fix(#2183): 信任链真·现场t2i+preheat接线+portrait_intercept降级纯t2v+timeout调大

- 信任链3处断链修复：
  1. run_viral_video_analyze VLM完成后立即启动_start_trust_chain_preheat（后台daemon与文案阶段并行）
  2. _step_render 预热不可用时真·同步调用Seedream t2i（而非打假日志直接传原图），120s超时
  3. ai_client.video_generation遇到portrait_intercept 400时自动移除所有参考图降级纯t2v重试一次
- LLM timeout调大（实测pro 1500tok输出需75.8s）：
  - _step_script_generation: fast-first/fast-retry 45→90s, pro-fallback 60→120s
  - _step_intent_parsing: 45→60s
  - _step_review: 30→45s
- generate_copy celery时限放宽：soft=360→600s, hard=420→660s（三级重试最坏~405s）
- 三层兜底：后台预热→现场t2i→400纯t2v，保证人像链路必出片

apps/worker/worker_app/tasks/viral_video.py

@@ -598,7 +598,7 @@ def _step_intent_parsing(job: ViralVideoJob, image_analysis: dict) -> dict:
     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=45)
+            result = call_llm(prompt, temperature=0.4, max_tokens=800, model=_m, timeout=60)  # #2183: pro 实测800tok约40s，给60s
             return (
                 result
                 if isinstance(result, dict)
@@ -1033,16 +1033,16 @@ def _step_script_generation(job: ViralVideoJob, intent: dict, image_analysis: di
 
     try:
         # 第一次：快模型 25s
-        normalized = _try_gen(_fast, 0.8, 2500, "fast-first", tmo=45)
+        normalized = _try_gen(_fast, 0.8, 2500, "fast-first", tmo=90)
         if normalized is not None:
             return normalized
-        # 第二次：快模型降温度+加大 max_tokens，25s
-        normalized = _try_gen(_fast, 0.6, 3200, "fast-retry", tmo=45)
+        # #2183: 实测pro 1500tok输出需75.8s，单次timeout提到90s
+        normalized = _try_gen(_fast, 0.6, 3200, "fast-retry", tmo=90)
         if normalized is not None:
             return normalized
-        # 第三次：用主力模型兜底，给 40s
+        # 第三次：用主力模型兜底，给 120s
         if _pro and _pro != _fast:
-            normalized = _try_gen(_pro, 0.7, 3500, "pro-fallback", tmo=60)
+            normalized = _try_gen(_pro, 0.7, 3500, "pro-fallback", tmo=120)
             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")
@@ -1079,7 +1079,7 @@ def _step_review(job: ViralVideoJob, copy_result: dict) -> dict:
     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=30)
+            result = call_llm(prompt, temperature=0.1, max_tokens=500, model=_m, timeout=45)  # #2183: 对齐pro响应
             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)
@@ -1251,13 +1251,44 @@ def _step_render(job: ViralVideoJob, copy_result: dict, tts_audio_url: str | Non
         pre_trusted = list(pti)
         logger.info("[爆款视频] 使用信任链预热结果 n=%d，跳过现场 Seedream AI 化", len(pre_trusted))
     elif all_portrait_urls and _mcfg.get("provider", "doubao") == "doubao":
+        # #2183: 真·现场跑信任链——同步调用 Seedream t2i，拿到 AI 人像 URL 后再传 Seedance
         logger.info(
-            "[爆款视频] 预热结果不可用(%s/%d张)，将现场跑信任链",
+            "[爆款视频] 预热结果不可用(%s/%d张)，现场同步跑信任链Seedream t2i",
             "缺失" if not pti else f"{len(pti)}/{len(all_portrait_urls)}",
             len(all_portrait_urls),
         )
+        try:
+            from packages.shared.ai_service import preheat_trust_chain
+            _ia = getattr(job, "image_analysis", None) or {}
+            _prods = (_ia.get("products") if isinstance(_ia, dict) else None) or []
+            _pdescs = []
+            if _prods:
+                _pdescs = [(pp.get("portrait_prompt") or "无人像") for pp in _prods]
+            elif isinstance(_ia, dict):
+                _pp0 = _ia.get("portrait_prompt") or "无人像"
+                if _pp0 and _pp0 != "无人像":
+                    _pdescs = [_pp0]
+            _valid = [d for d in _pdescs if d and isinstance(d, str) and "无人像" not in d and len(d) >= 10]
+            if _valid:
+                _t0 = time.time()
+                _live_urls = preheat_trust_chain(_valid, timeout=120)
+                if _live_urls and len(_live_urls) == len(all_portrait_urls):
+                    pre_trusted = list(_live_urls)
+                    logger.info(
+                        "[爆款视频] 现场信任链t2i完成 %d张 耗时%.1fs，将用AI人像传Seedance",
+                        len(pre_trusted), time.time()-_t0,
+                    )
+                else:
+                    logger.warning(
+                        "[爆款视频] 现场信任链t2i返回不匹配 urls=%s n_portraits=%d，回退原图+400降级纯t2v",
+                        _live_urls, len(all_portrait_urls),
+                    )
+            else:
+                logger.info("[爆款视频] 无有效人物描述（可能是商品图），无需现场跑信任链")
+        except Exception as _te:
+            logger.warning("[爆款视频] 现场跑信任链异常: %s，回退原图+400降级纯t2v", _te, exc_info=True)
 
-    # 第一次调用：带参考图/首帧/音频/参考视频
+    # 第一次调用：带参考图/首帧/音频/参考视频（pre_trusted有值→走信任链；无值→原图；若400 ai_client内部自动降级纯t2v）
     result = call_video_generation(
         prompt=prompt,
         image_url=first_image,
@@ -1550,8 +1581,20 @@ def run_viral_video_analyze(self: Task, job_id: str) -> dict:
 
         image_analysis = _step_image_analysis(job)
         job.image_analysis = image_analysis
+        # #2183/#2174: VLM完成后立即启动信任链t2i预热（后台daemon线程，与后续阶段并行）
+        if job.images:
+            try:
+                _products = (image_analysis or {}).get("products", []) or []
+                _portrait_descs = [(p.get("portrait_prompt") or "无人像") for p in _products] if _products else []
+                if not _portrait_descs and isinstance(image_analysis, dict):
+                    _pp = image_analysis.get("portrait_prompt") or "无人像"
+                    if _pp and _pp != "无人像":
+                        _portrait_descs = [_pp]
+                _start_trust_chain_preheat(job.id, _portrait_descs)
+            except Exception as _e:
+                logger.warning("[爆款视频][阶段1] 启动信任链t2i预热失败: %s", _e)
         _save_job(repo, job, session)
-        _emit_progress(job_id, ViralVideoStage.IMAGE_ANALYSIS, 60.0, "图片分析完成", {"result": image_analysis})
+        _emit_progress(job_id, ViralVideoStage.IMAGE_ANALYSIS, 60.0, "图片分析完成，AI人像预热中...", {"result": image_analysis, "trust_chain_preheating": True})
 
         style_guide = None
         if job.reference_video_url or job.style_template_id:
@@ -1598,8 +1641,8 @@ 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=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
+    soft_time_limit=600,  # #2183: pro长脚本~120s+三级重试最坏300s+intent/review ~105s，给到10min
+    time_limit=660,  # #2183: 11min 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/shared/ai_client.py

@@ -585,7 +585,7 @@ class DoubaoClient:
         # #2172/#2174: 信任链——使用预热好的 Seedream t2i 文生图（纯模型生成人像，是方舟信任产物，
         # 不会触发肖像审核）。预热在 VLM 分析后由 daemon 线程后台完成，结果通过 pre_trusted_images 传入。
         # - 预热结果有效 → 替换原参考图，走 omni_ref 模式
-        # - 预热结果不可用 → 直接用原图（若被400肖像拦截，#2166自动降级纯t2v），避免现场跑t2i阻塞渲染
+        # - 预热结果不可用 → 直接用原图（上层 _step_render 已现场同步跑 Seedream t2i 兜底；若再被400拦截，下方自动降级纯t2v）
         # 信任链只作用于 doubao provider；DashScope(Wan) 保持原行为。
         trust_chain_applied = False
         if provider == "doubao" and getattr(self, "trust_chain_enabled", True) and pre_trusted_images:
@@ -763,6 +763,28 @@ class DoubaoClient:
                 # 保留第二次的错误信息
                 sc, body = sc2, body2
 
+        # #2183: portrait_intercept / 真人肖像审核拦截 → 去掉所有参考图（含image_url首帧），纯 t2v 重试一次
+        # 信任链预热或现场 t2i 都失败时的最后兜底，保证能出片
+        if (
+            not task_id
+            and sc == 400
+        ):
+            _err_code_for_400, _ = _classify_video_error(sc, body, last_err)
+            if _err_code_for_400 == "portrait_intercept" and (image_url or ref_imgs):
+                logger.warning(
+                    "Seedance 创建因 portrait_intercept 失败，降级纯 t2v（移除所有参考图）重试: img=%d ref=%d",
+                    1 if image_url else 0, len(ref_imgs),
+                )
+                _t2v_payload = dict(create_payload)
+                _t2v_payload["content"] = [{"type": "text", "text": prompt.strip()}]
+                _t2v_payload["ratio"] = ratio or "9:16"
+                task_id, last_err, sc3, body3 = _do_create(_t2v_payload)
+                if task_id:
+                    sc, body = sc3, body3
+                    logger.info("[trust-chain] portrait_intercept 降级纯 t2v 成功 task_id=%s", task_id)
+                else:
+                    sc, body = sc3, body3
+
         if not task_id:
             err_code, user_msg = _classify_video_error(sc, body, last_err)
             self.last_video_error = {