完成极速成片

core/backend/apps/ai/script_agent.py

@@ -81,7 +81,7 @@ PERSONA_BRIEFS: dict[str, str] = {
     "urban": "25–32岁都市白领,工位或下班回家,说话像跟同事吐槽,不要主播腔",
     "bestie": "闺蜜分享口吻,带点兴奋,爱用「你懂的」「我跟你讲」",
     "ceo": "利落、判断句、少形容词,像拍板不是带货",
-    "reviewer": "先讲怎么试的再给结论,允许提一个小缺点才可信",
+    "reviewer": "先讲怎么试的再给结论；可信度来自过程、适用边界或意外发现，不编造小缺点",
     "mom": "带娃/家务间隙,讲省事,孩子或家人能沾边",
     "genz": "宿舍或通勤,短句,像给朋友发语音条",
 }
@@ -103,10 +103,18 @@ _SHOT_SIZE_MARKERS = (
     "手持", "跟拍", "俯拍", "仰拍", "推近", "拉远", "摇",
 )
 _MINOR_CHARACTER_RE = re.compile(
-    r"(?:婴儿|宝宝|宝贝|幼儿|儿童|小孩|小朋友|未成年|男童|女童|baby|toddler|infant)"
-    r"|(?:[0-9零一二三四五六七八九十]{1,3}\s*岁)",
+    r"(?:婴儿|宝宝|宝贝|幼儿|儿童|小孩|小朋友|未成年|男童|女童|baby|toddler|infant)",
     re.IGNORECASE,
 )
+_NUMERIC_AGE_RE = re.compile(r"(?<!\d)(\d{1,2})\s*岁")
+_CHINESE_MINOR_AGE_RE = re.compile(r"(?:[零一二三四五六七八九]岁|十岁|十[一二三四五六七]岁)")
+_CANNED_DEFECT_RE = re.compile(r"唯一(?:的)?(?:小)?(?:缺点|不足|遗憾)")
+_NATURAL_TURN_PHRASES = (
+    "我会额外留意的使用细节",
+    "换个场景更能看出差别的地方",
+    "下单前可以先确认的一点",
+    "实测时我更在意的细节",
+)
 
 
 _FORMAT_KEY_BY_LABEL = {label: key for key, label in PRESENTATION_FORMATS.items()}
@@ -231,7 +239,10 @@ def format_visual_beats(beats: list[tuple[int, int, str]]) -> str:
 
 def _is_minor_character(name: str, visual_prompt: str) -> bool:
     """角色基础资产不得是未成年人，避免真人生图审核拦截与儿童肖像风险。"""
-    return bool(_MINOR_CHARACTER_RE.search(f"{name or ''} {visual_prompt or ''}"))
+    text = f"{name or ''} {visual_prompt or ''}"
+    if _MINOR_CHARACTER_RE.search(text) or _CHINESE_MINOR_AGE_RE.search(text):
+        return True
+    return any(int(age) < 18 for age in _NUMERIC_AGE_RE.findall(text))
 
 
 def _product_only_visual(duration: int) -> str:
@@ -253,6 +264,14 @@ def _product_only_visual(duration: int) -> str:
     return "【本镜任务】用商品本身传达关键信息，不出现人物。\n【声音】旁白继续，画面不出现未成年人。\n【画面内容】\n" + format_visual_beats(beats)
 
 
+def _replace_canned_defect_phrase(text: str) -> str:
+    """不改模型给出的事实，只替换会让测评显得千篇一律的“唯一缺点”话术。"""
+    if not text or not _CANNED_DEFECT_RE.search(text):
+        return text
+    variant = _NATURAL_TURN_PHRASES[sum(map(ord, text)) % len(_NATURAL_TURN_PHRASES)]
+    return _CANNED_DEFECT_RE.sub(variant, text)
+
+
 # --------------------------------------------------------------------------- #
 # skill 加载(缓存)
 # --------------------------------------------------------------------------- #
@@ -613,6 +632,11 @@ def build_agent_messages(
         f"严格按已加载的「{PRESENTATION_FORMATS[fmt]} × {VIDEO_STRUCTURES[structure]}」套路写,"
         f"不要串成别的结构的套话。\n"
     )
+    authenticity_line = (
+        "【真实感转折】禁止使用「唯一缺点／唯一不足／唯一的小遗憾」这类模板句，也不要为了显得真实而编造缺点。"
+        "每版从以下角度自然选一个推进：测试过程里的意外发现、适用人群的边界、不同使用场景的反差、"
+        "一个可观察的细节、或使用习惯建议；必须由商品资料或画面可观察事实支持，不能每镜重复同一种。\n"
+    )
     head = (
         f"【画幅】{aspect_ratio}\n"
         f"【表现形式】{PRESENTATION_FORMATS[fmt]}(套路见 playbooks/format-{fmt}.md,已加载)\n"
@@ -623,6 +647,7 @@ def build_agent_messages(
         f"{beats_line}"
         f"{structure_line}"
         f"{combo_line}"
+        f"{authenticity_line}"
         f"【商品信息】\n{_product_context(project, selling_point_ids, persona)}"
     )
     if mode == "revise" and base_draft and target_index is not None:
@@ -1196,7 +1221,7 @@ def normalize_draft(
             if not line:
                 continue
             sp = d.get("speaker")
-            dialogue.append({"speaker": sp if sp in valid_ids else None, "line": line})
+            dialogue.append({"speaker": sp if sp in valid_ids else None, "line": _replace_canned_defect_phrase(line)})
         # 旁白:结构化对白/lines 优先,其次整句字符串 dialogue,再退到通用字段解析(narration/voiceover/caption/字幕…)
         narration = ""
         if isinstance(raw_dialogue, str) and raw_dialogue.strip():
@@ -1205,8 +1230,9 @@ def normalize_draft(
             narration = " ".join(d["line"] for d in dialogue)  # 扁平拼接,兼容下游字幕/配音
         if not narration:
             narration = _pick_field(seg, _NARRATION_EXACT, _NARRATION_FUZZY)
+        narration = _replace_canned_defect_phrase(narration)
         # 画面:优先收成秒级分镜;beats 数组会折进 visual,下游故事板/视频直接读这一段。
-        visual = compose_segment_visual(seg)
+        visual = _replace_canned_defect_phrase(compose_segment_visual(seg))
         norm_segments.append(
             {
                 "index": i,
@@ -1255,7 +1281,7 @@ def normalize_draft(
         elif index < len(segments) and isinstance(segments[index], dict):
             composed = compose_segment_visual(segments[index], seconds)
             if composed:
-                norm["visual"] = composed
+                norm["visual"] = _replace_canned_defect_phrase(composed)
 
     draft["segments"] = norm_segments
     draft["segment_count"] = len(norm_segments)

core/backend/apps/ai/services.py

@@ -2217,10 +2217,20 @@ def generate_person_triview(*, project, user, portrait_asset) -> AITask:
     ref_url = _asset_preview_url(portrait_asset)
     # 人物三视图提示词:正文可在 admin「提示词」页改(无占位符)
     tri_prompt = render_prompt("person_triview", THREE_VIEW_PROMPT)
+    portrait_label = ""
+    for group in project.base_asset_groups.filter(kind=BaseAssetGroup.Kind.PERSON):
+        meta = group.metadata or {}
+        if meta.get("triview_of"):
+            continue
+        candidates = [str(value) for value in (group.candidate_assets or [])]
+        if str(group.adopted_asset_id or "") == asset_key or asset_key in candidates:
+            portrait_label = str(meta.get("label") or "")
+            break
     payload = {
         "model": model_config.name,
         "prompt": tri_prompt,
         "kind": "person",
+        "label": portrait_label or str(portrait_asset.name or ""),
         "triview_of": asset_key,
         "reference_image": ref_url,
         "model_routing_v1": True,
@@ -3234,6 +3244,26 @@ def collect_video_review_blockers(project, only_segment: "VideoSegment | None" =
     return blockers
 
 
+_VIDEO_INFLIGHT_STATUSES = {
+    AITask.Status.CREATED,
+    AITask.Status.RESERVED,
+    AITask.Status.SUBMITTED,
+    AITask.Status.POLLING,
+    AITask.Status.POSTPROCESSING,
+}
+
+
+def video_segment_has_inflight_task(video_segment: VideoSegment) -> bool:
+    """同一镜头是否已有在途视频任务。防止极速成片并发推进把同一段提交两次。"""
+    segment_id = str(video_segment.id)
+    tasks = AITask.objects.filter(
+        project_id=video_segment.project_id,
+        task_type=AITask.Type.VIDEO_SEGMENT,
+        status__in=_VIDEO_INFLIGHT_STATUSES,
+    ).only("request_payload")
+    return any(str((task.request_payload or {}).get("video_segment_id") or "") == segment_id for task in tasks)
+
+
 def submit_video_segment(
     *,
     video_segment: VideoSegment,
@@ -3261,7 +3291,17 @@ def submit_video_segment(
         model_config = get_default_model(ModelConfig.Capability.VIDEO)
     if model_config is None:
         raise ValueError("no active video model configured")
-    project = video_segment.project
+
+    with transaction.atomic():
+        video_segment = VideoSegment.objects.select_for_update().select_related("project").get(pk=video_segment.pk)
+        project = video_segment.project
+        if video_segment.status in {VideoSegment.Status.RUNNING, VideoSegment.Status.SUCCEEDED}:
+            return None
+        if video_segment_has_inflight_task(video_segment):
+            return None
+        if video_segment.status != VideoSegment.Status.QUEUED:
+            video_segment.status = VideoSegment.Status.QUEUED
+            video_segment.save(update_fields=["status", "updated_at"])
 
     # 衔接:按 sort_order 把视频段绑到对应脚本镜,并织出跟住该镜的提示词。
     scene = None
@@ -3289,31 +3329,38 @@ def submit_video_segment(
         references=[],
         team=project.team,
     )
-    task = create_ai_task(
-        project=project,
-        user=user,
-        task_type=AITask.Type.VIDEO_SEGMENT,
-        model_config=model_config,
-        quote=quote,
-        reserve_amount=video_reserve_amount(quote.points),
-        request_payload={
-            "model": model_config.name,
-            "endpoint": model_config.endpoint,
-            "prompt": final_prompt,
-            "duration": video_segment.target_duration_seconds,
-            "ratio": aspect_ratio,
-            "resolution": resolution,
-            "estimated_tokens": est_tokens,
-            # 团队价格系数快照:按实结算用它,中途改价不影响在途任务(jimeng 同款纪律)
-            "price_multiplier": quote.meta.get("price_multiplier", "1"),
-            "video_segment_id": str(video_segment.id),
-            "reference_images": reference_images,
-            "model_routing_v1": True,
-        },
-    )
-    # 提交尝试的实际平台成本由 AIModelAttempt 累加；成片后再用实际模型的 usage true-up 覆盖。
-    task.base_cost = Decimal("0")
-    task.save(update_fields=["base_cost", "updated_at"])
+    with transaction.atomic():
+        video_segment = VideoSegment.objects.select_for_update().select_related("project").get(pk=video_segment.pk)
+        project = video_segment.project
+        if video_segment.status in {VideoSegment.Status.RUNNING, VideoSegment.Status.SUCCEEDED}:
+            return None
+        if video_segment_has_inflight_task(video_segment):
+            return None
+        task = create_ai_task(
+            project=project,
+            user=user,
+            task_type=AITask.Type.VIDEO_SEGMENT,
+            model_config=model_config,
+            quote=quote,
+            reserve_amount=video_reserve_amount(quote.points),
+            request_payload={
+                "model": model_config.name,
+                "endpoint": model_config.endpoint,
+                "prompt": final_prompt,
+                "duration": video_segment.target_duration_seconds,
+                "ratio": aspect_ratio,
+                "resolution": resolution,
+                "estimated_tokens": est_tokens,
+                # 团队价格系数快照:按实结算用它,中途改价不影响在途任务(jimeng 同款纪律)
+                "price_multiplier": quote.meta.get("price_multiplier", "1"),
+                "video_segment_id": str(video_segment.id),
+                "reference_images": reference_images,
+                "model_routing_v1": True,
+            },
+        )
+        # 提交尝试的实际平台成本由 AIModelAttempt 累加；成片后再用实际模型的 usage true-up 覆盖。
+        task.base_cost = Decimal("0")
+        task.save(update_fields=["base_cost", "updated_at"])
     try:
         routed = execute_routed_video_submit(
             task=task,

core/backend/apps/ai/test_script_duration_combo.py

@@ -159,6 +159,36 @@ class NormalizeDurationTests(SimpleTestCase):
         self.assertIn("不出现人物", draft["segments"][0]["visual"])
         self.assertNotIn("宝宝坐", draft["segments"][0]["visual"])
 
+    def test_adult_student_is_not_mistaken_for_a_minor(self):
+        import json
+
+        raw = {
+            "entities": [
+                {"id": "student", "type": "character", "name": "20岁大学生", "visual_prompt": "20岁成年学生，宿舍使用场景"},
+                {"id": "scene", "type": "scene", "name": "宿舍", "visual_prompt": "宿舍桌面"},
+            ],
+            "segments": [{"role": "钩子", "visual": "大学生在宿舍展示商品。", "entity_refs": ["student", "scene"]}],
+        }
+        draft = normalize_draft(json.dumps(raw, ensure_ascii=False), aspect_ratio="9:16", total_duration=15)
+        self.assertIn("student", [entity["id"] for entity in draft["entities"]])
+        self.assertIn("student", draft["segments"][0]["entity_refs"])
+        self.assertNotIn("不出现人物", draft["segments"][0]["visual"])
+
+    def test_canned_only_defect_phrase_is_replaced_without_dropping_content(self):
+        import json
+
+        raw = {
+            "segments": [{
+                "role": "卖点", "narration": "唯一缺点是瓶口需要慢一点倒，但质地很舒服。",
+                "visual": "近景展示唯一小缺点是瓶口开口较窄。",
+            }],
+        }
+        draft = normalize_draft(json.dumps(raw, ensure_ascii=False), aspect_ratio="9:16", total_duration=15)
+        joined = draft["segments"][0]["narration"] + draft["segments"][0]["visual"]
+        self.assertNotIn("唯一缺点", joined)
+        self.assertNotIn("唯一小缺点", joined)
+        self.assertIn("瓶口", joined)
+
 
 class ProductFactTests(SimpleTestCase):
     def test_missing_selling_point_is_rejected(self):
@@ -268,6 +298,8 @@ class PromptAssemblyTests(SimpleTestCase):
         self.assertIn("跟同事吐槽", user)
         self.assertIn("禁止一句 20 字收工", user)
         self.assertIn("秒级分镜", user)
+        self.assertIn("禁止使用「唯一缺点", user)
+        self.assertIn("测试过程里的意外发现", user)
 
     def test_video_digest_prompt_maps_camera_and_sound(self):
         digest = (

core/backend/apps/ai/test_video_segment_routing.py

@@ -141,6 +141,20 @@ class VideoSegmentRoutingTests(TestCase):
         self.assertEqual(self.ledger_count(task, CreditLedger.Type.CHARGE), 0)
         self.assertEqual(self.ledger_count(task, CreditLedger.Type.RELEASE), 0)
 
+    def test_second_submit_does_not_create_another_task(self):
+        primary = self.model(self.provider("video-primary-once", 20), "video-primary-once", outbound=True, default=True)
+        provider = self._provider_for(primary)
+        provider.create_video_task.return_value = {"id": "remote-once", "status": "queued"}
+
+        first = self._submit()
+        second = submit_video_segment(video_segment=self.segment, user=self.user, prompt="test")
+
+        self.assertIsNone(second)
+        self.assertEqual(AITask.objects.filter(task_type=AITask.Type.VIDEO_SEGMENT, team=self.team).count(), 1)
+        self.assertEqual(provider.create_video_task.call_count, 1)
+        first.refresh_from_db()
+        self.assertEqual(first.status, AITask.Status.SUBMITTED)
+
     @patch("apps.ai.services._store_generated_media")
     def test_retry_then_dynamic_fallback_polls_actual_model_and_charges_once(self, store_media):
         primary = self.model(self.provider("video-primary-fail", 20), "video-primary-fail", outbound=True, price=46, default=True)