diff --git a/beauty.png b/beauty.png new file mode 100644 index 0000000..a5b9323 Binary files /dev/null and b/beauty.png differ diff --git a/config.json b/config.json index fff8b35..15e7240 100644 --- a/config.json +++ b/config.json @@ -3,7 +3,7 @@ "base_url": "https://wolfai.top/v1", "sd_url": "http://127.0.0.1:7861", "mcp_url": "http://localhost:18060/mcp", - "model": "gemini-3-flash-preview", + "model": "deepseek-v3", "persona": "温柔知性的时尚博主", "auto_reply_enabled": false, "schedule_enabled": false, @@ -21,5 +21,5 @@ "base_url": "https://wolfai.top/v1" } ], - "xsec_token": "ABdAEbqP9ScgelmyolJxsnpCr_e645SCpnub2dLZJc4Ck=" + "xsec_token": "ABfkw0sdbz9Lf-js1d83biryHO6o13nCCPwPbVK6eGYR8=" } \ No newline at end of file diff --git a/llm_service.py b/llm_service.py index c7cc6b8..5f7d8c8 100644 --- a/llm_service.py +++ b/llm_service.py @@ -224,6 +224,9 @@ PROMPT_COPY_WITH_REFERENCE = """ class LLMService: """LLM API 服务封装""" + # 当主模型返回空内容时，依次尝试的备选模型列表 + FALLBACK_MODELS = ["deepseek-v3", "gemini-2.5-flash", "deepseek-v3.1"] + def __init__(self, api_key: str, base_url: str, model: str = "gpt-3.5-turbo"): self.api_key = api_key self.base_url = base_url.rstrip("/") @@ -231,7 +234,7 @@ class LLMService: def _chat(self, system_prompt: str, user_message: str, json_mode: bool = True, temperature: float = 0.8) -> str: - """底层聊天接口""" + """底层聊天接口（含空返回检测、json_mode 回退、模型降级）""" headers = { "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json", @@ -239,36 +242,149 @@ class LLMService: if json_mode: user_message = user_message + "\n请以json格式返回。" - payload = { - "model": self.model, - "messages": [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_message}, - ], - "temperature": temperature, - } - if json_mode: - payload["response_format"] = {"type": "json_object"} + # 构建要尝试的模型列表：主模型 + 备选模型（去重） + models_to_try = [self.model] + [m for m in self.FALLBACK_MODELS if m != self.model] - try: - resp = requests.post( - f"{self.base_url}/chat/completions", - headers=headers, json=payload, timeout=90 - ) - resp.raise_for_status() - content = resp.json()["choices"][0]["message"]["content"] - return content - except requests.exceptions.Timeout: - raise TimeoutError("LLM 请求超时，请检查网络或换一个模型") - except requests.exceptions.HTTPError as e: - raise ConnectionError(f"LLM API 错误 ({resp.status_code}): {resp.text[:200]}") - except Exception as e: - raise RuntimeError(f"LLM 调用异常: {e}") + last_error = None + for model_idx, current_model in enumerate(models_to_try): + payload = { + "model": current_model, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_message}, + ], + "temperature": temperature, + } + if json_mode: + payload["response_format"] = {"type": "json_object"} + + try: + resp = requests.post( + f"{self.base_url}/chat/completions", + headers=headers, json=payload, timeout=90 + ) + resp.raise_for_status() + content = resp.json()["choices"][0]["message"]["content"] + + # 检测空返回 — 如果启用了 json_mode 且返回为空，回退去掉 response_format 重试 + if not content or not content.strip(): + if json_mode: + logger.warning("[%s] LLM 返回空内容 (json_mode=True)，关闭 json_mode 回退重试...", current_model) + payload.pop("response_format", None) + resp2 = requests.post( + f"{self.base_url}/chat/completions", + headers=headers, json=payload, timeout=90 + ) + resp2.raise_for_status() + content = resp2.json()["choices"][0]["message"]["content"] + + if not content or not content.strip(): + # 当前模型完全无法返回内容，尝试下一个模型 + if model_idx < len(models_to_try) - 1: + next_model = models_to_try[model_idx + 1] + logger.warning("[%s] 返回空内容，自动降级到模型: %s", current_model, next_model) + continue + raise RuntimeError(f"所有模型均返回空内容（已尝试: {', '.join(models_to_try[:model_idx+1])}）") + + if model_idx > 0: + logger.info("模型降级成功: %s → %s", self.model, current_model) + return content + + except requests.exceptions.HTTPError as e: + status = getattr(resp, 'status_code', 0) + body = getattr(resp, 'text', '')[:300] + # 某些模型/提供商不支持 response_format，自动回退重试 + if json_mode and status in (400, 422, 500): + logger.warning("[%s] json_mode 请求失败 (HTTP %s)，关闭 response_format 回退重试...", current_model, status) + payload.pop("response_format", None) + try: + resp2 = requests.post( + f"{self.base_url}/chat/completions", + headers=headers, json=payload, timeout=90 + ) + resp2.raise_for_status() + content = resp2.json()["choices"][0]["message"]["content"] + if content and content.strip(): + if model_idx > 0: + logger.info("模型降级成功: %s → %s", self.model, current_model) + return content + except Exception: + pass + # 当前模型失败，尝试下一个 + last_error = ConnectionError(f"LLM API 错误 ({status}): {body}") + if model_idx < len(models_to_try) - 1: + logger.warning("[%s] HTTP %s 失败，降级到: %s", current_model, status, models_to_try[model_idx + 1]) + continue + raise last_error + + except requests.exceptions.Timeout: + last_error = TimeoutError(f"[{current_model}] LLM 请求超时") + if model_idx < len(models_to_try) - 1: + logger.warning("[%s] 请求超时，降级到: %s", current_model, models_to_try[model_idx + 1]) + continue + raise TimeoutError("LLM 请求超时，所有模型均超时，请检查网络") + + except (ConnectionError, RuntimeError): + raise + except Exception as e: + last_error = RuntimeError(f"LLM 调用异常: {e}") + if model_idx < len(models_to_try) - 1: + logger.warning("[%s] 调用异常 (%s)，降级到: %s", current_model, e, models_to_try[model_idx + 1]) + continue + raise last_error + + raise last_error or RuntimeError("LLM 调用失败: 未知错误") def _parse_json(self, text: str) -> dict: - """从 LLM 返回文本中解析 JSON""" - cleaned = re.sub(r"```json\s*|```", "", text).strip() - return json.loads(cleaned) + """从 LLM 返回文本中解析 JSON（多重容错）""" + if not text or not text.strip(): + raise ValueError("LLM 返回内容为空，无法解析 JSON") + + raw = text.strip() + + # 策略1: 去除 markdown 代码块 + cleaned = re.sub(r"```(?:json)?\s*", "", raw) + cleaned = re.sub(r"```", "", cleaned).strip() + + # 策略2: 直接解析 + try: + return json.loads(cleaned) + except json.JSONDecodeError: + pass + + # 策略3: 提取最外层的 { ... } 块 + match = re.search(r'(\{[\s\S]*\})', cleaned) + if match: + try: + return json.loads(match.group(1)) + except json.JSONDecodeError: + pass + + # 策略4: 逐行查找 JSON 开始位置 + for i, ch in enumerate(cleaned): + if ch == '{': + try: + return json.loads(cleaned[i:]) + except json.JSONDecodeError: + pass + break + + # 策略5: 尝试修复常见问题（尾部多余逗号、缺少闭合括号） + try: + # 去除尾部多余逗号 + fixed = re.sub(r',\s*([}\]])', r'\1', cleaned) + return json.loads(fixed) + except json.JSONDecodeError: + pass + + # 全部失败，打日志并抛出有用的错误信息 + preview = raw[:500] if len(raw) > 500 else raw + logger.error("JSON 解析全部失败，LLM 原始返回: %s", preview) + raise ValueError( + f"LLM 返回内容无法解析为 JSON。\n" + f"返回内容前200字: {raw[:200]}\n\n" + f"💡 可能原因: 模型不支持 JSON 输出格式，建议更换模型重试" + ) # ---------- 业务方法 ---------- @@ -290,51 +406,96 @@ class LLMService: return [] def generate_copy(self, topic: str, style: str) -> dict: - """生成小红书文案""" - content = self._chat( - PROMPT_COPYWRITING, - f"主题：{topic}\n风格：{style}", - temperature=0.92, - ) - data = self._parse_json(content) + """生成小红书文案（含重试逻辑）""" + last_error = None + for attempt in range(2): + try: + # 第二次尝试不使用 json_mode（兼容不支持的模型） + use_json_mode = (attempt == 0) + content = self._chat( + PROMPT_COPYWRITING, + f"主题：{topic}\n风格：{style}", + json_mode=use_json_mode, + temperature=0.92, + ) + data = self._parse_json(content) - # 强制标题长度限制 - title = data.get("title", "") - if len(title) > 20: - title = title[:20] - data["title"] = title + # 强制标题长度限制 + title = data.get("title", "") + if len(title) > 20: + title = title[:20] + data["title"] = title - # 去 AI 化后处理 - if "content" in data: - data["content"] = self._humanize_content(data["content"]) + # 去 AI 化后处理 + if "content" in data: + data["content"] = self._humanize_content(data["content"]) - return data + return data + + except (json.JSONDecodeError, ValueError) as e: + last_error = e + if attempt == 0: + logger.warning("文案生成 JSON 解析失败 (尝试 %d/2): %s，将关闭 json_mode 重试", attempt + 1, e) + continue + else: + logger.error("文案生成 JSON 解析失败 (尝试 %d/2): %s", attempt + 1, e) + + raise RuntimeError(f"文案生成失败: LLM 返回无法解析为 JSON，已重试 2 次。\n最后错误: {last_error}") def generate_copy_with_reference(self, topic: str, style: str, reference_notes: str) -> dict: - """参考热门笔记生成文案""" + """参考热门笔记生成文案（含重试逻辑）""" prompt = PROMPT_COPY_WITH_REFERENCE.format( reference_notes=reference_notes, topic=topic, style=style ) - content = self._chat(prompt, f"请创作关于「{topic}」的小红书笔记", - temperature=0.92) - data = self._parse_json(content) + last_error = None + for attempt in range(2): + try: + use_json_mode = (attempt == 0) + content = self._chat( + prompt, f"请创作关于「{topic}」的小红书笔记", + json_mode=use_json_mode, temperature=0.92, + ) + data = self._parse_json(content) - title = data.get("title", "") - if len(title) > 20: - data["title"] = title[:20] + title = data.get("title", "") + if len(title) > 20: + data["title"] = title[:20] - # 去 AI 化后处理 - if "content" in data: - data["content"] = self._humanize_content(data["content"]) + if "content" in data: + data["content"] = self._humanize_content(data["content"]) - return data + return data + + except (json.JSONDecodeError, ValueError) as e: + last_error = e + if attempt == 0: + logger.warning("参考文案生成 JSON 解析失败 (尝试 %d/2): %s，将关闭 json_mode 重试", attempt + 1, e) + continue + else: + logger.error("参考文案生成 JSON 解析失败 (尝试 %d/2): %s", attempt + 1, e) + + raise RuntimeError(f"参考文案生成失败: LLM 返回无法解析为 JSON，已重试 2 次。\n最后错误: {last_error}") def analyze_hotspots(self, feed_data: str) -> dict: - """分析热门内容趋势""" + """分析热门内容趋势（含重试逻辑）""" prompt = PROMPT_HOTSPOT_ANALYSIS.format(feed_data=feed_data) - content = self._chat(prompt, "请分析以上热门笔记数据") - return self._parse_json(content) + last_error = None + for attempt in range(2): + try: + use_json_mode = (attempt == 0) + content = self._chat(prompt, "请分析以上热门笔记数据", + json_mode=use_json_mode) + return self._parse_json(content) + except (json.JSONDecodeError, ValueError) as e: + last_error = e + if attempt == 0: + logger.warning("热点分析 JSON 解析失败 (尝试 %d/2): %s，将关闭 json_mode 重试", attempt + 1, e) + continue + else: + logger.error("热点分析 JSON 解析失败 (尝试 %d/2): %s", attempt + 1, e) + + raise RuntimeError(f"热点分析失败: LLM 返回无法解析为 JSON，已重试 2 次。\n最后错误: {last_error}") @staticmethod def _humanize_content(text: str) -> str: diff --git a/main.py b/main.py index 429ad27..676bb51 100644 --- a/main.py +++ b/main.py @@ -19,7 +19,7 @@ import matplotlib.pyplot as plt from config_manager import ConfigManager, OUTPUT_DIR from llm_service import LLMService -from sd_service import SDService, DEFAULT_NEGATIVE +from sd_service import SDService, DEFAULT_NEGATIVE, FACE_IMAGE_PATH from mcp_client import MCPClient, get_mcp_client # ================= matplotlib 中文字体配置 ================= @@ -262,6 +262,31 @@ def save_my_user_id(user_id_input): return f"✅ 用户 ID 已保存: `{uid}`" +# ================= 头像/换脸管理 ================= + +def upload_face_image(img): + """上传并保存头像图片""" + if img is None: + return None, "❌ 请上传头像图片" + try: + if isinstance(img, str) and os.path.isfile(img): + img = Image.open(img).convert("RGB") + elif not isinstance(img, Image.Image): + return None, "❌ 无法识别图片格式" + path = SDService.save_face_image(img) + return img, f"✅ 头像已保存至 {os.path.basename(path)}" + except Exception as e: + return None, f"❌ 保存失败: {e}" + + +def load_saved_face_image(): + """加载已保存的头像""" + img = SDService.load_face_image() + if img: + return img, "✅ 已加载保存的头像" + return None, "ℹ️ 尚未设置头像" + + def generate_copy(model, topic, style): """生成文案""" api_key, base_url, _ = _get_llm_config() @@ -284,20 +309,33 @@ def generate_copy(model, topic, style): return "", "", "", "", f"❌ 生成失败: {e}" -def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale): - """生成图片""" +def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale, face_swap_on, face_img): + """生成图片（可选 ReActor 换脸）""" if not model: return None, [], "❌ 未选择 SD 模型" try: svc = SDService(sd_url) + # 判断是否启用换脸 + face_image = None + if face_swap_on and face_img is not None: + if isinstance(face_img, Image.Image): + face_image = face_img + elif isinstance(face_img, str) and os.path.isfile(face_img): + face_image = Image.open(face_img).convert("RGB") + if face_swap_on and face_image is None: + # 尝试从默认路径加载 + face_image = SDService.load_face_image() + images = svc.txt2img( prompt=prompt, negative_prompt=neg_prompt, model=model, steps=int(steps), cfg_scale=float(cfg_scale), + face_image=face_image, ) - return images, images, f"✅ 生成 {len(images)} 张图片" + swap_hint = " (已换脸)" if face_image else "" + return images, images, f"✅ 生成 {len(images)} 张图片{swap_hint}" except Exception as e: logger.error("图片生成失败: %s", e) return None, [], f"❌ 绘图失败: {e}" @@ -1025,7 +1063,289 @@ DEFAULT_PERSONAS = [ RANDOM_PERSONA_LABEL = "🎲 随机人设（每次自动切换）" -# ================= 主题池 ================= +# ================= 人设 → 分类关键词/主题池映射 ================= +# 每个人设对应一组相符的评论关键词和主题，切换人设时自动同步 + +PERSONA_POOL_MAP = { + # ---- 时尚穿搭类 ---- + "温柔知性的时尚博主": { + "topics": [ + "春季穿搭", "通勤穿搭", "约会穿搭", "显瘦穿搭", "法式穿搭", + "极简穿搭", "氛围感穿搭", "一衣多穿", "秋冬叠穿", "夏日清凉穿搭", + "生活美学", "衣橱整理", "配色技巧", "基础款穿搭", "轻熟风穿搭", + ], + "keywords": [ + "穿搭", "ootd", "早春穿搭", "通勤穿搭", "显瘦", "法式穿搭", + "极简风", "氛围感", "轻熟风", "高级感穿搭", "配色", + ], + }, + "元气满满的大学生": { + "topics": [ + "学生党穿搭", "宿舍美食", "平价好物", "校园生活", "学生党护肤", + "期末复习", "社团活动", "寝室改造", "奶茶测评", "拍照打卡地", + "一人食食谱", "考研经验", "实习经验", "省钱攻略", + ], + "keywords": [ + "学生党", "平价好物", "宿舍", "校园", "奶茶", "探店", + "拍照", "省钱", "大学生活", "期末", "开学", "室友", + ], + }, + "30岁都市白领丽人": { + "topics": [ + "通勤穿搭", "职场干货", "面试技巧", "简历优化", "时间管理", + "理财入门", "轻熟风穿搭", "职场妆容", "咖啡探店", "高效工作法", + "副业分享", "自律生活", "下班后充电", "职场人际关系", + ], + "keywords": [ + "通勤穿搭", "职场", "面试", "理财", "自律", "高效", + "咖啡", "轻熟", "白领", "上班族", "时间管理", "副业", + ], + }, + "精致妈妈": { + "topics": [ + "育儿经验", "家居收纳", "辅食制作", "亲子游", "母婴好物", + "宝宝穿搭", "早教启蒙", "产后恢复", "家常菜做法", "小户型收纳", + "家庭教育", "孕期护理", "宝宝辅食", "妈妈穿搭", + ], + "keywords": [ + "育儿", "收纳", "辅食", "母婴", "亲子", "早教", + "宝宝", "家居", "待产", "产后", "妈妈", "家常菜", + ], + }, + "文艺青年摄影师": { + "topics": [ + "旅行攻略", "小众旅行地", "拍照打卡地", "城市citywalk", "古镇旅行", + "手机摄影技巧", "胶片摄影", "人像摄影", "风光摄影", "街拍", + "咖啡探店", "文艺书店", "展览打卡", "独立书店", + ], + "keywords": [ + "旅行", "摄影", "打卡", "citywalk", "胶片", "拍照", + "小众", "展览", "文艺", "街拍", "风光", "人像", + ], + }, + "健身达人营养师": { + "topics": [ + "减脂餐分享", "居家健身", "帕梅拉跟练", "跑步入门", "体态矫正", + "增肌餐", "蛋白质补充", "运动穿搭", "健身房攻略", "马甲线养成", + "热量计算", "健康早餐", "运动恢复", "减脂食谱", + ], + "keywords": [ + "减脂", "健身", "减脂餐", "蛋白质", "体态", "马甲线", + "帕梅拉", "跑步", "热量", "增肌", "运动", "健康餐", + ], + }, + "资深美妆博主": { + "topics": [ + "妆容教程", "眼妆教程", "唇妆合集", "底妆测评", "护肤心得", + "防晒测评", "学生党平价护肤", "敏感肌护肤", "美白攻略", + "成分党护肤", "换季护肤", "早C晚A护肤", "抗老护肤", + ], + "keywords": [ + "护肤", "化妆教程", "眼影", "口红", "底妆", "防晒", + "美白", "敏感肌", "成分", "平价", "测评", "粉底", + ], + }, + "独居女孩": { + "topics": [ + "独居生活", "租房改造", "氛围感房间", "一人食食谱", "好物分享", + "香薰推荐", "居家好物", "断舍离", "仪式感生活", "独居安全", + "解压方式", "emo急救指南", "桌面布置", "小户型装修", + ], + "keywords": [ + "独居", "租房改造", "好物", "氛围感", "一人食", "仪式感", + "解压", "居家", "香薰", "ins风", "房间", "断舍离", + ], + }, + "甜品烘焙爱好者": { + "topics": [ + "烘焙教程", "0失败甜品", "下午茶推荐", "蛋糕教程", "面包制作", + "饼干烘焙", "奶油裱花", "巧克力甜品", "网红甜品", "便当制作", + "早餐食谱", "咖啡配甜品", "节日甜品", "低卡甜品", + ], + "keywords": [ + "烘焙", "甜品", "蛋糕", "面包", "下午茶", "曲奇", + "裱花", "抹茶", "巧克力", "奶油", "食谱", "烤箱", + ], + }, + "数码科技女生": { + "topics": [ + "iPad生产力", "手机摄影技巧", "好用App推荐", "电子产品测评", + "桌面布置", "数码好物", "耳机测评", "平板学习", "生产力工具", + "手机壳推荐", "充电设备", "智能家居", + ], + "keywords": [ + "iPad", "App推荐", "数码", "测评", "手机", "耳机", + "桌面", "科技", "电子产品", "平板", "生产力", "充电", + ], + }, + "小镇姑娘在大城市打拼": { + "topics": [ + "省钱攻略", "成长日记", "平价好物", "租房改造", "副业分享", + "理财入门", "独居生活", "面试技巧", "通勤穿搭", "自律生活", + "城市生存指南", "女性成长", "攒钱计划", + ], + "keywords": [ + "省钱", "平价", "租房", "副业", "理财", "成长", + "自律", "打工", "攒钱", "面试", "独居", "北漂", + ], + }, + "中医养生爱好者": { + "topics": [ + "节气养生", "食疗方子", "泡脚养生", "体质调理", "艾灸", + "中药茶饮", "作息调整", "经络按摩", "养胃食谱", "祛湿方法", + "睡眠改善", "女性调理", "养生汤", "二十四节气", + ], + "keywords": [ + "养生", "食疗", "泡脚", "中医", "艾灸", "祛湿", + "节气", "体质", "养胃", "经络", "调理", "药膳", + ], + }, + "二次元coser": { + "topics": [ + "cos日常", "动漫周边", "漫展攻略", "cos化妆教程", "假发造型", + "lolita穿搭", "二次元好物", "手办收藏", "动漫推荐", "cos道具制作", + "jk穿搭", "谷子收藏", "二次元摄影", + ], + "keywords": [ + "cos", "动漫", "二次元", "漫展", "lolita", "手办", + "jk", "假发", "谷子", "周边", "番剧", "coser", + ], + }, + "北漂程序媛": { + "topics": [ + "高效工作法", "程序员日常", "好用App推荐", "副业分享", "自律生活", + "时间管理", "iPad生产力", "解压方式", "通勤穿搭", "理财入门", + "独居生活", "技术学习", "面试经验", "桌面布置", + ], + "keywords": [ + "程序员", "高效", "App推荐", "自律", "副业", "iPad", + "技术", "工作", "北漂", "面试", "代码", "桌面", + ], + }, + "复古穿搭博主": { + "topics": [ + "vintage风穿搭", "中古饰品", "复古妆容", "二手vintage", "古着穿搭", + "法式穿搭", "复古包包", "跳蚤市场", "旧物改造", "港风穿搭", + "文艺穿搭", "配饰搭配", "vintage探店", + ], + "keywords": [ + "vintage", "复古", "中古", "古着", "港风", "法式", + "饰品", "二手", "旧物", "跳蚤市场", "复古穿搭", "文艺", + ], + }, + "考研上岸学姐": { + "topics": [ + "考研经验", "英语学习方法", "书单推荐", "时间管理", "自律生活", + "考研择校", "政治复习", "数学刷题", "考研英语", "复试经验", + "专业课复习", "考研心态", "背诵技巧", "刷题方法", + ], + "keywords": [ + "考研", "英语学习", "书单", "自律", "学习方法", "上岸", + "刷题", "备考", "复习", "笔记", "时间管理", "择校", + ], + }, + "新手养猫人": { + "topics": [ + "养猫日常", "猫粮测评", "猫咪用品", "新手养宠指南", "猫咪健康", + "猫咪行为", "驱虫攻略", "猫砂测评", "猫玩具推荐", "猫咪拍照", + "多猫家庭", "领养代替购买", "猫咪绝育", + ], + "keywords": [ + "养猫", "猫粮", "猫咪", "宠物", "猫砂", "驱虫", + "铲屎官", "喵喵", "猫玩具", "猫零食", "新手养猫", "猫咪日常", + ], + }, + "咖啡重度爱好者": { + "topics": [ + "咖啡探店", "手冲咖啡", "咖啡豆推荐", "咖啡器具", "拿铁艺术", + "家庭咖啡", "咖啡配甜品", "独立咖啡馆", "冷萃咖啡", "咖啡知识", + "意式咖啡", "探店打卡", "咖啡拉花", + ], + "keywords": [ + "咖啡", "手冲", "拿铁", "探店", "咖啡豆", "美式", + "咖啡馆", "意式", "冷萃", "拉花", "咖啡器具", "独立咖啡馆", + ], + }, + "极简主义生活家": { + "topics": [ + "断舍离", "极简生活", "收纳技巧", "高质量生活", "减法生活", + "胶囊衣橱", "极简护肤", "环保生活", "数字断舍离", "极简穿搭", + "极简房间", "消费降级", "物欲管理", + ], + "keywords": [ + "断舍离", "极简", "收纳", "高质量", "减法", "胶囊衣橱", + "简约", "环保", "整理", "少即是多", "极简风", "质感", + ], + }, + "汉服爱好者": { + "topics": [ + "汉服穿搭", "国风穿搭", "传统文化", "汉服发型", "汉服配饰", + "汉服拍照", "古风妆容", "汉服日常", "汉服科普", "形制科普", + "古风摄影", "新中式穿搭", "汉服探店", + ], + "keywords": [ + "汉服", "国风", "传统文化", "古风", "新中式", "形制", + "发簪", "明制", "宋制", "唐制", "汉服日常", "古风摄影", + ], + }, + "插画师小姐姐": { + "topics": [ + "手绘教程", "创作灵感", "iPad绘画", "插画分享", "水彩教程", + "Procreate技巧", "配色方案", "角色设计", "头像绘制", "手账素材", + "接稿经验", "画师日常", "绘画工具推荐", + ], + "keywords": [ + "插画", "手绘", "Procreate", "画画", "iPad绘画", "水彩", + "配色", "创作", "画师", "手账", "教程", "素材", + ], + }, + "海归女孩": { + "topics": [ + "中西文化差异", "海外生活", "留学经验", "英语学习方法", "海归求职", + "旅行攻略", "异国美食", "海外好物", "文化冲击", "语言学习", + "签证攻略", "海归适应", "国外探店", + ], + "keywords": [ + "留学", "海归", "英语", "海外", "文化差异", "旅行", + "异国", "签证", "语言", "出国", "求职", "国外", + ], + }, + "瑜伽老师": { + "topics": [ + "瑜伽入门", "冥想练习", "体态矫正", "呼吸法", "居家瑜伽", + "拉伸教程", "肩颈放松", "瑜伽体式", "自律生活", "身心灵", + "瑜伽穿搭", "晨练瑜伽", "睡前瑜伽", + ], + "keywords": [ + "瑜伽", "冥想", "体态", "拉伸", "放松", "呼吸", + "柔韧", "健康", "自律", "晨练", "入门", "体式", + ], + }, + "美甲设计师": { + "topics": [ + "美甲教程", "流行甲型", "美甲合集", "简约美甲", "法式美甲", + "手绘美甲", "季节美甲", "显白美甲", "美甲配色", "短甲美甲", + "新娘美甲", "美甲工具推荐", "日式美甲", + ], + "keywords": [ + "美甲", "甲型", "法式美甲", "手绘", "显白", "短甲", + "指甲", "美甲教程", "配色", "日式美甲", "腮红甲", "猫眼甲", + ], + }, + "家居软装设计师": { + "topics": [ + "小户型改造", "氛围感布置", "软装搭配", "家居好物", "收纳技巧", + "客厅布置", "卧室改造", "灯光设计", "绿植布置", "装修避坑", + "北欧风格", "ins风家居", "墙面装饰", + ], + "keywords": [ + "家居", "软装", "改造", "收纳", "氛围感", "小户型", + "装修", "灯光", "绿植", "北欧", "ins风", "布置", + ], + }, +} + +# 为"随机人设"使用的全量池（兼容旧逻辑） DEFAULT_TOPICS = [ # 穿搭类 "春季穿搭", "通勤穿搭", "约会穿搭", "显瘦穿搭", "小个子穿搭", @@ -1064,7 +1384,7 @@ DEFAULT_STYLES = [ "知识科普", "经验分享", "清单合集", "对比测评", "沉浸式体验", ] -# ================= 评论关键词池 ================= +# 全量评论关键词池（兼容旧逻辑 / 随机人设） DEFAULT_COMMENT_KEYWORDS = [ # 穿搭时尚 "穿搭", "ootd", "早春穿搭", "通勤穿搭", "显瘦", "小个子穿搭", @@ -1087,6 +1407,77 @@ DEFAULT_COMMENT_KEYWORDS = [ ] +def _match_persona_pools(persona_text: str) -> dict | None: + """根据人设文本模糊匹配对应的关键词池和主题池 + 返回 {"topics": [...], "keywords": [...]} 或 None（未匹配） + """ + if not persona_text or persona_text == RANDOM_PERSONA_LABEL: + return None + # 精确匹配 + for key, pools in PERSONA_POOL_MAP.items(): + if key in persona_text or persona_text in key: + return pools + # 关键词模糊匹配 + _CATEGORY_HINTS = { + "时尚|穿搭|搭配|衣服": "温柔知性的时尚博主", + "大学|学生|校园": "元气满满的大学生", + "白领|职场|通勤|上班": "30岁都市白领丽人", + "妈妈|育儿|宝宝|母婴": "精致妈妈", + "摄影|旅行|旅游|文艺": "文艺青年摄影师", + "健身|运动|减脂|增肌|营养": "健身达人营养师", + "美妆|化妆|护肤|美白": "资深美妆博主", + "独居|租房|一人": "独居女孩", + "烘焙|甜品|蛋糕|面包": "甜品烘焙爱好者", + "数码|科技|App|电子": "数码科技女生", + "小镇|打拼|省钱|攒钱": "小镇姑娘在大城市打拼", + "中医|养生|食疗|节气": "中医养生爱好者", + "二次元|cos|动漫|漫展": "二次元coser", + "程序|代码|开发|码农": "北漂程序媛", + "复古|vintage|中古|古着": "复古穿搭博主", + "考研|备考|上岸|学习方法": "考研上岸学姐", + "猫|铲屎|喵": "新手养猫人", + "咖啡|手冲|拿铁": "咖啡重度爱好者", + "极简|断舍离|简约": "极简主义生活家", + "汉服|国风|传统文化": "汉服爱好者", + "插画|手绘|画画|绘画": "插画师小姐姐", + "海归|留学|海外": "海归女孩", + "瑜伽|冥想|身心灵": "瑜伽老师", + "美甲|甲型|指甲": "美甲设计师", + "家居|软装|装修|改造": "家居软装设计师", + } + for hints, persona_key in _CATEGORY_HINTS.items(): + if any(h in persona_text for h in hints.split("|")): + return PERSONA_POOL_MAP.get(persona_key) + return None + + +def get_persona_topics(persona_text: str) -> list[str]: + """获取人设对应的主题池，未匹配则返回全量池""" + pools = _match_persona_pools(persona_text) + return pools["topics"] if pools else DEFAULT_TOPICS + + +def get_persona_keywords(persona_text: str) -> list[str]: + """获取人设对应的评论关键词池，未匹配则返回全量池""" + pools = _match_persona_pools(persona_text) + return pools["keywords"] if pools else DEFAULT_COMMENT_KEYWORDS + + +def on_persona_changed(persona_text: str): + """人设切换时联动更新评论关键词池和主题池""" + keywords = get_persona_keywords(persona_text) + topics = get_persona_topics(persona_text) + keywords_str = ", ".join(keywords) + topics_str = ", ".join(topics) + matched = _match_persona_pools(persona_text) + if matched: + label = persona_text[:15] if len(persona_text) > 15 else persona_text + hint = f"✅ 已切换至「{label}」专属关键词/主题池" + else: + hint = "ℹ️ 使用通用全量关键词/主题池" + return keywords_str, topics_str, hint + + def _auto_log_append(msg: str): """记录自动化日志""" ts = datetime.now().strftime("%H:%M:%S") @@ -1122,7 +1513,9 @@ def auto_comment_once(keywords_str, mcp_url, model, persona_text): return f"🚫 今日评论已达上限 ({DAILY_LIMITS['comments']})" persona_text = _resolve_persona(persona_text) - keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else DEFAULT_COMMENT_KEYWORDS + # 如果用户未手动修改关键词池，则使用人设匹配的专属关键词池 + persona_keywords = get_persona_keywords(persona_text) + keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else persona_keywords keyword = random.choice(keywords) _auto_log_append(f"🔍 搜索关键词: {keyword}") @@ -1367,9 +1760,9 @@ def auto_favorite_once(keywords_str, fav_count, mcp_url): return f"❌ 收藏失败: {e}" -def _auto_publish_with_log(topics_str, mcp_url, sd_url_val, sd_model_name, model): +def _auto_publish_with_log(topics_str, mcp_url, sd_url_val, sd_model_name, model, face_swap_on=False): """一键发布 + 同步刷新日志""" - msg = auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model) + msg = auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model, face_swap_on=face_swap_on) return msg, get_auto_log() @@ -1540,7 +1933,7 @@ def auto_reply_once(max_replies, mcp_url, model, persona_text): return f"❌ 自动回复失败: {e}" -def auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model): +def auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model, face_swap_on=False): """一键发布：自动生成文案 → 生成图片 → 本地备份 → 发布到小红书（含限额）""" try: if _is_in_cooldown(): @@ -1551,7 +1944,7 @@ def auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model): topics = [t.strip() for t in topics_str.split(",") if t.strip()] if topics_str else DEFAULT_TOPICS topic = random.choice(topics) style = random.choice(DEFAULT_STYLES) - _auto_log_append(f"📝 主题: {topic} | 风格: {style}") + _auto_log_append(f"📝 主题: {topic} | 风格: {style} (主题池: {len(topics)} 个)") # 生成文案 api_key, base_url, _ = _get_llm_config() @@ -1575,7 +1968,15 @@ def auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model): return "❌ SD WebUI 未连接或未选择模型，请先在全局设置中连接" sd_svc = SDService(sd_url_val) - images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name) + # 自动发布也支持换脸 + face_image = None + if face_swap_on: + face_image = SDService.load_face_image() + if face_image: + _auto_log_append("🎭 换脸已启用") + else: + _auto_log_append("⚠️ 换脸已启用但未找到头像，跳过换脸") + images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name, face_image=face_image) if not images: _record_error() return "❌ 图片生成失败：没有返回图片" @@ -1648,7 +2049,7 @@ def _scheduler_loop(comment_enabled, publish_enabled, reply_enabled, like_enable fav_min, fav_max, fav_count_per_run, op_start_hour, op_end_hour, keywords, topics, mcp_url, sd_url_val, sd_model_name, - model, persona_text): + model, persona_text, face_swap_on=False): """后台定时调度循环（含运营时段、冷却、收藏、统计）""" _auto_log_append("🤖 自动化调度器已启动") _auto_log_append(f"⏰ 运营时段: {int(op_start_hour)}:00 - {int(op_end_hour)}:00") @@ -1742,7 +2143,7 @@ def _scheduler_loop(comment_enabled, publish_enabled, reply_enabled, like_enable if publish_enabled and now >= next_publish: try: _auto_log_append("--- 🔄 执行自动发布 ---") - msg = auto_publish_once(topics, mcp_url, sd_url_val, sd_model_name, model) + msg = auto_publish_once(topics, mcp_url, sd_url_val, sd_model_name, model, face_swap_on=face_swap_on) _auto_log_append(msg) except Exception as e: _auto_log_append(f"❌ 自动发布异常: {e}") @@ -1781,7 +2182,7 @@ def start_scheduler(comment_on, publish_on, reply_on, like_on, favorite_on, fav_min, fav_max, fav_count_per_run, op_start_hour, op_end_hour, keywords, topics, mcp_url, sd_url_val, sd_model_name, - model, persona_text): + model, persona_text, face_swap_on=False): """启动定时自动化""" global _auto_thread if _auto_running.is_set(): @@ -1807,6 +2208,7 @@ def start_scheduler(comment_on, publish_on, reply_on, like_on, favorite_on, op_start_hour, op_end_hour, keywords, topics, mcp_url, sd_url_val, sd_model_name, model, persona_text), + kwargs={"face_swap_on": face_swap_on}, daemon=True, ) _auto_thread.start() @@ -2066,6 +2468,38 @@ with gr.Blocks( ) status_bar = gr.Markdown("🔄 等待连接...") + gr.Markdown("---") + gr.Markdown("#### 🎭 AI 换脸 (ReActor)") + gr.Markdown( + "> 上传你的头像，生成含人物的图片时自动替换为你的脸\n" + "> 需要 SD WebUI 已安装 [ReActor](https://github.com/Gourieff/sd-webui-reactor) 扩展" + ) + with gr.Row(): + face_image_input = gr.Image( + label="上传头像 (正面清晰照片效果最佳)", + type="pil", + height=180, + scale=1, + ) + face_image_preview = gr.Image( + label="当前头像", + type="pil", + height=180, + interactive=False, + value=SDService.load_face_image(), + scale=1, + ) + with gr.Row(): + btn_save_face = gr.Button("💾 保存头像", variant="primary", size="sm") + face_swap_toggle = gr.Checkbox( + label="🎭 生成图片时启用 AI 换脸", + value=os.path.isfile(FACE_IMAGE_PATH), + interactive=True, + ) + face_status = gr.Markdown( + "✅ 头像已就绪" if os.path.isfile(FACE_IMAGE_PATH) else "ℹ️ 尚未设置头像" + ) + gr.Markdown("---") gr.Markdown("#### 🖥️ 系统设置") with gr.Row(): @@ -2415,6 +2849,9 @@ with gr.Blocks( "> 一键评论引流 + 一键点赞 + 一键收藏 + 一键回复 + 一键发布 + 随机定时全自动\n\n" "⚠️ **注意**: 请确保已连接 LLM、SD WebUI 和 MCP 服务" ) + persona_pool_hint = gr.Markdown( + value=f"🎭 当前人设池: **{config.get('persona', '随机')[:20]}** → 关键词/主题池已匹配", + ) with gr.Row(): # 左栏: 一键操作 @@ -2425,9 +2862,9 @@ with gr.Blocks( "每次随机选关键词搜索，从结果中随机选笔记" ) auto_comment_keywords = gr.Textbox( - label="评论关键词池 (逗号分隔)", - value=", ".join(DEFAULT_COMMENT_KEYWORDS), - placeholder="关键词1, 关键词2, ...", + label="评论关键词池 (逗号分隔，随人设自动切换)", + value=", ".join(get_persona_keywords(config.get("persona", ""))), + placeholder="关键词1, 关键词2, ... (切换人设自动更新)", ) btn_auto_comment = gr.Button( "💬 一键评论 (单次)", variant="primary", size="lg", @@ -2482,9 +2919,9 @@ with gr.Blocks( "> 随机选主题+风格 → AI 生成文案 → SD 生成图片 → 自动发布" ) auto_publish_topics = gr.Textbox( - label="主题池 (逗号分隔)", - value=", ".join(random.sample(DEFAULT_TOPICS, min(15, len(DEFAULT_TOPICS)))), - placeholder="主题会从池中随机选取，可自行修改", + label="主题池 (逗号分隔，随人设自动切换)", + value=", ".join(get_persona_topics(config.get("persona", ""))), + placeholder="主题会从池中随机选取，切换人设自动更新", ) btn_auto_publish = gr.Button( "🚀 一键发布 (单次)", variant="primary", size="lg", @@ -2642,6 +3079,13 @@ with gr.Blocks( outputs=[status_bar], ) + # ---- 头像/换脸管理 ---- + btn_save_face.click( + fn=upload_face_image, + inputs=[face_image_input], + outputs=[face_image_preview, face_status], + ) + # ---- Tab 1: 内容创作 ---- btn_gen_copy.click( fn=generate_copy, @@ -2651,7 +3095,8 @@ with gr.Blocks( btn_gen_img.click( fn=generate_images, - inputs=[sd_url, res_prompt, neg_prompt, sd_model, steps, cfg_scale], + inputs=[sd_url, res_prompt, neg_prompt, sd_model, steps, cfg_scale, + face_swap_toggle, face_image_preview], outputs=[gallery, state_images, status_bar], ) @@ -2798,6 +3243,13 @@ with gr.Blocks( ) # ---- Tab 6: 自动运营 ---- + # 人设切换 → 联动更新评论关键词池和主题池 + persona.change( + fn=on_persona_changed, + inputs=[persona], + outputs=[auto_comment_keywords, auto_publish_topics, persona_pool_hint], + ) + btn_auto_comment.click( fn=_auto_comment_with_log, inputs=[auto_comment_keywords, mcp_url, llm_model, persona], @@ -2820,7 +3272,7 @@ with gr.Blocks( ) btn_auto_publish.click( fn=_auto_publish_with_log, - inputs=[auto_publish_topics, mcp_url, sd_url, sd_model, llm_model], + inputs=[auto_publish_topics, mcp_url, sd_url, sd_model, llm_model, face_swap_toggle], outputs=[auto_publish_result, auto_log_display], ) btn_start_sched.click( @@ -2833,7 +3285,8 @@ with gr.Blocks( sched_fav_min, sched_fav_max, sched_fav_count, sched_start_hour, sched_end_hour, auto_comment_keywords, auto_publish_topics, - mcp_url, sd_url, sd_model, llm_model, persona], + mcp_url, sd_url, sd_model, llm_model, persona, + face_swap_toggle], outputs=[sched_result], ) btn_stop_sched.click( diff --git a/my_face.png b/my_face.png new file mode 100644 index 0000000..2739f95 Binary files /dev/null and b/my_face.png differ diff --git a/myself.jpg b/myself.jpg new file mode 100644 index 0000000..b7650bd Binary files /dev/null and b/myself.jpg differ diff --git a/sd_service.py b/sd_service.py index 266b9b4..f3fe76a 100644 --- a/sd_service.py +++ b/sd_service.py @@ -1,16 +1,20 @@ """ Stable Diffusion 服务模块 -封装对 SD WebUI API 的调用，支持 txt2img 和 img2img +封装对 SD WebUI API 的调用，支持 txt2img 和 img2img，支持 ReActor 换脸 """ import requests import base64 import io import logging +import os from PIL import Image logger = logging.getLogger(__name__) -SD_TIMEOUT = 900 # 图片生成可能需要较长时间 +SD_TIMEOUT = 1800 # 图片生成可能需要较长时间 + +# 头像文件默认保存路径 +FACE_IMAGE_PATH = os.path.join(os.path.dirname(__file__), "my_face.png") # 默认反向提示词（针对 JuggernautXL / SDXL 优化，偏向东方审美） DEFAULT_NEGATIVE = ( @@ -31,6 +35,100 @@ class SDService: def __init__(self, sd_url: str = "http://127.0.0.1:7860"): self.sd_url = sd_url.rstrip("/") + # ---------- 工具方法 ---------- + + @staticmethod + def _image_to_base64(img: Image.Image) -> str: + """PIL Image → base64 字符串""" + buf = io.BytesIO() + img.save(buf, format="PNG") + return base64.b64encode(buf.getvalue()).decode("utf-8") + + @staticmethod + def load_face_image(path: str = None) -> Image.Image | None: + """加载头像图片，不存在则返回 None""" + path = path or FACE_IMAGE_PATH + if path and os.path.isfile(path): + try: + return Image.open(path).convert("RGB") + except Exception as e: + logger.warning("头像加载失败: %s", e) + return None + + @staticmethod + def save_face_image(img: Image.Image, path: str = None) -> str: + """保存头像图片，返回保存路径""" + path = path or FACE_IMAGE_PATH + img = img.convert("RGB") + img.save(path, format="PNG") + logger.info("头像已保存: %s", path) + return path + + def _build_reactor_args(self, face_image: Image.Image) -> dict: + """构建 ReActor 换脸参数（alwayson_scripts 格式） + + 参数索引对照 (reactor script-info): + 0: source_image (base64) 1: enable 2: source_faces + 3: target_faces 4: model 5: restore_face + 6: restore_visibility 7: restore_first 8: upscaler + 9: scale 10: upscaler_vis 11: swap_in_source + 12: swap_in_generated 13: log_level 14: gender_source + 15: gender_target 16: save_original 17: codeformer_weight + 18: source_hash_check 19: target_hash_check 20: exec_provider + 21: face_mask_correction 22: select_source 23: face_model + 24: source_folder 25: multiple_sources 26: random_image + 27: force_upscale 28: threshold 29: max_faces + 30: tab_single + """ + face_b64 = self._image_to_base64(face_image) + return { + "reactor": { + "args": [ + face_b64, # 0: source image (base64) + True, # 1: enable ReActor + "0", # 2: source face index + "0", # 3: target face index + "inswapper_128.onnx", # 4: swap model + "CodeFormer", # 5: restore face method + 1, # 6: restore face visibility + True, # 7: restore face first, then upscale + "None", # 8: upscaler + 1, # 9: scale + 1, # 10: upscaler visibility + False, # 11: swap in source + True, # 12: swap in generated + "Minimum", # 13: log level + "No", # 14: gender detection (source) + "No", # 15: gender detection (target) + False, # 16: save original + 0.6, # 17: CodeFormer weight (fidelity) + True, # 18: source hash check + False, # 19: target hash check + "CUDA", # 20: execution provider + True, # 21: face mask correction + "Image(s)", # 22: select source type + "None", # 23: face model name + "", # 24: source folder + None, # 25: multiple source images + False, # 26: random image + False, # 27: force upscale + 0.5, # 28: detection threshold + 0, # 29: max faces (0 = no limit) + "tab_single", # 30: tab + ], + } + } + + def has_reactor(self) -> bool: + """检查 SD WebUI 是否安装了 ReActor 扩展""" + try: + resp = requests.get(f"{self.sd_url}/sdapi/v1/scripts", timeout=5) + scripts = resp.json() + all_scripts = scripts.get("txt2img", []) + scripts.get("img2img", []) + return any("reactor" in s.lower() for s in all_scripts) + except Exception: + return False + def check_connection(self) -> tuple[bool, str]: """检查 SD 服务是否可用""" try: @@ -74,8 +172,13 @@ class SDService: seed: int = -1, sampler_name: str = "DPM++ 2M", scheduler: str = "Karras", + face_image: Image.Image = None, ) -> list[Image.Image]: - """文生图（参数针对 JuggernautXL 优化）""" + """文生图（参数针对 JuggernautXL 优化） + + Args: + face_image: 头像 PIL Image，传入后自动启用 ReActor 换脸 + """ if model: self.switch_model(model) @@ -92,6 +195,11 @@ class SDService: "scheduler": scheduler, } + # 如果提供了头像，通过 ReActor 换脸 + if face_image is not None: + payload["alwayson_scripts"] = self._build_reactor_args(face_image) + logger.info("🎭 ReActor 换脸已启用") + resp = requests.post( f"{self.sd_url}/sdapi/v1/txt2img", json=payload, diff --git a/zjz.png b/zjz.png new file mode 100644 index 0000000..a4055f5 Binary files /dev/null and b/zjz.png differ