apps/worker/worker_app/tasks/viral_video.py

@@ -438,7 +438,7 @@ def _analyze_single_image(
 
     # 第二次：pro 降级重试
     if pro_fallback_model and pro_fallback_model != vision_model:
-        pro_raw = _call(pro_fallback_model, max(60, timeout))
+        pro_raw = _call(pro_fallback_model, 25)  # #2173: pro 25s 上限（lite 已失败过，快速兜底）
         pro_result = _normalize(pro_raw, "pro_fallback")
         if _is_vision_result_usable(pro_result):
             pro_result["_fallback_used"] = True
@@ -472,7 +472,7 @@ def _step_image_analysis(job: ViralVideoJob) -> dict:
         if _s.doubao_vision_use_lite:
             vision_model = _s.doubao_vision_lite_model
             pro_model = _s.doubao_vision_model
-            vision_timeout = 45  # lite 给到 45s 避免首轮就 timeout 降级 pro
+            vision_timeout = 15  # #2173: lite 15s 快速失败转 pro（实测 lite 持续超时时白等137s是P0）
         else:
             vision_model = _s.doubao_vision_model
             pro_model = None  # 已经是 pro，不再降级
@@ -581,17 +581,20 @@ def _step_intent_parsing(job: ViralVideoJob, image_analysis: dict) -> dict:
 
     _s = get_shared_settings()
     _fast = _s.doubao_fast_model
-    try:
-        # 用快模型提速（结构化输出任务，不需要推理模型）
-        result = call_llm(prompt, temperature=0.4, max_tokens=800, model=_fast)
-        return (
-            result
-            if isinstance(result, dict)
-            else {"intent": str(result)[:200], "key_messages": [], "tone": "专业", "suggested_title": ""}
-        )
-    except Exception as e:
-        logger.warning("[爆款视频] 意图解析失败: %s", e)
-        return {"intent": "推广产品", "key_messages": ["产品亮点"], "tone": "专业", "suggested_title": ""}
+    _pro = _s.doubao_model
+    for _m, _lbl in [(_fast, "fast"), (_pro, "pro-fallback")]:
+        try:
+            logger.info("[爆款视频] 意图解析 model=%s label=%s", _m, _lbl)
+            result = call_llm(prompt, temperature=0.4, max_tokens=800, model=_m, timeout=25)
+            return (
+                result
+                if isinstance(result, dict)
+                else {"intent": str(result)[:200], "key_messages": [], "tone": "专业", "suggested_title": ""}
+            )
+        except Exception as e:
+            logger.warning("[爆款视频] 意图解析失败 label=%s err=%s", _lbl, e)
+            continue
+    return {"intent": "推广产品", "key_messages": ["产品亮点"], "tone": "专业", "suggested_title": ""}
 
 
 # ── 编导分镜脚本生成（核心，v1.6 新 prompt） ──────────────────────────────
@@ -982,9 +985,9 @@ def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: di
     _fast = _s.doubao_fast_model
     _pro = getattr(_s, "doubao_model", None) or _fast
 
-    def _try_gen(model: str, temp: float, max_tok: int, label: str):
-        logger.info("[爆款视频] 编导脚本生成 model=%s label=%s", model, label)
-        r = call_llm(prompt, temperature=temp, max_tokens=max_tok, model=model)
+    def _try_gen(model: str, temp: float, max_tok: int, label: str, tmo: int = 25):
+        logger.info("[爆款视频] 编导脚本生成 model=%s label=%s timeout=%d", model, label, tmo)
+        r = call_llm(prompt, temperature=temp, max_tokens=max_tok, model=model, timeout=tmo)
         if r is None:
             logger.warning("[爆款视频] 编导脚本返回None label=%s", label)
             return None
@@ -1016,17 +1019,17 @@ def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: di
         return normalized
 
     try:
-        # 第一次：快模型
-        normalized = _try_gen(_fast, 0.8, 2500, "fast-first")
+        # 第一次：快模型 25s
+        normalized = _try_gen(_fast, 0.8, 2500, "fast-first", tmo=25)
         if normalized is not None:
             return normalized
-        # 第二次：快模型降温度+加大 max_tokens
-        normalized = _try_gen(_fast, 0.6, 3200, "fast-retry")
+        # 第二次：快模型降温度+加大 max_tokens，25s
+        normalized = _try_gen(_fast, 0.6, 3200, "fast-retry", tmo=25)
         if normalized is not None:
             return normalized
-        # 第三次：用主力模型兜底
+        # 第三次：用主力模型兜底，给 40s
         if _pro and _pro != _fast:
-            normalized = _try_gen(_pro, 0.7, 3500, "pro-fallback")
+            normalized = _try_gen(_pro, 0.7, 3500, "pro-fallback", tmo=40)
             if normalized is not None:
                 return normalized
         logger.warning("[爆款视频] 编导脚本三次都未生成合格结果，使用兜底脚本")
@@ -1059,13 +1062,16 @@ def _step_review(job: ViralVideoJob, copy_result: dict) -> dict:
 - issues: 需要修改的问题列表（如有）"""
     _s = get_shared_settings()
     _fast = _s.doubao_fast_model
-    try:
-        # 合规审核用快模型 + 短输出（结构化判断）
-        result = call_llm(prompt, temperature=0.1, max_tokens=500, model=_fast)
-        return result if isinstance(result, dict) else {"passed": True, "score": 80, "details": {}}
-    except Exception as e:
-        logger.warning("[爆款视频] 合规审核失败: %s", e)
-        return {"passed": True, "score": 75, "details": {d: "默认通过" for d in dimensions}}
+    _pro = _s.doubao_model
+    for _m, _lbl in [(_fast, "fast"), (_pro, "pro-fallback")]:
+        try:
+            logger.info("[爆款视频] 合规审核 model=%s label=%s", _m, _lbl)
+            result = call_llm(prompt, temperature=0.1, max_tokens=500, model=_m, timeout=20)
+            return result if isinstance(result, dict) else {"passed": True, "score": 80, "details": {}}
+        except Exception as e:
+            logger.warning("[爆款视频] 合规审核失败 label=%s err=%s", _lbl, e)
+            continue
+    return {"passed": True, "score": 75, "details": {d: "默认通过" for d in dimensions}}
 
 
 def _step_tts(job: ViralVideoJob, voiceover_script: str):
@@ -1573,8 +1579,8 @@ def run_viral_video_analyze(self: Task, job_id: str) -> dict:
     bind=True,
     max_retries=1,
     name="worker.run_viral_video_generate_copy",
-    soft_time_limit=180,  # 3min（编导脚本生成含三级重试）
-    time_limit=240,
+    soft_time_limit=360,  # #2173: 6min（编导脚本含意图+三级重试+审核，fast超时转pro）
+    time_limit=420,  # #2173: 7min hard limit
 )
 def run_viral_video_generate_copy(self: Task, job_id: str) -> dict:
     """v1.6 阶段2（v1.6.1 提速版）：意图解析 → 编导分镜脚本生成 → 直接返回，合规审核后置到出片前。

packages/config/base.py

@@ -108,8 +108,14 @@ class SharedSettings(BaseSettings):
     doubao_video_model: str = "doubao-seedance-2-5-260628"
     doubao_video_timeout: int = 600  # 视频生成轮询总超时（秒）
     doubao_video_poll_interval: int = 10  # 轮询间隔（秒）
-    doubao_image_model: str = "doubao-seedream-5-0-pro-260628"  # 图生图/文生图（信任链真人照片AI化）
-    doubao_image_timeout: int = 120  # 图片生成超时（秒）
+    doubao_image_model: str = (
+        "doubao-seedream-5-0-flash-260915"  # #2173: 信任链 Seedream 改 flash 模型（实测 pro 46.5s→flash 13s；pro AI化图仍被Seedance拦截）
+    )
+    doubao_image_size: str = "1K"  # #2173: 1K 已足够做 Seedance 参考图，2K 在 flash 下也 22s，1K 13s
+    doubao_image_timeout: int = 60  # #2173: flash+1K 通常15s内，给60s余量
+    doubao_trust_chain_enabled: bool = (
+        True  # #2173: 信任链总开关；若Seedream产物仍被Seedance拦截，可配 False 关闭直接t2v降级
+    )
 
     # ── DashScope (阿里云百炼 Wan 3.0 等) ─────────────────────────────────
     dashscope_api_key: str = ""

packages/shared/ai_client.py

@@ -182,7 +182,9 @@ class DoubaoClient:
         self.fast_model: str = settings.doubao_fast_model
         self.embedding_model: str = settings.doubao_embedding_model
         self.image_model: str = settings.doubao_image_model
-        self.image_timeout: int = getattr(settings, "doubao_image_timeout", 120) or 120
+        self.image_size: str = getattr(settings, "doubao_image_size", "1K") or "1K"
+        self.image_timeout: int = getattr(settings, "doubao_image_timeout", 60) or 60
+        self.trust_chain_enabled: bool = getattr(settings, "doubao_trust_chain_enabled", True)
         # 最近一次视频生成的详细错误（error_code + user_message + raw detail），供上层读取后展示给用户
         self.last_video_error: dict = {}
         # 最近一次图片生成的详细错误，供上层读取
@@ -240,6 +242,7 @@ class DoubaoClient:
         temperature: float = 0.7,
         max_tokens: int = 1024,
         model: str | None = None,
+        timeout: int | None = None,
     ) -> Optional[str]:
         """调用 Chat Completion 接口.
 
@@ -270,18 +273,19 @@ class DoubaoClient:
         _t0 = time.time()
         for attempt in range(self.max_retries + 1):
             try:
+                _req_timeout = timeout if timeout is not None else self.timeout
                 response = httpx.post(
                     url,
                     headers=headers,
                     json=payload,
-                    timeout=self.timeout,
+                    timeout=_req_timeout,
                 )
                 response.raise_for_status()
                 data = response.json()
                 content = data["choices"][0]["message"]["content"]
                 _elapsed = time.time() - _t0
                 logger.info(
-                    "[doubao] chat_completion 完成 model=%s tokens_in=%d tokens_out=%d elapsed=%.1fs attempt=%d",
+                    "[doubao] chat_completion 完成 model=%s tokens_in=%d tokens_out=%d elapsed=%.1fs attempt=%d timeout=%d",
                     payload.get("model"),
                     data.get("usage", {}).get("prompt_tokens", 0),
                     data.get("usage", {}).get("completion_tokens", 0),
@@ -417,15 +421,17 @@ class DoubaoClient:
         self,
         portrait_urls: list[str],
         *,
-        timeout: int = 120,
+        timeout: int | None = None,
+        size: str | None = None,
     ) -> list[str] | None:
         """#2172 信任链预热：对一组人像 URL 执行 Seedream AI 化，返回 AI 化后的 URL 列表。
 
         - 全部成功返回 list[str]（顺序与输入一致）
         - 任何一张失败返回 None（保留上层回退原图直传的路径）
+        - 若 trust_chain_enabled=False 直接返回 None（#2173: 可配置关闭）
         - 供 worker 在视频生成前并行预热使用；video_generation 内部若收到 preheated 结果会直接使用，不再现场跑。
         """
-        if not portrait_urls or not self.is_available:
+        if not portrait_urls or not self.is_available or not getattr(self, "trust_chain_enabled", True):
             return None
         seedream_prompt = (
             "保持此人五官特征、发型、肤色、面部轮廓、年龄感，生成一张高清写实人像照片，"
@@ -438,8 +444,8 @@ class DoubaoClient:
             sd_result = self.image_generation(
                 prompt=sd_prompt,
                 reference_images=[raw_url],
-                size="2K",
-                timeout=timeout,
+                size=size or getattr(self, "image_size", "1K"),
+                timeout=timeout or getattr(self, "image_timeout", 60),
             )
             if not sd_result:
                 logger.warning(
@@ -564,7 +570,7 @@ class DoubaoClient:
         #       预热结果有效则直接使用，否则现场跑一次 Seedream AI 化。
         # 信任链只作用于 doubao provider；DashScope(Wan) 保持原行为。
         trust_chain_applied = False
-        if provider == "doubao":
+        if provider == "doubao" and getattr(self, "trust_chain_enabled", True):
             raw_portrait_urls: list[str] = []
             if image_url:
                 raw_portrait_urls.append(image_url)
@@ -953,6 +959,7 @@ class DoubaoClient:
             return None
 
         img_model = model or self.image_model
+        _img_size = size or getattr(self, "image_size", "1K")
         url = f"{self.base_url}/images/generations"
         headers = {
             "Authorization": f"Bearer {self.api_key}",
@@ -961,7 +968,7 @@ class DoubaoClient:
         payload: dict[str, Any] = {
             "model": img_model,
             "prompt": prompt.strip(),
-            "size": size,
+            "size": _img_size,
             "response_format": "url",
             "output_format": output_format,
             "watermark": bool(watermark),
@@ -973,7 +980,7 @@ class DoubaoClient:
             else:
                 payload["image"] = ref_imgs_local[:10]
 
-        req_timeout = timeout or self.image_timeout
+        req_timeout = timeout or getattr(self, "image_timeout", 60)
         last_err: Exception | None = None
         last_sc = 0
         last_body = ""
@@ -1003,7 +1010,7 @@ class DoubaoClient:
                             "Seedream 图片生成成功 model=%s ref_imgs=%d size=%s elapsed=%.1fs attempt=%d",
                             img_model,
                             len(ref_imgs_local),
-                            size,
+                            _img_size,
                             time.time() - _img_t0,
                             attempt + 1,
                         )

packages/shared/ai_service.py

@@ -502,6 +502,7 @@ def call_llm(
     max_tokens: int = 2048,
     model: str | None = None,
     system_prompt: str | None = None,
+    timeout: int | None = None,
 ) -> object:
     """调用豆包大模型（文本对话），返回解析后的 JSON（dict/list）或原文字符串；失败返回 None。
 
@@ -521,7 +522,7 @@ def call_llm(
         {"role": "system", "content": system_prompt},
         {"role": "user", "content": prompt},
     ]
-    raw = client.chat_completion(messages, temperature=temperature, max_tokens=max_tokens, model=model)
+    raw = client.chat_completion(messages, temperature=temperature, max_tokens=max_tokens, model=model, timeout=timeout)
     if raw is None:
         return None
     try: