diff --git a/plugins/ai_image.py b/plugins/ai_image.py
new file mode 100644
index 0000000..945f318
--- /dev/null
+++ b/plugins/ai_image.py
@@ -0,0 +1,590 @@
+"""
+AI 生图插件 - 基于 ai.2x.nz 自然语言生图接口
+支持自然语言描述、直接 Tag、工作流切换、画风选择等
+"""
+
+import os
+import json
+import time
+import threading
+import hashlib
+import re
+from datetime import datetime
+from urllib.parse import quote
+
+try:
+    import requests
+    REQUESTS_AVAILABLE = True
+except ImportError:
+    REQUESTS_AVAILABLE = False
+
+try:
+    import websocket
+    WEBSOCKET_AVAILABLE = True
+except ImportError:
+    WEBSOCKET_AVAILABLE = False
+
+
+class Plugin:
+    """AI 生图插件 - 调用 ai.2x.nz 接口生成图片"""
+
+    BASE_URL = "https://ai.2x.nz"
+    WS_URL = "wss://ai.2x.nz/ws/run"
+    API_URL = "https://ai.2x.nz/api"
+
+    # 保存目录
+    DEFAULT_SAVE_DIR = "generated_images"
+
+    def __init__(self):
+        self.usage = """AI 生图工具 - 基于自然语言描述生成图片
+
+基本用法:
+  generate <描述>                     - 用自然语言描述生成图片
+  tag <直接Tag>                       - 用 Danbooru Tag 生成图片
+  both <Tag> <<<>>> <描述>            - 同时使用 Tag 和自然语言
+
+高级用法:
+  workflows                           - 列出所有可用工作流
+  switch <工作流名>                   - 切换工作流
+  width <数值>                        - 设置图片宽度 (最大1344)
+  height <数值>                       - 设置图片高度 (最大1344)
+  size <宽> <高>                      - 同时设置宽高
+  rewrite on|off                      - 改写模式开关
+  override on|off                     - 覆写模式开关
+  status                              - 查看当前状态和配置
+
+示例:
+  ai_image generate 一个可爱的猫耳少女，樱花背景
+  ai_image tag 1girl, cat_ears, cherry_blossoms, smile
+  ai_image both 1girl, cat_ears <<<>>> 一个可爱的猫耳少女在樱花树下微笑
+  ai_image workflows
+  ai_image switch WAI - 37.json
+  ai_image size 1024 1024
+  ai_image rewrite on
+"""
+        self.cli = None
+        self.save_dir = self.DEFAULT_SAVE_DIR
+
+        # 配置状态
+        self.current_workflow = None
+        self.direct_prompt = ""
+        self.nl_prompt = ""
+        self.rewrite = False
+        self.override = False
+        self.width = None
+        self.height = None
+        self.workflows_cache = None
+        self.is_generating = False
+
+    def set_cli(self, cli):
+        """设置 CLI 实例引用"""
+        self.cli = cli
+        # 注册插件命令
+        if hasattr(cli, 'register_liugin_command'):
+            cli.register_liugin_command('ai_image', self.command_handler)
+
+    def get_tool_info(self):
+        return {
+            "name": "ai_image",
+            "description": "AI 生图工具 — 基于 ai.2x.nz 接口，支持自然语言描述和 Danbooru Tag 生成图片。当用户想要生成图片、画图、AI绘画、生图时使用此工具。",
+            "keywords": [
+                "生图", "画图", "图片", "AI绘画", "生成图片", "image", "generate",
+                "draw", "绘画", "插画", "二次元", "动漫", "Danbooru", "tag",
+                "工作流", "workflow", "ComfyUI"
+            ],
+            "usage": self.usage
+        }
+
+    def get_mcp_definition(self):
+        return {
+            "name": "ai_image",
+            "description": "AI 生图工具，基于 ai.2x.nz 接口生成图片",
+            "inputSchema": {
+                "type": "object",
+                "properties": {
+                    "operation": {
+                        "type": "string",
+                        "enum": ["generate", "tag", "both", "workflows", "switch", "size", "status"],
+                        "description": "操作类型"
+                    },
+                    "prompt": {
+                        "type": "string",
+                        "description": "生成提示词"
+                    }
+                },
+                "required": ["operation"]
+            }
+        }
+
+    def convert_mcp_args(self, arguments):
+        op = arguments.get("operation", "")
+        prompt = arguments.get("prompt", "")
+        return f"{op} {prompt}"
+
+    def command_handler(self, args):
+        """处理 /ai_image 命令"""
+        return self.handle(args)
+
+    def handle(self, args):
+        """处理 AI 生图请求"""
+        if not REQUESTS_AVAILABLE:
+            return "❌ 缺少 requests 库，请运行: pip install requests"
+
+        args = args.strip()
+        if not args:
+            return self.usage
+
+        # 解析操作
+        parts = args.split(maxsplit=1)
+        operation = parts[0].lower()
+        remaining = parts[1] if len(parts) > 1 else ""
+
+        try:
+            if operation in ["generate", "gen", "g"]:
+                return self._cmd_generate(remaining)
+            elif operation in ["tag", "t", "direct", "d"]:
+                return self._cmd_tag(remaining)
+            elif operation in ["both", "b"]:
+                return self._cmd_both(remaining)
+            elif operation in ["workflows", "wf", "list"]:
+                return self._cmd_workflows()
+            elif operation in ["switch", "sw"]:
+                return self._cmd_switch(remaining)
+            elif operation in ["width", "w"]:
+                return self._cmd_set_width(remaining)
+            elif operation in ["height", "h"]:
+                return self._cmd_set_height(remaining)
+            elif operation in ["size", "s"]:
+                return self._cmd_set_size(remaining)
+            elif operation in ["rewrite", "rw"]:
+                return self._cmd_set_rewrite(remaining)
+            elif operation in ["override", "ov"]:
+                return self._cmd_set_override(remaining)
+            elif operation in ["status", "st"]:
+                return self._cmd_status()
+            elif operation in ["help", "?"]:
+                return self.usage
+            else:
+                # 尝试当作自然语言描述处理
+                return self._cmd_generate(args)
+        except Exception as e:
+            return f"❌ 操作失败: {str(e)}"
+
+    # ── 命令实现 ──
+
+    def _cmd_generate(self, prompt):
+        """自然语言生成图片"""
+        if not prompt.strip():
+            return "❌ 请提供描述内容。示例: ai_image generate 一个可爱的猫耳少女"
+
+        self.nl_prompt = prompt.strip()
+        self.direct_prompt = ""
+        self.override = False
+        return self._run_generation()
+
+    def _cmd_tag(self, tags):
+        """使用直接 Tag 生成图片"""
+        if not tags.strip():
+            return "❌ 请提供 Tag 内容。示例: ai_image tag 1girl, cat_ears, smile"
+
+        self.direct_prompt = tags.strip()
+        self.nl_prompt = ""
+        self.override = True
+        return self._run_generation()
+
+    def _cmd_both(self, args):
+        """同时使用 Tag 和自然语言"""
+        if "<<<>>>" in args:
+            parts = args.split("<<<>>>", 1)
+            self.direct_prompt = parts[0].strip()
+            self.nl_prompt = parts[1].strip()
+        else:
+            # 尝试用 || 分隔
+            if "||" in args:
+                parts = args.split("||", 1)
+                self.direct_prompt = parts[0].strip()
+                self.nl_prompt = parts[1].strip()
+            else:
+                return "❌ 格式错误。用法: ai_image both <Tag> <<<>>> <描述>"
+
+        self.override = False
+        return self._run_generation()
+
+    def _cmd_workflows(self):
+        """列出所有工作流"""
+        workflows = self._fetch_workflows()
+        if not workflows:
+            return "❌ 获取工作流列表失败"
+
+        result = f"📂 共 {len(workflows)} 个工作流:\n\n"
+        for i, wf in enumerate(workflows, 1):
+            name = wf.get("path", "未知")
+            current = " ← [当前]" if name == self.current_workflow else ""
+            result += f"  {i:2d}. {name}{current}\n"
+
+        result += f"\n💡 使用 'ai_image switch <工作流名>' 切换工作流"
+        return result
+
+    def _cmd_switch(self, name):
+        """切换工作流"""
+        if not name.strip():
+            return "❌ 请提供工作流名称。先用 'ai_image workflows' 查看列表"
+
+        # 模糊匹配
+        workflows = self._fetch_workflows()
+        if not workflows:
+            return "❌ 获取工作流列表失败"
+
+        matched = None
+        name_lower = name.strip().lower()
+        for wf in workflows:
+            wf_path = wf.get("path", "")
+            if name_lower == wf_path.lower():
+                matched = wf_path
+                break
+            if name_lower in wf_path.lower():
+                matched = wf_path
+
+        if not matched:
+            # 尝试数字索引
+            try:
+                idx = int(name.strip()) - 1
+                if 0 <= idx < len(workflows):
+                    matched = workflows[idx].get("path", "")
+            except ValueError:
+                pass
+
+        if not matched:
+            return f"❌ 未找到匹配的工作流: {name}\n💡 先用 'ai_image workflows' 查看列表"
+
+        self.current_workflow = matched
+        return f"✅ 已切换工作流: {matched}"
+
+    def _cmd_set_width(self, val):
+        """设置宽度"""
+        try:
+            w = int(val.strip())
+            if w < 64 or w > 1344:
+                return "❌ 宽度范围: 64 ~ 1344"
+            self.width = w
+            return f"✅ 宽度已设置为: {w}"
+        except ValueError:
+            return "❌ 请输入有效数字"
+
+    def _cmd_set_height(self, val):
+        """设置高度"""
+        try:
+            h = int(val.strip())
+            if h < 64 or h > 1344:
+                return "❌ 高度范围: 64 ~ 1344"
+            self.height = h
+            return f"✅ 高度已设置为: {h}"
+        except ValueError:
+            return "❌ 请输入有效数字"
+
+    def _cmd_set_size(self, val):
+        """同时设置宽高"""
+        parts = val.strip().split()
+        if len(parts) < 2:
+            return "❌ 用法: ai_image size <宽> <高>"
+        try:
+            w, h = int(parts[0]), int(parts[1])
+            if w < 64 or w > 1344 or h < 64 or h > 1344:
+                return "❌ 宽高范围: 64 ~ 1344"
+            self.width = w
+            self.height = h
+            return f"✅ 尺寸已设置为: {w} x {h}"
+        except ValueError:
+            return "❌ 请输入有效数字"
+
+    def _cmd_set_rewrite(self, val):
+        """设置改写模式"""
+        val = val.strip().lower()
+        if val in ["on", "开", "1", "true", "yes"]:
+            self.rewrite = True
+            return "✅ 改写模式已开启 — LLM 将智能重写 prompt"
+        elif val in ["off", "关", "0", "false", "no"]:
+            self.rewrite = False
+            return "✅ 改写模式已关闭 — 普通翻译/追加模式"
+        else:
+            self.rewrite = not self.rewrite
+            status = "开启" if self.rewrite else "关闭"
+            return f"✅ 改写模式已{status}"
+
+    def _cmd_set_override(self, val):
+        """设置覆写模式"""
+        val = val.strip().lower()
+        if val in ["on", "开", "1", "true", "yes"]:
+            self.override = True
+            return "✅ 覆写模式已开启 — 忽略工作流内置 prompt"
+        elif val in ["off", "关", "0", "false", "no"]:
+            self.override = False
+            return "✅ 覆写模式已关闭 — 保留工作流内置 prompt"
+        else:
+            self.override = not self.override
+            status = "开启" if self.override else "关闭"
+            return f"✅ 覆写模式已{status}"
+
+    def _cmd_status(self):
+        """查看当前状态"""
+        result = " AI 生图状态:\n\n"
+        result += f"  工作流: {self.current_workflow or '(默认)'}\n"
+        result += f"  直接 Tag: {self.direct_prompt or '(未设置)'}\n"
+        result += f"  自然语言: {self.nl_prompt or '(未设置)'}\n"
+        result += f"  改写模式: {'✅ 开启' if self.rewrite else '❌ 关闭'}\n"
+        result += f"  覆写模式: {'✅ 开启' if self.override else '❌ 关闭'}\n"
+        result += f"  宽度: {self.width or '(默认)'}\n"
+        result += f"  高度: {self.height or '(默认)'}\n"
+        result += f"  保存目录: {self.save_dir}\n"
+        result += f"  生成中: {'是' if self.is_generating else '否'}\n"
+        return result
+
+    # ── 核心生图逻辑 ──
+
+    def _fetch_workflows(self):
+        """获取工作流列表"""
+        if self.workflows_cache:
+            return self.workflows_cache
+
+        try:
+            resp = requests.get(f"{self.API_URL}/workflows", timeout=15)
+            if resp.status_code == 200:
+                data = resp.json()
+                self.workflows_cache = data.get("workflows", [])
+                return self.workflows_cache
+        except Exception as e:
+            pass
+        return []
+
+    def _run_generation(self):
+        """执行图片生成"""
+        if self.is_generating:
+            return "⚠️ 已有生成任务在进行中，请等待完成"
+
+        if not self.direct_prompt and not self.nl_prompt:
+            return "❌ 请提供至少一种 prompt（直接 Tag 或自然语言描述）"
+
+        self.is_generating = True
+        result_lines = []
+        image_paths = []
+
+        try:
+            # 获取默认工作流（如果未指定则使用列表中第一个）
+            workflow = self.current_workflow
+            if not workflow:
+                workflows = self._fetch_workflows()
+                if workflows:
+                    # 优先选 "无Lora" 工作流，否则用第一个
+                    for wf in workflows:
+                        if "无lora" in wf.get("path", "").lower():
+                            workflow = wf["path"]
+                            break
+                    if not workflow:
+                        workflow = workflows[0].get("path", "")
+
+            # 构建 payload
+            payload = {
+                "workflow_path": workflow or "",
+                "inline_workflow": None,
+                "direct_prompt": self.direct_prompt,
+                "nl_prompt": self.nl_prompt,
+                "rewrite": self.rewrite,
+                "override": self.override,
+                "width": self.width,
+                "height": self.height,
+            }
+
+            result_lines.append("  正在连接 ai.2x.nz ...")
+            result_lines.append(f"  直接 Tag: {self.direct_prompt[:80] or '(无)'}")
+            result_lines.append(f"  自然语言: {self.nl_prompt[:80] or '(无)'}")
+            result_lines.append(f"  工作流: {workflow or '(默认)'}")
+            result_lines.append("")
+
+            # 使用同步方式通过 REST + 轮询获取结果
+            # 由于 websocket-client 可能未安装，提供降级方案
+            if WEBSOCKET_AVAILABLE:
+                images = self._generate_via_ws(payload, result_lines)
+            else:
+                # 降级：尝试使用 requests + SSE 或提示安装
+                result_lines.append("⚠️ websocket-client 未安装，尝试安装...")
+                images = self._generate_via_ws_fallback(payload, result_lines)
+
+            if images:
+                image_paths = images
+                result_lines.append(f"\n✅ 生成完成！共 {len(images)} 张图片")
+                for i, path in enumerate(images, 1):
+                    result_lines.append(f"   {i}. {path}")
+            else:
+                result_lines.append("\n❌ 未生成任何图片")
+
+        except Exception as e:
+            result_lines.append(f"\n❌ 生成失败: {str(e)}")
+        finally:
+            self.is_generating = False
+
+        return "\n".join(result_lines)
+
+    def _generate_via_ws(self, payload, result_lines):
+        """通过 WebSocket 生成图片"""
+        images = []
+        final_prompt = ""
+        error_msg = None
+
+        ws_result = {"images": [], "error": None, "final_prompt": "", "done": False}
+
+        def on_message(ws, message):
+            try:
+                m = json.loads(message)
+                msg_type = m.get("type", "")
+
+                if msg_type == "log":
+                    result_lines.append(f"  {m.get('message', '')}")
+                elif msg_type == "llm_start":
+                    result_lines.append(" LLM 正在处理 prompt...")
+                elif msg_type == "llm_chunk":
+                    pass  # 流式输出，不逐块显示
+                elif msg_type == "llm_done":
+                    result_lines.append(" LLM 处理完成")
+                elif msg_type == "progress":
+                    node = m.get("node", "")
+                    value = m.get("value", 0)
+                    max_val = m.get("max", 0)
+                    if max_val and max_val > 1:
+                        pct = int(value * 100 / max_val)
+                        result_lines.append(f"⏳ 进度: {node} {value}/{max_val} ({pct}%)")
+                    else:
+                        result_lines.append(f"⏳ 执行: {node}")
+                elif msg_type == "prompt_id":
+                    fp = m.get("final_prompt", "")
+                    if fp:
+                        ws_result["final_prompt"] = fp
+                        result_lines.append(f"\n  最终 Prompt:\n{fp[:300]}{'...' if len(fp) > 300 else ''}\n")
+                elif msg_type == "image":
+                    url = m.get("url", "")
+                    filename = m.get("filename", "")
+                    if url:
+                        # 下载图片
+                        saved = self._download_image(url, filename)
+                        if saved:
+                            ws_result["images"].append(saved)
+                            result_lines.append(f"  收到图片: {filename}")
+                elif msg_type == "done":
+                    count = m.get("count", 0)
+                    result_lines.append(f"✅ 服务端完成，共 {count} 张")
+                    ws_result["done"] = True
+                    ws.close()
+                elif msg_type == "error":
+                    ws_result["error"] = m.get("message", "未知错误")
+                    result_lines.append(f"❌ 错误: {ws_result['error']}")
+                    ws.close()
+            except Exception as e:
+                result_lines.append(f"⚠️ 解析消息出错: {e}")
+
+        def on_error(ws, error):
+            ws_result["error"] = str(error)
+            result_lines.append(f"❌ WebSocket 错误: {error}")
+
+        def on_close(ws, close_status_code, close_msg):
+            ws_result["done"] = True
+
+        def on_open(ws):
+            ws.send(json.dumps(payload))
+
+        try:
+            ws = websocket.WebSocketApp(
+                self.WS_URL,
+                on_message=on_message,
+                on_error=on_error,
+                on_close=on_close,
+                on_open=on_open
+            )
+
+            # 在独立线程中运行 WebSocket
+            ws_thread = threading.Thread(target=ws.run_forever, kwargs={"ping_timeout": 10})
+            ws_thread.daemon = True
+            ws_thread.start()
+
+            # 等待完成（最多 5 分钟）
+            timeout = 300
+            start = time.time()
+            while not ws_result["done"] and (time.time() - start) < timeout:
+                time.sleep(1)
+
+            if ws_result["error"]:
+                raise Exception(ws_result["error"])
+
+            if not ws_result["done"]:
+                ws.close()
+                raise Exception("生成超时（超过 5 分钟）")
+
+            return ws_result["images"]
+
+        except Exception as e:
+            raise Exception(f"WebSocket 连接失败: {e}")
+
+    def _generate_via_ws_fallback(self, payload, result_lines):
+        """降级方案：尝试自动安装 websocket-client"""
+        try:
+            import subprocess
+            result_lines.append("  正在安装 websocket-client ...")
+            subprocess.run(
+                ["pip", "install", "websocket-client"],
+                capture_output=True, timeout=60
+            )
+            import importlib
+            global websocket
+            websocket = importlib.import_module("websocket")
+            global WEBSOCKET_AVAILABLE
+            WEBSOCKET_AVAILABLE = True
+            result_lines.append("✅ websocket-client 安装成功")
+            return self._generate_via_ws(payload, result_lines)
+        except Exception as e:
+            result_lines.append(f"❌ 无法安装 websocket-client: {e}")
+            result_lines.append("💡 请手动运行: pip install websocket-client")
+            return []
+
+    def _download_image(self, url, filename):
+        """下载图片到本地"""
+        try:
+            # 确保 URL 是完整的
+            if url.startswith("/"):
+                url = f"{self.BASE_URL}{url}"
+            elif not url.startswith("http"):
+                url = f"{self.BASE_URL}/api/output/file?path={quote(url)}"
+
+            # 创建保存目录
+            os.makedirs(self.save_dir, exist_ok=True)
+
+            # 生成文件名
+            if not filename:
+                filename = f"img_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png"
+
+            # 清理文件名
+            filename = re.sub(r'[<>:"/\\|?*]', '_', filename)
+            save_path = os.path.join(self.save_dir, filename)
+
+            # 如果文件已存在，添加序号
+            if os.path.exists(save_path):
+                name, ext = os.path.splitext(filename)
+                for i in range(1, 100):
+                    new_path = os.path.join(self.save_dir, f"{name}_{i}{ext}")
+                    if not os.path.exists(new_path):
+                        save_path = new_path
+                        break
+
+            # 下载
+            resp = requests.get(url, timeout=60, stream=True)
+            if resp.status_code == 200:
+                with open(save_path, 'wb') as f:
+                    for chunk in resp.iter_content(chunk_size=8192):
+                        f.write(chunk)
+                return save_path
+            else:
+                return None
+
+        except Exception as e:
+            return None
+
+
+# 兼容 Liugin 类名
+Liugin = Plugin