：
+- 模型选择做成「厂商 → 模型」两级级联：先在 vendor 下拉里选厂商，
+  再在 model 下拉里选该厂商的模型（联动由配套前端 zenmux_cascade.js 完成）。
+- 每个模型选项后面直接标注输入/输出价格（USD / 百万 token）。
+- 具备常规 API 节点的完整参数：api_key、system/user prompt、seed、
+  temperature、top_p、max_tokens、以及可选的多模态图像输入与代理。
+- 默认模型 openai/gpt-5.4-nano。
+- 随节点分发 models_snapshot.json，无网络也能列出模型；价格与列表可用
+  build_snapshot.py 重新拉取更新。
+
+注：ZenMux 采用 OpenAI 兼容协议，chat 端点为
+    https://zenmux.ai/api/v1/chat/completions
+"""
+import base64
+import io
+import json
+import os
+import re
+
+import numpy as np
+import requests
+from PIL import Image
+
+from .model_registry import (
+    DEFAULT_MODEL_ID,
+    default_model_label,
+    all_model_labels,
+    all_vendors,
+    label_to_model_id,
+)
+
+# ZenMux 平台固定地址（OpenAI 兼容）
+DEFAULT_BASE_URL = "https://zenmux.ai/api/v1"
+# 输入框默认值不带 "://"——本仓库实测 ComfyUI 前端会吞掉文本框里的
+# 协议片段（见 openai_node.py 的同款处理），后端 _build_chat_url 会自动补 https。
+DEFAULT_BASE_URL_INPUT = "zenmux.ai/api/v1"
+
+
+def _clear_proxy_env():
+    """清除可能干扰 requests 的代理环境变量（仅本模块加载时执行一次）。"""
+    for key in ('HTTP_PROXY', 'HTTPS_PROXY', 'http_proxy', 'https_proxy'):
+        os.environ.pop(key, None)
+
+
+_clear_proxy_env()
+
+
+def _build_chat_url(base_url: str) -> str:
+    """由 base_url 拼出 chat/completions 端点，容忍用户填了/没填结尾斜杠。"""
+    s = (base_url or "").strip()
+    if not s:
+        s = DEFAULT_BASE_URL
+    # 补协议
+    if not re.match(r'^https?://', s, flags=re.IGNORECASE):
+        s = 'https://' + s.lstrip('/')
+    s = s.rstrip('/')
+    # 用户可能已经把 /chat/completions 填进去了
+    if s.lower().endswith('/chat/completions'):
+        return s
+    return s + '/chat/completions'
+
+
+def _build_proxy_url(raw_proxy: str) -> str:
+    """把 '127.0.0.1:7890' 或 'http://127.0.0.1:7890' 统一成带协议的地址。"""
+    s = (raw_proxy or "").strip()
+    if not s:
+        return ''
+    if re.match(r'^https?://', s, flags=re.IGNORECASE):
+        return s
+    s = re.sub(r'^https?\s*:\s*/*/?\s*', '', s, flags=re.IGNORECASE).strip('/')
+    return ('http://' + s) if s else ''
+
+
+class ZenMuxNode:
+    """ZenMux API 连接节点。"""
+
+    # ────────── 输入定义 ──────────
+    @classmethod
+    def INPUT_TYPES(cls):
+        vendors = all_vendors()
+        model_labels = all_model_labels()
+        default_label = default_model_label()
+        return {
+            "required": {
+                "api_key": ("STRING", {
+                    "default": "",
+                    "multiline": False,
+                }),
+                # vendor 只用于前端级联筛选；后端不依赖它（真实模型从 model 解析）。
+                # 默认选中 openai。
+                "vendor": (vendors, {
+                    "default": "openai" if "openai" in vendors else (vendors[0] if vendors else "openai"),
+                }),
+                # model 的候选是「全量」模型标签（含价格），保证 ComfyUI 后端校验通过；
+                # 前端 JS 会按 vendor 把可见项收窄到该厂商。
+                "model": (model_labels, {
+                    "default": default_label,
+                }),
+                "system_prompt": ("STRING", {
+                    "default": "You are a helpful assistant.",
+                    "multiline": True,
+                }),
+                "user_prompt": ("STRING", {
+                    "default": "",
+                    "multiline": True,
+                }),
+                "seed": ("INT", {
+                    "default": 0,
+                    "min": 0,
+                    "max": 0xffffffffffffffff,
+                }),
+            },
+            "optional": {
+                "temperature": ("FLOAT", {
+                    "default": 0.7,
+                    "min": 0.0,
+                    "max": 2.0,
+                    "step": 0.1,
+                }),
+                "top_p": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 1.0,
+                    "step": 0.05,
+                }),
+                "max_tokens": ("INT", {
+                    "default": 1024,
+                    "min": 1,
+                    "max": 200000,
+                }),
+                # 多模态图像输入（模型需支持 image 输入才有意义）
+                "image_1": ("IMAGE",),
+                "image_2": ("IMAGE",),
+                "image_3": ("IMAGE",),
+                "image_4": ("IMAGE",),
+                "image_5": ("IMAGE",),
+                "image_6": ("IMAGE",),
+                "detail": (["auto", "low", "high"], {
+                    "default": "auto",
+                }),
+                "image_max_size": ("INT", {
+                    "default": 1024,
+                    "min": 256,
+                    "max": 4096,
+                    "step": 64,
+                }),
+                # 高级：一般无需改动，留空即用官方地址（无需写 https://，会自动补全）
+                "base_url": ("STRING", {
+                    "default": DEFAULT_BASE_URL_INPUT,
+                    "multiline": False,
+                }),
+                "proxy_url": ("STRING", {
+                    "default": "",
+                    "multiline": False,
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING")
+    RETURN_NAMES = ("text", "model_id")
+    FUNCTION = "generate"
+    CATEGORY = "Rui-Node🐶/AI模型🤖"
+
+    # ────────── 宽松校验 ──────────
+    @classmethod
+    def VALIDATE_INPUTS(cls, vendor, model):
+        """
+        接管 vendor / model 两个下拉的校验，替代 ComfyUI 内置的
+        「值必须在候选列表里」检查：价格快照更新后，旧工作流里保存的
+        标签（带旧价格）不再逐字匹配新列表，但只要能解析出 model id
+        就应放行，避免整个工作流被判为无效。
+        """
+        if label_to_model_id(model) is None:
+            return f"无法从 '{model}' 解析出 ZenMux 模型 id"
+        return True
+
+    # ────────── 图像编码 ──────────
+    @staticmethod
+    def _encode_image(img_tensor, max_size):
+        """把单张图像张量 [H,W,C]（0~1）编码为 base64 JPEG。"""
+        img_np = np.clip(img_tensor.cpu().numpy(), 0, 1)
+        pil = Image.fromarray((img_np * 255).astype(np.uint8), 'RGB')
+        w, h = pil.size
+        if max(w, h) > max_size:
+            r = max_size / max(w, h)
+            pil = pil.resize((max(1, int(w * r)), max(1, int(h * r))), Image.LANCZOS)
+        buf = io.BytesIO()
+        pil.save(buf, format='JPEG', quality=85)
+        return base64.b64encode(buf.getvalue()).decode('utf-8')
+
+    # ────────── 主函数 ──────────
+    def generate(
+        self,
+        api_key,
+        vendor,
+        model,
+        system_prompt,
+        user_prompt,
+        seed,
+        temperature=0.7,
+        top_p=1.0,
+        max_tokens=1024,
+        image_1=None,
+        image_2=None,
+        image_3=None,
+        image_4=None,
+        image_5=None,
+        image_6=None,
+        detail="auto",
+        image_max_size=1024,
+        base_url=DEFAULT_BASE_URL,
+        proxy_url="",
+    ):
+        # ---- 1. 从下拉标签解析真实 model id ----
+        model_id = label_to_model_id(model) or DEFAULT_MODEL_ID
+        chat_url = _build_chat_url(base_url)
+        print(f"[Rui-Node] ZenMux -> {chat_url}  model={model_id}")
+
+        if not (api_key or "").strip():
+            return ("（错误：未填写 api_key，请在节点里填入 ZenMux 的 API Key）", model_id)
+
+        # ---- 2. 收集图像 ----
+        images = [
+            img for img in (image_1, image_2, image_3, image_4, image_5, image_6)
+            if img is not None
+        ]
+
+        # ---- 3. 构造 messages ----
+        messages = []
+        if system_prompt and system_prompt.strip():
+            messages.append({"role": "system", "content": system_prompt})
+
+        if images:
+            parts = []
+            if user_prompt and user_prompt.strip():
+                parts.append({"type": "text", "text": user_prompt})
+            for img in images:
+                b64 = self._encode_image(img[0], image_max_size)
+                parts.append({
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/jpeg;base64,{b64}",
+                        "detail": detail,
+                    },
+                })
+            if not parts:
+                parts.append({"type": "text", "text": " "})
+            messages.append({"role": "user", "content": parts})
+        else:
+            text = (user_prompt or "").strip()
+            if not text:
+                return ("（错误：未提供图片也未提供提示词，请至少填写 user_prompt）", model_id)
+            messages.append({"role": "user", "content": text})
+
+        # ---- 4. 请求 ----
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key.strip()}",
+        }
+        payload = {
+            "model": model_id,
+            "messages": messages,
+            "seed": seed,
+            "temperature": temperature,
+            "top_p": top_p,
+            "max_tokens": max_tokens,
+        }
+
+        proxies = None
+        p = _build_proxy_url(proxy_url)
+        if p:
+            proxies = {"http": p, "https": p}
+
+        resp = None
+        try:
+            resp = requests.post(chat_url, headers=headers, json=payload,
+                                 proxies=proxies, timeout=180)
+            resp.raise_for_status()
+            data = resp.json()
+            if data.get("choices"):
+                msg = data["choices"][0].get("message", {})
+                content = msg.get("content", "")
+                if isinstance(content, list):  # 少数模型返回分段内容
+                    content = "".join(
+                        seg.get("text", "") for seg in content if isinstance(seg, dict)
+                    )
+                return (content or "", model_id)
+            return (f"API 返回格式异常: {json.dumps(data, ensure_ascii=False)[:800]}", model_id)
+        except requests.exceptions.ConnectionError as e:
+            return (f"连接失败（请检查网络/代理）: {e}", model_id)
+        except requests.exceptions.Timeout:
+            return ("请求超时（180s），请检查网络或 ZenMux 服务状态。", model_id)
+        except requests.exceptions.HTTPError:
+            code = resp.status_code if resp is not None else "?"
+            body = resp.text[:600] if resp is not None else ""
+            return (f"HTTP 错误 {code}: {body}", model_id)
+        except Exception as e:
+            return (f"请求异常: {type(e).__name__}: {e}", model_id)
+
+
+# ────────── ComfyUI 注册 ──────────
+NODE_CLASS_MAPPINGS = {
+    "ZenMuxAPINode": ZenMuxNode,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ZenMuxAPINode": "ZenMux API 连接 / ZenMux API Connector",
+}