feat: 新增图像生成插件 + 记忆系统插件 + 7工具横评页面
- plugins/image_generator.py: 基于 ai.2x.nz ComfyUI 的图像生成插件 - 自然语言/Tag 双模式，支持中文描述 - WebSocket 实时进度，28 个角色工作流 - 分辨率可调（最大 1344x1344） - plugins/memory_plugin.py: 包装 xcli_core/memory.py 的记忆插件 - AI 可写日记、读日记、搜索日记 - 全局搜索（MEMORY + 日记 + 聊天记录） - 自动保存对话 + 上下文压缩 - 长期记忆 MEMORY.md 管理 - compare.html: 7 款 AI 编程助手横评页面 - Claude Code / Codex CLI / Gemini CLI / Aider / Copilot CLI / 小狸 / TG HELPER - 25+ 维度功能对比 + 评分 + 场景推荐 - .gitignore: 排除运行时生成的记忆数据和图片
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"""
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图像生成插件 - 基于 ai.2x.nz 的 ComfyUI 在线服务
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支持自然语言描述和英文 Tag 两种模式
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"""
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import os
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import json
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import asyncio
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import urllib.request
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import time
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from typing import Optional
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class Plugin:
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"""图像生成工具 — 自然语言/Tag 生图，基于 ComfyUI 在线服务"""
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def __init__(self):
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self.usage = """图像生成工具 — 基于 ai.2x.nz 的 ComfyUI 在线服务
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操作:
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generate <描述> - 自然语言描述生成图片（中文/英文均可）
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tag <英文tags> - 直接 Tag 模式（逗号分隔的英文标签）
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list - 列出可用的工作流/角色
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status - 查看 GPU 状态
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示例:
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image_generator generate 一个白发蓝眼的猫耳少女在写代码
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image_generator tag masterpiece, 1girl, white hair, blue eyes, cat ears, coding
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image_generator generate 画一个赛博朋克风格的城市夜景 --width 1024 --height 768
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说明:
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- 自然语言模式会经过 LLM 翻译为 Tag，效果更好
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- 直接 Tag 模式适合熟悉 Danbooru 标签的用户
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- 默认分辨率 1024x1344（WAI 推荐）
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- 最大分辨率 1344x1344
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- 生成的图片保存在 screenshots/ 目录
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"""
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self.cli = None
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self.api_base = "https://ai.2x.nz"
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self.ws_url = "wss://ai.2x.nz/ws/run"
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self.default_workflow = "WAI - 无Lora.json"
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self.output_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "screenshots")
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def set_cli(self, cli):
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self.cli = cli
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def get_tool_info(self):
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return {
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"name": "image_generator",
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"description": "图像生成 — 自然语言/Tag 生图，基于 ComfyUI 在线服务（ai.2x.nz），支持中文描述",
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"keywords": ["图像", "生成", "画图", "绘图", "AI画", "生图", "image", "generate", "draw", "paint",
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"diffusion", "comfyui", "stable", "sd", "文生图", "text2img"],
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"usage": self.usage
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}
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def handle(self, args: str) -> str:
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try:
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parts = args.strip().split(maxsplit=1)
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if not parts:
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return "错误：请提供操作类型\n\n" + self.usage
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op = parts[0].lower()
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rest = parts[1] if len(parts) > 1 else ""
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handlers = {
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"generate": self._generate,
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"tag": self._generate_tag,
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"gen": self._generate,
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"g": self._generate,
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"list": self._list_workflows,
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"ls": self._list_workflows,
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"status": self._gpu_status,
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}
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handler = handlers.get(op)
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if not handler:
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# 如果没有指定操作，把整个 args 当作自然语言描述
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return self._generate(args)
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return handler(rest)
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except Exception as e:
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return f"图像生成错误: {e}"
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def _generate(self, args: str) -> str:
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"""自然语言模式生成图片"""
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# 解析 --width 和 --height 参数
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width, height = 1024, 1344
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prompt = args.strip()
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if "--width" in prompt or "--height" in prompt or "--w" in prompt or "--h" in prompt:
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import re
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w_match = re.search(r'--w(?:idth)?\s+(\d+)', prompt)
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h_match = re.search(r'--h(?:eight)?\s+(\d+)', prompt)
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if w_match:
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width = int(w_match.group(1))
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prompt = prompt[:w_match.start()] + prompt[w_match.end():]
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if h_match:
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height = int(h_match.group(1))
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prompt = prompt[:h_match.start()] + prompt[h_match.end():]
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prompt = prompt.strip()
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if not prompt:
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return "错误：请提供图片描述\n示例: image_generator generate 一个白发猫耳少女"
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# 限制分辨率
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width = min(width, 1344)
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height = min(height, 1344)
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return self._run_generation(prompt, "", width, height, rewrite=True, override=False)
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def _generate_tag(self, args: str) -> str:
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"""直接 Tag 模式"""
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width, height = 1024, 1344
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prompt = args.strip()
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if "--width" in prompt or "--height" in prompt or "--w" in prompt or "--h" in prompt:
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import re
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w_match = re.search(r'--w(?:idth)?\s+(\d+)', prompt)
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h_match = re.search(r'--h(?:eight)?\s+(\d+)', prompt)
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if w_match:
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width = int(w_match.group(1))
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prompt = prompt[:w_match.start()] + prompt[w_match.end():]
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if h_match:
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height = int(h_match.group(1))
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prompt = prompt[:h_match.start()] + prompt[h_match.end():]
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prompt = prompt.strip()
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if not prompt:
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return "错误：请提供 Tag\n示例: image_generator tag masterpiece, 1girl, white hair, blue eyes"
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width = min(width, 1344)
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height = min(height, 1344)
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return self._run_generation("", prompt, width, height, rewrite=False, override=False)
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def _run_generation(self, nl_prompt: str, direct_prompt: str, width: int, height: int,
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rewrite: bool = False, override: bool = False) -> str:
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"""执行图像生成（同步包装）"""
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try:
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import websocket as ws_lib
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except ImportError:
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# 回退：用 urllib 的方式不行，WebSocket 必须用专用库
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# 尝试用 subprocess 调用 python 脚本
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return self._run_generation_subprocess(nl_prompt, direct_prompt, width, height, rewrite, override)
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try:
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start_time = time.time()
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ws = ws_lib.create_connection(self.ws_url, timeout=180)
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payload = {
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"workflow_path": self.default_workflow,
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"inline_workflow": None,
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"direct_prompt": direct_prompt,
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"nl_prompt": nl_prompt,
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"rewrite": rewrite,
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"override": override,
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"width": width,
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"height": height,
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}
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ws.send(json.dumps(payload))
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image_path = None
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final_prompt = ""
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error_msg = ""
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while True:
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try:
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raw = ws.recv()
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data = json.loads(raw)
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msg_type = data.get("type", "")
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if msg_type == "image":
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url = data.get("url", "")
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filename = data.get("filename", "")
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if url:
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image_path = self._download_image(url, filename)
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elif msg_type == "final_prompt":
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final_prompt = data.get("final_prompt", "") if isinstance(data.get("final_prompt"), str) else data.get("prompt", "")
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elif msg_type == "done":
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break
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elif msg_type == "error":
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error_msg = data.get("message", "未知错误")
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break
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except Exception as e:
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error_msg = str(e)
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break
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ws.close()
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elapsed = time.time() - start_time
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if error_msg:
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return f" 生成失败: {error_msg}"
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if image_path:
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size = os.path.getsize(image_path)
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size_str = f"{size/1024:.0f}KB" if size < 1024*1024 else f"{size/1024/1024:.1f}MB"
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result = f" 图片生成成功！\n"
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result += f" 路径: {image_path}\n"
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result += f" 大小: {size_str}\n"
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result += f" 分辨率: {width}x{height}\n"
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result += f"⏱ 耗时: {elapsed:.1f}s\n"
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if final_prompt:
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result += f" Prompt: {final_prompt[:120]}...\n"
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result += f"\n💡 提示: 用 'send_image {image_path}' 可以推送到手机"
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return result
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else:
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return " 生成完成但未收到图片数据"
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except Exception as e:
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return f" 连接失败: {e}\n💡 请检查网络连接或稍后重试"
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def _run_generation_subprocess(self, nl_prompt: str, direct_prompt: str, width: int, height: int,
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rewrite: bool, override: bool) -> str:
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"""用子进程执行 WebSocket 生成（无需 websocket 库）"""
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import subprocess
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import tempfile
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script = f'''
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import json, asyncio, urllib.request, os, sys
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async def main():
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try:
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import websockets
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except ImportError:
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print("ERROR:需要安装 websockets: pip install websockets")
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sys.exit(1)
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uri = "wss://ai.2x.nz/ws/run"
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async with websockets.connect(uri) as ws:
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payload = {{
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"workflow_path": "{self.default_workflow}",
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"inline_workflow": None,
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"direct_prompt": {json.dumps(direct_prompt)},
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"nl_prompt": {json.dumps(nl_prompt)},
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"rewrite": {"true" if rewrite else "false"},
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"override": {"true" if override else "false"},
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"width": {width},
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"height": {height},
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}}
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await ws.send(json.dumps(payload))
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image_url = None
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while True:
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try:
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msg = await asyncio.wait_for(ws.recv(), timeout=180)
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data = json.loads(msg)
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t = data.get("type", "")
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if t == "image":
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image_url = data.get("url", "")
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elif t == "done":
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break
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elif t == "error":
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print(f"ERROR:{{data.get('message','')}}")
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break
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except asyncio.TimeoutError:
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print("ERROR:超时")
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break
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if image_url:
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full_url = f"https://ai.2x.nz{{image_url}}"
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out_path = os.path.join("{self.output_dir}", f"generated_{{os.path.basename(image_url)}}")
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os.makedirs(os.path.dirname(out_path), exist_ok=True)
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urllib.request.urlretrieve(full_url, out_path)
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print(f"OK:{{out_path}}")
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asyncio.run(main())
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'''
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script_path = os.path.join(self.output_dir, "_gen_temp.py")
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os.makedirs(self.output_dir, exist_ok=True)
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with open(script_path, 'w') as f:
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f.write(script)
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try:
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result = subprocess.run(
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["python3", script_path],
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capture_output=True, text=True, timeout=200
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)
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output = result.stdout.strip()
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if output.startswith("OK:"):
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image_path = output[3:]
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size = os.path.getsize(image_path)
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size_str = f"{size/1024:.0f}KB" if size < 1024*1024 else f"{size/1024/1024:.1f}MB"
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res = f" 图片生成成功！\n"
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res += f" 路径: {image_path}\n"
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res += f" 大小: {size_str}\n"
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res += f" 分辨率: {width}x{height}\n"
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res += f"\n💡 提示: 用 'send_image {image_path}' 可以推送到手机"
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return res
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elif output.startswith("ERROR:"):
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return f" {output[6:]}"
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else:
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return f" 生成异常:\n{result.stdout}\n{result.stderr}"
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except subprocess.TimeoutExpired:
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return " 生成超时（>200s），请稍后重试"
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finally:
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try:
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os.remove(script_path)
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except:
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pass
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def _download_image(self, url: str, filename: str) -> str:
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"""下载生成的图片"""
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full_url = f"{self.api_base}{url}"
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os.makedirs(self.output_dir, exist_ok=True)
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out_path = os.path.join(self.output_dir, f"gen_{filename}")
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urllib.request.urlretrieve(full_url, out_path)
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return out_path
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def _list_workflows(self, args: str) -> str:
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"""列出可用的工作流"""
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try:
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import urllib.request
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req = urllib.request.Request(f"{self.api_base}/api/workflows")
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with urllib.request.urlopen(req, timeout=10) as resp:
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data = json.loads(resp.read())
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workflows = data.get("workflows", [])
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result = f" 可用工作流 ({len(workflows)} 个):\n\n"
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lora_workflows = []
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no_lora = []
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for w in workflows:
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name = w.get("path", "")
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if "无Lora" in name or "no" in name.lower():
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no_lora.append(name)
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else:
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lora_workflows.append(name)
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if no_lora:
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result += " 通用（无角色绑定）:\n"
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for w in no_lora:
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result += f" • {w}\n"
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if lora_workflows:
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result += "\n 角色工作流:\n"
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for w in lora_workflows:
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result += f" • {w}\n"
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result += "\n💡 使用 'image_generator generate <描述>' 自然语言生图"
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result += "\n💡 使用 'image_generator tag <tags>' 直接 Tag 生图"
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return result
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except Exception as e:
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return f" 获取工作流列表失败: {e}"
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def _gpu_status(self, args: str) -> str:
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"""查看 GPU 状态"""
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try:
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import urllib.request
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req = urllib.request.Request(f"{self.api_base}/api/gpu")
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with urllib.request.urlopen(req, timeout=10) as resp:
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data = json.loads(resp.read())
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result = " 服务器 GPU 状态:\n"
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for gpu in data.get("gpus", []):
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name = gpu.get("name", "未知")
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util = gpu.get("utilization", 0)
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mem_used = gpu.get("memory_used", 0)
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mem_total = gpu.get("memory_total", 0)
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temp = gpu.get("temperature", 0)
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result += f" {name}\n"
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result += f" GPU 负载: {util}%\n"
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result += f" 显存: {mem_used}/{mem_total} MiB\n"
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result += f" 温度: {temp}°C\n"
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return result
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except Exception as e:
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return f" 获取 GPU 状态失败: {e}"
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@@ -0,0 +1,185 @@
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"""
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记忆系统插件 — 包装 xcli_core/memory.py 中的 MemoryManager
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使 AI 可以通过工具接口读写日记、搜索记忆、管理聊天记录
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"""
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import os
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import sys
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# 添加项目根目录到 path
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(current_dir)
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sys.path.insert(0, project_root)
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from xcli_core.memory import MemoryManager
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class Plugin:
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"""持久化记忆系统 — AI 可写日记、搜索记忆、管理聊天记录"""
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def __init__(self):
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self.cli = None
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self.memory = None
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self.usage = """持久化记忆系统工具
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操作:
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diary <内容> - 写当日日记
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diary_ai <内容> - AI 写观察日记（标记为 AI 观察）
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remember <内容> - 记录到长期记忆 (MEMORY.md)
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search <关键词> - 全局搜索（日记+记忆+聊天记录）
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context - 查看当前注入的记忆上下文
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diary_list - 列出所有日记文件
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diary_read <日期> - 读取指定日期的日记（如 2026-04-27）
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chat_list - 列出最近的聊天记录
|
||||
chat_search <关键词> - 在聊天记录中搜索
|
||||
memory_read - 读取 MEMORY.md 长期记忆
|
||||
memory_write <内容> - 写入 MEMORY.md（追加）
|
||||
|
||||
JSON 格式示例:
|
||||
{"action": "use_tool", "tool": "memory_plugin", "args": "diary 今天完成了API对接"}
|
||||
{"action": "use_tool", "tool": "memory_plugin", "args": "search 定时任务"}
|
||||
{"action": "use_tool", "tool": "memory_plugin", "args": "remember 用户偏好深色主题"}
|
||||
"""
|
||||
|
||||
def set_cli(self, cli):
|
||||
self.cli = cli
|
||||
# 初始化 MemoryManager
|
||||
project_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
self.memory = MemoryManager(project_dir)
|
||||
# 挂载到 cli 实例上，让 cli_core.py 能访问
|
||||
cli.memory_manager = self.memory
|
||||
|
||||
def get_tool_info(self):
|
||||
return {
|
||||
"name": "memory_plugin",
|
||||
"description": "持久化记忆系统 — AI 可写日记、记录长期记忆、搜索历史对话和日记。支持自动压缩上下文。",
|
||||
"keywords": ["记忆", "memory", "日记", "diary", "搜索", "search", "历史", "history",
|
||||
"remember", "聊天记录", "chat", "上下文", "context", "压缩", "compress"],
|
||||
"usage": self.usage
|
||||
}
|
||||
|
||||
def handle(self, args: str) -> str:
|
||||
if not self.memory:
|
||||
return "错误：记忆系统未初始化"
|
||||
|
||||
if not args or not args.strip():
|
||||
return self._help()
|
||||
|
||||
parts = args.strip().split(None, 1)
|
||||
action = parts[0].lower()
|
||||
rest = parts[1].strip() if len(parts) > 1 else ""
|
||||
|
||||
handlers = {
|
||||
"diary": self._diary,
|
||||
"diary_ai": self._diary_ai,
|
||||
"remember": self._remember,
|
||||
"search": self._search,
|
||||
"context": self._context,
|
||||
"diary_list": self._diary_list,
|
||||
"diary_read": self._diary_read,
|
||||
"chat_list": self._chat_list,
|
||||
"chat_search": self._chat_search,
|
||||
"memory_read": self._memory_read,
|
||||
"memory_write": self._memory_write,
|
||||
"help": lambda _: self._help(),
|
||||
}
|
||||
|
||||
handler = handlers.get(action)
|
||||
if not handler:
|
||||
return f"未知操作: {action}\n" + self._help()
|
||||
|
||||
return handler(rest)
|
||||
|
||||
def _diary(self, content: str) -> str:
|
||||
if not content:
|
||||
return "错误：请提供日记内容\n用法: diary <内容>"
|
||||
self.memory.write_diary(content)
|
||||
return f" 日记已写入 {self.memory._today_file()}"
|
||||
|
||||
def _diary_ai(self, content: str) -> str:
|
||||
if not content:
|
||||
return "错误：请提供 AI 观察内容\n用法: diary_ai <内容>"
|
||||
self.memory.write_ai_diary(content)
|
||||
return f" AI 观察日记已写入"
|
||||
|
||||
def _remember(self, content: str) -> str:
|
||||
if not content:
|
||||
return "错误：请提供要记住的内容\n用法: remember <内容>"
|
||||
self.memory.append_memory("备忘", f"- {content}")
|
||||
return f" 已记录到 MEMORY.md 长期记忆"
|
||||
|
||||
def _search(self, keyword: str) -> str:
|
||||
if not keyword:
|
||||
return "错误：请提供搜索关键词\n用法: search <关键词>"
|
||||
results = self.memory.search_memory(keyword)
|
||||
if not results:
|
||||
return f" 未找到与 '{keyword}' 相关的记忆"
|
||||
lines = [f" 搜索 '{keyword}' 的结果 ({len(results)} 条):"]
|
||||
for r in results:
|
||||
source = r.get("source", "")
|
||||
snippet = r.get("snippet", "")
|
||||
role = r.get("role", "")
|
||||
line_num = r.get("line", "")
|
||||
prefix = f"[{role}] " if role else ""
|
||||
line_info = f" (L{line_num})" if line_num else ""
|
||||
lines.append(f" {source}{line_info}: {prefix}{snippet}")
|
||||
return "\n".join(lines)
|
||||
|
||||
def _context(self, _: str) -> str:
|
||||
ctx = self.memory.build_memory_context()
|
||||
return ctx if ctx else " 当前没有记忆上下文"
|
||||
|
||||
def _diary_list(self, _: str) -> str:
|
||||
files = self.memory.list_diary_files()
|
||||
if not files:
|
||||
return " 还没有日记文件"
|
||||
return " 日记文件:\n" + "\n".join(f" {f}" for f in files[:20])
|
||||
|
||||
def _diary_read(self, date_str: str) -> str:
|
||||
if not date_str:
|
||||
return "错误：请提供日期\n用法: diary_read <日期，如 2026-04-27>"
|
||||
content = self.memory.load_diary(date_str)
|
||||
return content if content else f" 没有找到 {date_str} 的日记"
|
||||
|
||||
def _chat_list(self, _: str) -> str:
|
||||
chats = self.memory.list_chats()
|
||||
if not chats:
|
||||
return " 没有保存的聊天记录"
|
||||
lines = [" 最近的聊天记录:"]
|
||||
for c in chats:
|
||||
lines.append(f" {c['name']} ({c['time']}, {c['size_kb']}KB)")
|
||||
return "\n".join(lines)
|
||||
|
||||
def _chat_search(self, keyword: str) -> str:
|
||||
if not keyword:
|
||||
return "错误：请提供关键词\n用法: chat_search <关键词>"
|
||||
results = self.memory.search_chats(keyword)
|
||||
if not results:
|
||||
return f" 未在聊天记录中找到 '{keyword}'"
|
||||
lines = [f" 聊天记录搜索 '{keyword}':"]
|
||||
for r in results:
|
||||
lines.append(f" {r['file']} [{r['role']}]: {r['snippet']}")
|
||||
return "\n".join(lines)
|
||||
|
||||
def _memory_read(self, _: str) -> str:
|
||||
content = self.memory.load_memory()
|
||||
return content if content else " MEMORY.md 为空"
|
||||
|
||||
def _memory_write(self, content: str) -> str:
|
||||
if not content:
|
||||
return "错误：请提供内容\n用法: memory_write <内容>"
|
||||
self.memory.append_memory("备忘", f"- {content}")
|
||||
return f" 已写入 MEMORY.md"
|
||||
|
||||
def _help(self) -> str:
|
||||
return """ 记忆系统用法:
|
||||
diary <内容> - 写当日日记
|
||||
diary_ai <内容> - AI 写观察日记
|
||||
remember <内容> - 记到长期记忆 (MEMORY.md)
|
||||
search <关键词> - 全局搜索记忆
|
||||
context - 查看记忆上下文
|
||||
diary_list - 列出日记
|
||||
diary_read <日期> - 读取日记
|
||||
chat_list - 列出聊天记录
|
||||
chat_search <关键词> - 搜索聊天记录
|
||||
memory_read - 读取 MEMORY.md
|
||||
memory_write <内容> - 写入 MEMORY.md"""
|
||||
Reference in new issue
Block a user