Merge pull request #2251: Ditto 使用用户视频+ffmpeg循环补长

- lipsync_ditto.py 重写驱动视频逻辑：
  - 默认使用用户上传的 job.video_url（签名后），default_video_url 仅兜底
  - 新增 _prepare_driver_video()：下载视频→ffprobe时长→
    视频<音频+2s时用 ffmpeg stream_loop -1 循环到目标时长（-c copy优先，失败回退libx264重编码）
    循环视频上传临时 OSS ditto-tmp/ 路径
  - 视频足够长直接使用原URL，不做处理
  - 预处理异常时回退直接使用（交给Ditto侧处理）
- ditto_service.py 更新注释：明确video_url优先用户视频，default仅兜底
- config/base.py 更新注释：ditto_default_video_url 为兜底用途
- 音频下载提前统一（情绪分析+视频预处理共用一次下载）
- 所有异常路径保持三层兜底（Ditto→GPU→MediaKit）

alembic/versions/096_feature_billing_fields.py

@@ -0,0 +1,61 @@
+"""功能计费积分字段（爆款/对口型/智能剪辑 DB 化计费）。
+
+给 gpu_lipsync_tasks / generation_tasks / lipsync_jobs 三张表加积分字段：
+- credits_prepaid: 提交任务时预扣积分
+- credits_cost: 最终结算积分
+- credits_transaction_id: 预扣流水 ID
+
+注意：feature_pricing_configs 配置表由 xiaoxia-admin 侧 migration 建立，
+本仓库只读，不在此创建。
+
+Revision ID: 096_feature_billing_fields
+Revises: 095_viral_video_prompt_templates
+Create Date: 2026-10-05
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "096_feature_billing_fields"
+down_revision = "095_viral_video_prompt_templates"
+branch_labels = None
+depends_on = None
+
+_TABLES = ("gpu_lipsync_tasks", "generation_tasks", "lipsync_jobs")
+_COLUMNS = (
+    ("credits_prepaid", sa.Float(), "0"),
+    ("credits_cost", sa.Float(), "0"),
+    ("credits_transaction_id", sa.String(36), ""),
+)
+
+
+def _table_exists(conn, name: str) -> bool:
+    return name in sa.inspect(conn).get_table_names()
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    for table in _TABLES:
+        if not _table_exists(conn, table):
+            continue
+        existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
+        for col_name, col_type, default in _COLUMNS:
+            if col_name in existing:
+                continue
+            op.add_column(
+                table,
+                sa.Column(col_name, col_type, nullable=False, server_default=default),
+            )
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+    for table in _TABLES:
+        if not _table_exists(conn, table):
+            continue
+        existing = {c["name"] for c in sa.inspect(conn).get_columns(table)}
+        for col_name, _col_type, _default in _COLUMNS:
+            if col_name not in existing:
+                continue
+            op.drop_column(table, col_name)

alembic/versions/099_ai_model_router_seed.py

@@ -0,0 +1,222 @@
+# -*- coding: utf-8 -*-
+"""099: AI 模型路由层 seed — 补齐缺失模型和能力配置.
+
+幂等：所有 INSERT 先检查存在性。
+- ai_models: 补齐 qwen3.7-plus, seedream, seedance, embedding, wan3.0 等
+- ai_capability_configs: 补齐 image_generation, video_generation, embedding
+- 更新已有 capability 的 lite_model_id
+"""
+
+import json
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "099_ai_model_router_seed"
+down_revision = "098_viral_video_image_analysis_v5"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+
+    # CI 环境下 ai_models 表可能尚未创建（由 ORM 自动建表，非 migration）
+    # 如果表不存在则跳过 seed，由应用启动时 ORM 建表后首次访问时生效
+    table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
+    if not table_check:
+        # ai_models 表不存在，跳过所有 seed（CI 环境）
+        return
+
+    # ── 1. 补齐 ai_models 缺失记录 ────────────────────────────────────────────
+    existing_models = {
+        row[0]
+        for row in conn.execute(
+            sa.text("SELECT model_key FROM ai_models WHERE deleted_at IS NULL")
+        ).fetchall()
+    }
+
+    # 从已有 active 记录获取 API key（复用，不硬编码）
+    dashscope_key_row = conn.execute(
+        sa.text(
+            "SELECT api_key FROM ai_models WHERE provider='dashscope' AND deleted_at IS NULL AND api_key IS NOT NULL AND api_key != '' LIMIT 1"
+        )
+    ).first()
+    dashscope_key = dashscope_key_row[0] if dashscope_key_row else ""
+
+    volcengine_key_row = conn.execute(
+        sa.text(
+            "SELECT api_key FROM ai_models WHERE provider='volcengine' AND deleted_at IS NULL AND api_key IS NOT NULL AND api_key != '' LIMIT 1"
+        )
+    ).first()
+    volcengine_key = volcengine_key_row[0] if volcengine_key_row else ""
+
+    new_models = [
+        {
+            "model_key": "qwen3.7-plus",
+            "name": "通义千问3.7 Plus（VLM 兜底）",
+            "provider": "dashscope",
+            "api_key": dashscope_key,
+            "api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
+            "description": "阿里云百炼 Qwen3.7 Plus 多模态模型，用于 VLM 兜底分析",
+        },
+        {
+            "model_key": "doubao-seedream-5-0-flash-260915",
+            "name": "Seedream 5.0 Flash（图片生成）",
+            "provider": "volcengine",
+            "api_key": volcengine_key,
+            "api_base": "https://ark.cn-beijing.volces.com/api/v3",
+            "description": "火山引擎 Seedream 5.0 Flash 文生图模型",
+        },
+        {
+            "model_key": "doubao-seedance-2-5-260628",
+            "name": "Seedance 2.5（视频生成）",
+            "provider": "volcengine",
+            "api_key": volcengine_key,
+            "api_base": "https://ark.cn-beijing.volces.com/api/v3",
+            "description": "火山引擎 Seedance 2.5 图/文生视频模型",
+        },
+        {
+            "model_key": "doubao-embedding-vision-251215",
+            "name": "豆包多模态向量嵌入",
+            "provider": "volcengine",
+            "api_key": volcengine_key,
+            "api_base": "https://ark.cn-beijing.volces.com/api/v3",
+            "description": "火山引擎豆包多模态向量嵌入模型",
+        },
+        {
+            "model_key": "wan3.0-video",
+            "name": "Wan 3.0 视频生成",
+            "provider": "dashscope",
+            "api_key": dashscope_key,
+            "api_base": "https://dashscope.aliyuncs.com/api/v1",
+            "description": "阿里云百炼 Wan 3.0 视频生成模型",
+        },
+        {
+            "model_key": "doubao-seed-2-1-pro-260915",
+            "name": "豆包 Seed 2.1 Pro（高精度推理）",
+            "provider": "volcengine",
+            "api_key": volcengine_key,
+            "api_base": "https://ark.cn-beijing.volces.com/api/v3",
+            "description": "火山引擎豆包 Seed 2.1 Pro 深度思考+多模态",
+        },
+    ]
+
+    for m in new_models:
+        if m["model_key"] not in existing_models:
+            conn.execute(
+                sa.text(
+                    """
+                    INSERT INTO ai_models (id, name, provider, model_key, api_key, api_base, description, status, is_default, usage_today, created_at, updated_at)
+                    VALUES (gen_random_uuid()::text, :name, :provider, :model_key, :api_key, :api_base, :description, 'active', false, 0, now(), now())
+                    """
+                ),
+                m,
+            )
+
+    # ── 2. 补齐 ai_capability_configs 缺失项 ──────────────────────────────────
+    cap_table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
+    if not cap_table_check:
+        return
+
+    existing_caps = {
+        row[0]
+        for row in conn.execute(
+            sa.text("SELECT capability_key FROM ai_capability_configs")
+        ).fetchall()
+    }
+
+    def _get_model_id(model_key: str) -> str | None:
+        row = conn.execute(
+            sa.text(
+                "SELECT id FROM ai_models WHERE model_key = :key AND deleted_at IS NULL AND status = 'active' LIMIT 1"
+            ),
+            {"key": model_key},
+        ).first()
+        return row[0] if row else None
+
+    # image_generation
+    if "image_generation" not in existing_caps:
+        mid = _get_model_id("doubao-seedream-5-0-flash-260915")
+        if mid:
+            conn.execute(
+                sa.text(
+                    """
+                    INSERT INTO ai_capability_configs (id, capability_key, capability_name, primary_model_id, timeout_seconds, max_retries, concurrency, extra_params, is_enabled, created_at, updated_at)
+                    VALUES (gen_random_uuid()::text, :ck, :cn, :pm, 60, 1, 2, :ep, true, now(), now())
+                    """
+                ),
+                {
+                    "ck": "image_generation",
+                    "cn": "图片生成（Seedream）",
+                    "pm": mid,
+                    "ep": json.dumps({"size": "1K"}),
+                },
+            )
+
+    # video_generation
+    if "video_generation" not in existing_caps:
+        mid = _get_model_id("doubao-seedance-2-5-260628")
+        fb_mid = _get_model_id("wan3.0-video")
+        if mid:
+            conn.execute(
+                sa.text(
+                    """
+                    INSERT INTO ai_capability_configs (id, capability_key, capability_name, primary_model_id, fallback_model_id, timeout_seconds, max_retries, concurrency, extra_params, is_enabled, created_at, updated_at)
+                    VALUES (gen_random_uuid()::text, :ck, :cn, :pm, :fm, 600, 1, 1, :ep, true, now(), now())
+                    """
+                ),
+                {
+                    "ck": "video_generation",
+                    "cn": "视频生成（Seedance/Wan）",
+                    "pm": mid,
+                    "fm": fb_mid,
+                    "ep": json.dumps({}),
+                },
+            )
+
+    # embedding
+    if "embedding" not in existing_caps:
+        mid = _get_model_id("doubao-embedding-vision-251215")
+        if mid:
+            conn.execute(
+                sa.text(
+                    """
+                    INSERT INTO ai_capability_configs (id, capability_key, capability_name, primary_model_id, timeout_seconds, max_retries, concurrency, extra_params, is_enabled, created_at, updated_at)
+                    VALUES (gen_random_uuid()::text, :ck, :cn, :pm, 30, 2, 5, :ep, true, now(), now())
+                    """
+                ),
+                {
+                    "ck": "embedding",
+                    "cn": "向量嵌入",
+                    "pm": mid,
+                    "ep": json.dumps({}),
+                },
+            )
+
+    # ── 3. 更新 image_analysis 的 lite_model_id ─────────────────────────────
+    lite_model_id = _get_model_id("qwen3.8-flash")
+    if lite_model_id:
+        conn.execute(
+            sa.text(
+                "UPDATE ai_capability_configs SET lite_model_id = :lite WHERE capability_key = 'image_analysis' AND lite_model_id IS NULL"
+            ),
+            {"lite": lite_model_id},
+        )
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+    # 安全检查表是否存在
+    table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
+    if not table_check:
+        return
+    conn.execute(
+        sa.text("DELETE FROM ai_capability_configs WHERE capability_key IN ('image_generation', 'video_generation', 'embedding')")
+    )
+    conn.execute(
+        sa.text(
+            "DELETE FROM ai_models WHERE model_key IN ('qwen3.7-plus', 'doubao-seedream-5-0-flash-260915', 'doubao-seedance-2-5-260628', 'doubao-embedding-vision-251215', 'wan3.0-video', 'doubao-seed-2-1-pro-260915') AND deleted_at IS NULL"
+        )
+    )

alembic/versions/100_fix_capability_model_bindings.py

@@ -0,0 +1,107 @@
+# -*- coding: utf-8 -*-
+"""100: 修正已有 capability 的模型绑定.
+
+幂等：仅当 primary_model_id 当前绑定到旧模型 (doubao-seed-1-6) 时才更新，
+避免覆盖用户在后台的自定义配置。
+
+- 更新 5 个 LLM capability (intent_parsing, copy_fusion, storyboard, copy_review, asset_classify)
+  的 primary_model_id 从 doubao-seed-1-6 改为 doubao-seed-2-1-pro-260915
+- 更新 image_analysis 的 primary/lite/fallback 模型绑定
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "100_fix_capability_model_bindings"
+down_revision = "099_ai_model_router_seed"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+
+    # Check tables exist
+    table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
+    if not table_check:
+        return
+
+    config_table_check = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
+    if not config_table_check:
+        return
+
+    # Look up model IDs by model_key (not hardcoded UUIDs)
+    pro_model_row = conn.execute(
+        sa.text(
+            "SELECT id FROM ai_models WHERE model_key = 'doubao-seed-2-1-pro-260915' AND deleted_at IS NULL LIMIT 1"
+        )
+    ).first()
+    if not pro_model_row:
+        return
+    pro_model_id = pro_model_row[0]
+
+    old_model_row = conn.execute(
+        sa.text("SELECT id FROM ai_models WHERE model_key = 'doubao-seed-1-6-250615' LIMIT 1")
+    ).first()
+    old_model_id = old_model_row[0] if old_model_row else None
+
+    llm_capabilities = [
+        "intent_parsing",
+        "copy_fusion",
+        "storyboard",
+        "copy_review",
+        "asset_classify",
+    ]
+
+    for cap_key in llm_capabilities:
+        if old_model_id:
+            conn.execute(
+                sa.text(
+                    "UPDATE ai_capability_configs SET primary_model_id = :new_id, updated_at = NOW() "
+                    "WHERE capability_key = :cap_key AND primary_model_id = :old_id"
+                ),
+                {"new_id": pro_model_id, "old_id": old_model_id, "cap_key": cap_key},
+            )
+
+    # Update image_analysis
+    qwen38_row = conn.execute(
+        sa.text("SELECT id FROM ai_models WHERE model_key = 'qwen3.8-flash' AND deleted_at IS NULL LIMIT 1")
+    ).first()
+    qwen37_row = conn.execute(
+        sa.text("SELECT id FROM ai_models WHERE model_key = 'qwen3.7-plus' AND deleted_at IS NULL LIMIT 1")
+    ).first()
+
+    if qwen38_row and qwen37_row:
+        qwen38_id = qwen38_row[0]
+        qwen37_id = qwen37_row[0]
+
+        current_ia = conn.execute(
+            sa.text(
+                "SELECT primary_model_id, lite_model_id, fallback_model_id "
+                "FROM ai_capability_configs WHERE capability_key = 'image_analysis'"
+            )
+        ).first()
+
+        if current_ia:
+            current_primary, current_lite, current_fallback = current_ia
+            updates = {}
+            if current_primary != qwen38_id:
+                updates["primary_model_id"] = qwen38_id
+            if current_lite != qwen38_id:
+                updates["lite_model_id"] = qwen38_id
+            if current_fallback != qwen37_id:
+                updates["fallback_model_id"] = qwen37_id
+
+            if updates:
+                set_clause = ", ".join([f"{k} = :{k}" for k in updates.keys()])
+                set_clause += ", updated_at = NOW()"
+                updates["cap_key"] = "image_analysis"
+                conn.execute(
+                    sa.text(f"UPDATE ai_capability_configs SET {set_clause} WHERE capability_key = :cap_key"),
+                    updates,
+                )
+
+
+def downgrade() -> None:
+    pass

alembic/versions/101_qwen_vl_plus_and_max_tokens.py

@@ -0,0 +1,153 @@
+# -*- coding: utf-8 -*-
+"""101: 补齐 qwen-vl-plus 视觉模型并修正 image_analysis 绑定与 max_tokens.
+
+背景：
+- qwen-vl-plus 做图片识别时返回 JSON 约 500-600 tokens，旧硬编码
+  max_tokens=350 导致 JSON 被截断、解析失败返回"未识别"。
+- 代码侧已移除硬编码，改由 capability 的 DB 配置决定 max_tokens。
+
+幂等：
+- qwen-vl-plus 已存在则不插入；
+- 仅当 image_analysis 当前 primary_model 不是 qwen-vl-plus 时才更新绑定，
+  避免覆盖后台手动配置。
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "101_qwen_vl_plus_and_max_tokens"
+down_revision = "100_fix_capability_model_bindings"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+
+    models_table = conn.execute(sa.text("SELECT to_regclass('public.ai_models')")).scalar()
+    if not models_table:
+        return
+
+    caps_table = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
+    if not caps_table:
+        return
+
+    # ── c. 补全其他 capability 的 max_tokens 默认值（幂等）──────────────────
+    # 放在 image_analysis 特定逻辑之前，确保任何分支 return 都不会跳过本段。
+    # 仅在当前值为 NULL 或过小 (<100) 时更新，不覆盖已有合理配置。
+    # embedding / tts / voice_clone 不走 chat 接口，无需设置。
+    default_max_tokens = {
+        "intent_parsing": 500,
+        "copy_fusion": 2500,
+        "storyboard": 4000,
+        "copy_review": 1000,
+        "asset_classify": 500,
+        "image_generation": 500,
+        "video_generation": 500,
+    }
+    for cap_key, mt in default_max_tokens.items():
+        conn.execute(
+            sa.text(
+                "UPDATE ai_capability_configs "
+                "SET max_tokens = :mt, updated_at = now() "
+                "WHERE capability_key = :key "
+                "AND (max_tokens IS NULL OR max_tokens < 100)"
+            ),
+            {"mt": mt, "key": cap_key},
+        )
+
+    # ── a. 确保 qwen-vl-plus 模型存在 ────────────────────────────────────────
+    conn.execute(sa.text("""
+            INSERT INTO ai_models (id, name, provider, model_key, api_key, api_base,
+                                   description, status, is_default, usage_today,
+                                   created_at, updated_at)
+            SELECT gen_random_uuid()::text,
+                   '通义千问VL Plus',
+                   'dashscope',
+                   'qwen-vl-plus',
+                   COALESCE(
+                       (SELECT api_key FROM ai_models
+                        WHERE provider = 'dashscope' AND deleted_at IS NULL
+                          AND api_key IS NOT NULL AND api_key != ''
+                        LIMIT 1),
+                       ''
+                   ),
+                   'https://dashscope.aliyuncs.com/compatible-mode/v1',
+                   '阿里云视觉理解模型（图片识别/分析）',
+                   'active', false, 0, now(), now()
+            WHERE NOT EXISTS (
+                SELECT 1 FROM ai_models
+                WHERE model_key = 'qwen-vl-plus' AND deleted_at IS NULL
+            )
+            """))
+
+    qwen_vl_row = conn.execute(
+        sa.text(
+            "SELECT id FROM ai_models WHERE model_key = 'qwen-vl-plus' "
+            "AND deleted_at IS NULL AND status = 'active' LIMIT 1"
+        )
+    ).first()
+    if not qwen_vl_row:
+        return
+    qwen_vl_id = qwen_vl_row[0]
+
+    qwen37_row = conn.execute(
+        sa.text(
+            "SELECT id FROM ai_models WHERE model_key = 'qwen3.7-plus' "
+            "AND deleted_at IS NULL AND status = 'active' LIMIT 1"
+        )
+    ).first()
+    qwen37_id = qwen37_row[0] if qwen37_row else None
+
+    # ── b. 仅当当前 primary 不是 qwen-vl-plus 时修正绑定与 max_tokens ───────
+    current = conn.execute(
+        sa.text(
+            "SELECT primary_model_id, lite_model_id, fallback_model_id, max_tokens "
+            "FROM ai_capability_configs WHERE capability_key = 'image_analysis'"
+        )
+    ).first()
+
+    if current is None:
+        # capability 不存在则创建
+        conn.execute(
+            sa.text("""
+                INSERT INTO ai_capability_configs
+                    (id, capability_key, capability_name, primary_model_id,
+                     lite_model_id, fallback_model_id, timeout_seconds,
+                     max_retries, max_tokens, concurrency, extra_params,
+                     is_enabled, created_at, updated_at)
+                VALUES (gen_random_uuid()::text, 'image_analysis', '图片分析',
+                        :primary, :primary, :fallback, 30, 1, 1000, 2,
+                        '{}'::jsonb, true, now(), now())
+                """),
+            {"primary": qwen_vl_id, "fallback": qwen37_id},
+        )
+        return
+
+    current_primary = current[0]
+    if current_primary == qwen_vl_id:
+        # 已经绑定 qwen-vl-plus：视为后台/数据迁移已处理，不覆盖任何配置
+        return
+
+    set_parts = [
+        "primary_model_id = :vl_id",
+        "lite_model_id = :vl_id",
+        "max_tokens = 1000",
+        "updated_at = now()",
+    ]
+    params: dict = {"vl_id": qwen_vl_id}
+    if qwen37_id is not None:
+        set_parts.insert(2, "fallback_model_id = :qwen37_id")
+        params["qwen37_id"] = qwen37_id
+
+    conn.execute(
+        sa.text(
+            "UPDATE ai_capability_configs SET " + ", ".join(set_parts) + " WHERE capability_key = 'image_analysis'"
+        ),
+        params,
+    )
+
+
+def downgrade() -> None:
+    pass

alembic/versions/102_image_analysis_max_tokens_1500.py

@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+"""102: image_analysis max_tokens 1200 -> 1500.
+
+v6 prompt 更长、字段更多，旧 max_tokens 容易截断 JSON。
+仅在 image_analysis 当前 max_tokens < 1500 时更新（幂等，不覆盖后台已调到 >=1500 的配置）。
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "102_image_analysis_max_tokens_1500"
+down_revision = "101_qwen_vl_plus_and_max_tokens"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+
+    caps_table = conn.execute(sa.text("SELECT to_regclass('public.ai_capability_configs')")).scalar()
+    if not caps_table:
+        return
+
+    conn.execute(
+        sa.text(
+            "UPDATE ai_capability_configs "
+            "SET max_tokens = 1500, updated_at = now() "
+            "WHERE capability_key = 'image_analysis' "
+            "AND (max_tokens IS NULL OR max_tokens < 1500)"
+        )
+    )
+
+
+def downgrade() -> None:
+    pass

alembic/versions/103_v7_prompt_and_tokens_3000.py

@@ -0,0 +1,194 @@
+# -*- coding: utf-8 -*-
+"""image_analysis v7 prompt + max_tokens 3000 + max_retries 3
+
+Revision ID: 103_v7_prompt_and_tokens_3000
+Revises: 102_image_analysis_max_tokens_1500
+Create Date: 2026-10-07
+
+变更：
+1. 插入v7精简prompt（~1KB，v6 ~4.5KB，删除few-shot/冗长规则，减少输出token占用），设为active
+2. v6停用（is_active=False），保留历史
+3. image_analysis capability: max_tokens 1500→3000，max_retries 1→3
+
+ai_capability_configs 由应用 create_all 创建，全新 alembic-only 库可能不存在，
+故第3步做 to_regclass 守卫（同 102）。
+"""
+
+from sqlalchemy import text
+
+from alembic import op
+
+revision = "103_v7_prompt_and_tokens_3000"
+down_revision = "102_image_analysis_max_tokens_1500"
+branch_labels = None
+depends_on = None
+
+V7_SYSTEM = """# 角色
+你是一位专业的图片分析师，擅长准确识别图片中的场景、人物、物体、文字、氛围。
+
+# 任务
+对用户上传的图片逐张分析，描述你看到的内容，输出JSON格式。
+
+## 技能
+
+### 技能1：判断图片类型
+判断图片属于哪种类型，type字段填对应的英文值：
+- 商品图（product）：单个或多个商品、产品包装
+- 门店场景图（store）：店铺内部、门头招牌、货架陈列
+- 人物图（person）：人物形象、穿搭造型、肖像照片
+- 风景图（scene）：风景、动物、美食、街景
+- 其他（other）：以上都不是
+
+### 技能2：描述通用信息
+不管什么图都要描述：
+- type：图片类型，填product/store/person/scene/other其中一个
+- scene：一句话描述场景，例如"理疗养生店内部，摆着多张理疗床和产品货架"
+- mood：整体氛围，2-4个词，例如"整洁专业"、"热闹温馨"
+- colors：主要颜色，最多5个，写具体颜色名（亮红色/米白色/深蓝色，不写笼统的红色蓝色）
+- visible_text：图片里看到的文字，说明什么字、在什么位置，最多5条；没看到就空数组
+- lighting：光线情况，例如"明亮柔光"、"自然光"、"室内暖黄灯"
+- composition：怎么拍的，例如"居中特写"、"中景平视"、"俯拍"
+- has_person：有没有人，true或false
+
+### 技能3：描述门店场景
+如果是门店场景图（type="store"），还要描述：
+- store_type：什么类型的店，例如"养生馆"、"便利店"、"餐饮店"、"母婴店"
+- brand_signage：招牌上写了什么字、有什么品牌标识
+- visual_elements：看到哪些显眼的东西（招牌样式、灯光、货架、商品陈列、海报、收银台等），最多8个
+- product_categories：看到哪些品类的商品，例如"饮料零食"、"养生产品"
+- promotion_elements：有没有促销活动（打折海报、满减吊旗等），没有就空数组
+- atmosphere：店内什么氛围，例如"亲民生活化"、"老字号专业感"
+- cleanliness：店内干净程度，例如"干净整洁"、"货架整齐"
+- 看到顾客或店员要描述他们在做什么，has_person填true
+
+### 技能4：描述商品
+如果是商品图（type="product"），逐个商品描述：
+- product_name：商品名称，尽量具体，例如"OMO奥妙除菌除螨洗衣液"；看不出来填null
+- brand：什么牌子，看不出来填null
+- category：类目，从以下选一个：服饰鞋包/美妆/数码/食品/家居清洁/母婴/配饰/其他
+- package_type：什么包装，例如"瓶装"、"盒装"、"罐装"、"袋装"、"多瓶装"
+- package_color：包装主要颜色，写具体色（亮红色不写红色）
+- body_shape：瓶身或包装形状，例如"圆润胖瓶"、"竖款带把手瓶身"
+- label_design：标签设计，例如"红色标签印白色品牌logo"
+- key_text_on_package：包装上最显眼的文字（品牌名、功能词、卖点词），最多5个
+- product_features：包装特征，3-6个短语，包含颜色、瓶盖、形状、标签图案
+- key_selling_points：核心卖点，1-3个短语
+
+### 技能5：描述人物
+如果是人物图（type="person"），描述：
+- person_count：几个人
+- gender：性别（男/女/无法判断）
+- age_range：年龄段（儿童/青少年/青年/中年/老年/无法判断）
+- outfit_style：穿搭风格，例如"休闲日常"、"通勤商务"、"街头潮流"
+- upper_wear：上装（颜色+款式+材质），穿裙装不填
+- lower_wear：下装（颜色+款式+版型），穿裙装不填
+- dress_wear：裙装描述，穿上下装不填
+- outerwear：外套
+- shoes：鞋子
+- bag：包袋，没有填null
+- accessories：配饰（眼镜/帽子/项链/耳环/手表/手链/围巾/腰带等），没有填空数组
+- hairstyle：发型
+- makeup：妆容，男生或看不出填null
+- expression：表情，例如"微笑看镜头"、"冷酷无表情"
+- pose：姿势动作，例如"身直立正对镜头"、"单手撩发"
+- body_type：身材，例如"纤细苗条"、"高挑身材"、"丰满匀称"
+- portrait_prompt：80-150字详细描述人物形象（后面用来AI生成肖像图），要写清年龄段、穿搭完整细节、发型发色、妆容、表情、姿势、场景、光线、风格感觉，语言要有画面感
+
+### 技能6：描述风景
+如果是风景图（type="scene"），描述：
+- scene_type：什么场景，例如"自然风景"、"城市街景"、"动物"、"美食"
+- main_subject：画面主体是什么
+- key_elements：关键元素，最多8个
+- environment_objects：周围环境物体，最多8个
+- atmosphere：整体氛围，例如"秋日慵懒氛围感"、"清新自然氧气感"
+- 有人物就描述人物特征
+
+## 限制
+- 只输出JSON，不要任何解释文字，不要markdown代码块包裹，不要写"好的""以下是分析结果"这种废话
+- 颜色写具体色调（亮红色/米白色/深蓝色/翠绿色），不写笼统词汇
+- 瓶身、包装、招牌上的文字尽量识别出来（品牌名、功能词、卖点词）
+- 多个商品、多个人物分开描述，不要合并
+- 看不出来、不确定的字段填null或空数组，布尔值填true/false，绝对不要瞎编
+- 确保JSON格式合法，所有大括号、中括号、引号正确闭合
+- 数组字段控制数量：colors最多5个，visible_text最多5条，visual_elements最多8个，accessories最多10个"""
+V7_USER = "请分析这张图片，按系统消息的JSON结构输出。"
+
+
+def _capability_table_exists(bind) -> bool:
+    return bool(bind.execute(text("SELECT to_regclass('public.ai_capability_configs')")).scalar())
+
+
+def upgrade() -> None:
+    bind = op.get_bind()
+    # 1. 停用旧的active image_analysis prompt（含v6）
+    bind.execute(
+        text(
+            "UPDATE viral_video_prompt_templates SET is_active = FALSE "
+            "WHERE prompt_type = 'image_analysis' AND is_active = TRUE"
+        )
+    )
+    # 2. 幂等插入v7（存在则更新并重新激活）
+    existing = bind.execute(
+        text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 7")
+    ).fetchone()
+    if existing:
+        bind.execute(
+            text(
+                "UPDATE viral_video_prompt_templates SET is_active = TRUE, "
+                "system_prompt = :sys, user_prompt_template = :usr, "
+                "name = 'v7 精简结构化分析', updated_at = NOW() "
+                "WHERE prompt_type = 'image_analysis' AND version = 7"
+            ),
+            {"sys": V7_SYSTEM, "usr": V7_USER},
+        )
+    else:
+        bind.execute(
+            text(
+                "INSERT INTO viral_video_prompt_templates "
+                "(prompt_type, version, name, system_prompt, user_prompt_template, "
+                "is_active, created_at, updated_at) "
+                "VALUES ('image_analysis', 7, 'v7 精简结构化分析', "
+                ":sys, :usr, TRUE, NOW(), NOW())"
+            ),
+            {"sys": V7_SYSTEM, "usr": V7_USER},
+        )
+    # 3. capability max_tokens=3000、max_retries=3（表不存在则跳过）
+    if _capability_table_exists(bind):
+        bind.execute(
+            text(
+                "UPDATE ai_capability_configs SET max_tokens = 3000, "
+                "updated_at = NOW() "
+                "WHERE capability_key = 'image_analysis' AND "
+                "(max_tokens IS NULL OR max_tokens < 3000)"
+            )
+        )
+        bind.execute(
+            text(
+                "UPDATE ai_capability_configs SET max_retries = 3, updated_at = NOW() "
+                "WHERE capability_key = 'image_analysis' AND "
+                "(max_retries IS NULL OR max_retries < 3)"
+            )
+        )
+
+
+def downgrade() -> None:
+    bind = op.get_bind()
+    # 删除v7
+    bind.execute(
+        text("DELETE FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 7")
+    )
+    # 恢复v6为active
+    bind.execute(
+        text(
+            "UPDATE viral_video_prompt_templates SET is_active = TRUE "
+            "WHERE prompt_type = 'image_analysis' AND version = 6"
+        )
+    )
+    # tokens/retries回退
+    if _capability_table_exists(bind):
+        bind.execute(
+            text(
+                "UPDATE ai_capability_configs SET max_tokens = 1500, max_retries = 1, "
+                "updated_at = NOW() WHERE capability_key = 'image_analysis'"
+            )
+        )

alembic/versions/104_v8_image_analysis_and_storyboard_v3.py

@@ -0,0 +1,242 @@
+# -*- coding: utf-8 -*-
+"""image_analysis v8 prompt + storyboard v3 prompt - 用户端展示格式 markdown 控制
+
+Revision ID: 104_v8_display_markdown
+Revises: 103_v7_prompt_and_tokens_3000
+Create Date: 2026-10-07
+
+变更：
+1. image_analysis v8: 在 v7 基础上 system_prompt 末尾追加「## 用户端展示格式」章节，
+   要求 VLM 在每张图的 JSON 里输出 summary_markdown 字段（markdown 格式的图片描述），
+   v8 设 is_active=true，v7 设 is_active=false。
+2. storyboard v3: 在 v2 基础上 system_prompt 追加要求 LLM 在 copy_result 中
+   输出 copy_display_markdown 字段（markdown 格式的完整文案展示），
+   v3 设 is_active=true，v2 设 is_active=false。
+"""
+
+from sqlalchemy import text
+
+from alembic import op
+
+revision = "104_v8_display_markdown"
+down_revision = "103_v7_prompt_and_tokens_3000"
+branch_labels = None
+depends_on = None
+
+# ── v8 追加的 system prompt 内容 ──────────────────────────────────────
+V8_SYSTEM_APPEND = """
+
+## 用户端展示格式
+
+对于每张分析的图片，在 JSON 中额外输出一个 **summary_markdown** 字段，用 markdown 格式写出给用户看的图片描述。
+
+格式要求（根据图片类型自适应）：
+
+**商品图（type=product）**示例：
+### 商品名称
+**品牌**：品牌名 | **类目**：服饰鞋包/美妆/数码/...
+**核心特征**
+- 特征1：描述
+- 特征2：描述
+**外观**：颜色+材质+设计描述
+**包装**：包装类型描述
+**文字信息**：包装上看到的文字
+
+**门店场景图（type=store）**示例：
+### 门店名称/类型
+**类型**：奶茶店/便利店/养生馆/...
+**品牌标识**：招牌文字描述
+**环境氛围**：店内整体感觉
+**陈列亮点**
+- 亮点1
+- 亮点2
+**氛围**：亲民/专业/时尚/...
+
+**人物图（type=person）**示例：
+### 人物描述
+**形象**：年龄段 + 风格
+**穿搭**
+- 上装：颜色+款式
+- 下装：颜色+款式
+- 配饰：...
+**气质**：表情+姿势+整体感觉
+
+**风景/场景图（type=scene）**示例：
+### 场景名称
+**类型**：自然风景/城市街景/动物/美食
+**主体**：画面主要元素
+**氛围**：整体感觉描述
+
+要求：
+- 内容真实具体，从实际图片分析得出
+- 用 markdown 语法：**加粗**、列表、标题
+- 控制在 100-200 字
+- 不要编造图片中没有的信息
+"""
+
+# ── storyboard v3 追加的 system prompt 内容 ──────────────────────────
+V3_STORYBOARD_APPEND = """
+
+## 用户端展示格式
+
+在输出分镜脚本的同时，在顶层输出一个 **copy_display_markdown** 字段（用 XML 标签 <copy_display_markdown> 包裹），用 markdown 格式写出完整文案展示。
+
+格式示例：
+# 标题/主题
+
+## 整体概要
+一句话描述视频内容
+
+## 分镜预览
+
+### 镜头1（0-3秒）
+**景别**：近景俯拍，缓慢推镜
+**画面**：场景描述
+**台词**：口播文本
+**动作**：人物动作描述
+
+### 镜头2（3-9秒）
+...
+
+## 完整口播
+完整口播文案文本
+
+要求：
+- 把所有分镜按时间顺序整理成易读的格式
+- 用 markdown 语法组织，**加粗**标签、##二级标题、列表等
+- 控制在 300-500 字
+- 让用户一眼看懂视频会拍成什么样
+"""
+
+
+def upgrade() -> None:
+    bind = op.get_bind()
+
+    # ── 1. image_analysis v8 ──────────────────────────────────────────
+    # 停用所有 active image_analysis prompt
+    bind.execute(
+        text(
+            "UPDATE viral_video_prompt_templates SET is_active = FALSE "
+            "WHERE prompt_type = 'image_analysis' AND is_active = TRUE"
+        )
+    )
+
+    # 读取 v7 的 prompt 内容作为基础
+    v7_row = bind.execute(
+        text(
+            "SELECT system_prompt, user_prompt_template, COALESCE(example_output, '') "
+            "FROM viral_video_prompt_templates "
+            "WHERE prompt_type = 'image_analysis' "
+            "ORDER BY version DESC LIMIT 1"
+        )
+    ).fetchone()
+
+    if v7_row:
+        v7_system = v7_row[0] or ""
+        v8_system = v7_system + V8_SYSTEM_APPEND
+        v8_user = v7_row[1] or "{image_url}"
+        v8_example = v7_row[2] or ""
+
+        # 幂等：已有 v8 则更新，否则插入
+        existing_v8 = bind.execute(
+            text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 8")
+        ).fetchone()
+        if existing_v8:
+            bind.execute(
+                text(
+                    "UPDATE viral_video_prompt_templates SET is_active = TRUE, "
+                    "system_prompt = :sys, user_prompt_template = :usr, "
+                    "example_output = :ex, name = 'v8 用户端展示格式', "
+                    "updated_at = NOW() "
+                    "WHERE prompt_type = 'image_analysis' AND version = 8"
+                ),
+                {"sys": v8_system, "usr": v8_user, "ex": v8_example},
+            )
+        else:
+            bind.execute(
+                text(
+                    "INSERT INTO viral_video_prompt_templates "
+                    "(prompt_type, version, name, system_prompt, user_prompt_template, "
+                    "example_output, is_active, created_at, updated_at) "
+                    "VALUES ('image_analysis', 8, 'v8 用户端展示格式', "
+                    ":sys, :usr, :ex, TRUE, NOW(), NOW())"
+                ),
+                {"sys": v8_system, "usr": v8_user, "ex": v8_example},
+            )
+
+    # ── 2. storyboard v3 ─────────────────────────────────────────────
+    # 停用所有 active storyboard prompt
+    bind.execute(
+        text(
+            "UPDATE viral_video_prompt_templates SET is_active = FALSE "
+            "WHERE prompt_type = 'storyboard' AND is_active = TRUE"
+        )
+    )
+
+    # 读取当前 storyboard prompt
+    sb_row = bind.execute(
+        text(
+            "SELECT system_prompt, user_prompt_template, COALESCE(example_output, '') "
+            "FROM viral_video_prompt_templates "
+            "WHERE prompt_type = 'storyboard' "
+            "ORDER BY version DESC LIMIT 1"
+        )
+    ).fetchone()
+
+    if sb_row:
+        sb_system = sb_row[0] or ""
+        v3_system = sb_system + V3_STORYBOARD_APPEND
+        v3_user = sb_row[1] or ""
+        v3_example = sb_row[2] or ""
+
+        existing_v3 = bind.execute(
+            text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = 'storyboard' AND version = 3")
+        ).fetchone()
+        if existing_v3:
+            bind.execute(
+                text(
+                    "UPDATE viral_video_prompt_templates SET is_active = TRUE, "
+                    "system_prompt = :sys, user_prompt_template = :usr, "
+                    "example_output = :ex, name = 'v3 用户端展示格式', "
+                    "updated_at = NOW() "
+                    "WHERE prompt_type = 'storyboard' AND version = 3"
+                ),
+                {"sys": v3_system, "usr": v3_user, "ex": v3_example},
+            )
+        else:
+            bind.execute(
+                text(
+                    "INSERT INTO viral_video_prompt_templates "
+                    "(prompt_type, version, name, system_prompt, user_prompt_template, "
+                    "example_output, is_active, created_at, updated_at) "
+                    "VALUES ('storyboard', 3, 'v3 用户端展示格式', "
+                    ":sys, :usr, :ex, TRUE, NOW(), NOW())"
+                ),
+                {"sys": v3_system, "usr": v3_user, "ex": v3_example},
+            )
+
+
+def downgrade() -> None:
+    bind = op.get_bind()
+
+    # 删除 v8
+    bind.execute(
+        text("DELETE FROM viral_video_prompt_templates " "WHERE prompt_type = 'image_analysis' AND version = 8")
+    )
+    # 恢复 v7 active
+    bind.execute(
+        text(
+            "UPDATE viral_video_prompt_templates SET is_active = TRUE, updated_at = NOW() "
+            "WHERE prompt_type = 'image_analysis' AND version = 7"
+        )
+    )
+
+    # 删除 v3
+    bind.execute(text("DELETE FROM viral_video_prompt_templates " "WHERE prompt_type = 'storyboard' AND version = 3"))
+    # 恢复 storyboard v2 active
+    bind.execute(
+        text(
+            "UPDATE viral_video_prompt_templates SET is_active = TRUE, updated_at = NOW() "
+            "WHERE prompt_type = 'storyboard' AND version = 2"
+        )
+    )

alembic/versions/105_narration_first.py

@@ -0,0 +1,116 @@
+# -*- coding: utf-8 -*-
+"""image_analysis v8 + storyboard v3 叙述优先重写版（架构大简化）
+
+Revision ID: 105_narration_first
+Revises: 104_v8_display_markdown
+Create Date: 2026-10-07
+
+变更：
+1. image_analysis v8：用「叙述优先」版整体替换 104 的 append 版——VLM 主交付物是
+   自然叙述 summary_markdown，结构化字段仅保留 type/name/brand/has_person，
+   顶层 products 改名 images；v8 active，其余 image_analysis 全部 deactivate。
+2. storyboard v3：整体替换为风格重写版（口播口语化、画面有画面感、
+   copy_display_markdown 流畅叙述）；v3 active，其余 storyboard deactivate。
+3. intent_parsing 类型模板全部 deactivate（意图解析步骤已删除）。
+模板内容直接取自 packages.application.viral_video.prompts.DEFAULT_TEMPLATES，
+保证代码默认值与 DB seed 完全一致。
+"""
+
+from sqlalchemy import text
+
+from alembic import op
+from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
+
+revision = "105_narration_first"
+down_revision = "104_v8_display_markdown"
+branch_labels = None
+depends_on = None
+
+
+def _tpl(prompt_type: str, version: int) -> dict:
+    for t in DEFAULT_TEMPLATES:
+        if t["prompt_type"] == prompt_type and t["version"] == version:
+            return t
+    raise RuntimeError("default template missing: %s v%s" % (prompt_type, version))
+
+
+def _upsert(bind, t: dict) -> None:
+    existing = bind.execute(
+        text("SELECT id FROM viral_video_prompt_templates " "WHERE prompt_type = :pt AND version = :ver"),
+        {"pt": t["prompt_type"], "ver": t["version"]},
+    ).fetchone()
+    params = {
+        "pt": t["prompt_type"],
+        "ver": t["version"],
+        "name": t["name"],
+        "sys": t["system_prompt"],
+        "usr": t["user_prompt_template"],
+        "ex": t.get("example_output", "") or "",
+    }
+    if existing:
+        bind.execute(
+            text(
+                "UPDATE viral_video_prompt_templates SET name = :name, "
+                "system_prompt = :sys, user_prompt_template = :usr, "
+                "example_output = :ex, is_active = TRUE, updated_at = NOW() "
+                "WHERE prompt_type = :pt AND version = :ver"
+            ),
+            params,
+        )
+    else:
+        bind.execute(
+            text(
+                "INSERT INTO viral_video_prompt_templates "
+                "(prompt_type, version, name, system_prompt, user_prompt_template, "
+                "example_output, is_active, created_at, updated_at) "
+                "VALUES (:pt, :ver, :name, :sys, :usr, :ex, TRUE, NOW(), NOW())"
+            ),
+            params,
+        )
+
+
+def upgrade() -> None:
+    bind = op.get_bind()
+
+    # 1. image_analysis：停用全部后写入叙述优先 v8
+    bind.execute(
+        text("UPDATE viral_video_prompt_templates SET is_active = FALSE " "WHERE prompt_type = 'image_analysis'")
+    )
+    _upsert(bind, _tpl("image_analysis", 8))
+
+    # 2. storyboard：停用全部后写入重写版 v3
+    bind.execute(text("UPDATE viral_video_prompt_templates SET is_active = FALSE " "WHERE prompt_type = 'storyboard'"))
+    _upsert(bind, _tpl("storyboard", 3))
+
+    # 3. intent_parsing 已废弃：全部停用
+    bind.execute(
+        text("UPDATE viral_video_prompt_templates SET is_active = FALSE " "WHERE prompt_type = 'intent_parsing'")
+    )
+
+    # 4. review 模板确保 active
+    bind.execute(text("UPDATE viral_video_prompt_templates SET is_active = TRUE " "WHERE prompt_type = 'review'"))
+
+
+def downgrade() -> None:
+    bind = op.get_bind()
+    # 恢复 104 的 v8/v3 无法重建（内容已替换），仅把版本 active 状态回退：
+    # 停用新版，尝试恢复 v7 / v2
+    bind.execute(
+        text(
+            "UPDATE viral_video_prompt_templates SET is_active = FALSE "
+            "WHERE prompt_type IN ('image_analysis','storyboard') "
+            "AND version IN (8, 3)"
+        )
+    )
+    bind.execute(
+        text(
+            "UPDATE viral_video_prompt_templates SET is_active = TRUE "
+            "WHERE prompt_type = 'image_analysis' AND version = 7"
+        )
+    )
+    bind.execute(
+        text(
+            "UPDATE viral_video_prompt_templates SET is_active = TRUE "
+            "WHERE prompt_type = 'storyboard' AND version = 2"
+        )
+    )

alembic/versions/106_system_settings.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+"""system_settings 正式建表（#2246）
+
+Revision ID: 106_system_settings
+Revises: 105_narration_first
+Create Date: 2026-10-08
+
+system_settings 表此前在 staging 手工创建（对应 034 占位迁移），
+此处补正式迁移保证其他环境一致。CREATE TABLE/INDEX 使用 IF NOT EXISTS，
+对已手工建表的环境幂等。
+"""
+
+from alembic import op
+
+revision = "106_system_settings"
+down_revision = "105_narration_first"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.execute("""
+        CREATE TABLE IF NOT EXISTS system_settings (
+            id VARCHAR(36) NOT NULL,
+            setting_key VARCHAR(100) NOT NULL,
+            setting_value TEXT,
+            setting_type VARCHAR(20) NOT NULL,
+            description VARCHAR(255) NOT NULL DEFAULT '',
+            is_public BOOLEAN NOT NULL DEFAULT FALSE,
+            updated_by VARCHAR(36),
+            category VARCHAR(50) NOT NULL DEFAULT 'general',
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(),
+            updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT NOW(),
+            CONSTRAINT pk_system_settings PRIMARY KEY (id),
+            CONSTRAINT uq_system_settings_setting_key UNIQUE (setting_key)
+        )
+        """)
+    op.execute("CREATE INDEX IF NOT EXISTS ix_system_settings_category " "ON system_settings (category)")
+
+
+def downgrade() -> None:
+    op.execute("DROP INDEX IF EXISTS ix_system_settings_category")
+    op.execute("DROP TABLE IF EXISTS system_settings")

alembic/versions/107_add_language_to_viral_video_jobs.py

@@ -0,0 +1,23 @@
+"""add language column to viral_video_jobs
+
+Revision ID: 107
+Revises: 106
+Create Date: 2026-10-09
+
+"""
+
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision = "107"
+down_revision = "106"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.execute("ALTER TABLE viral_video_jobs " "ADD COLUMN IF NOT EXISTS language VARCHAR(20) NOT NULL DEFAULT 'zh-CN'")
+
+
+def downgrade() -> None:
+    op.execute("ALTER TABLE viral_video_jobs DROP COLUMN IF EXISTS language")

apps/api/app/api/router.py

@@ -1,3 +1,4 @@
+from app.api.routes.admin.ditto_emotion import router as admin_ditto_emotion_router
 from app.api.routes.ai import router as ai_router
 from app.api.routes.ai_avatar_render import router as ai_avatar_render_router
 from app.api.routes.asset_diagnosis import router as asset_diagnosis_router
@@ -242,3 +243,6 @@ api_router.include_router(
     tags=["GPU Worker"],
 )
 api_router.include_router(viral_video_router, prefix="/viral-video", tags=["爆款视频"])
+
+# #2246：后台 Ditto 表情配置（router 自带 /admin/ditto-emotion 前缀）
+api_router.include_router(admin_ditto_emotion_router)

apps/api/app/api/routes/admin/__init__.py

@@ -0,0 +1 @@
+"""后台管理路由（#2246 起）."""

apps/api/app/api/routes/admin/ditto_emotion.py

@@ -0,0 +1,163 @@
+"""Ditto 数字人表情后台配置 — #2246.
+
+路由前缀 /api/v1/admin/ditto-emotion，全部使用 _verify_internal_api_key 鉴权
+（X-API-Key header）。仅开放 5 项白名单配置：
+- ditto_emotion_enabled / ditto_emotion_model / ditto_emotion_temperature
+- ditto_emotion_prompt / ditto_blend_frames
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from fastapi import APIRouter, Depends
+from pydantic import BaseModel
+
+from packages.application.system_config_service import get_system_config_service
+from packages.config import get_api_settings
+from packages.domain.system_setting import (
+    SETTING_TYPE_BOOL,
+    SETTING_TYPE_FLOAT,
+    SETTING_TYPE_INT,
+    SETTING_TYPE_STRING,
+)
+
+from ..auth import _verify_internal_api_key
+
+router = APIRouter(
+    prefix="/admin/ditto-emotion",
+    tags=["Admin"],
+    dependencies=[Depends(_verify_internal_api_key)],
+)
+
+MODEL_OPTIONS = [
+    "doubao-seed-2-1-lite-250915",
+    "doubao-seed-2-1-pro-250915",
+    "deepseek-v3",
+]
+
+# key → (类型, 分类)
+_WHITELIST: dict[str, str] = {
+    "ditto_emotion_enabled": SETTING_TYPE_BOOL,
+    "ditto_emotion_model": SETTING_TYPE_STRING,
+    "ditto_emotion_temperature": SETTING_TYPE_FLOAT,
+    "ditto_emotion_prompt": SETTING_TYPE_STRING,
+    "ditto_blend_frames": SETTING_TYPE_INT,
+}
+
+_DESCRIPTIONS: dict[str, str] = {
+    "ditto_emotion_enabled": "LLM 情绪分析开关。关闭时回退到原有关键词匹配模式，不影响正常出片。",
+    "ditto_emotion_model": "用于分析文案情绪的大模型。",
+    "ditto_emotion_temperature": "模型温度，0-1，越低越稳定保守。",
+    "ditto_emotion_prompt": "情绪分析提示词，核心调优入口，必须包含 {文案} 占位符。",
+    "ditto_blend_frames": "表情切换过渡帧数（6-30），越大越柔和。",
+}
+
+
+def _settings():
+    return get_api_settings()
+
+
+def _default_value(key: str) -> Any:
+    return getattr(_settings(), key)
+
+
+def _build_config_item(key: str) -> dict[str, Any]:
+    item: dict[str, Any] = {
+        "key": key,
+        "type": _WHITELIST[key],
+        "description": _DESCRIPTIONS.get(key, ""),
+        "default": _default_value(key),
+    }
+    service = get_system_config_service()
+    item["value"] = service.get_config(key, _default_value(key))
+    if key == "ditto_emotion_model":
+        item["model_options"] = list(MODEL_OPTIONS)
+    return item
+
+
+class ConfigUpdatePayload(BaseModel):
+    configs: dict[str, Any]
+
+
+class TestPayload(BaseModel):
+    test_text: str
+
+
+def _validate_value(key: str, value: Any) -> Any:
+    st = _WHITELIST[key]
+    if st == SETTING_TYPE_BOOL:
+        if not isinstance(value, bool):
+            raise ValueError(f"{key} 必须是布尔值")
+    elif st == SETTING_TYPE_INT:
+        if isinstance(value, bool) or not isinstance(value, int):
+            raise ValueError(f"{key} 必须是整数")
+        if not 6 <= value <= 30:
+            raise ValueError(f"{key} 必须在 6-30 之间")
+    elif st == SETTING_TYPE_FLOAT:
+        if isinstance(value, bool):
+            raise ValueError(f"{key} 必须是数字")
+        try:
+            value = float(value)
+        except (TypeError, ValueError) as exc:
+            raise ValueError(f"{key} 必须是数字") from exc
+        if not 0.0 <= value <= 1.0:
+            raise ValueError(f"{key} 必须在 0-1 之间")
+    elif st == SETTING_TYPE_STRING:
+        if not isinstance(value, str):
+            raise ValueError(f"{key} 必须是字符串")
+        if key == "ditto_emotion_prompt" and value.strip() and "{文案}" not in value:
+            raise ValueError("提示词必须包含 {文案} 占位符")
+        if key == "ditto_emotion_model" and value not in MODEL_OPTIONS:
+            raise ValueError(f"模型必须是以下之一：{', '.join(MODEL_OPTIONS)}")
+    return value
+
+
+@router.get("/config")
+def get_config() -> dict[str, Any]:
+    return {"configs": [_build_config_item(k) for k in _WHITELIST]}
+
+
+@router.put("/config")
+def update_config(
+    payload: ConfigUpdatePayload,
+    x_api_key: str = Depends(_verify_internal_api_key),
+) -> dict[str, Any]:
+    configs = payload.configs
+    illegal = [k for k in configs if k not in _WHITELIST]
+    if illegal:
+        return {
+            "ok": False,
+            "error": f"不允许修改的配置项：{', '.join(illegal)}",
+        }
+    service = get_system_config_service()
+    updated: dict[str, Any] = {}
+    for key, raw in configs.items():
+        try:
+            value = _validate_value(key, raw)
+        except ValueError as exc:
+            return {"ok": False, "error": str(exc)}
+        service.set_config(
+            key,
+            value,
+            setting_type=_WHITELIST[key],
+            updated_by=x_api_key[:8] if x_api_key else None,
+        )
+        updated[key] = value
+    return {"ok": True, "updated": updated}
+
+
+@router.post("/config/test")
+def test_config(payload: TestPayload) -> dict[str, Any]:
+    text = (payload.test_text or "").strip()
+    if not text:
+        return {"ok": False, "error": "test_text 不能为空"}
+    from packages.application.ditto_emotion_service import get_ditto_emotion_service
+
+    service = get_ditto_emotion_service()
+    segments = service.analyze(text)
+    return {
+        "ok": True,
+        "enabled": service.enabled,
+        "segments": [s.to_dict() for s in segments],
+    }

apps/api/app/api/routes/generation_tasks.py

@@ -44,6 +44,7 @@ from packages.application import (
     GetGenerationTaskUseCase,
     ListGeneratedVideosByTaskUseCase,
 )
+from packages.domain import feature_pricing_service
 from packages.domain.smart_match import smart_select_assets
 
 # #2035：文案关键词 → 素材分类 映射表（用于 smart_match category_match 维度）
@@ -163,7 +164,6 @@ def _infer_expected_categories(script_tags: set[str] | None) -> set[str] | None:
     return matched or None
 
 
-
 logger = logging.getLogger(__name__)
 
 router = APIRouter()
@@ -700,6 +700,17 @@ def create_generation_task(
         logger.info("画中画已下线，strategy_id %s → one_take", effective_strategy_id)
         effective_strategy_id = "one_take"
 
+    # ── smart_edit 计费预扣（全局 points 开关 + 功能开关均开才扣） ──
+    # 首期固定价：dynamic_cost=0，price=(0+fixed_cost)×multiplier，price_cap 封顶。
+    # 预览任务不扣费；按任务条数扣费，任一任务预扣失败（余额不足）整体拒绝。
+    smart_edit_charge = 0.0
+    charged_task_count = 0
+    if not request.is_preview and feature_pricing_service.is_feature_enabled("smart_edit"):
+        unit_credits, _bd = feature_pricing_service.calculate_price("smart_edit", 0.0)
+        if unit_credits > 0:
+            smart_edit_charge = round(unit_credits * count, 2)
+            charged_task_count = count
+
     # 批量生成（count>1）：每个变体必须走与单视频完全相同的独立选片流程（#1743/#1749）。
     # - 变体 0：clone 源 plan（不污染源 plan），变体 1..N-1 用 reselect_plan_for_variant
     #   完整重跑选片（素材级去重：fresh 优先 → 受控复用 overlap≤20% → 短素材禁复用）；
@@ -930,6 +941,42 @@ def create_generation_task(
             )
             # 变体序号写入 extra_meta（响应/排查时可辨识）
             task.extra_meta["variant_index"] = task_index
+
+            # smart_edit 逐条预扣（首期固定价，credits_cost=prepaid，不做结算）
+            task_txn_id = ""
+            if charged_task_count > 0:
+                from packages.domain.points_service import PointsService
+
+                unit_credits = round(smart_edit_charge / count, 2)
+                res = PointsService().deduct_points(
+                    user_id=user_id,
+                    amount=unit_credits,
+                    source="smart_edit",
+                    db=db,
+                    description="智能剪辑生成预扣",
+                    ref_id=task.id,
+                )
+                if not res.get("success"):
+                    # 余额不足：退还本次请求已扣积分后整体拒绝
+                    already_charged = round(unit_credits * task_index, 2)
+                    if already_charged > 0:
+                        PointsService().refund_points(
+                            user_id=user_id,
+                            amount=already_charged,
+                            source="smart_edit",
+                            db=db,
+                            ref_id=task.id,
+                            description="智能剪辑批量提交失败退回",
+                        )
+                    raise HTTPException(
+                        status_code=402,
+                        detail=(f"积分不足：智能剪辑每条需 {unit_credits:.2f} 积分，当前余额 {res.get('balance', 0)}"),
+                    )
+                task_txn_id = str(res.get("transaction_id") or "")
+                task.credits_prepaid = unit_credits
+                task.credits_cost = unit_credits
+                task.credits_transaction_id = task_txn_id
+                generation_task_repository.update(task)
             try:
                 # 兜底关联编辑计划：前端未传 source_edit_plan_id 时，
                 # 通过 template_id + user_id 在 DB 层直接查找最新的 plan。

apps/api/app/api/routes/viral_video.py

@@ -142,6 +142,7 @@ def _to_response(job) -> ViralVideoJobResponse:
         copy_result=_build_copy_result(job),
         voice_id=getattr(job, "voice_id", "") or "",
         voice_source=getattr(job, "voice_source", "") or "",
+        language=getattr(job, "language", "zh-CN") or "zh-CN",
         video_ratio=getattr(job, "video_ratio", "9:16") or "9:16",
         video_model=getattr(job, "video_model", "") or "",
         intent_result=job.intent_result,
@@ -243,6 +244,7 @@ def analyze_images(
         style_strength=request.style_strength or "medium",
         voice_id=request.voice_id or "",
         voice_source=request.voice_source or "",
+        language=getattr(request, "language", "zh-CN") or "zh-CN",
         video_ratio=request.video_ratio or "9:16",
         video_model=request.video_model or "",
         video_resolution=getattr(request, "video_resolution", "720p") or "720p",
@@ -280,11 +282,18 @@ def generate_copy(
         raise HTTPException(status_code=404, detail="任务不存在")
     if job.user_id != authenticated_user.user.id:
         raise HTTPException(status_code=403, detail="无权操作此任务")
-    if job.status not in (ViralVideoStatus.IMAGE_ANALYZED, ViralVideoStatus.PENDING, ViralVideoStatus.FAILED):
+    # 允许首次进入（IMAGE_ANALYZED/PENDING）、失败重试（FAILED）、文案重新生成（COPY_GENERATED/COMPLETED）
+    if job.status not in (
+        ViralVideoStatus.IMAGE_ANALYZED,
+        ViralVideoStatus.PENDING,
+        ViralVideoStatus.FAILED,
+        ViralVideoStatus.COPY_GENERATED,
+        ViralVideoStatus.COMPLETED,
+    ):
         raise HTTPException(status_code=409, detail=f"任务当前状态 {job.status} 不能生成文案")
 
-    # 允许失败任务重试：重置
-    if job.status == ViralVideoStatus.FAILED:
+    # 失败重试 / 重新生成：retry_count 自增
+    if job.status in (ViralVideoStatus.FAILED, ViralVideoStatus.COPY_GENERATED, ViralVideoStatus.COMPLETED):
         job.retry_count += 1
         job.error_msg = ""
 
@@ -307,6 +316,7 @@ def generate_copy(
         job.style_guide = request.style_guide
     job.voice_id = request.voice_id or job.voice_id
     job.voice_source = request.voice_source or job.voice_source
+    job.language = getattr(request, "language", "") or job.language or "zh-CN"
     job.video_ratio = request.video_ratio or job.video_ratio or "9:16"
     job.video_model = request.video_model or job.video_model or ""
     job.video_resolution = getattr(request, "video_resolution", "") or job.video_resolution or "720p"
@@ -341,6 +351,37 @@ def confirm_copy(
         raise HTTPException(status_code=403, detail="无权操作此任务")
     if job.status != ViralVideoStatus.COPY_GENERATED:
         raise HTTPException(status_code=409, detail=f"任务当前状态 {job.status} 不能确认文案（需 copy_generated）")
+    # #2218: 额外校验 copy_result 完整性，防止孤儿/脏数据进入渲染
+    if not isinstance(job.copy_result, dict) or not job.copy_result:
+        raise HTTPException(status_code=409, detail="文案数据缺失，请先点击「生成文案」")
+
+    # Bug1 fix: 用户 confirm 时允许修改 video_model/video_resolution/video_ratio/duration
+    old_duration = int(getattr(job, "duration", 15) or 15)
+    old_resolution = getattr(job, "video_resolution", "720p") or "720p"
+    old_ratio = getattr(job, "video_ratio", "9:16") or "9:16"
+    old_model = getattr(job, "video_model", None) or "seedance-2.5"
+
+    if request.duration is not None:
+        job.duration = max(5, min(30, int(request.duration)))
+    if request.video_resolution is not None:
+        job.video_resolution = request.video_resolution
+    if request.video_ratio is not None:
+        job.video_ratio = request.video_ratio
+    if request.video_model is not None:
+        job.video_model = request.video_model
+    if request.voice_id is not None:
+        job.voice_id = request.voice_id
+    if request.voice_source is not None:
+        job.voice_source = request.voice_source
+    if getattr(request, "language", None) is not None:
+        job.language = request.language
+
+    param_changed = (
+        (request.duration is not None and int(request.duration) != old_duration)
+        or (request.video_resolution is not None and request.video_resolution != old_resolution)
+        or (request.video_ratio is not None and request.video_ratio != old_ratio)
+        or (request.video_model is not None and request.video_model != old_model)
+    )
 
     # 积分预扣（已扣过/重试任务跳过）
     from app.config import settings as _settings
@@ -349,7 +390,51 @@ def confirm_copy(
         already_paid = (float(getattr(job, "credits_prepaid", 0) or 0) > 0) or (
             float(getattr(job, "credits_cost", 0) or 0) > 0
         )
-        if not already_paid:
+        if param_changed and already_paid:
+            # 参数变更：回退旧预扣，按新参数重新预扣
+            from packages.domain.points_rules import calculate_viral_video_credits, resolve_video_dimensions
+            from packages.domain.points_service import PointsService
+
+            old_w, old_h = resolve_video_dimensions(old_resolution, old_ratio)
+            old_est = calculate_viral_video_credits(old_duration, old_w, old_h, old_model)
+            new_w, new_h = resolve_video_dimensions(
+                getattr(job, "video_resolution", "720p") or "720p",
+                job.video_ratio or "9:16",
+            )
+            new_est = calculate_viral_video_credits(
+                int(job.duration or 15), new_w, new_h, job.video_model or "seedance-2.5"
+            )
+            svc = PointsService()
+            # 退回旧预扣
+            if getattr(job, "credits_transaction_id", None):
+                svc.refund_points(
+                    user_id=authenticated_user.user.id,
+                    amount=float(job.credits_prepaid),
+                    source="viral_video",
+                    db=session,
+                    ref_id=job.credits_transaction_id,
+                    description="confirm-copy 参数变更退还旧预扣",
+                )
+            # 预扣新金额
+            if new_est > 0:
+                res = svc.deduct_viral_video(authenticated_user.user.id, new_est, job.id, session)
+                if not res.get("success"):
+                    balance = res.get("balance", 0)
+                    raise HTTPException(
+                        status_code=402,
+                        detail={
+                            "code": "INSUFFICIENT_POINTS",
+                            "message": f"积分不足，需要 {new_est} 积分，当前余额 {balance}",
+                            "required": new_est,
+                            "balance": balance,
+                        },
+                    )
+                job.credits_prepaid = new_est
+                job.credits_transaction_id = res.get("transaction_id", "") or ""
+            logger.info(
+                "[爆款视频][confirm-copy] 参数变更，积分重算: old=%d new=%d job_id=%s", old_est, new_est, job.id
+            )
+        elif not already_paid:
             from packages.domain.points_rules import calculate_viral_video_credits, resolve_video_dimensions
             from packages.domain.points_service import PointsService
 
@@ -880,13 +965,28 @@ async def viral_video_websocket(websocket: WebSocket, job_id: str) -> None:
         job = job_repo.get(job_id)
         if job is not None:
             status_val = job.status.value if hasattr(job.status, "value") else str(job.status)
+            # #P0: 初始快照必须包含前端重连/刷新所需的业务字段，
+            # 结构对齐 worker 推送的 image_analyzed / copy_generated 事件。
+            data: dict = {"status": status_val}
+            ia = getattr(job, "image_analysis", None)
+            if isinstance(ia, dict) and ia:
+                data["image_analysis"] = ia
+            cr = _build_copy_result(job)
+            if isinstance(cr, dict) and cr:
+                data["copy_result"] = cr
+            gct = getattr(job, "generated_copy_text", "") or ""
+            if gct:
+                data["generated_copy_text"] = gct
+            sb = getattr(job, "storyboard", None) or []
+            if sb:
+                data["storyboard"] = sb
             initial = {
                 "type": "viral_video:progress",
                 "job_id": job_id,
                 "stage": _stage_from_status(job),
                 "progress": _estimate_progress(job),
                 "message": _initial_message(job),
-                "data": {"status": status_val},
+                "data": data,
             }
             await websocket.send_json(initial)
             # 已经终态 → 再发一条终态事件后立即关闭，避免占连接

apps/api/app/schemas/viral_video.py

@@ -87,6 +87,7 @@ class CreateViralVideoRequest(BaseModel):
     style_template_id: str = ""
     voice_id: str = ""
     voice_source: str = ""
+    language: str = "zh-CN"
     video_ratio: str = "9:16"
     video_model: str = ""
     video_resolution: str = "720p"
@@ -121,6 +122,7 @@ class AnalyzeImagesRequest(BaseModel):
     video_model: str = ""
     video_resolution: str = "720p"
     duration: int = Field(default=15, ge=5, le=30)
+    language: str = "zh-CN"
 
 
 class GenerateCopyRequest(BaseModel):
@@ -142,6 +144,7 @@ class GenerateCopyRequest(BaseModel):
     style_guide: dict | None = None
     voice_id: str = ""
     voice_source: str = ""
+    language: str = "zh-CN"
     video_ratio: str = "9:16"
     video_model: str = ""
     video_resolution: str = "720p"
@@ -167,6 +170,13 @@ class ConfirmCopyRequest(BaseModel):
     """v1.5+ 阶段3：用户确认/编辑口播后开始渲染（TTS+单次Seedance）。"""
 
     edited_copy: str = Field(default="", description="用户编辑后的口播文案；为空则用 AI 生成的 voiceover_script")
+    video_model: str | None = Field(default=None, description="用户选定的视频生成模型（confirm时可选）")
+    video_resolution: str | None = Field(default=None, description="用户选定的分辨率（confirm时可选）")
+    video_ratio: str | None = Field(default=None, description="用户选定的比例（confirm时可选）")
+    duration: int | None = Field(default=None, ge=5, le=30, description="用户选定的时长秒数（confirm时可选，5~30）")
+    voice_id: str | None = Field(default=None, description="用户选定的音色ID（confirm时可选）")
+    voice_source: str | None = Field(default=None, description="用户选定的音色来源（confirm时可选）")
+    language: str | None = Field(default=None, description="用户选定的语言（confirm时可选）")
 
 
 class ConfirmIntentRequest(BaseModel):
@@ -218,6 +228,7 @@ class ViralVideoJobResponse(BaseModel):
     # 音色/视频参数
     voice_id: str = ""
     voice_source: str = ""
+    language: str = "zh-CN"
     video_ratio: str = "9:16"
     video_model: str = ""
     intent_result: dict | None = None

apps/api/app/services/gpu_lipsync_service.py

@@ -228,6 +228,8 @@ class GpuLipsyncService:
         lipsync_job_id: str = "",
         user_id: str = "",
         project_id: str = "",
+        credits_prepaid: float = 0.0,
+        credits_transaction_id: str = "",
     ) -> GpuLipsyncTaskModel:
         task_id = str(uuid.uuid4())
         now = datetime.now(UTC)
@@ -240,6 +242,8 @@ class GpuLipsyncService:
             audio_url=audio_url,
             status="pending",
             attempt=0,
+            credits_prepaid=float(credits_prepaid or 0.0),
+            credits_transaction_id=str(credits_transaction_id or ""),
             created_at=now,
             updated_at=now,
         )

apps/api/app/services/lipsync_service.py

@@ -38,6 +38,7 @@ from sqlalchemy.orm import Session
 from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
 from packages.application.cosyvoice_service import CosyVoiceError
 from packages.config import get_api_settings
+from packages.domain import feature_pricing_service
 from packages.domain.sentence_timings import (
     compute_sentence_timings,
     probe_audio_duration,
@@ -222,7 +223,47 @@ class LipsyncService:
         if timings:
             job.sentence_timings = timings
 
-        # 4. 检查是否走 GPU 路径：开关打开 + 有可用 Worker
+        # 4. 检查是否走 Ditto（蚂蚁数字人，#2076）：开关 + 配置完整
+        use_ditto = False
+        if self.settings.use_ditto_lipsync:
+            try:
+                from packages.application.ditto_service import get_ditto_client
+
+                ditto = get_ditto_client()
+                if ditto.is_configured:
+                    use_ditto = True
+                    logger.info("[lipsync] 优先走 Ditto 蚂蚁数字人: job_id=%s", job.id)
+                else:
+                    logger.info(
+                        "[lipsync] Ditto 开关已开但配置不完整（base_url=%s, template=%s），继续判断 GPU: job_id=%s",
+                        bool(ditto.base_url),
+                        bool(ditto.default_video_url),
+                        job.id,
+                    )
+            except Exception as exc:
+                logger.warning("[lipsync] Ditto 初始化失败，继续判断 GPU: job_id=%s err=%s", job.id, exc)
+
+        if use_ditto:
+            try:
+                # Ditto 使用预置人物模板视频，不用用户上传的 video_url；
+                # 但保留用户 video_url 以便失败回退到 GPU/MediaKit。
+                job.status = "processing"
+                job.mediakit_task_id = "ditto:submitted"
+                job.updated_at = datetime.now(UTC)
+                self.db.commit()
+                from app.tasks.lipsync_ditto import lipsync_ditto_process_async
+
+                lipsync_ditto_process_async.apply_async(args=(job.id, job.user_id))
+                logger.info("[lipsync] Ditto 任务已异步派发: job_id=%s", job.id)
+                return
+            except Exception as exc:
+                logger.warning("[lipsync] Ditto 派发失败，回退 GPU/MediaKit: job_id=%s err=%s", job.id, exc)
+                try:
+                    self.db.rollback()
+                except Exception:
+                    pass
+
+        # 5. 检查是否走 GPU 路径：开关打开 + 有可用 Worker
         use_gpu = False
         if self.settings.use_gpu_lipsync:
             try:
@@ -368,6 +409,8 @@ class LipsyncService:
             lipsync_job_id=job.id,
             user_id=job.user_id,
             project_id=job.project_id,
+            credits_prepaid=float(getattr(job, "credits_prepaid", 0) or 0),
+            credits_transaction_id=str(getattr(job, "credits_transaction_id", "") or ""),
         )
         logger.info(
             "[lipsync] 已创建 GPU 任务（异步）: job_id=%s gpu_task=%s",
@@ -415,6 +458,121 @@ class LipsyncService:
             job.output_duration,
         )
 
+    # ── lip_sync 计费辅助 ────────────────────────────────────────────────
+
+    @staticmethod
+    def _estimate_duration(
+        *,
+        audio_duration: Optional[float] = None,
+        sentence_timings: Optional[list] = None,
+        script_text: str = "",
+    ) -> float:
+        """预估音频/成片秒数。
+
+        优先级：audio_duration（预合成前端已 ffprobe）> timings 末句 end_time >
+        脚本字数 / 5 字每秒 > 默认 10 秒。
+        """
+        if audio_duration and float(audio_duration) > 0:
+            return float(audio_duration)
+        if sentence_timings:
+            max_end = 0.0
+            for item in sentence_timings:
+                if isinstance(item, dict):
+                    end = item.get("end_time") or item.get("end") or 0.0
+                else:
+                    end = 0.0
+                try:
+                    max_end = max(max_end, float(end))
+                except (TypeError, ValueError):
+                    continue
+            if max_end > 0:
+                return max_end
+        text = (script_text or "").strip()
+        if text:
+            return max(1.0, len(text) / 5.0)
+        return 10.0
+
+    def _settle_lip_sync(self, job: LipsyncJobModel, actual_duration: float) -> None:
+        """按实际时长结算（首期只退不补：final < prepaid 退差额，> 不补）。
+
+        幂等：credits_cost 已 > 0 说明结算过，直接跳过。
+        结算失败不阻塞业务（结果已产出），仅记录日志。
+        """
+        try:
+            prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
+            if prepaid <= 0:
+                return
+            if float(getattr(job, "credits_cost", 0) or 0) > 0:
+                return
+            feature_cfg = feature_pricing_service.get_feature_config("lip_sync")
+            unit_cost = float(feature_cfg.dynamic_unit_cost) if feature_cfg is not None else 0.0
+            duration = float(actual_duration or 0.0)
+            if duration <= 0:
+                duration = self._estimate_duration(
+                    sentence_timings=job.sentence_timings,
+                    script_text=job.script_text,
+                )
+            final_price, _bd = feature_pricing_service.calculate_price("lip_sync", duration * unit_cost)
+            final_price = round(float(final_price), 2)
+            job.credits_cost = final_price
+            if final_price < prepaid - 0.009:
+                refund = round(prepaid - final_price, 2)
+                from packages.domain.points_service import PointsService
+
+                res = PointsService().refund_points(
+                    user_id=job.user_id,
+                    amount=refund,
+                    source="lip_sync",
+                    db=self.db,
+                    ref_id=str(job.credits_transaction_id or job.id),
+                    description="对口型结算退费",
+                )
+                if not res.get("success"):
+                    logger.warning(
+                        "[lip_sync] 结算退费失败 job_id=%s refund=%.2f（不阻塞）",
+                        job.id,
+                        refund,
+                    )
+            # final > prepaid：首期只退不补，不补扣
+            self.db.commit()
+        except Exception:  # noqa: BLE001
+            logger.exception("[lip_sync] 结算异常 job_id=%s（不阻塞结果）", job.id)
+            try:
+                self.db.rollback()
+            except Exception:  # noqa: BLE001
+                pass
+
+    def _refund_lip_sync(self, job: LipsyncJobModel) -> None:
+        """任务失败/取消时全额退还预扣积分（credits_cost 已结算则退实际未消耗部分）。"""
+        try:
+            prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
+            if prepaid <= 0:
+                return
+            txn_id = str(getattr(job, "credits_transaction_id", "") or "")
+            cost = float(getattr(job, "credits_cost", 0) or 0)
+            refund = round(prepaid - cost, 2) if cost > 0 else round(prepaid, 2)
+            if refund <= 0:
+                return
+            from packages.domain.points_service import PointsService
+
+            res = PointsService().refund_points(
+                user_id=job.user_id,
+                amount=refund,
+                source="lip_sync",
+                db=self.db,
+                ref_id=txn_id or job.id,
+                description="对口型失败/取消退款",
+            )
+            if res.get("success"):
+                job.credits_cost = prepaid  # 标记已全额退回，防重复退
+                self.db.commit()
+        except Exception:  # noqa: BLE001
+            logger.exception("[lip_sync] 退款异常 job_id=%s", job.id)
+            try:
+                self.db.rollback()
+            except Exception:  # noqa: BLE001
+                pass
+
     # ── 创建任务 ──────────────────────────────────────────────────────────
 
     def create_job(
@@ -466,6 +624,35 @@ class LipsyncService:
             if not isinstance(sentence_timings, list) or len(sentence_timings) == 0:
                 raise MediaKitError("预合成模式 sentence_timings 不能为空", code="InvalidInput")
 
+        # 0.5 lip_sync 计费预扣（全局 points 开关 + 功能开关均开才扣）
+        prepaid_credits = 0.0
+        prepaid_txn_id = ""
+        if feature_pricing_service.is_feature_enabled("lip_sync"):
+            est_duration = self._estimate_duration(
+                audio_duration=audio_duration,
+                sentence_timings=sentence_timings,
+                script_text=script_text,
+            )
+            feature_cfg = feature_pricing_service.get_feature_config("lip_sync")
+            unit_cost = float(feature_cfg.dynamic_unit_cost) if feature_cfg is not None else 0.0
+            dynamic_cost = est_duration * unit_cost
+            prepaid_credits, _bd = feature_pricing_service.calculate_price("lip_sync", dynamic_cost)
+            if prepaid_credits > 0:
+                from packages.domain.points_service import PointsService
+
+                res = PointsService().deduct_points(
+                    user_id=user_id,
+                    amount=prepaid_credits,
+                    source="lip_sync",
+                    db=self.db,
+                    description="对口型生成预扣",
+                )
+                if not res.get("success"):
+                    raise ValueError(
+                        f"积分不足：本次对口型需 {prepaid_credits:.2f} 积分，当前余额 {res.get('balance', 0)}"
+                    )
+                prepaid_txn_id = str(res.get("transaction_id") or "")
+
         # 1. 创建数据库记录
         job_id = str(uuid.uuid4())
         job = LipsyncJobModel(
@@ -482,6 +669,8 @@ class LipsyncService:
             emotion=emotion or "",
             # 音频直传（含预合成）直接进入 pending（后续同步改为 submitted）；TTS 模式进入 tts_processing
             status="tts_processing" if is_tts_mode else "pending",
+            credits_prepaid=prepaid_credits,
+            credits_transaction_id=prepaid_txn_id,
         )
         self.db.add(job)
         self.db.flush()
@@ -656,6 +845,30 @@ class LipsyncService:
         if job.status in (STATUS_COMPLETED, "failed"):
             return job
 
+        # Ditto 异步路径：mediakit_task_id 以 "ditto:" 开头，由 Celery 任务异步更新
+        # 不做 MediaKit 轮询，只检查是否卡住太久（>10 分钟）则标失败
+        if job.mediakit_task_id and job.mediakit_task_id.startswith("ditto:"):
+            if job.status in ("processing", "submitted"):
+                _now = datetime.now(UTC)
+                _upd = job.updated_at
+                if _upd is not None and _upd.tzinfo is None:
+                    _upd = _upd.replace(tzinfo=UTC)
+                stale_minutes = 10
+                if _upd and (_now - _upd).total_seconds() > stale_minutes * 60:
+                    logger.warning(
+                        "Ditto 异步任务超时（>%d 分钟），标记失败: job_id=%s",
+                        stale_minutes,
+                        job_id,
+                    )
+                    job.status = "failed"
+                    job.error_message = f"Ditto 处理超时（>{stale_minutes} 分钟）"
+                    job.error_code = "DittoTimeout"
+                    job.completed_at = _now
+                    job.updated_at = _now
+                    self.db.commit()
+                    self._refund_lip_sync(job)
+            return job
+
         # GPU 异步路径：mediakit_task_id 以 "gpu:" 开头，由 Celery 任务异步更新
         # 不做 MediaKit 轮询，只检查是否卡住太久（>30 分钟）则标失败
         if job.mediakit_task_id and job.mediakit_task_id.startswith("gpu:"):
@@ -677,6 +890,8 @@ class LipsyncService:
                     job.completed_at = _now
                     job.updated_at = _now
                     self.db.commit()
+                    # lip_sync 超时全额退款
+                    self._refund_lip_sync(job)
             return job
 
         # 未提交的任务不轮询
@@ -702,6 +917,8 @@ class LipsyncService:
                 job.completed_at = datetime.now(UTC)
                 job.updated_at = datetime.now(UTC)
                 self.db.commit()
+                # lip_sync 结算（只退不补）
+                self._settle_lip_sync(job, float(job.output_duration or 0.0))
                 # 异步转存自家 OSS
                 try:
                     from app.tasks.lipsync_tts import persist_output_video_task
@@ -719,6 +936,8 @@ class LipsyncService:
                 job.error_message = error.get("message", "任务执行失败")
                 job.error_code = error.get("code", "TaskFailed")
                 job.completed_at = datetime.now(UTC)
+                # lip_sync 失败全额退款（先退款再统一 commit）
+                self._refund_lip_sync(job)
             else:
                 # 中间状态（running/processing/queued 等）同步到 DB，避免前端永远卡在 submitted
                 if isinstance(mk_status, str) and mk_status:
@@ -812,6 +1031,8 @@ class LipsyncService:
             job.status = "cancelled"
             job.updated_at = datetime.now(UTC)
             self.db.commit()
+            # lip_sync 取消全额退款
+            self._refund_lip_sync(job)
             self.db.refresh(job)
 
         return job

apps/api/app/tasks/lipsync_ditto.py

@@ -0,0 +1,552 @@
+"""Ditto 蚂蚁数字人口型异步任务 — #2076.
+
+把 Ditto 同步 HTTP 调用（30-120s）从 API 请求移到 Celery 后台执行：
+1. 加载 LipsyncJob
+2. 确定驱动视频：用户上传的 video_url 优先，无则用 settings.ditto_default_video_url 兜底
+3. 视频时长对齐：若视频 < 音频+2s，用 ffmpeg 循环视频到足够长度后上传临时文件
+4. 调 DittoClient.generate_and_persist
+5. 成功：标记 completed，写入 output_video_url（Ditto 输出自带音频，无需二次混流/超分）
+6. 失败：回退 GPU MuseTalk → 再失败回退 MediaKit
+
+注意：
+- 保留 MuseTalk 代码不动；Ditto 优先，失败按原链路兜底
+- Ditto 默认使用用户上传的视频作为驱动模板，default_video_url 仅作兜底
+- 用户视频短于音频时，ffmpeg stream_loop 循环到音频时长+2s余量
+- 不传 GFPGAN 超分，不需要 ffmpeg 音视频混流（Ditto 输出已带音视频）
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import subprocess
+import tempfile
+from datetime import UTC, datetime
+from typing import TYPE_CHECKING, Optional
+
+from celery import shared_task
+from sqlalchemy.orm import Session
+
+if TYPE_CHECKING:
+    from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
+
+logger = logging.getLogger(__name__)
+
+_DITTO_URL_TTL_SECONDS = 7 * 24 * 3600  # Ditto 结果 OSS URL 7 天有效
+_VIDEO_LOOP_MARGIN_SECONDS = 2.0  # 循环视频时比音频多留 2 秒余量
+_MAX_VIDEO_PREPROCESS_SIZE = 200 * 1024 * 1024  # 用户视频最大 200MB
+
+
+def _get_db_session() -> Session:
+    try:
+        from worker_app.db import SessionLocal  # type: ignore
+    except ImportError:
+        from app.db import SessionLocal  # type: ignore
+    return SessionLocal()
+
+
+def _sign_media_url(url: str) -> str:
+    """对自家 OSS URL 签 7 天预签名。"""
+    if not url:
+        return url
+    try:
+        from urllib.parse import urlparse
+
+        from packages.shared.storage import get_shared_storage_service
+
+        storage = get_shared_storage_service()
+        public_base = getattr(storage, "public_url", "")
+        if not isinstance(public_base, str) or not public_base:
+            return url
+        own_host = urlparse(public_base).netloc.lower()
+        host = urlparse(url).netloc.lower()
+        if not own_host or host != own_host:
+            return url
+        return storage.get_download_url(url, expires_seconds=_DITTO_URL_TTL_SECONDS)
+    except Exception:
+        return url
+
+
+def _probe_media_duration(path_or_bytes, *, is_bytes: bool = False) -> float:
+    """用 ffprobe 探测视频/音频时长（秒）；失败返回 0。
+
+    Args:
+        path_or_bytes: 文件路径(str) 或 字节数据(bytes)
+        is_bytes: 传入的是 bytes 还是文件路径
+    """
+    tmp_path = None
+    try:
+        if is_bytes:
+            with tempfile.NamedTemporaryFile(suffix=".bin", delete=False) as tmp:
+                tmp.write(path_or_bytes)
+                tmp_path = tmp.name
+            target = tmp_path
+        else:
+            target = path_or_bytes
+        out = subprocess.check_output(
+            [
+                "ffprobe",
+                "-v",
+                "error",
+                "-show_entries",
+                "format=duration",
+                "-of",
+                "default=noprint_wrappers=1:nokey=1",
+                target,
+            ],
+            stderr=subprocess.STDOUT,
+            timeout=15,
+        )
+        return float(out.decode().strip() or 0)
+    except Exception as exc:
+        logger.warning("[ditto_task] ffprobe 失败: %s", exc)
+        return 0.0
+    finally:
+        if tmp_path and os.path.exists(tmp_path):
+            try:
+                os.unlink(tmp_path)
+            except Exception:
+                pass
+
+
+def _probe_video_duration(video_bytes: bytes) -> float:
+    """用 ffprobe 探测视频时长（秒）；失败返回 0。"""
+    return _probe_media_duration(video_bytes, is_bytes=True)
+
+
+def _prepare_driver_video(
+    *,
+    user_video_url: str,
+    default_video_url: str,
+    audio_duration: float,
+    job_id: str,
+    user_id: str,
+) -> tuple[str, bool]:
+    """准备传给 Ditto 的驱动视频 URL。
+
+    逻辑：
+    1. 优先使用用户上传的视频（user_video_url），无则用 default_video_url 兜底
+    2. 下载视频，探测时长
+    3. 若视频时长 >= 音频时长+2s 余量：直接用原 URL（签名后）
+    4. 若视频时长 < 音频时长+2s：ffmpeg stream_loop 循环到目标时长，上传临时 OSS，返回临时 URL
+    5. 任何异常：回退到 default_video_url（兜底）
+
+    Returns:
+        (video_url_for_ditto, is_temporary) — is_temporary=True 表示 URL 是本次临时生成的
+    """
+    from packages.shared.storage import get_shared_storage_service
+    from packages.shared.url_security import safe_download_bytes
+
+    storage = get_shared_storage_service()
+
+    # 1. 选择源 URL（用户视频优先）
+    source_url = user_video_url or default_video_url
+    source_label = "user" if user_video_url else "default"
+    if not source_url:
+        raise RuntimeError("无可用驱动视频（用户视频和默认模板都为空）")
+
+    # 2. 下载视频
+    try:
+        # 用户视频需要签名才能下载
+        signed_source = _sign_media_url(source_url) if user_video_url else source_url
+        video_bytes = safe_download_bytes(
+            signed_source,
+            purpose="ditto-driver-video",
+            max_size=_MAX_VIDEO_PREPROCESS_SIZE,
+            allowed_mime_types=("video/mp4", "video/quicktime", "video/x-msvideo", "video/webm"),
+            timeout=60,
+        )
+    except Exception as dl_exc:
+        logger.warning(
+            "[ditto_task] 下载驱动视频失败（%s），回退默认模板: job=%s err=%s",
+            source_label,
+            job_id,
+            dl_exc,
+        )
+        if user_video_url and default_video_url:
+            return default_video_url, False
+        raise
+
+    # 3. 探测视频时长
+    video_duration = _probe_media_duration(video_bytes, is_bytes=True)
+    target_duration = audio_duration + _VIDEO_LOOP_MARGIN_SECONDS
+
+    # 4. 视频足够长：直接用签名后的原 URL
+    if video_duration >= target_duration:
+        logger.info(
+            "[ditto_task] 驱动视频足够长（%.2fs >= %.2fs），直接使用: job=%s source=%s",
+            video_duration,
+            target_duration,
+            job_id,
+            source_label,
+        )
+        return _sign_media_url(source_url) if user_video_url else source_url, False
+
+    # 5. 视频不够长：ffmpeg 循环到目标时长
+    logger.info(
+        "[ditto_task] 驱动视频不够长（%.2fs < %.2fs），ffmpeg 循环延长: job=%s",
+        video_duration,
+        target_duration,
+        job_id,
+    )
+    looped_path = None
+    try:
+        # 写临时文件
+        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as src_tmp:
+            src_tmp.write(video_bytes)
+            src_path = src_tmp.name
+        looped_fd, looped_path = tempfile.mkstemp(suffix=".mp4")
+        os.close(looped_fd)
+
+        # ffmpeg: stream_loop -1 循环输入，-t 截到目标时长
+        # 使用 -c copy 快速复制流（不重编码），速度快无画质损失
+        cmd = [
+            "ffmpeg",
+            "-y",
+            "-stream_loop",
+            "-1",
+            "-i",
+            src_path,
+            "-t",
+            str(target_duration),
+            "-c",
+            "copy",
+            "-movflags",
+            "+faststart",
+            looped_path,
+        ]
+        try:
+            subprocess.run(cmd, check=True, capture_output=True, timeout=60)
+        except subprocess.CalledProcessError:
+            # copy 模式失败（编码不兼容），回退重编码
+            logger.warning("[ditto_task] stream_loop copy 失败，回退重编码: job=%s", job_id)
+            cmd = [
+                "ffmpeg",
+                "-y",
+                "-stream_loop",
+                "-1",
+                "-i",
+                src_path,
+                "-t",
+                str(target_duration),
+                "-c:v",
+                "libx264",
+                "-preset",
+                "veryfast",
+                "-crf",
+                "23",
+                "-c:a",
+                "aac",
+                "-movflags",
+                "+faststart",
+                looped_path,
+            ]
+            subprocess.run(cmd, check=True, capture_output=True, timeout=120)
+
+        # 上传临时 OSS
+        looped_key = f"ditto-tmp/{user_id}/{job_id}_looped.mp4"
+        with open(looped_path, "rb") as f:
+            tmp_url = storage.upload_file(
+                f,
+                looped_key,
+                content_type="video/mp4",
+            )
+        # 临时文件需要签名（private bucket）
+        signed_tmp_url = _sign_media_url(tmp_url)
+        logger.info(
+            "[ditto_task] 循环视频已上传: job=%s key=%s dur=%.2fs",
+            job_id,
+            looped_key,
+            target_duration,
+        )
+        return signed_tmp_url, True
+
+    except Exception as loop_exc:
+        logger.warning(
+            "[ditto_task] 视频循环处理失败，回退直接使用（Ditto 侧处理）: job=%s err=%s",
+            job_id,
+            loop_exc,
+        )
+        # 兜底：直接用原视频（交给 Ditto 侧处理时长不一致）
+        return _sign_media_url(source_url) if user_video_url else source_url, False
+    finally:
+        for p in (locals().get("src_path"), looped_path):
+            if p and os.path.exists(p):
+                try:
+                    os.unlink(p)
+                except Exception:
+                    pass
+
+
+def _refund_lip_sync(db: Session, job: "LipsyncJobModel") -> None:
+    """Ditto 失败/取消时全额退款（复用 lipsync_service 的退款逻辑）。"""
+    try:
+        from app.services.lipsync_service import LipsyncService
+
+        LipsyncService(db)._refund_lip_sync(job)
+    except Exception:
+        logger.exception("[ditto_task] lip_sync 退款异常 job_id=%s", job.id)
+
+
+def _settle_lip_sync(db: Session, job: "LipsyncJobModel", duration: float) -> None:
+    """Ditto 成功后按实际时长结算。"""
+    try:
+        from app.services.lipsync_service import LipsyncService
+
+        LipsyncService(db)._settle_lip_sync(job, duration)
+    except Exception:
+        logger.exception("[ditto_task] lip_sync 结算异常 job_id=%s（不阻塞）", job.id)
+
+
+def _fallback_to_gpu_then_mediakit(db: Session, job: "LipsyncJobModel") -> None:
+    """Ditto 失败后：优先回退 GPU MuseTalk，再回退 MediaKit 云端。
+
+    复用 lipsync_service 现有路径逻辑以保证兜底一致性。
+    """
+    # 先尝试走 GPU MuseTalk（若可用）
+    try:
+        from app.services.gpu_lipsync_service import GpuLipsyncService
+        from app.tasks.lipsync_gpu import lipsync_gpu_process_async
+
+        gpu_svc = GpuLipsyncService(db)
+        if gpu_svc.has_available_worker():
+            logger.info("[ditto_task] 回退 GPU MuseTalk: job_id=%s", job.id)
+            from app.services.lipsync_service import LipsyncService
+
+            svc = LipsyncService(db)
+            storage = _shared_storage()
+            persisted_audio = None
+            try:
+                persisted_audio = svc._persist_external_audio_for_gpu(job=job, storage=storage)
+            except Exception as exc:
+                logger.warning("[ditto_task] GPU 外部音频转存失败: %s", exc)
+            audio_url_for_task = persisted_audio or job.audio_url
+            gpu_task = gpu_svc.create_task(
+                video_url=job.video_url,
+                audio_url=audio_url_for_task,
+                lipsync_job_id=job.id,
+                user_id=job.user_id,
+            )
+            if gpu_task is not None:
+                job.mediakit_task_id = f"gpu:{gpu_task.id}"
+                job.status = "processing"
+                job.updated_at = datetime.now(UTC)
+                db.commit()
+                lipsync_gpu_process_async.apply_async(args=(job.id, job.user_id, gpu_task.id))
+                return
+            db.rollback()
+    except Exception as exc:
+        logger.warning("[ditto_task] GPU MuseTalk 回退失败，转 MediaKit: %s", exc)
+        try:
+            db.rollback()
+        except Exception:
+            pass
+
+    # 最后兜底：MediaKit 云端
+    try:
+        from app.services.mediakit_client import get_mediakit_client
+
+        client = get_mediakit_client()
+        video_url = _sign_media_url(job.video_url)
+        signed_audio_url = _sign_media_url(job.audio_url)
+        result = client.submit_lipsync(
+            video_url=video_url,
+            audio_url=signed_audio_url,
+            enable_video_loop=job.enable_video_loop,
+            client_token=job.id,
+        )
+        job.mediakit_task_id = result["task_id"]
+        job.status = "submitted"
+        job.submitted_at = datetime.now(UTC)
+        job.updated_at = datetime.now(UTC)
+        db.commit()
+        logger.info("[ditto_task] 已回退 MediaKit: job_id=%s task_id=%s", job.id, result["task_id"])
+    except Exception as exc:
+        job.status = "failed"
+        job.error_message = f"Ditto/GPU/MediaKit 均失败: {exc}"
+        job.error_code = "AllBackendsFailed"
+        job.updated_at = datetime.now(UTC)
+        db.commit()
+        logger.error("[ditto_task] 所有兜底均失败: job_id=%s err=%s", job.id, exc)
+
+
+def _shared_storage():
+    from packages.shared.storage import get_shared_storage_service
+
+    return get_shared_storage_service()
+
+
+@shared_task(
+    name="lipsync_ditto_process_async",
+    bind=True,
+    max_retries=0,
+    acks_late=True,
+    time_limit=600,
+    soft_time_limit=540,
+)
+def lipsync_ditto_process_async(self, job_id: str, user_id: str) -> None:
+    """异步调用 Ditto 生成口型视频。
+
+    Args:
+        job_id: LipsyncJob ID
+        user_id: 用户 ID
+    """
+    from packages.application.ditto_emotion_service import get_ditto_emotion_service
+    from packages.application.ditto_service import DittoError, get_ditto_client
+    from packages.config import get_api_settings
+    from packages.domain.sentence_timings import probe_audio_duration
+    from packages.shared.url_security import safe_download_bytes
+
+    db: Session = _get_db_session()
+    job: Optional[LipsyncJobModel] = None
+    prepared_video_url: str = ""
+    try:
+        from packages.adapters.sqlalchemy_impl.models import LipsyncJobModel
+
+        job = db.query(LipsyncJobModel).filter_by(id=job_id, user_id=user_id).first()
+        if job is None:
+            logger.error("[ditto_task] job 不存在: job_id=%s", job_id)
+            return
+
+        if job.status != "processing":
+            logger.warning(
+                "[ditto_task] job 状态异常（非 processing），跳过: job_id=%s status=%s",
+                job_id,
+                job.status,
+            )
+            return
+
+        audio_url = job.audio_url or ""
+        script = job.script_text or ""
+        user_video_url = job.video_url or ""
+        if not audio_url:
+            raise DittoError("job.audio_url 为空，无法调用 Ditto", code="InvalidParam")
+
+        settings = get_api_settings()
+        default_video_url = settings.ditto_default_video_url or ""
+
+        logger.info(
+            "[ditto_task] 开始 Ditto 生成: job_id=%s has_user_video=%s script_len=%d",
+            job_id,
+            bool(user_video_url),
+            len(script),
+        )
+
+        # ── 0. 探测音频时长（情绪分析 + 视频循环都需要）──
+        audio_duration = 0.0
+        audio_bytes_for_probe = None
+        try:
+            audio_bytes_for_probe = safe_download_bytes(
+                audio_url,
+                allowed_mime_types=("audio/mpeg", "audio/wav", "audio/x-wav", "audio/mp3"),
+                timeout=30,
+            )
+            audio_duration = probe_audio_duration(audio_bytes_for_probe)
+            logger.info("[ditto_task] 音频时长: %.2fs", audio_duration)
+        except Exception as audio_exc:
+            logger.warning("[ditto_task] 音频时长探测失败: %s", audio_exc)
+            audio_duration = 0.0
+
+        # ── 1. LLM 情绪分析（生成 emo_timeline）──
+        emo_timeline = ""
+        try:
+            emo_svc = get_ditto_emotion_service()
+            if emo_svc.enabled and script and audio_duration > 0:
+                sentence_timings = getattr(job, "sentence_timings", None)
+                emo_timeline = emo_svc.build_timeline(
+                    text=script,
+                    audio_duration=audio_duration,
+                    sentence_timings=sentence_timings,
+                )
+                if emo_timeline:
+                    logger.info("[ditto_task] 情绪时间线已生成: segments≈%d", len(emo_timeline) // 50)
+        except Exception as emo_exc:
+            logger.warning("[ditto_task] 情绪分析异常（降级中性）: %s", emo_exc)
+            emo_timeline = ""
+
+        # ── 2. 准备驱动视频（用户视频优先，必要时循环延长）──
+        prepared_video_url, _is_tmp = _prepare_driver_video(
+            user_video_url=user_video_url,
+            default_video_url=default_video_url,
+            audio_duration=audio_duration if audio_duration > 0 else 10.0,  # 探测失败时按10s估
+            job_id=job_id,
+            user_id=user_id,
+        )
+
+        # ── 3. 调用 Ditto ──
+        client = get_ditto_client()
+        result = client.generate_and_persist(
+            job_id=job_id,
+            user_id=user_id,
+            audio_url=audio_url,
+            script=script,
+            video_url=prepared_video_url,
+            emo_timeline=emo_timeline,
+        )
+
+        # Ditto 返回的 MP4 自带音频，签名 OSS URL（7天有效）后标记完成
+        job.output_video_url = _sign_media_url(result.video_url)
+        # 探测时长（用于计费）
+        duration = _probe_video_duration(result.video_bytes)
+        if duration <= 0:
+            # 兜底：按音频时长估算
+            if audio_bytes_for_probe is not None:
+                try:
+                    duration = probe_audio_duration(audio_bytes_for_probe)
+                except Exception:
+                    duration = 0.0
+            if duration <= 0 and audio_duration > 0:
+                duration = audio_duration
+        job.output_duration = duration
+        job.status = "completed"
+        job.completed_at = datetime.now(UTC)
+        job.updated_at = datetime.now(UTC)
+        db.commit()
+        logger.info(
+            "[ditto_task] Ditto 完成: job_id=%s url=%s duration=%.2fs rtf=%.2f frames=%d",
+            job_id,
+            result.video_url[:100],
+            duration,
+            result.rtf,
+            result.frames,
+        )
+        _settle_lip_sync(db, job, duration)
+
+    except DittoError as exc:
+        logger.error("[ditto_task] Ditto 失败，回退: job_id=%s code=%s err=%s", job_id, exc.code, exc)
+        if job is not None:
+            try:
+                db.rollback()
+                job = db.query(type(job)).filter_by(id=job_id).first() if hasattr(job, "id") else job
+                _fallback_to_gpu_then_mediakit(db, job)
+            except Exception as fallback_exc:
+                logger.exception("[ditto_task] 回退也失败 job_id=%s err=%s", job_id, fallback_exc)
+                try:
+                    if job:
+                        job.status = "failed"
+                        job.error_message = f"Ditto 失败且回退异常: {exc}; fallback: {fallback_exc}"
+                        job.error_code = "FallbackError"
+                        job.updated_at = datetime.now(UTC)
+                        db.commit()
+                except Exception:
+                    pass
+    except Exception as exc:
+        logger.exception("[ditto_task] 未预期异常: job_id=%s err=%s", job_id, exc)
+        if job is not None:
+            try:
+                db.rollback()
+                job = db.query(type(job)).filter_by(id=job_id).first()
+                _fallback_to_gpu_then_mediakit(db, job)
+            except Exception as fallback_exc:
+                logger.exception("[ditto_task] 回退也失败 job_id=%s err=%s", job_id, fallback_exc)
+                try:
+                    if job:
+                        job.status = "failed"
+                        job.error_message = f"Ditto 异常: {exc}"
+                        job.error_code = "DittoAsyncError"
+                        job.updated_at = datetime.now(UTC)
+                        db.commit()
+                except Exception:
+                    pass
+    finally:
+        db.close()

apps/api/app/tasks/lipsync_gpu.py

@@ -104,6 +104,7 @@ def lipsync_gpu_process_async(self, job_id: str, user_id: str, gpu_task_id: str)
             job.updated_at = datetime.now(UTC)
             db.commit()
             logger.info("[lipsync_gpu_async] GPU 任务已被用户取消: job_id=%s", job_id)
+            _refund_lip_sync(db, job)
             return
 
         if final_task.status != "done":
@@ -141,6 +142,7 @@ def lipsync_gpu_process_async(self, job_id: str, user_id: str, gpu_task_id: str)
             job_id,
             job.output_duration,
         )
+        _settle_lip_sync(db, job, final_task)
     except Exception as exc:
         logger.exception("[lipsync_gpu_async] 异常: job_id=%s err=%s", job_id, exc)
         try:
@@ -157,6 +159,33 @@ def lipsync_gpu_process_async(self, job_id: str, user_id: str, gpu_task_id: str)
         db.close()
 
 
+def _settle_lip_sync(db: Session, job: LipsyncJobModel, gpu_task) -> None:
+    """GPU 成功后结算：同步 credits_cost 到 gpu 任务并按实际时长多退少不补。"""
+    try:
+        from app.services.lipsync_service import LipsyncService
+
+        # GPU 任务表先同步结算结果（标记用）
+        LipsyncService._settle_lip_sync(job, float(getattr(gpu_task, "result_duration", 0) or 0.0))
+        gpu_task.credits_cost = float(job.credits_cost or 0.0)
+        db.commit()
+    except Exception:  # noqa: BLE001
+        logger.exception("[lipsync_gpu_async] lip_sync 结算异常 job_id=%s（不阻塞）", job.id)
+        try:
+            db.rollback()
+        except Exception:  # noqa: BLE001
+            pass
+
+
+def _refund_lip_sync(db: Session, job: LipsyncJobModel) -> None:
+    """GPU 取消/失败路径全额退款。"""
+    try:
+        from app.services.lipsync_service import LipsyncService
+
+        LipsyncService(db)._refund_lip_sync(job)
+    except Exception:  # noqa: BLE001
+        logger.exception("[lipsync_gpu_async] lip_sync 退款异常 job_id=%s", job.id)
+
+
 def _fallback_to_mediakit(db: Session, job: LipsyncJobModel) -> None:
     """GPU 失败时回退到 MediaKit 云端渲染。"""
     try:

apps/api/app/tasks/lipsync_tts.py

@@ -261,7 +261,33 @@ def tts_synthesize_and_submit(
                 "[lipsync_tts] 句子时间戳计算失败（不影响主流程）: job_id=%s err=%s", job_id, _st_err, exc_info=True
             )
 
-        # 3. 签名 URL 并提交到 MediaKit（复用模块内 _sign_media_url，避免对 LipsyncService 的耦合）
+        # 3. 优先走 Ditto（#2076）：开关打开且配置完整时，派发 Ditto 异步任务，不再走 MediaKit
+        ditto_dispatched = False
+        try:
+            from packages.config import get_api_settings as _get_settings
+
+            _settings = _get_settings()
+            if _settings.use_ditto_lipsync and _settings.ditto_api_base_url and _settings.ditto_default_video_url:
+                from app.tasks.lipsync_ditto import lipsync_ditto_process_async
+
+                job.status = "processing"
+                job.mediakit_task_id = "ditto:tts-submitted"
+                job.updated_at = datetime.now(UTC)
+                db.commit()
+                lipsync_ditto_process_async.apply_async(args=(job_id, user_id))
+                logger.info("[lipsync_tts] TTS 完成，已派发 Ditto 任务: job_id=%s", job_id)
+                ditto_dispatched = True
+        except Exception as _ditto_err:
+            logger.warning("[lipsync_tts] Ditto 派发失败，回退 MediaKit: job_id=%s err=%s", job_id, _ditto_err)
+            try:
+                db.rollback()
+            except Exception:
+                pass
+
+        if ditto_dispatched:
+            return
+
+        # 4. 签名 URL 并提交到 MediaKit（复用模块内 _sign_media_url，避免对 LipsyncService 的耦合）
         audio_url = _sign_media_url(job.audio_url)
         video_url = _sign_media_url(job.video_url)
 

apps/web/package-lock.json

@@ -14,6 +14,7 @@
         "axios": "^1.7.2",
         "classnames": "^2.5.1",
         "dayjs": "^1.11.23",
+        "marked": "^12.0.2",
         "mp4box": "^2.4.1",
         "react": "^18.3.1",
         "react-dom": "^18.3.1",
@@ -4502,6 +4503,17 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/marked": {
+      "version": "12.0.2",
+      "resolved": "https://registry.npmmirror.com/marked/-/marked-12.0.2.tgz",
+      "integrity": "sha512-qXUm7e/YKFoqFPYPa3Ukg9xlI5cyAtGmyEIzMfW//m6kXwCy2Ps9DYf5ioijFKQ8qyuscrHoY04iJGctu2Kg0Q==",
+      "bin": {
+        "marked": "bin/marked.js"
+      },
+      "engines": {
+        "node": ">= 18"
+      }
+    },
     "node_modules/math-intrinsics": {
       "version": "1.1.0",
       "resolved": "https://registry.npmjs.org/math-intrinsics/-/math-intrinsics-1.1.0.tgz",

apps/web/package.json

@@ -25,6 +25,7 @@
     "axios": "^1.7.2",
     "classnames": "^2.5.1",
     "dayjs": "^1.11.23",
+    "marked": "^12.0.2",
     "mp4box": "^2.4.1",
     "react": "^18.3.1",
     "react-dom": "^18.3.1",

apps/web/src/api/admin/dittoEmotion.ts

@@ -0,0 +1,76 @@
+/**
+ * 后台管理 API client（#2246）
+ *
+ * 独立 axios 实例：不经过主 apiClient 的 Bearer token / 401 刷新逻辑，
+ * 后台鉴权使用 X-API-Key（存 localStorage，不硬编码）。
+ */
+import axios from "axios"
+
+export const ADMIN_API_KEY_STORAGE = "ditto_admin_api_key"
+
+export type ConfigType = "bool" | "int" | "float" | "string" | "json"
+
+export interface ConfigItem {
+  key: string
+  value: unknown
+  default: unknown
+  type: ConfigType
+  description: string
+  model_options?: string[]
+}
+
+export interface TestResult {
+  ok: boolean
+  enabled?: boolean
+  segments?: Array<{ text: string; emo: number; intensity: number }>
+  error?: string
+}
+
+export function getAdminApiKey(): string {
+  return localStorage.getItem(ADMIN_API_KEY_STORAGE) || ""
+}
+
+export function setAdminApiKey(key: string): void {
+  localStorage.setItem(ADMIN_API_KEY_STORAGE, key)
+}
+
+export function clearAdminApiKey(): void {
+  localStorage.removeItem(ADMIN_API_KEY_STORAGE)
+}
+
+function createAdminClient() {
+  const client = axios.create({
+    baseURL: "/api/v1",
+    timeout: 60000,
+    headers: { "Content-Type": "application/json" },
+  })
+  client.interceptors.request.use((config) => {
+    const key = getAdminApiKey()
+    if (key && config.headers) {
+      config.headers["X-API-Key"] = key
+    }
+    return config
+  })
+  return client
+}
+
+const adminClient = createAdminClient()
+
+export async function fetchConfig(): Promise<ConfigItem[]> {
+  const { data } = await adminClient.get("/admin/ditto-emotion/config")
+  return data.configs as ConfigItem[]
+}
+
+export async function updateConfig(
+  configs: Record<string, unknown>,
+): Promise<{ ok: boolean; updated?: Record<string, unknown>; error?: string }> {
+  const { data } = await adminClient.put("/admin/ditto-emotion/config", { configs })
+  return data
+}
+
+export async function testConfig(testText: string): Promise<TestResult> {
+  const { data } = await adminClient.post("/admin/ditto-emotion/config/test", {
+    test_text: testText,
+  })
+  return data as TestResult
+}

apps/web/src/api/viral-video/types.ts

@@ -65,27 +65,23 @@ export function isAnalysisStage(stage: ViralVideoStage | undefined): boolean {
   return isImageAnalysisStage(stage) || isCopyStage(stage)
 }
 
-/** 单张图片 VLM 识别出的商品信息 */
+/** 单张图片 VLM 识别结果（v8 叙述优先，仅保留最少结构化字段） */
 export interface ImageProductAnalysis {
+  /** store / product / person / scene */
+  type?: string
   name?: string
-  category?: string
   brand?: string
-  colors?: string[]
-  material_or_texture?: string
-  key_features?: string[]
-  visual_style?: string
-  scene?: string
-  target_audience_hint?: string
-  text_on_image?: string
-  /** 旧字段兼容 */
-  spec?: string
-  features?: string[] | string
-  label_text?: string
-  selling_points?: string
-  image_index?: number
+  has_person?: boolean
+  /** v8: 用户端展示用的叙述 markdown（由提示词控制排版） */
+  summary_markdown?: string
+  /** 标题行兼容字段 */
+  category?: string
 }
 
 export interface ImageAnalysisResult {
+  /** v8 字段 */
+  images?: ImageProductAnalysis[]
+  /** 老数据兼容 */
   products?: ImageProductAnalysis[]
 }
 
@@ -132,6 +128,8 @@ export interface CopyResult {
   /** 向后兼容：= voiceover_script */
   suggested_copy?: string
   title?: string
+  /** v3 storyboard: 用户端展示用的 markdown 文案（由提示词控制排版） */
+  copy_display_markdown?: string
   /** v1.5 旧字段兼容（老数据降级时可能出现） */
   scenes?: Array<{ shot: string; narration: string; duration?: number }>
 }

apps/web/src/pages/admin/DittoEmotionConfig.tsx

@@ -0,0 +1,311 @@
+import React, { useEffect, useMemo, useState } from "react"
+import {
+  Alert,
+  Button,
+  Card,
+  Form,
+  Input,
+  InputNumber,
+  Modal,
+  Select,
+  Slider,
+  Space,
+  Spin,
+  Switch,
+  message,
+} from "antd"
+import {
+  clearAdminApiKey,
+  fetchConfig,
+  getAdminApiKey,
+  setAdminApiKey,
+  testConfig,
+  updateConfig,
+  type ConfigItem,
+} from "@/api/admin/dittoEmotion"
+import "./Admin.css"
+
+const EMO_LABELS: Record<number, string> = {
+  3: "开心",
+  4: "中性",
+  5: "伤心",
+  6: "惊讶",
+}
+
+const DittoEmotionConfig: React.FC = () => {
+  const [hasKey, setHasKey] = useState<boolean>(!!getAdminApiKey())
+  const [keyInput, setKeyInput] = useState<string>("")
+  const [loading, setLoading] = useState<boolean>(false)
+  const [saving, setSaving] = useState<boolean>(false)
+  const [testing, setTesting] = useState<boolean>(false)
+  const [items, setItems] = useState<ConfigItem[]>([])
+  const [modelOptions, setModelOptions] = useState<string[]>([])
+  const [testResult, setTestResult] = useState<string>("")
+  const [form] = Form.useForm()
+
+  const [testInput, setTestInput] = useState<string>("")
+
+  const load = React.useCallback(async () => {
+    setLoading(true)
+    try {
+      const configs = await fetchConfig()
+      setItems(configs)
+      const values: Record<string, unknown> = {}
+      configs.forEach((c) => {
+        values[c.key] = c.value
+        if (c.model_options) setModelOptions(c.model_options)
+      })
+      form.setFieldsValue(values)
+    } catch {
+      // 401/403 等 → 提示 key 可能无效
+      message.error("加载配置失败，请检查 X-API-Key 是否正确")
+    } finally {
+      setLoading(false)
+    }
+  }, [form])
+
+  useEffect(() => {
+    if (hasKey) {
+      void load()
+    }
+  }, [hasKey, load])
+
+  const defaults = useMemo(() => {
+    const m: Record<string, unknown> = {}
+    items.forEach((c) => {
+      m[c.key] = c.default
+    })
+    return m
+  }, [items])
+
+  const submitKey = () => {
+    if (!keyInput.trim()) {
+      message.warning("请输入 X-API-Key")
+      return
+    }
+    setAdminApiKey(keyInput.trim())
+    setHasKey(true)
+  }
+
+  const changeKey = () => {
+    clearAdminApiKey()
+    setHasKey(false)
+    setKeyInput("")
+  }
+
+  const resetDefaults = () => {
+    form.setFieldsValue(defaults)
+    message.info("已填入默认值，点击「保存配置」后生效")
+  }
+
+  const validateBeforeSave = async (): Promise<Record<string, unknown> | null> => {
+    try {
+      const values = await form.validateFields()
+      const prompt = (values.ditto_emotion_prompt || "") as string
+      if (prompt.trim() && !prompt.includes("{文案}")) {
+        message.error("提示词必须包含 {文案} 占位符")
+        return null
+      }
+      return values as Record<string, unknown>
+    } catch {
+      return null
+    }
+  }
+
+  const onSave = async () => {
+    const values = await validateBeforeSave()
+    if (!values) return
+    setSaving(true)
+    try {
+      const res = await updateConfig(values)
+      if (res.ok) {
+        message.success("配置已保存并立即生效")
+        await load()
+      } else {
+        message.error(res.error || "保存失败")
+      }
+    } catch {
+      message.error("保存失败，请检查网络或 X-API-Key")
+    } finally {
+      setSaving(false)
+    }
+  }
+
+  const onTest = async () => {
+    const testText = (testInput || "").trim()
+    if (!testText) {
+      message.warning("请先在下方输入测试文案")
+      return
+    }
+    setTesting(true)
+    setTestResult("")
+    try {
+      const res = await testConfig(testText)
+      if (!res.ok) {
+        message.error(res.error || "测试失败")
+      } else if (!res.enabled) {
+        message.info("当前表情开关为关闭状态，无情绪结果，可先开启后再测")
+      } else {
+        const lines = (res.segments || []).map(
+          (s) => `【${EMO_LABELS[s.emo] ?? s.emo} ${s.intensity}】${s.text}`,
+        )
+        setTestResult(lines.join("\n") || "未解析到情绪结果")
+      }
+    } catch {
+      message.error("测试失败，请检查网络或 X-API-Key")
+    } finally {
+      setTesting(false)
+    }
+  }
+
+  if (!hasKey) {
+    return (
+      <div className="admin-coming-soon-page">
+        <Modal
+          title="请输入后台 X-API-Key"
+          open
+          closable={false}
+          footer={[
+            <Button type="primary" key="ok" onClick={submitKey}>
+              确认
+            </Button>,
+          ]}
+        >
+          <Alert
+            type="info"
+            showIcon
+            style={{ marginBottom: 12 }}
+            message="Key 仅保存在本机浏览器 localStorage，用于后台接口鉴权（X-API-Key）。"
+          />
+          <Input.Password
+            autoFocus
+            placeholder="X-API-Key"
+            value={keyInput}
+            onChange={(e) => setKeyInput(e.target.value)}
+            onPressEnter={submitKey}
+          />
+        </Modal>
+      </div>
+    )
+  }
+
+  return (
+    <div className="admin-coming-soon-page">
+      <div style={{ maxWidth: 860, width: "100%" }}>
+        <Card
+          title="Ditto 数字人表情设置"
+          extra={
+            <Button size="small" onClick={changeKey}>
+              更换 X-API-Key
+            </Button>
+          }
+          className="xx-card"
+        >
+          <Alert
+            type="success"
+            showIcon
+            style={{ marginBottom: 16 }}
+            message="修改保存后立即生效，无需重启或发版。"
+            action={
+              <Button size="small" onClick={resetDefaults}>
+                重置默认
+              </Button>
+            }
+          />
+
+          <Spin spinning={loading}>
+            <Form form={form} layout="vertical">
+              <Form.Item
+                name="ditto_emotion_enabled"
+                label="启用 LLM 情绪分析"
+                valuePropName="checked"
+                extra="关闭后立即回退到原有关键词匹配模式，不影响正常出片。"
+              >
+                <Switch />
+              </Form.Item>
+
+              <Form.Item
+                name="ditto_emotion_model"
+                label="情绪分析模型"
+                rules={[{ required: true, message: "请选择模型" }]}
+              >
+                <Select
+                  options={(modelOptions.length ? modelOptions : []).map((m) => ({
+                    label: m,
+                    value: m,
+                  }))}
+                />
+              </Form.Item>
+
+              <Form.Item name="ditto_emotion_temperature" label="温度（0-1，越低越稳定）">
+                <Space style={{ width: "100%" }} align="center">
+                  <Slider min={0} max={1} step={0.1} style={{ width: 320 }} />
+                  <InputNumber min={0} max={1} step={0.1} />
+                </Space>
+              </Form.Item>
+
+              <Form.Item
+                name="ditto_emotion_prompt"
+                label="情绪分析提示词（必须包含 {文案} 占位符）"
+                rules={[
+                  {
+                    validator: (_, value) =>
+                      !value || !String(value).trim() || String(value).includes("{文案}")
+                        ? Promise.resolve()
+                        : Promise.reject(new Error("必须包含 {文案} 占位符")),
+                  },
+                ]}
+              >
+                <Input.TextArea
+                  rows={15}
+                  placeholder="留空则使用系统默认提示词"
+                  style={{ fontFamily: "monospace" }}
+                />
+              </Form.Item>
+
+              <Form.Item
+                name="ditto_blend_frames"
+                label="表情过渡帧数（6-30，越大越柔和）"
+                rules={[{ required: true, message: "请输入过渡帧数" }]}
+              >
+                <InputNumber min={6} max={30} step={1} precision={0} />
+              </Form.Item>
+            </Form>
+
+            <Space style={{ marginTop: 8 }}>
+              <Button type="primary" loading={saving} onClick={onSave}>
+                保存配置
+              </Button>
+            </Space>
+          </Spin>
+        </Card>
+
+        <Card title="配置测试" className="xx-card" style={{ marginTop: 16 }}>
+          <Input.TextArea
+            rows={3}
+            placeholder="输入测试文案，例如：这款面膜超级好用！今天补水效果太棒了。"
+            value={testInput}
+            onChange={(e) => setTestInput(e.target.value)}
+          />
+          <Space style={{ marginTop: 12 }}>
+            <Button loading={testing} onClick={onTest}>
+              用当前配置测试
+            </Button>
+          </Space>
+          {testResult && (
+            <Input.TextArea
+              readOnly
+              rows={6}
+              value={testResult}
+              style={{ marginTop: 12, fontFamily: "monospace", whiteSpace: "pre-wrap" }}
+            />
+          )}
+        </Card>
+      </div>
+    </div>
+  )
+}
+
+export default DittoEmotionConfig
+
+export const Component = DittoEmotionConfig

apps/web/src/pages/viral-video/ViralVideoPage.css

@@ -1050,10 +1050,13 @@
   flex-direction: column;
   align-items: center;
   justify-content: center;
+  height: 360px;
   padding: 28px 16px;
   gap: 10px;
   background: #fff;
   border: 1px solid #e5e7eb;
+  border-radius: 10px;
+  margin-top: 8px;
 }
 .vv-copy-loading .vv-spinner {
   width: 28px;
@@ -1082,18 +1085,38 @@
 
 /* ── Storyboard (linear doc style) ── */
 .vv-storyboard {
-  padding: 6px 2px;
-  background: transparent;
-  border: none;
+  display: flex;
+  flex-direction: column;
+  height: 360px;
+  padding: 10px 12px;
+  background: #fff;
+  border: 1px solid #e5e7eb;
+  border-radius: 10px;
+  margin-top: 8px;
+  overflow: hidden;
 }
 
 .vv-sb-doc {
+  flex: 1 1 auto;
   display: flex;
   flex-direction: column;
   gap: 3px;
   color: #1f2937;
   font-size: 13px;
   line-height: 1.55;
+  overflow-y: auto;
+  padding-right: 4px;
+  margin-right: -4px;
+}
+.vv-sb-doc::-webkit-scrollbar {
+  width: 6px;
+}
+.vv-sb-doc::-webkit-scrollbar-thumb {
+  background: #d8c4ff;
+  border-radius: 3px;
+}
+.vv-sb-doc::-webkit-scrollbar-track {
+  background: transparent;
 }
 .vv-sb-h {
   margin: 6px 0 2px;
@@ -1383,12 +1406,14 @@
 /* 口播稿 —— 复用 vv-sb-field 样式，无额外需求 */
 
 .vv-sb-actions {
+  flex-shrink: 0;
   display: flex;
   align-items: center;
   gap: 8px;
   margin-top: 8px;
   padding-top: 8px;
   border-top: 1px solid #e5e7eb;
+  background: #fff;
 }
 .vv-sb-actions .vv-btn-ghost {
   padding: 6px 14px;
@@ -1948,3 +1973,99 @@
   padding-bottom: 6px;
   border-bottom: 1px dashed #e5e7eb;
 }
+
+/* ─────────── markdown 渲染（提示词控制展示格式） ─────────── */
+.vv-recog-md {
+  padding: 4px 0;
+}
+.vv-copy-preview {
+  margin-bottom: 14px;
+  padding: 12px 14px;
+  background: linear-gradient(180deg, #faf7ff 0%, #f6f2ff 100%);
+  border: 1px solid #ece4fb;
+  border-radius: 10px;
+}
+.vv-copy-preview-h {
+  margin: 0 0 8px;
+  border-bottom: none;
+  padding-bottom: 0;
+}
+.vv-md-body {
+  font-size: 13px;
+  line-height: 1.7;
+  color: #374151;
+  word-break: break-word;
+}
+.vv-md-body h1,
+.vv-md-body h2,
+.vv-md-body h3,
+.vv-md-body h4 {
+  margin: 10px 0 6px;
+  font-weight: 600;
+  color: #1f2937;
+  line-height: 1.4;
+}
+.vv-md-body h1 {
+  font-size: 18px;
+}
+.vv-md-body h2 {
+  font-size: 16px;
+}
+.vv-md-body h3 {
+  font-size: 15px;
+}
+.vv-md-body h4 {
+  font-size: 14px;
+}
+.vv-md-body p {
+  margin: 6px 0;
+}
+.vv-md-body ul,
+.vv-md-body ol {
+  margin: 6px 0;
+  padding-left: 20px;
+}
+.vv-md-body li {
+  margin: 3px 0;
+}
+.vv-md-body strong {
+  color: #111827;
+  font-weight: 600;
+}
+.vv-md-body blockquote {
+  margin: 8px 0;
+  padding: 4px 12px;
+  border-left: 3px solid #7c3aed;
+  background: rgba(124, 58, 237, 0.05);
+  color: #4b5563;
+}
+.vv-md-body code {
+  padding: 1px 5px;
+  background: #f3f4f6;
+  border-radius: 4px;
+  font-size: 12px;
+  color: #be185d;
+}
+.vv-md-body a {
+  color: #7c3aed;
+  text-decoration: none;
+}
+.vv-md-body a:hover {
+  text-decoration: underline;
+}
+.vv-md-body table {
+  border-collapse: collapse;
+  margin: 8px 0;
+  width: 100%;
+}
+.vv-md-body th,
+.vv-md-body td {
+  border: 1px solid #e5e7eb;
+  padding: 6px 10px;
+  text-align: left;
+}
+.vv-md-body hr {
+  border: none;
+  border-top: 1px solid #e5e7eb;
+  margin: 12px 0;
+}

apps/web/src/pages/viral-video/ViralVideoPage.tsx

@@ -1,5 +1,6 @@
 import React, { useCallback, useEffect, useRef, useState } from "react"
 import axios from "axios"
+import { marked } from "marked"
 import {
   PlusOutlined,
   CloseOutlined,
@@ -150,6 +151,16 @@ type TabTask = {
   audioInst: HTMLAudioElement | null
 }
 
+/* ── marked 配置：禁用 mangle/headerIds，输出干净 HTML ── */
+marked.setOptions({ gfm: true, breaks: false })
+const renderMarkdown = (md: string): string => {
+  try {
+    return marked.parse(md ?? "", { async: false }) as string
+  } catch {
+    return (md ?? "").replace(/&/g, "&amp;").replace(/</g, "&lt;")
+  }
+}
+
 /* ─────────── 常量 ─────────── */
 
 const LANGUAGES = ["中文(普通话)", "粤语", "英语", "日语", "韩语"]
@@ -296,6 +307,8 @@ interface Storyboard {
   hard_constraints: string[]
   negative_prompts: string[]
   voiceover_script: string
+  /** v3: 用户端展示用 markdown 文案（由提示词控制排版） */
+  copy_display_markdown: string
 }
 
 /** 兼容旧 copy_result（final_copy/title/scenes）→ 新 Storyboard 结构 */
@@ -323,6 +336,7 @@ function copyResultToStoryboard(cr: CopyResult | null | undefined): Storyboard |
       hard_constraints: Array.isArray(cr.hard_constraints) ? cr.hard_constraints : [],
       negative_prompts: Array.isArray(cr.negative_prompts) ? cr.negative_prompts : [],
       voiceover_script: cr.voiceover_script || cr.final_copy || cr.suggested_copy || "",
+      copy_display_markdown: cr.copy_display_markdown || "",
     }
   }
   // 兜底：旧结构转简单分镜
@@ -358,6 +372,7 @@ function copyResultToStoryboard(cr: CopyResult | null | undefined): Storyboard |
     hard_constraints: [],
     negative_prompts: [],
     voiceover_script: finalCopy,
+    copy_display_markdown: cr.copy_display_markdown || "",
   }
 }
 
@@ -412,6 +427,7 @@ const MOCK_STORYBOARD: Storyboard = {
   negative_prompts: ["冷色调", "模糊", "变形", "水印文字", "卡通风格", "空无一人"],
   voiceover_script:
     "还在为餐桌选不到好桌子发愁？这张北美黑胡桃木餐桌，一家人坐下来吃饭刚刚好。全实木、无贴皮，纹理好看又耐刮。点小黄车，给家里添一张好桌子。",
+  copy_display_markdown: "",
 }
 
 const fmtSize = (bytes: number | undefined) => {
@@ -1192,8 +1208,10 @@ const ViralVideoPage: React.FC = () => {
 
   /* ── 识别描述汇览渲染 ── */
   const renderRecognition = () => {
-    const products: ImageProductAnalysis[] =
-      (task.imageAnalysis?.products as ImageProductAnalysis[] | undefined) || []
+    const images: ImageProductAnalysis[] =
+      (task.imageAnalysis?.images as ImageProductAnalysis[] | undefined) ||
+      (task.imageAnalysis?.products as ImageProductAnalysis[] | undefined) ||
+      []
     if (task.uiStep === "step1_analyzing") {
       return (
         <div className="vv-recog">
@@ -1201,83 +1219,36 @@ const ViralVideoPage: React.FC = () => {
             <LoadingOutlined style={{ color: "#7c3aed", marginRight: 6 }} />
             识别描述汇览
           </div>
-          <div className="vv-muted">AI 正在识别商品特征…</div>
+          <div className="vv-muted">AI 正在识别画面…</div>
         </div>
       )
     }
-    if (products.length === 0) return null
-    const featureText = (f: string[] | string | undefined) => {
-      if (!f) return ""
-      if (Array.isArray(f)) return f.join("；")
-      return f
-    }
+    if (images.length === 0) return null
     return (
       <div className="vv-recog">
         <div className="vv-recog-title">
           <CheckCircleFilled style={{ color: "#10b981" }} />
           识别描述汇览
         </div>
-        {products.map((p, i) => (
-          <div key={i} className="vv-recog-item">
-            <div className="vv-recog-line">
-              <span className="vv-recog-k">图片{i + 1}：</span>
-              <span>
-                {p.name || "未识别"}
-                {p.spec && <span className="vv-recog-meta">（{p.spec}）</span>}
-                {p.brand && <span className="vv-recog-meta"> · {p.brand}</span>}
-                {p.category && <span className="vv-recog-meta"> · {p.category}</span>}
-              </span>
+        {images.map((p, i) => {
+          const meta = [p.name || "未识别", p.brand, p.category].filter(Boolean)
+          return (
+            <div key={i} className="vv-recog-item vv-recog-md">
+              <div className="vv-recog-line">
+                <span className="vv-recog-k">图片{i + 1}：</span>
+                <span>{meta.join(" · ")}</span>
+              </div>
+              {p.summary_markdown ? (
+                <div
+                  className="vv-md-body"
+                  dangerouslySetInnerHTML={{ __html: renderMarkdown(p.summary_markdown) }}
+                />
+              ) : (
+                <div className="vv-muted">（暂无叙述描述）</div>
+              )}
             </div>
-            {featureText(p.key_features ?? p.features) && (
-              <div className="vv-recog-line">
-                <span className="vv-recog-k">核心特征：</span>
-                <span className="vv-recog-v">{featureText(p.key_features ?? p.features)}</span>
-              </div>
-            )}
-            {p.colors && p.colors.length > 0 && (
-              <div className="vv-recog-line">
-                <span className="vv-recog-k">主色调：</span>
-                <span className="vv-recog-v">{p.colors.join(" / ")}</span>
-              </div>
-            )}
-            {p.material_or_texture && (
-              <div className="vv-recog-line">
-                <span className="vv-recog-k">材质/纹理：</span>
-                <span className="vv-recog-v">{p.material_or_texture}</span>
-              </div>
-            )}
-            {p.visual_style && (
-              <div className="vv-recog-line">
-                <span className="vv-recog-k">视觉风格：</span>
-                <span className="vv-recog-v">{p.visual_style}</span>
-              </div>
-            )}
-            {p.scene && (
-              <div className="vv-recog-line">
-                <span className="vv-recog-k">场景：</span>
-                <span className="vv-recog-v">{p.scene}</span>
-              </div>
-            )}
-            {p.target_audience_hint && (
-              <div className="vv-recog-line">
-                <span className="vv-recog-k">目标人群：</span>
-                <span className="vv-recog-v">{p.target_audience_hint}</span>
-              </div>
-            )}
-            {(p.text_on_image || p.label_text) && (
-              <div className="vv-recog-line">
-                <span className="vv-recog-k">包装文字：</span>
-                <span className="vv-recog-v">{p.text_on_image || p.label_text}</span>
-              </div>
-            )}
-            {p.selling_points && (
-              <div className="vv-recog-line">
-                <span className="vv-recog-k">卖点：</span>
-                <span className="vv-recog-v">{p.selling_points}</span>
-              </div>
-            )}
-          </div>
-        ))}
+          )
+        })}
       </div>
     )
   }
@@ -1416,6 +1387,19 @@ const ViralVideoPage: React.FC = () => {
     return (
       <div className="vv-copy-box vv-storyboard">
         <div className="vv-sb-doc">
+          {/* 文案预览（提示词控制排版，只读；编辑在下方分镜字段中进行） */}
+          {sb.copy_display_markdown && (
+            <div className="vv-copy-preview">
+              <h4 className="vv-sb-h vv-copy-preview-h">
+                <FileTextOutlined style={{ color: "#7c3aed", marginRight: 6 }} />
+                文案预览
+              </h4>
+              <div
+                className="vv-md-body"
+                dangerouslySetInnerHTML={{ __html: renderMarkdown(sb.copy_display_markdown) }}
+              />
+            </div>
+          )}
           {/* 视频总览 */}
           <h4 className="vv-sb-h">视频总览</h4>
           <p className="vv-sb-inline-row">

apps/web/src/router/appRoutes.tsx

@@ -121,7 +121,11 @@ const appChildren: RouteObject[] = [
     children: [
       {
         index: true,
-        lazy: lazyRoute(() => import("@/pages/admin/AdminComingSoon")),
+        element: <Navigate to="/app/admin/ditto-emotion" replace />,
+      },
+      {
+        path: "ditto-emotion",
+        lazy: lazyRoute(() => import("@/pages/admin/DittoEmotionConfig")),
       },
       {
         path: "users",

apps/worker/worker_app/celery_app.py

@@ -53,6 +53,9 @@ celery_app.conf.imports = (
     # #1998 GPU MuseTalk 异步推理：wait_for_result→签名 URL→回写 lipsync_jobs
     # 必须在 Worker 侧注册，否则 apply_async 消息无人消费，job 永远卡在 processing
     "app.tasks.lipsync_gpu",
+    # #2076 Ditto 蚂蚁数字人异步推理：同步 HTTP 调用 Ditto → MP4 流转存 OSS → 回写 lipsync_jobs
+    # 必须在 Worker 侧注册；失败回退 GPU MuseTalk → MediaKit
+    "app.tasks.lipsync_ditto",
 )
 
 # Celery Beat 定时任务调度

apps/worker/worker_app/tasks/generation.py

@@ -387,6 +387,41 @@ BATCH_RENDER_SIMILARITY_LIMIT = 0.20
 """批次内成片查重相似度阈值：超过则重选独立 plan 重渲一次（20%）。"""
 
 
+def _refund_smart_edit_prepaid(task_id: str) -> None:
+    """智能剪辑任务最终失败时退还预扣积分（幂等）。"""
+    session = SessionLocal()
+    try:
+        from packages.adapters.sqlalchemy_impl.generation_task_repository import (
+            SQLAlchemyGenerationTaskRepository,
+        )
+        from packages.domain.points_service import PointsService
+
+        repo = SQLAlchemyGenerationTaskRepository(session)
+        task = repo.get(task_id)
+        if not task:
+            return
+        prepaid = float(getattr(task, "credits_prepaid", 0) or 0)
+        if prepaid <= 0:
+            return
+        txn_id = getattr(task, "credits_transaction_id", "") or ""
+        res = PointsService().refund_points(
+            user_id=task.user_id,
+            amount=prepaid,
+            source="smart_edit",
+            db=session,
+            ref_id=task.id,
+            related_transaction_id=txn_id or None,
+            description="智能剪辑任务失败退回",
+        )
+        task.credits_cost = 0.0
+        task.credits_prepaid = 0.0
+        repo.update(task)
+        if not res.get("success"):
+            logger.warning("[task_id=%s] 失败退积分未成功: %s", task_id, res)
+    finally:
+        session.close()
+
+
 def should_rerender_for_batch_dedup(*, batch_id: str, render_attempt: int, batch_similarity) -> bool:
     """批次内查重后判定是否需要重选 plan 重渲。
 
@@ -1167,6 +1202,10 @@ def generate_video(self, task_id: str) -> dict:
                 "mark_failed",
                 error_message="source_edit_plan_id is required. Please create a preview task first.",
             )
+            try:
+                _refund_smart_edit_prepaid(task_id)
+            except Exception:
+                logger.warning("[task_id=%s] 失败退积分异常", task_id, exc_info=True)
             return {
                 "status": "failed",
                 "task_id": task_id,
@@ -1205,6 +1244,7 @@ def generate_video(self, task_id: str) -> dict:
         )
 
         # ── 自动重试逻辑 ──────────────────────────────────────────────────
+        will_retry = False
         try:
             from packages.adapters.sqlalchemy_impl.generation_task_repository import (
                 SQLAlchemyGenerationTaskRepository,
@@ -1217,6 +1257,7 @@ def generate_video(self, task_id: str) -> dict:
                 if _task and _task.auto_retry_enabled and _task.auto_retry_max > 0:
                     current_retry = _task.retry_count or 0
                     if current_retry < _task.auto_retry_max:
+                        will_retry = True
                         logger.info(
                             "[task_id=%s] 触发自动重试: 当前重试次数=%d, 最大重试次数=%d",
                             task_id,
@@ -1250,6 +1291,13 @@ def generate_video(self, task_id: str) -> dict:
                 exc_info=True,
             )
 
+        # 最终失败（不再重试）：退还 smart_edit 预扣积分
+        if not will_retry:
+            try:
+                _refund_smart_edit_prepaid(task_id)
+            except Exception:
+                logger.warning("[task_id=%s] 失败退积分异常", task_id, exc_info=True)
+
         return {
             "status": "failed",
             "task_id": task_id,

apps/worker/worker_app/tasks/vision/_prompt.py

@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+"""V2 prompt 解析：优先读后台 viral_video_prompt_templates（prompt_type='image_analysis'
+且 is_active=true），30s TTL 热加载；DB 无有效记录/异常时，fallback 到 prompts.py 的
+image_analysis v8 默认 system/user。
+
+规则：
+- DB 有 is_active=true 的 image_analysis 记录：system 原样用 DB.system_prompt
+  （自带完整输出格式，不追加任何硬编码 schema），user 用 DB.user_prompt_template
+  渲染（填入 image_url / ocr_text）；
+- DB 无记录/异常：system/user 用 prompts.py 里的 v8 默认模板。
+"""
+
+from __future__ import annotations
+
+import logging
+import threading
+import time
+
+logger = logging.getLogger(__name__)
+
+
+def _default_template() -> dict:
+    # 延迟导入：避免模块加载时拉起整个 packages 依赖链（也便于旧 Python 收集测试）
+    from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
+
+    for item in DEFAULT_TEMPLATES:
+        if item["prompt_type"] == "image_analysis":
+            return item
+    raise RuntimeError("image_analysis 默认模板缺失")
+
+
+_cache_lock = threading.Lock()
+_cache: dict[str, tuple[float, tuple[str, str]]] = {}
+_CACHE_TTL = 30.0
+
+
+def _load_db_template():
+    """查 DB is_active=true 的 image_analysis 记录；不可达/无记录返回 None。"""
+    try:
+        from packages.application.viral_video.prompt_loader import _load_from_db
+
+        return _load_from_db("image_analysis")
+    except Exception as e:  # noqa: BLE001
+        logger.warning("[vision.v2] 查询DB image_analysis prompt失败: %s", e)
+        return None
+
+
+def _render_user(user_tpl: str, image_url: str, ocr_text: str) -> str:
+    try:
+        return user_tpl.format(image_url=image_url, ocr_text=ocr_text or "无")
+    except Exception:  # noqa: BLE001
+        return user_tpl
+
+
+def _resolve(kind: str, image_url: str = "", ocr_text: str = "") -> tuple[str, str]:
+    now = time.time()
+    cache_key = f"prompt_{kind}"
+    with _cache_lock:
+        hit = _cache.get(cache_key)
+        if hit and now - hit[0] < _CACHE_TTL:
+            sys_prompt, usr_prompt = hit[1]
+            return sys_prompt, _render_user(usr_prompt, image_url, ocr_text)
+
+    default = _default_template()
+    sys_prompt = default["system_prompt"]
+    usr_prompt = default["user_prompt_template"]
+
+    tpl = _load_db_template()
+    if tpl is not None:
+        db_sys = (getattr(tpl, "system_prompt", "") or "").strip()
+        if db_sys:
+            sys_prompt = db_sys
+            db_usr = getattr(tpl, "user_prompt_template", "") or usr_prompt
+            usr_prompt = db_usr or usr_prompt
+            logger.info(
+                "[vision.v2] 使用DB image_analysis prompt version=%s",
+                getattr(tpl, "version", "?"),
+            )
+
+    with _cache_lock:
+        _cache[cache_key] = (now, (sys_prompt, usr_prompt))
+    return sys_prompt, _render_user(usr_prompt, image_url, ocr_text)
+
+
+def resolve_fast_prompt(image_url: str = "", ocr_text: str = "") -> tuple[str, str]:
+    return _resolve("fast", image_url, ocr_text)
+
+
+def resolve_pro_prompt(image_url: str = "", ocr_text: str = "") -> tuple[str, str]:
+    return _resolve("pro", image_url, ocr_text)
+
+
+def invalidate_cache() -> None:
+    with _cache_lock:
+        _cache.clear()

apps/worker/worker_app/tasks/vision/assembler.py

@@ -1,307 +1,106 @@
-# -*- coding: utf-8 -*-
-"""把 fast_json VLM 输出 + OCR 文本组装为与旧 _normalize() 完全一致的 dict。
+"""V2 结果组装（v8 叙述优先，大幅精简）。
 
-目标：下游（信任链t2i/intent_parsing/script_generation）零改动。
-必出字段：name, brand, category, appearance, packaging, text_on_package,
-         key_features, scene, mood, portrait_prompt, summary, _source
+设计原则：VLM 直接输出最终给用户看的 summary_markdown，assembler 只负责
+- 解析 fast JSON（兼容顶层 {"images":[...]} 与 {"products":[...]} 两种键）；
+- 补齐 5 个必备字段（type/name/brand/has_person/summary_markdown）；
+- summary_markdown 缺失（异常）时才拼一句最基础的兜底文字。
+
+正常情况下不改写、不“润色”VLM 输出，不做 brand 多级兜底，不处理任何
+colors/material/key_features 等细分字段。
 """
 
 from __future__ import annotations
 
+import logging
 from typing import Any
 
-# ---------- portrait_prompt 模板 ----------
-# 目标：60-100 字的人物穿搭描述，用于 Seedream 纯文生图。要求具体、风格化、视觉细节丰富。
-# 旧 VLM 输出格式参考："一位25岁左右的亚洲女性，身穿白色V领短袖T恤，黑色高腰阔腿裤，
-# 搭配银色项链，长发披肩，表情自信，街拍风格，阳光明媚的城市街头"
+logger = logging.getLogger(__name__)
+
+_VALID_TYPES = ("store", "product", "person", "scene")
 
 
-def _join_parts(*parts: str | None) -> str:
-    return "".join(p for p in parts if p)
+def _coerce_bool(value: Any) -> bool:
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, (int, float)):
+        return value != 0
+    if isinstance(value, str):
+        return value.strip().lower() in ("true", "1", "yes", "是")
+    return False
 
 
-_AGE_PREFIX = {
-    "青年": "年轻",
-    "中年": "中年",
-    "老年": "老年",
-}
-# gender 后缀
-_GENDER_WORD = {"男": "男性", "女": "女性"}
+def _basic_markdown(image: dict[str, Any]) -> str:
+    """异常兜底：VLM 没给 summary_markdown 时只拼一句基础文字。"""
+    name = (image.get("name") or "").strip() or "未识别"
+    brand = (image.get("brand") or "").strip()
+    typ = image.get("type") or "scene"
+    label = f"{brand}{name}" if brand and brand not in name else (brand or name)
+    if typ == "store":
+        return f"这是{label}的门店场景，画面细节识别不完整。"
+    if typ == "person":
+        return f"画面中的人物与{label}相关，细节识别不完整。"
+    if typ == "product":
+        return f"这是{label}的商品图片，具体外观细节识别不完整。"
+    return f"画面内容为{label}，细节识别不完整。"
 
 
-def _person_subject(fj: dict[str, Any]) -> str:
-    """人物主语：年轻女性 / 中年男性 / 少女 / 小男孩 / 人物 等。"""
-    gender = fj.get("gender") or ""
-    age = fj.get("age_range") or ""
-    gw = _GENDER_WORD.get(gender, "")
-    if age == "儿童":
-        if gender == "女":
-            return "小女孩"
-        if gender == "男":
-            return "小男孩"
-        return "儿童"
-    if age == "青少年":
-        if gender == "女":
-            return "少女"
-        if gender == "男":
-            return "少年"
-        return "青少年"
-    prefix = _AGE_PREFIX.get(age, "")
-    if gw:
-        return f"{prefix}{gw}" if prefix else gw
-    return f"{prefix}人物" if prefix else "人物"
+def _normalize_image(raw: Any, idx: int) -> dict[str, Any]:
+    """把一条 VLM 输出归一化为 5 字段 dict。"""
+    if not isinstance(raw, dict):
+        raw = {}
 
+    typ = str(raw.get("type") or "").strip().lower()
+    if typ not in _VALID_TYPES:
+        typ = "scene"
 
-def _build_wear_sentence(fj: dict[str, Any]) -> str:
-    """穿搭段：上装+下装/连衣裙，带颜色+材质+图案。"""
-    upper = fj.get("upper_wear") or ""
-    upper_color = fj.get("upper_color") or ""
-    lower = fj.get("lower_wear") or ""
-    lower_color = fj.get("lower_color") or ""
-    dress_color = fj.get("dress_color") or ""
-    material = fj.get("material") or ""
-    pattern = fj.get("pattern") or ""
+    name = str(raw.get("name") or "").strip() or "未识别"
+    brand = str(raw.get("brand") or "").strip()
+    has_person = _coerce_bool(raw.get("has_person"))
+    if typ == "person" and not has_person:
+        # type=person 通常意味着主体是人，保持一致（仅异常补全）
+        has_person = True
 
-    is_dress = ("连衣裙" in upper) or ("裙" in upper and not lower)
-    if is_dress:
-        c = dress_color or upper_color
-        wear = f"{c}{upper}" if c else upper
-        if material and material not in wear:
-            wear = f"{material}{wear}"
-        if pattern and pattern not in wear and pattern != "纯色":
-            wear += f"，{pattern}图案"
-        return f"身穿{wear}"
+    summary = raw.get("summary_markdown")
+    summary = summary.strip() if isinstance(summary, str) else ""
 
-    parts: list[str] = []
-    if upper:
-        up = f"{upper_color}{upper}" if upper_color else upper
-        if material and material not in up:
-            up = f"{material}{up}"
-        if pattern and pattern != "纯色" and pattern not in up:
-            up += f"（{pattern}）"
-        parts.append(f"上身{up}" if up else "")
-    if lower:
-        lo = f"{lower_color}{lower}" if lower_color else lower
-        parts.append(f"下身{lo}" if lo else "")
-    return "，".join(p for p in parts if p)
-
-
-def _build_portrait_prompt(fj: dict[str, Any]) -> str:
-    """组装最终 portrait_prompt（目标 60-100 字，用于 Seedream 纯文生图）。"""
-    if not fj.get("has_person"):
-        # 非人像：用商品+场景+mood 拼一段
-        name = fj.get("product_name") or "商品"
-        brand = fj.get("brand") or ""
-        colors = fj.get("colors") or []
-        style = fj.get("style") or ""
-        scene = fj.get("scene") or ""
-        mood = fj.get("mood") or ""
-        pieces = []
-        if brand:
-            pieces.append(brand)
-        pieces.append(name)
-        if colors:
-            pieces.append("、".join(colors[:3]) + "配色")
-        if style:
-            pieces.append(style + "风格")
-        if mood:
-            pieces.append(mood + "氛围")
-        if scene and scene not in ("通用",):
-            pieces.append(scene + "场景")
-        pieces.append("产品特写")
-        prompt = "，".join(p for p in pieces if p)
-        return prompt if len(prompt) >= 10 else "产品展示图，特写镜头"
-
-    subject = _person_subject(fj)
-    wear = _build_wear_sentence(fj)
-
-    accessories = fj.get("accessories") or []
-    if isinstance(accessories, str):
-        accessories = [accessories]
-    acc_str = ""
-    if accessories:
-        acc_str = "，佩戴" + "、".join(str(a) for a in accessories if a)
-
-    hairstyle = fj.get("hairstyle") or ""
-    expression = fj.get("expression") or ""
-    pose = fj.get("pose") or ""
-    style = fj.get("style") or ""
-    scene = fj.get("scene") or ""
-    mood = fj.get("mood") or ""
-
-    detail_parts: list[str] = []
-    if hairstyle:
-        detail_parts.append(hairstyle)
-    if expression and expression not in ("自然", "平静"):
-        detail_parts.append(f"神情{expression}")
-    if pose and pose not in ("站立",):
-        detail_parts.append(pose)
-
-    style_parts: list[str] = []
-    if style:
-        style_parts.append(style)
-    if mood:
-        style_parts.append(mood)
-    if scene and scene not in ("通用",):
-        style_parts.append(scene)
-
-    pieces = [f"一位{subject}"]
-    if wear:
-        pieces.append(wear)
-    if acc_str:
-        pieces.append(acc_str.lstrip("，"))
-    if detail_parts:
-        pieces.append("，".join(detail_parts))
-    if style_parts:
-        # 风格词之间不用逗号，用空格紧凑
-        pieces.append("".join(style_parts) + "风格")
-    else:
-        pieces.append("人像写真")
-
-    full = "，".join(p for p in pieces if p)
-    # 过短补充镜头词
-    if len(full) < 40:
-        full += "，自然光线下人像特写，画面清晰"
-    # 过长截断
-    if len(full) > 120:
-        full = full[:120].rstrip("，") + "。"
-    return full
-
-
-# ---------- 商品字段 ----------
-
-
-def _infer_name(fj: dict[str, Any], ocr_texts: list[str]) -> str:
-    pname = fj.get("product_name")
-    if pname and pname != "未识别":
-        return str(pname)
-    # 人物图 → name 用穿搭主件
-    if fj.get("has_person"):
-        up = fj.get("upper_wear") or ""
-        if "连衣裙" in up:
-            return up
-        return up or "人物穿搭"
-    if ocr_texts:
-        # 商品名可能是 OCR 最长的一行（品牌/产品名）
-        return max(ocr_texts, key=len)
-    return "未识别"
-
-
-def _infer_brand(fj: dict[str, Any], ocr_texts: list[str]) -> str:
-    brand = fj.get("brand")
-    if brand:
-        return str(brand)
-    # OCR 里短的、纯字母/汉字短串可能是 brand
-    for t in ocr_texts:
-        if 1 < len(t) <= 12:
-            return t
-    return "无法判断"
-
-
-def _infer_category(fj: dict[str, Any]) -> str:
-    cat = fj.get("category")
-    if cat:
-        return str(cat)
-    if fj.get("has_person"):
-        return "服饰"
-    return "非产品图"
-
-
-def _build_appearance(fj: dict[str, Any]) -> str:
-    """外观描述：颜色+款式+材质+图案 拼成一段。"""
-    parts: list[str] = []
-    for key, _label in [
-        ("upper_color", "主色"),
-        ("upper_wear", "款式"),
-        ("material", "材质"),
-        ("pattern", "图案"),
-    ]:
-        v = fj.get(key)
-        if v and v not in ("无法判断", "未知", "纯色"):
-            parts.append(str(v))
-    if not parts:
-        if fj.get("has_person"):
-            return "人像穿搭整体造型"
-        return "无法判断"
-    return "、".join(parts)
-
-
-def _build_key_features(fj: dict[str, Any], ocr_texts: list[str]) -> list[str]:
-    feats: list[str] = []
-    for key in (
-        "upper_wear",
-        "lower_wear",
-        "upper_color",
-        "lower_color",
-        "dress_color",
-        "material",
-        "pattern",
-        "style",
-        "accessories",
-    ):
-        v = fj.get(key)
-        if not v:
-            continue
-        if isinstance(v, list):
-            feats.extend(str(x) for x in v if x)
-        elif isinstance(v, str) and v not in ("无法判断", "未知", "纯色"):
-            feats.append(v)
-    if ocr_texts:
-        feats.append(f"画面文字: {'/'.join(ocr_texts[:3])}")
-    # 去重
-    out: list[str] = []
-    seen: set[str] = set()
-    for f in feats:
-        f = f.strip()
-        if f and f not in seen and len(f) <= 30:
-            seen.add(f)
-            out.append(f)
-    return out[:6] if out else ["无法判断"]
-
-
-def assemble_result(
-    idx: int,
-    fast_json: dict[str, Any] | None,
-    ocr_texts: list[str],
-) -> dict[str, Any]:
-    """把 fast_json 结果 + OCR 文本组装成下游兼容的 product dict。"""
-    fj = fast_json or {}
-    ocr_texts = ocr_texts or []
-
-    portrait_prompt = _build_portrait_prompt(fj)
-    name = _infer_name(fj, ocr_texts)
-    brand = _infer_brand(fj, ocr_texts)
-    category = _infer_category(fj)
-    appearance = _build_appearance(fj)
-    key_features = _build_key_features(fj, ocr_texts)
-    scene = fj.get("scene") or "通用"
-    mood = fj.get("mood") or ""
-    packaging = "无法判断"  # 包装细节专用API无，保留占位
-    text_on_package = ocr_texts[:8]
-    summary = _build_summary(fj, name, brand, category)
-
-    return {
+    image: dict[str, Any] = {
+        "type": typ,
         "name": name,
         "brand": brand,
-        "category": category,
-        "appearance": appearance,
-        "packaging": packaging,
-        "text_on_package": text_on_package,
-        "key_features": key_features,
-        "scene": scene,
-        "mood": mood,
-        "portrait_prompt": portrait_prompt,
-        "summary": summary,
-        "_source": "v2_fast_json",
+        "has_person": has_person,
+        "summary_markdown": summary,
     }
+    if not summary:
+        image["summary_markdown"] = _basic_markdown(image)
+        image["_source"] = "summary_missing"
+        logger.info("[assembler] 图片 #%s 缺少 summary_markdown，使用基础兜底", idx)
+    return image
 
 
-def _build_summary(fj: dict, name: str, brand: str, category: str) -> str:
-    if fj.get("has_person"):
-        up = fj.get("upper_wear") or "穿搭"
-        style = fj.get("style") or ""
-        base = f"{style}{up}" if style and style not in up else up
-        return base
-    if brand != "无法判断" and name != brand:
-        return f"{brand} {name}"
-    return name
+def _extract_items(fast_json: Any) -> list[Any]:
+    """从 fast JSON 中取出图片条目：优先 images，兼容 products。"""
+    if not isinstance(fast_json, dict):
+        return []
+    items = fast_json.get("images")
+    if not isinstance(items, list):
+        items = fast_json.get("products")
+    return items if isinstance(items, list) else []
+
+
+def assemble_result(idx: int, fast_json: Any, ocr_texts: list[str] | None = None) -> dict[str, Any]:
+    """组装单张图片分析结果。
+
+    每次调用对应一张图片；fast_json 形如 {"images": [{...}]}（v8）。
+    返回单条 image dict（5 字段，必要时带 _source）。
+    """
+    items = _extract_items(fast_json)
+    if items:
+        image = _normalize_image(items[0], idx)
+    else:
+        # 极端异常：fast 无任何可用条目，OCR 文字可作为名称线索
+        ocr_hint = ""
+        if ocr_texts:
+            ocr_hint = "、".join(t for t in ocr_texts if t)[:40]
+        image = _normalize_image({"name": ocr_hint or "未识别", "summary_markdown": ""}, idx)
+        image["_source"] = "empty_fast_json"
+    return image

apps/worker/worker_app/tasks/vision/fast_path.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
-"""V2 图片分析主路径：每图并行 OCR（火山专用API）+ lite JSON VLM，失败时单次 pro VLM 兜底。
+"""V2 图片分析主路径（v8 叙述优先）：每图并行 OCR（火山 MediaKit，未配置自动跳过）
++ fast VLM 强约束 JSON；失败时单次 pro VLM 兜底。
 
-设计原则（灵应10-05要求）：
-- 主力路径简洁：单图2路并行，外层N图全并发
-- 兜底简单：单次 pro VLM 调用，无竞速/重试/复杂超时
-- 输出 dict 格式与旧版完全一致，下游零改动
+架构：
+- 单图 2 路并行（OCR + fast VLM），外层 N 图全并发（workers=8）；
+- 兜底单次 pro VLM，无竞速/复杂重试；
+- 输出统一为 5 字段 image dict（type/name/brand/has_person/summary_markdown）。
 """
 
 from __future__ import annotations
@@ -19,111 +20,85 @@ from . import assembler, ocr_volc, vlm_fallback, vlm_fast_json
 
 logger = logging.getLogger(__name__)
 
-# 可通过环境变量调参（有默认值，无需配置即可跑）
 _IMG_WORKERS = int(os.environ.get("VISION_V2_IMG_WORKERS", "8"))
-_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "8"))
-_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "8"))
+_FAST_TIMEOUT = float(os.environ.get("VISION_V2_FAST_TIMEOUT", "20"))
+_FAST_JSON_TIMEOUT = float(os.environ.get("VISION_V2_FAST_JSON_TIMEOUT", "20"))
 _OCR_TIMEOUT = float(os.environ.get("VISION_V2_OCR_TIMEOUT", "6"))
 _PRO_TIMEOUT = float(os.environ.get("VISION_V2_PRO_TIMEOUT", "45"))
 
-_FALLBACK_RESULT = {
-    "name": "未识别",
-    "brand": "无法判断",
-    "category": "非产品图",
-    "appearance": "无法判断",
-    "packaging": "无法判断",
-    "text_on_package": [],
-    "key_features": ["无法判断"],
-    "scene": "通用",
-    "mood": "",
-    "portrait_prompt": "无法判断",
-    "summary": "未识别",
-}
+
+def _is_usable(r: dict[str, Any] | None) -> bool:
+    if not isinstance(r, dict):
+        return False
+    return bool((r.get("summary_markdown") or "").strip())
 
 
-def _is_usable(r: dict[str, Any]) -> bool:
-    """结果可用判定：portrait_prompt 是核心，有效就算 usable。"""
-    pp = (r.get("portrait_prompt") or "").strip()
-    if pp and pp not in ("无人像", "无法判断", "未识别"):
-        return True
-    name = (r.get("name") or "").strip()
-    if name and name not in ("未识别", "无法判断", "未知"):
-        return True
-    return False
+def _basic_failure(ocr_result: list[str], fast_elapsed: float, source: str) -> dict[str, Any]:
+    image = assembler.assemble_result(-1, {}, ocr_result)
+    image["_source"] = source
+    image["_fast_elapsed"] = round(fast_elapsed, 2)
+    return image
 
 
 def analyze_image_v2(idx: int, img_url: str) -> dict[str, Any]:
-    """单张图片 V2 分析。"""
     t0 = time.time()
 
-    # 第1层：OCR + lite JSON VLM 并行
     fj_result: dict[str, Any] | None = None
     ocr_result: list[str] = []
-    with ThreadPoolExecutor(max_workers=2) as pool:
-        f_fj = pool.submit(vlm_fast_json.call_fast_json, img_url, timeout=_FAST_JSON_TIMEOUT)
-        f_ocr = pool.submit(ocr_volc.call_ocr, img_url, timeout=_OCR_TIMEOUT)
-        try:
-            for fut in as_completed([f_fj, f_ocr], timeout=_FAST_TIMEOUT):
-                try:
-                    res = fut.result(timeout=1)
-                except Exception as e:
-                    logger.warning("[vision.v2] 图片 #%d 子任务异常: %s", idx, e)
-                    continue
-                if fut is f_fj and isinstance(res, dict):
-                    fj_result = res
-                elif fut is f_ocr and isinstance(res, list):
-                    ocr_result = res
-        except TimeoutError:
-            # fast 整体超时，取消还没跑完的子任务，继续走 pro 兜底
-            for f in (f_fj, f_ocr):
-                if not f.done():
-                    f.cancel()
-            logger.warning("[vision.v2] 图片 #%d fast路径超时(%.0fs)，走pro兜底", idx, _FAST_TIMEOUT)
+    pool = ThreadPoolExecutor(max_workers=2)
+    f_fj = pool.submit(vlm_fast_json.call_fast_json, img_url, timeout=_FAST_JSON_TIMEOUT)
+    f_ocr = pool.submit(ocr_volc.call_ocr, img_url, timeout=_OCR_TIMEOUT)
+    try:
+        for fut in as_completed([f_fj, f_ocr], timeout=_FAST_TIMEOUT):
+            try:
+                res = fut.result(timeout=1)
+            except Exception as e:  # noqa: BLE001
+                logger.warning("[vision.v2] 图片 #%d 子任务异常: %s", idx, e)
+                continue
+            if fut is f_fj and isinstance(res, dict):
+                fj_result = res
+            elif fut is f_ocr and isinstance(res, list):
+                ocr_result = res
+    except TimeoutError:
+        for f in (f_fj, f_ocr):
+            if not f.done():
+                f.cancel()
+        logger.warning("[vision.v2] 图片 #%d fast路径超时(%.0fs)，走pro兜底", idx, _FAST_TIMEOUT)
+    finally:
+        fast_elapsed = time.time() - t0
+        pool.shutdown(wait=False)
 
-    fast_elapsed = time.time() - t0
-
-    # 组装 fast 结果
     if fj_result:
         assembled = assembler.assemble_result(idx, fj_result, ocr_result)
         if _is_usable(assembled):
             assembled["_fast_elapsed"] = round(fast_elapsed, 2)
-            logger.info(
-                "[vision.v2] 图片 #%d fast命中 elapsed=%.2fs pp=%s",
-                idx,
-                fast_elapsed,
-                (assembled.get("portrait_prompt") or "")[:40],
-            )
+            logger.info("[vision.v2] 图片 #%d fast命中 elapsed=%.2fs", idx, fast_elapsed)
             return assembled
 
-    # 第2层：pro VLM 单次兜底
-    pro_t0 = time.time()
-    pro_result = vlm_fallback.call_pro_vlm(img_url, idx, timeout=_PRO_TIMEOUT)
-    if pro_result and _is_usable(pro_result):
+    pro_result = vlm_fallback.call_pro_vlm(img_url, idx, ocr_hint=ocr_result, timeout=_PRO_TIMEOUT)
+    if _is_usable(pro_result):
         pro_result["_fallback_used"] = True
         pro_result["_fast_elapsed"] = round(fast_elapsed, 2)
-        pro_result["_pro_elapsed"] = round(time.time() - pro_t0, 2)
-        if ocr_result and not pro_result.get("text_on_package"):
-            pro_result["text_on_package"] = ocr_result[:8]
         logger.info("[vision.v2] 图片 #%d pro兜底命中 total=%.2fs", idx, time.time() - t0)
         return pro_result
 
-    # 最终：返回最小可用结果
     logger.warning("[vision.v2] 图片 #%d 全路径失败 elapsed=%.2fs", idx, time.time() - t0)
-    out = dict(_FALLBACK_RESULT)
-    out["_source"] = "v2_all_failed"
-    out["text_on_package"] = ocr_result[:8]
-    out["_fast_elapsed"] = round(fast_elapsed, 2)
-    return out
+    return _basic_failure(ocr_result, fast_elapsed, "v2_all_failed")
 
 
 def analyze_images_v2(img_urls: list[str]) -> list[dict[str, Any]]:
-    """批量图片 V2 分析，外层全并发。"""
     if not img_urls:
         return []
     workers = min(_IMG_WORKERS, len(img_urls), 16)
     results: list[dict[str, Any] | None] = [None] * len(img_urls)
 
-    logger.info("[vision.v2] 开始图片分析 n=%d workers=%d fast_timeout=%.0fs", len(img_urls), workers, _FAST_TIMEOUT)
+    logger.info(
+        "[vision.v2] 开始图片分析 n=%d workers=%d fast_timeout=%.0fs pro_timeout=%.0fs",
+        len(img_urls),
+        workers,
+        _FAST_TIMEOUT,
+        _PRO_TIMEOUT,
+    )
     t0 = time.time()
     with ThreadPoolExecutor(max_workers=workers) as pool:
         future_to_idx = {pool.submit(analyze_image_v2, idx, url): idx for idx, url in enumerate(img_urls)}
@@ -131,14 +106,19 @@ def analyze_images_v2(img_urls: list[str]) -> list[dict[str, Any]]:
             idx = future_to_idx[fut]
             try:
                 results[idx] = fut.result()
-            except Exception as e:
+            except Exception as e:  # noqa: BLE001
                 logger.warning("[vision.v2] 图片 #%d future异常: %s", idx, e, exc_info=True)
-                r = dict(_FALLBACK_RESULT)
-                r["_source"] = "v2_future_exception"
-                results[idx] = r
+                results[idx] = assembler.assemble_result(idx, {}, [])
+                results[idx]["_source"] = "v2_future_exception"  # type: ignore[index]
 
     elapsed = time.time() - t0
-    succ = sum(1 for r in results if r and _is_usable(r))
+    succ = sum(1 for r in results if _is_usable(r))
     fb = sum(1 for r in results if r and r.get("_fallback_used"))
-    logger.info("[vision.v2] 完成 n=%d usable=%d pro_fallback=%d elapsed=%.2fs", len(img_urls), succ, fb, elapsed)
-    return [r for r in results if r is not None]
+    logger.info(
+        "[vision.v2] 完成 n=%d usable=%d pro_fallback=%d elapsed=%.2fs",
+        len(img_urls),
+        succ,
+        fb,
+        elapsed,
+    )
+    return [r for r in results if r is not None]  # type: ignore[misc]

apps/worker/worker_app/tasks/vision/json_utils.py

@@ -0,0 +1,138 @@
+# -*- coding: utf-8 -*-
+"""VLM 返回文本的稳健 JSON 提取工具。
+
+背景：复杂门店图 VLM 输出经常被 max_tokens 截断（finish_reason=length），
+json.loads 失败后整个结果被丢弃，导致"未识别"。本工具提供：
+1. markdown 代码块剥离（含只开不闭的截断场景）
+2. 最外层 { } 切片
+3. 非法控制字符清理
+4. 直接 json.loads
+5. 截断 JSON 括号/引号栈补全修复
+6. 尾部逐字符截断重试（去除最后一个不完整 token 后修复）
+
+成功返回 dict；截断修复产物带 _partial=True 标记；彻底失败返回 None。
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+
+logger = logging.getLogger(__name__)
+
+_CODE_FENCE_RE = re.compile(r"^```(?:json)?\s*\n?(.*?)\n?```\s*$", re.DOTALL)
+
+
+def _strip_code_fence(s: str) -> str:
+    s = s.strip()
+    m = _CODE_FENCE_RE.match(s)
+    if m:
+        return m.group(1).strip()
+    # 兼容开头 ```json 但结尾无 ```（截断场景）
+    if s.startswith("```"):
+        lines = s.split("\n")
+        if lines and lines[0].startswith("```"):
+            lines = lines[1:]
+        s = "\n".join(lines).strip()
+    return s
+
+
+def _repair_truncated_json(text: str) -> str:
+    """尝试补全被截断的JSON：维护 bracket/quote 栈，在末尾补闭合符。"""
+    stack: list[str] = []
+    in_string = False
+    escape = False
+    for ch in text:
+        if escape:
+            escape = False
+            continue
+        if ch == "\\" and in_string:
+            escape = True
+            continue
+        if ch == '"':
+            in_string = not in_string
+            continue
+        if in_string:
+            continue
+        if ch in "{[":
+            stack.append(ch)
+        elif ch == "}":
+            if stack and stack[-1] == "{":
+                stack.pop()
+        elif ch == "]":
+            if stack and stack[-1] == "[":
+                stack.pop()
+    repair = ""
+    if in_string:
+        repair += '"'
+    for opener in reversed(stack):
+        repair += "}" if opener == "{" else "]"
+    if repair:
+        logger.info(
+            "[json_utils] 截断JSON修复: 补全%d个闭合符 in_string=%s",
+            len(repair),
+            in_string,
+        )
+    return text + repair
+
+
+def _clean_invalid_chars(text: str) -> str:
+    """清理JSON中非法的控制字符（tab/newline 之外的 0x00-0x1f 段）。"""
+    return re.sub(r"[\x00-\x08\x0b\x0c\x0e-\x1f]", "", text)
+
+
+def extract_json_object(text: str) -> dict | None:
+    """从VLM返回文本中稳健提取JSON对象。
+
+    返回 dict 或 None。成功的 dict 可能带 _partial=True 标记，
+    表示原始文本被截断、经括号补全后得到的产物。
+    """
+    if not text or not isinstance(text, str):
+        return None
+    # 1. 剥离 markdown
+    text = _strip_code_fence(text)
+    # 2. 找最外层 { }
+    lpos = text.find("{")
+    if lpos < 0:
+        return None
+    rpos = text.rfind("}")
+    if rpos > lpos:
+        text = text[lpos : rpos + 1]
+    else:
+        # 截断场景：无任何闭合 }，取到末尾交给修复器
+        text = text[lpos:]
+    # 3. 清理非法控制字符
+    text = _clean_invalid_chars(text)
+    # 4. 直接 loads
+    try:
+        obj = json.loads(text)
+        return obj if isinstance(obj, dict) else None
+    except json.JSONDecodeError:
+        pass
+    # 5. 尝试截断修复
+    repaired = _repair_truncated_json(text)
+    try:
+        obj = json.loads(repaired)
+        if isinstance(obj, dict):
+            obj["_partial"] = True
+            return obj
+    except json.JSONDecodeError:
+        pass
+    # 6. 尾部逐字符截断重试（去除最后一个不完整 token）
+    for _ in range(50):
+        last_comma = repaired.rfind(",")
+        last_brace = max(repaired.rfind("}"), repaired.rfind("]"))
+        cut = max(last_comma, last_brace)
+        if cut < 10:
+            break
+        repaired = repaired[: cut + 1]
+        repaired = _repair_truncated_json(repaired)
+        try:
+            obj = json.loads(repaired)
+            if isinstance(obj, dict):
+                obj["_partial"] = True
+                return obj
+        except json.JSONDecodeError:
+            continue
+    return None

apps/worker/worker_app/tasks/vision/vlm_fallback.py

@@ -1,226 +1,109 @@
 # -*- coding: utf-8 -*-
-"""VLM 兜底：专用API路径失败时的最后一道防线，单次调用 doubao-seed-2.1-pro。
+"""V2 兜底路径：vision client（fallback 变体）单图调用，走 v8 叙述优先 prompt。
 
-设计原则：简单、直接、无竞速、无复杂超时逻辑。只在 fast_json 结果不可用时调用。
+fast 超时/非 JSON/为空时单次调用；输出统一走 assembler.assemble_result 组装，
+与 fast 路径同为 5 字段 image dict。
 """
 
 from __future__ import annotations
 
-import json
 import logging
-import re
 import time
 from typing import Any
 
+from . import _prompt, assembler
+
 logger = logging.getLogger(__name__)
 
-DEFAULT_PRO_MODEL = "doubao-seed-2-1-pro-260915"
-DEFAULT_TIMEOUT = 45
-DEFAULT_MAX_TOKENS = 800
-
-
-def _strip_code_fence(s: str) -> str:
-    s = s.strip()
-    if s.startswith("```"):
-        lines = s.split("\n")
-        if lines and lines[0].startswith("```"):
-            lines = lines[1:]
-        if lines and lines[-1].strip().startswith("```"):
-            lines = lines[:-1]
-        s = "\n".join(lines).strip()
-    return s
-
-
-def _xml_text(tag: str, xml: str) -> str:
-    m = re.search(rf"<{tag}[^>]*>(.*?)</{tag}>", xml, re.S)
-    return (m.group(1) if m else "").strip()
-
-
-def _xml_attr(tag: str, attr: str, xml: str) -> str:
-    m = re.search(rf"<{tag}[^>]*\b{attr}\s*=\s*[\"']([^\"']*)[\"']", xml)
-    return (m.group(1) if m else "").strip()
-
-
-def _xml_to_product(raw: str, idx: int) -> dict[str, Any]:
-    """解析 VLM 输出的 XML 格式（简化版）。"""
-    scene = _xml_text("scene", raw) or "通用"
-    mood = _xml_text("mood", raw) or ""
-
-    portrait_prompt = "无人像"
-    p_has = _xml_attr("people", "has_person", raw)
-    if p_has and p_has.lower() != "false":
-        gender = _xml_attr("people", "gender", raw) or ""
-        age = _xml_attr("people", "age_range", raw) or ""
-        outfit = _xml_attr("people", "outfit", raw) or ""
-        hair = _xml_attr("people", "hair", raw) or "自然发型"
-        pose = _xml_attr("people", "pose", raw) or ""
-        expr = _xml_attr("people", "expression", raw) or "自然"
-        parts: list[str] = []
-        if gender:
-            parts.append(gender + ("性" if not gender.endswith("性") else ""))
-        if age:
-            parts.append(age)
-        parts.append("人物")
-        parts.append(hair)
-        if outfit:
-            parts.append(f"身着{outfit}")
-        if pose:
-            parts.append(f"姿态{pose}")
-        parts.append(f"表情{expr}")
-        portrait_prompt = "，".join(parts)
-
-    m = re.search(r"<product[^>]*>(.*?)</product>", raw, re.S)
-    if m:
-        pbody = m.group(1)
-        name = _xml_attr("product", "name", raw) or _xml_text("name", pbody) or "未识别"
-        brand = _xml_attr("product", "brand", raw) or _xml_text("brand", pbody) or "无法判断"
-        category = _xml_attr("product", "category", raw) or _xml_text("category", pbody) or "无法判断"
-        appearance = _xml_attr("product", "appearance", raw) or _xml_text("appearance", pbody) or "无法判断"
-        packaging = _xml_attr("product", "packaging", raw) or _xml_text("packaging", pbody) or "无法判断"
-        feat = _xml_attr("product", "features", raw) or _xml_text("features", pbody) or ""
-        feat_list = [x.strip() for x in re.split(r"[,，;；]", feat) if x.strip()] if feat else ["无法判断"]
-        top_text = _xml_attr("product", "text_on_package", raw) or _xml_text("text_on_package", pbody) or ""
-        text_list = [x.strip() for x in re.split(r"[,，;；]", top_text) if x.strip()] if top_text else []
-        summary = _xml_attr("product", "summary", raw) or _xml_text("summary", pbody) or f"{brand} {name}"
-        pp_attr = _xml_attr("product", "portrait_prompt", raw)
-        if pp_attr and pp_attr != "无人像":
-            portrait_prompt = pp_attr
-        return {
-            "name": name,
-            "brand": brand,
-            "category": category,
-            "appearance": appearance,
-            "packaging": packaging,
-            "text_on_package": text_list,
-            "key_features": feat_list,
-            "scene": scene,
-            "mood": mood,
-            "portrait_prompt": portrait_prompt,
-            "summary": summary,
-            "_source": "vlm_pro_xml",
-        }
-
-    if portrait_prompt != "无人像":
-        return {
-            "name": "未识别",
-            "brand": "无法判断",
-            "category": "无法判断",
-            "appearance": "无法判断",
-            "packaging": "无法判断",
-            "text_on_package": [],
-            "key_features": ["无法判断"],
-            "scene": scene,
-            "mood": mood,
-            "portrait_prompt": portrait_prompt,
-            "summary": "未识别",
-            "_source": "vlm_pro_no_product",
-        }
-    return {
-        "name": "未识别",
-        "brand": "无法判断",
-        "category": "无法判断",
-        "appearance": "无法判断",
-        "packaging": "无法判断",
-        "text_on_package": [],
-        "key_features": ["无法判断"],
-        "scene": scene,
-        "mood": mood,
-        "portrait_prompt": "无人像",
-        "summary": "未识别",
-        "_source": "vlm_pro_no_tag",
-    }
+_DEFAULT_TIMEOUT = 45
 
 
 def call_pro_vlm(
     img_url: str,
     idx: int,
     *,
-    model: str | None = None,
-    timeout: int = DEFAULT_TIMEOUT,
+    ocr_hint: list[str] | None = None,
+    timeout: int = _DEFAULT_TIMEOUT,
+    max_tokens: int | None = None,
 ) -> dict[str, Any] | None:
-    """单次调用 pro VLM，解析后返回 product dict；失败返回 None。"""
     t0 = time.time()
-    try:
-        from packages.application.viral_video.prompt_loader import (
-            get_template,
-            render_system_prompt,
-            render_user_prompt,
-        )
-        from packages.shared.ai_client import get_doubao_client
-    except ImportError as e:
-        logger.warning("[vision.vlm] 导入失败: %s", e)
-        return None
+    ocr_text = "、".join(t for t in (ocr_hint or []) if t)[:200]
 
     try:
-        template = get_template("image_analysis")
-        system = render_system_prompt(template)
-        user = render_user_prompt(template, image_count=1, industry="通用", image_urls=f"第1张：{img_url}")
-    except Exception as e:
-        logger.warning("[vision.vlm] 模板加载失败: %s", e)
+        from packages.shared.ai_router import ai_router
+
+        client = ai_router.get_vision_client("image_analysis", variant="fallback")
+        if not client or not client.is_available:
+            logger.warning("[vision.v2] pro vision client 不可用，跳过")
+            return None
+    except Exception as e:  # noqa: BLE001
+        logger.warning("[vision.v2] ai_router 获取失败: %s", e)
         return None
 
-    client = get_doubao_client()
-    if not client.is_available:
-        return None
+    system_prompt, user_prompt = _prompt.resolve_pro_prompt(img_url, ocr_text)
+
+    messages: list[dict[str, Any]] = [
+        {"role": "system", "content": system_prompt},
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": img_url}},
+                {"type": "text", "text": user_prompt},
+            ],
+        },
+    ]
 
-    use_model = model or DEFAULT_PRO_MODEL
-    _orig_retries = client.max_retries
-    client.max_retries = 0
     try:
-        raw = client.vision_completion(
-            messages=[{"role": "system", "content": system}, {"role": "user", "content": user}],
-            images=[img_url],
-            temperature=0.3,
-            max_tokens=DEFAULT_MAX_TOKENS,
-            timeout=timeout,
-            model=use_model,
-        )
-    except Exception as e:
-        logger.warning("[vision.vlm] 图片 #%d pro VLM 调用失败 elapsed=%.1fs err=%s", idx, time.time() - t0, e)
-        client.max_retries = _orig_retries
-        return None
-    client.max_retries = _orig_retries
+        call_kwargs: dict[str, Any] = {
+            "messages": messages,
+            "images": None,
+            "temperature": 0.3,
+            "timeout": timeout,
+            "enable_thinking": False,
+            "response_format": {"type": "json_object"},
+            "max_tokens": max_tokens if max_tokens is not None else 4000,
+        }
 
-    elapsed = time.time() - t0
-    if not raw:
-        logger.warning("[vision.vlm] 图片 #%d pro VLM 返回空 elapsed=%.1fs", idx, elapsed)
-        return None
+        from .json_utils import extract_json_object
 
-    text = _strip_code_fence(raw)
-    l, r = text.find("{"), text.rfind("}")
-    if l >= 0 and r > l:
-        try:
-            obj = json.loads(text[l : r + 1])
-            if isinstance(obj, dict):
-                logger.info("[vision.vlm] 图片 #%d pro VLM JSON 完成 elapsed=%.1fs", idx, elapsed)
-                return {
-                    "name": obj.get("name") or "未识别",
-                    "brand": obj.get("brand") or "无法判断",
-                    "category": obj.get("category") or "无法判断",
-                    "appearance": obj.get("appearance") or "无法判断",
-                    "packaging": obj.get("packaging") or "无法判断",
-                    "text_on_package": obj.get("text_on_package") or [],
-                    "key_features": obj.get("key_features") or obj.get("features") or ["无法判断"],
-                    "scene": obj.get("scene") or "通用",
-                    "mood": obj.get("mood") or "",
-                    "portrait_prompt": obj.get("portrait_prompt") or "无人像",
-                    "summary": obj.get("summary") or f"{obj.get('brand','')} {obj.get('name','')}",
-                    "_source": "vlm_pro_json",
+        obj = None
+        for _outer in range(2):
+            kw = dict(call_kwargs)
+            if _outer == 1:
+                kw.pop("response_format", None)
+                msgs2 = [dict(messages[0]), dict(messages[1])]
+                cont = [dict(c) for c in msgs2[1]["content"]]
+                cont[-1] = {
+                    "type": "text",
+                    "text": user_prompt + "\n严格只输出JSON对象，不要解释或markdown。",
                 }
-        except json.JSONDecodeError:
-            pass
+                msgs2[1] = {"role": "user", "content": cont}
+                kw["messages"] = msgs2
+            raw = client.vision_completion(**kw)
+            if not raw:
+                continue
+            obj = extract_json_object(raw)
+            if obj is not None:
+                break
+            logger.warning("[vision.v2] pro 非JSON(100字) outer=%s: %s", _outer, raw[:100])
 
-    try:
-        result = _xml_to_product(text, idx)
+        elapsed = time.time() - t0
+        if obj is None:
+            logger.warning("[vision.v2] pro 两次均未得到JSON elapsed=%.1fs", elapsed)
+            return None
+        if obj.get("_partial"):
+            logger.warning("[vision.v2] pro 截断JSON(partial) elapsed=%.1fs", elapsed)
+
+        result = assembler.assemble_result(idx, obj, ocr_hint or [])
+        result["_source"] = "vlm_pro"
         result["_fallback_used"] = True
-        result["_pro_elapsed"] = round(elapsed, 2)
-        logger.info(
-            "[vision.vlm] 图片 #%d pro VLM XML 完成 elapsed=%.2fs pp=%s",
-            idx,
-            elapsed,
-            (result.get("portrait_prompt") or "")[:40],
-        )
+        logger.info("[vision.v2] pro 完成 model=%s elapsed=%.1fs", client.model, elapsed)
         return result
-    except Exception as e:
-        logger.warning("[vision.vlm] 图片 #%d 解析失败 elapsed=%.1fs err=%s head=%s", idx, elapsed, e, raw[:200])
+    except Exception as e:  # noqa: BLE001
+        logger.warning(
+            "[vision.v2] pro 异常 elapsed=%.1fs err=%s",
+            time.time() - t0,
+            e,
+            exc_info=True,
+        )
         return None

apps/worker/worker_app/tasks/vision/vlm_fast_json.py

@@ -1,206 +1,111 @@
 # -*- coding: utf-8 -*-
-"""doubao-seed-2.1-lite 强约束 JSON-only 调用。
+"""V2 快速路径：vision client（默认 image_analysis 能力）强约束 JSON-only 调用。
 
-目标：替代"人体属性/商品检测/图像标签"三个火山不存在的专用云端 API。
-设计要点：
-- system prompt 极致精简，只给字段 schema 和强约束（禁止自然语言、禁止 markdown）
-- max_tokens=350（比旧 VLM 的 1200 小很多，降低延迟）
-- temperature=0.1（极低，稳定输出 JSON）
-- timeout=8s（够快，失败则由外层走 pro VLM 兜底）
-- 期望返回纯 JSON object（无 ```json 包裹、无解释文字）
+要点：
+- 通过 ai_router.get_vision_client() 获取 client；
+- enable_thinking=False 关闭推理链，response_format=json_object 强约束 JSON；
+- system/user prompt 优先读后台模板（v8 叙述优先），DB 不可用时用 prompts.py 默认；
+- temperature=0.1（稳定输出 JSON）；两次尝试（第二次去 json_object 约束）。
 """
 
 from __future__ import annotations
 
-import json
 import logging
 import time
 from typing import Any
 
+from . import _prompt
+
 logger = logging.getLogger(__name__)
 
-# 极简 system prompt：只给字段定义 + 硬性输出要求
-_FAST_SYSTEM = (
-    "你是图片结构化识别器。严格按下方 JSON schema 返回一个对象，不要任何解释、"
-    "不要markdown、不要代码块、不要前后缀文字。字段值不确定时填 null 或空数组。\n"
-    "{\n"
-    '  "has_person": true/false,  // 图中是否有人\n'
-    '  "gender": "男"/"女"/null,\n'
-    '  "age_range": "儿童"/"青少年"/"青年"/"中年"/"老年"/null,\n'
-    '  "upper_wear": "上装款式，如T恤/衬衫/卫衣/毛衣/西装/夹克/连衣裙/吊带/背心/外套等",\n'
-    '  "upper_color": "上装主色",\n'
-    '  "lower_wear": "下装款式，如牛仔裤/休闲裤/短裙/长裙/短裤/西裤/运动裤等；穿连衣裙时填null",\n'
-    '  "lower_color": "下装主色",\n'
-    '  "dress_color": "连衣裙主色（穿连衣裙时填）",\n'
-    '  "accessories": ["眼镜"/"帽子"/"项链"/"耳环"/"背包"/"手表"等数组],\n'
-    '  "hairstyle": "发型，如短发/长发/马尾/卷发/丸子头/光头等",\n'
-    '  "expression": "表情，如微笑/严肃/酷/开心等",\n'
-    '  "pose": "姿势，如站立/坐姿/侧身/行走等",\n'
-    '  "scene": "场景，如室内/街拍/户外/办公室/家居/海边/雪景/森林等",\n'
-    '  "style": "风格，如休闲/商务/运动/复古/潮流/甜美/酷飒/优雅/街头/法式等",\n'
-    '  "has_product": true/false,  // 是否有明确商品展示\n'
-    '  "category": "产品类目：服饰/鞋包/美妆/数码/食品/家居/配饰/母婴/非产品图",\n'
-    '  "product_name": "产品名称，非产品图填null",\n'
-    '  "brand": "品牌或文字标识，无则null",\n'
-    '  "material": "材质，如棉质/牛仔/皮革/真丝/针织/涤纶等",\n'
-    '  "pattern": "图案，如纯色/条纹/波点/格子/印花/碎花/Logo等",\n'
-    '  "colors": ["主色数组"],\n'
-    '  "mood": "整体氛围/情绪，如清新/活力/高级/温暖/冷峻/甜美/复古等"\n'
-    "}"
-)
-
-_FAST_USER = "识别这张图片的人物穿搭与主体信息，只返回JSON对象。"
-
-# 默认模型
-DEFAULT_LITE_MODEL = "doubao-seed-2-1-lite-260915"
-DEFAULT_TIMEOUT = 8
-DEFAULT_MAX_TOKENS = 350
-
-
-def _strip_code_fence(s: str) -> str:
-    """剥离 ```json ... ``` 包裹（即使要求纯 JSON，模型偶尔仍会包代码块）。"""
-    s = s.strip()
-    if s.startswith("```"):
-        lines = s.split("\n")
-        # 去掉首行 ```json
-        if lines and lines[0].startswith("```"):
-            lines = lines[1:]
-        # 去掉尾行 ```
-        if lines and lines[-1].strip().startswith("```"):
-            lines = lines[:-1]
-        s = "\n".join(lines).strip()
-    return s
+_DEFAULT_TIMEOUT = 20
 
 
 def call_fast_json(
     img_url: str,
     *,
-    model: str | None = None,
-    timeout: int = DEFAULT_TIMEOUT,
-    max_tokens: int = DEFAULT_MAX_TOKENS,
+    timeout: int = _DEFAULT_TIMEOUT,
+    max_tokens: int | None = None,
 ) -> dict[str, Any] | None:
-    """调用 lite VLM 返回结构化 dict；失败/非 JSON 返回 None。
-
-    直接用 httpx 发最小 payload（关闭 thinking），不走 ai_client 包装：
-    - 关闭 thinking/推理链（reasoning_tokens 是延迟主因，单次要10-12s）
-    - 单次调用不重试（失败由外层走 pro 兜底）
-    - 温度=0.1 稳定输出 JSON
-    """
     t0 = time.time()
-    import httpx
 
     try:
-        from packages.shared import get_shared_settings
+        from packages.shared.ai_router import ai_router
 
-        settings = get_shared_settings()
-        api_key = settings.doubao_api_key
-        base_url = (settings.doubao_base_url or "https://ark.cn-beijing.volces.com/api/v3").rstrip("/")
-        if not api_key:
-            logger.warning("[vision.v2] doubao api_key 未配置，跳过 fast_json")
+        client = ai_router.get_vision_client("image_analysis", variant="primary")
+        if not client or not client.is_available:
+            logger.warning("[vision.v2] vision client 不可用，跳过 fast_json")
             return None
+    except Exception as e:  # noqa: BLE001
+        logger.warning("[vision.v2] ai_router 获取失败: %s", e)
+        return None
 
-        use_model = model or DEFAULT_LITE_MODEL
-        url = f"{base_url}/chat/completions"
-        payload: dict[str, Any] = {
-            "model": use_model,
-            "messages": [
-                {"role": "system", "content": _FAST_SYSTEM},
-                {
-                    "role": "user",
-                    "content": [
-                        {"type": "image_url", "image_url": {"url": img_url}},
-                        {"type": "text", "text": _FAST_USER},
-                    ],
-                },
+    system_prompt, user_prompt = _prompt.resolve_fast_prompt(img_url, "")
+
+    messages: list[dict[str, Any]] = [
+        {"role": "system", "content": system_prompt},
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": img_url}},
+                {"type": "text", "text": user_prompt},
             ],
+        },
+    ]
+
+    try:
+        call_kwargs: dict[str, Any] = {
+            "messages": messages,
+            "images": None,
             "temperature": 0.1,
-            "max_tokens": max_tokens,
-            "stream": False,
+            "timeout": timeout,
+            "enable_thinking": False,
+            "response_format": {"type": "json_object"},
         }
-        # 关键：关闭 thinking（避免产生 reasoning_tokens 拖慢响应）
-        # 方舟/豆包 2.x 模型支持 thinking.type=disabled
-        try:
-            payload["thinking"] = {"type": "disabled"}
-        except Exception:
-            pass
-        # 部分模型用 reasoning_effort 控制思考深度
-        payload["reasoning_effort"] = "low"
+        if max_tokens is not None:
+            call_kwargs["max_tokens"] = max_tokens
 
-        resp = httpx.post(
-            url,
-            headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
-            json=payload,
-            timeout=timeout,
-        )
-        elapsed = time.time() - t0
-        if resp.status_code != 200:
-            logger.warning(
-                "[vision.v2] fast_json HTTP %d elapsed=%.1fs body=%s", resp.status_code, elapsed, resp.text[:200]
-            )
-            # 如果400说明不支持thinking参数，降级重试一次
-            if resp.status_code == 400 and "thinking" in resp.text.lower():
-                payload.pop("thinking", None)
-                payload.pop("reasoning_effort", None)
-                time.time()
-                resp = httpx.post(
-                    url,
-                    headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
-                    json=payload,
-                    timeout=timeout,
-                )
-                elapsed = time.time() - t0
-                if resp.status_code != 200:
-                    logger.warning("[vision.v2] fast_json 降级后 HTTP %d elapsed=%.1fs", resp.status_code, elapsed)
-                    return None
-            else:
-                return None
-        data = resp.json()
-        raw = (data.get("choices") or [{}])[0].get("message", {}).get("content")
-        if raw is None:
-            logger.warning("[vision.v2] fast_json 返回 None elapsed=%.1fs", elapsed)
-            return None
-        usage = data.get("usage") or {}
-        logger.info(
-            "[vision.v2] fast_json 直连完成 model=%s elapsed=%.1fs in=%d out=%d reasoning=%d",
-            use_model,
-            elapsed,
-            usage.get("prompt_tokens", 0),
-            usage.get("completion_tokens", 0),
-            usage.get("reasoning_tokens", 0),
-        )
-        elapsed = time.time() - t0
-        if raw is None:
-            logger.warning("[vision.v2] fast_json 返回 None elapsed=%.1fs model=%s", elapsed, use_model)
-            return None
+        from .json_utils import extract_json_object
 
-        text = _strip_code_fence(raw)
-        # 截到第一个 { 和最后一个 } 之间，容忍前后偶发文字
-        l = text.find("{")
-        r = text.rfind("}")
-        if l >= 0 and r > l:
-            text = text[l : r + 1]
-        try:
-            obj = json.loads(text)
-        except json.JSONDecodeError:
-            logger.warning(
-                "[vision.v2] fast_json JSON 解析失败 elapsed=%.1fs head=%s",
-                elapsed,
-                raw[:200],
-            )
-            return None
-        if not isinstance(obj, dict):
-            logger.warning("[vision.v2] fast_json 非 dict: %s", type(obj))
+        obj = None
+        for _outer in range(2):
+            kw = dict(call_kwargs)
+            if _outer == 1:
+                kw.pop("response_format", None)
+                msgs2 = [dict(messages[0]), dict(messages[1])]
+                cont = [dict(c) for c in msgs2[1]["content"]]
+                cont[-1] = {
+                    "type": "text",
+                    "text": user_prompt + "\n严格只输出JSON对象，不要解释或markdown。",
+                }
+                msgs2[1] = {"role": "user", "content": cont}
+                kw["messages"] = msgs2
+            raw = client.vision_completion(**kw)
+            if not raw:
+                continue
+            obj = extract_json_object(raw)
+            if obj is not None:
+                break
+            logger.warning("[vision.v2] fast_json 非JSON(100字) outer=%s: %s", _outer, raw[:100])
+
+        elapsed = time.time() - t0
+        if obj is None:
+            logger.warning("[vision.v2] fast_json 两次均未得到JSON elapsed=%.1fs", elapsed)
             return None
+        if obj.get("_partial"):
+            logger.warning("[vision.v2] fast_json 截断JSON(partial) elapsed=%.1fs", elapsed)
         logger.info(
-            "[vision.v2] fast_json 完成 model=%s elapsed=%.1fs has_person=%s has_product=%s category=%s",
-            use_model,
+            "[vision.v2] fast_json 完成 model=%s elapsed=%.1fs type=%s",
+            client.model,
             elapsed,
-            obj.get("has_person"),
-            obj.get("has_product"),
-            obj.get("category"),
+            obj.get("type"),
         )
         return obj
-    except Exception as e:
-        elapsed = time.time() - t0
-        logger.warning("[vision.v2] fast_json 异常 elapsed=%.1fs err=%s", elapsed, e, exc_info=True)
+    except Exception as e:  # noqa: BLE001
+        logger.warning(
+            "[vision.v2] fast_json 异常 elapsed=%.1fs err=%s",
+            time.time() - t0,
+            e,
+            exc_info=True,
+        )
         return None

deploy/configs/.env.staging

@@ -311,3 +311,10 @@ GPU_ENCODE_CRF=23
 GPU_ENCODE_FALLBACK_CPU=true
 GPU_ENCODE_MEZZANINE_TRANSPORT=oss
 GPU_ENCODE_OSS_TMP_PREFIX=tmp/gpu-mezzanine/
+
+# ==================== Ditto 蚂蚁数字人口型 ====================
+# 注意：这些值必须写死在模板里（不是 CI Secret），否则每次 CI 重新渲染 .env 都会被丢弃，
+# 导致 staging 发版后 Ditto 口型服务静默降级到 GPU/MediaKit（P0 防复发）。
+USE_DITTO_LIPSYNC=true
+DITTO_API_BASE_URL=http://100.76.80.23:8000
+DITTO_DEFAULT_VIDEO_URL=https://xiaoxia-autocut.oss-cn-hangzhou.aliyuncs.com/uploads/default_avatar.mp4

packages/adapters/sqlalchemy_impl/models.py

@@ -335,6 +335,10 @@ class GenerationTaskModel(Base):
     bgm_config = Column(JSON, nullable=False, default=dict)
     extra_meta = Column("metadata", JSON, nullable=False, default=dict)
     logs = Column(Text, nullable=False, default="[]", server_default="[]")
+    # 功能计费（smart_edit）：预扣积分 / 最终积分 / 预扣流水 ID
+    credits_prepaid = Column(Float, nullable=False, default=0.0, server_default="0")
+    credits_cost = Column(Float, nullable=False, default=0.0, server_default="0")
+    credits_transaction_id = Column(String(36), nullable=False, default="", server_default="")
     created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
     updated_at = Column(
         DateTime,
@@ -727,6 +731,11 @@ class LipsyncJobModel(Base):
     # 精确句子时间戳（TTS 合成后由 silencedetect 计算，用于 B-roll 精确定位）
     sentence_timings = Column(JSON, nullable=True)  # list[{index,text,start_time,end_time}]
 
+    # 功能计费（lip_sync）：预扣积分 / 最终积分 / 预扣流水 ID
+    credits_prepaid = Column(Float, nullable=False, default=0.0, server_default="0")
+    credits_cost = Column(Float, nullable=False, default=0.0, server_default="0")
+    credits_transaction_id = Column(String(36), nullable=False, default="", server_default="")
+
     # 时间戳
     submitted_at = Column(DateTime, nullable=True)
     completed_at = Column(DateTime, nullable=True)
@@ -905,6 +914,11 @@ class GpuLipsyncTaskModel(Base):
     # 心跳：worker 最近一次 poll/result 的时间，用于判定 worker 失联
     last_heartbeat_at = Column(DateTime, nullable=True)
 
+    # 功能计费（lip_sync）：预扣积分 / 最终积分 / 预扣流水 ID
+    credits_prepaid = Column(Float, nullable=False, default=0.0, server_default="0")
+    credits_cost = Column(Float, nullable=False, default=0.0, server_default="0")
+    credits_transaction_id = Column(String(36), nullable=False, default="", server_default="")
+
 
 class GpuWorkerModel(Base):
     """GPU Worker 注册表 — 反向轮询模式下用于心跳与监控."""
@@ -947,6 +961,7 @@ class ViralVideoJobModel(Base):
     # v1.5 音频/视频参数
     voice_id = Column(String(200), nullable=False, default="")
     voice_source = Column(String(20), nullable=False, default="")
+    language = Column(String(20), nullable=False, default="zh-CN")
     video_ratio = Column(String(10), nullable=False, default="9:16")
     video_model = Column(String(100), nullable=False, default="")
     # 结果与状态
@@ -1005,3 +1020,20 @@ class ViralVideoPromptTemplateModel(Base):
     is_active = Column(Boolean, nullable=False, default=True)
     created_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
     updated_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
+
+
+class SystemSettingModel(Base):
+    """系统配置表 ORM 模型（#2246：后台可配置项，表已手工存在于 staging）."""
+
+    __tablename__ = "system_settings"
+
+    id = Column(String(36), primary_key=True)
+    setting_key = Column(String(100), nullable=False, unique=True)
+    setting_value = Column(Text, nullable=True)
+    setting_type = Column(String(20), nullable=False)
+    description = Column(String(255), nullable=False, default="", server_default="")
+    is_public = Column(Boolean, nullable=False, default=False, server_default="false")
+    updated_by = Column(String(36), nullable=True)
+    category = Column(String(50), nullable=False, default="general", server_default="general")
+    created_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
+    updated_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))

packages/adapters/sqlalchemy_impl/system_setting_repository.py

@@ -0,0 +1,65 @@
+"""system_settings 表 SQLAlchemy Repository — #2246."""
+
+from __future__ import annotations
+
+from sqlalchemy.orm import Session
+
+from packages.adapters.sqlalchemy_impl.models import SystemSettingModel
+from packages.domain.system_setting import SystemSetting
+
+
+class SQLAlchemySystemSettingRepository:
+    def __init__(self, session: Session):
+        self.session = session
+
+    def get_by_key(self, setting_key: str) -> SystemSetting | None:
+        model = self.session.query(SystemSettingModel).filter(SystemSettingModel.setting_key == setting_key).first()
+        if model is None:
+            return None
+        return self._to_domain(model)
+
+    def list_all(self, category: str | None = None) -> list[SystemSetting]:
+        query = self.session.query(SystemSettingModel)
+        if category is not None:
+            query = query.filter(SystemSettingModel.category == category)
+        return [self._to_domain(m) for m in query.all()]
+
+    def upsert(self, setting: SystemSetting) -> SystemSetting:
+        model = (
+            self.session.query(SystemSettingModel).filter(SystemSettingModel.setting_key == setting.setting_key).first()
+        )
+        if model is None:
+            model = SystemSettingModel(id=setting.id)
+            self.session.add(model)
+        model.setting_key = setting.setting_key
+        model.setting_value = setting.setting_value
+        model.setting_type = setting.setting_type
+        model.description = setting.description
+        model.is_public = setting.is_public
+        model.category = setting.category
+        model.updated_by = setting.updated_by
+        self.session.commit()
+        return setting
+
+    def delete_by_key(self, setting_key: str) -> bool:
+        model = self.session.query(SystemSettingModel).filter(SystemSettingModel.setting_key == setting_key).first()
+        if model is None:
+            return False
+        self.session.delete(model)
+        self.session.commit()
+        return True
+
+    @staticmethod
+    def _to_domain(model: SystemSettingModel) -> SystemSetting:
+        return SystemSetting(
+            id=model.id,
+            setting_key=model.setting_key,
+            setting_value=model.setting_value,
+            setting_type=model.setting_type,
+            description=model.description or "",
+            is_public=bool(model.is_public),
+            category=model.category or "general",
+            updated_by=model.updated_by,
+            created_at=model.created_at,
+            updated_at=model.updated_at,
+        )

packages/adapters/sqlalchemy_impl/viral_video_repository.py

@@ -49,6 +49,7 @@ def _to_domain(model: ViralVideoJobModel) -> ViralVideoJob:
         style_template_id=getattr(model, "style_template_id", "") or "",
         voice_id=getattr(model, "voice_id", "") or "",
         voice_source=getattr(model, "voice_source", "") or "",
+        language=getattr(model, "language", "zh-CN") or "zh-CN",
         video_ratio=getattr(model, "video_ratio", "9:16") or "9:16",
         video_model=getattr(model, "video_model", "") or "",
         status=ViralVideoStatus(model.status) if model.status else ViralVideoStatus.PENDING,
@@ -102,6 +103,7 @@ class SQLAlchemyViralVideoJobRepository:
             style_template_id=job.style_template_id,
             voice_id=job.voice_id,
             voice_source=job.voice_source,
+            language=getattr(job, "language", "zh-CN") or "zh-CN",
             video_ratio=job.video_ratio,
             video_model=job.video_model,
             status=job.status,
@@ -169,6 +171,7 @@ class SQLAlchemyViralVideoJobRepository:
         model.style_template_id = job.style_template_id
         model.voice_id = job.voice_id or ""
         model.voice_source = job.voice_source or ""
+        model.language = getattr(job, "language", "zh-CN") or "zh-CN"
         model.video_ratio = job.video_ratio or "9:16"
         model.video_model = job.video_model or ""
         model.updated_at = datetime.now(timezone.utc)

packages/application/auth/password_hasher.py

@@ -3,10 +3,24 @@
 使用 bcrypt 安全存储密码
 """
 
+import hashlib
 from typing import Optional
 
 import bcrypt
 
+# bcrypt 只对前 72 字节有效，且 bcrypt>=4.1 会对超长输入直接抛 ValueError。
+# 超长密码先做一次 SHA-256（定长 hex），再交给 bcrypt，
+# 既绕过长度限制又保持对超长不同密码的区分度。
+_BCRYPT_MAX_BYTES = 72
+
+
+def _prepare_password_bytes(password: str) -> bytes:
+    raw = password.encode("utf-8")
+    if len(raw) > _BCRYPT_MAX_BYTES:
+        return hashlib.sha256(raw).hexdigest().encode("utf-8")
+    return raw
+
+
 from packages.domain.auth.password_hasher import PasswordHasherPort, PasswordValidatorPort
 
 
@@ -42,8 +56,8 @@ class PasswordHasher(PasswordHasherPort):
         if not password:
             raise ValueError("Password cannot be empty")
 
-        # bcrypt 需要 bytes
-        password_bytes = password.encode("utf-8")
+        # bcrypt 需要 bytes（超长密码先 SHA-256 以兼容 72 字节限制）
+        password_bytes = _prepare_password_bytes(password)
 
         # 生成 salt 并哈希
         salt = bcrypt.gensalt(rounds=self.rounds)
@@ -67,7 +81,7 @@ class PasswordHasher(PasswordHasherPort):
             return False
 
         try:
-            password_bytes = password.encode("utf-8")
+            password_bytes = _prepare_password_bytes(password)
             hashed_bytes = hashed_password.encode("utf-8")
 
             return bcrypt.checkpw(password_bytes, hashed_bytes)

packages/application/cosyvoice_service.py

@@ -351,12 +351,25 @@ class CosyVoiceService:
                 用于私有 bucket 下，将裸 URL 转为预签名 URL，
                 确保 CosyVoice 服务器能下载参考音频.
         """
+        # 优先从 ai_router 获取 DB 配置
+        _router_key, _router_url, _router_model = "", "", ""
+        try:
+            from packages.shared.ai_router import ai_router
+
+            tts_client = ai_router.get_tts_client("tts")
+            if tts_client and tts_client.is_available:
+                _router_key = tts_client.api_key
+                _router_url = tts_client.base_url
+                _router_model = tts_client.model
+        except Exception:
+            pass
+
         settings = get_shared_settings()
 
-        self._api_key = api_key or settings.cosyvoice_api_key
-        self._base_url = base_url or settings.cosyvoice_base_url
-        self._model = model or settings.cosyvoice_model
-        self._clone_model = clone_model or getattr(settings, "cosyvoice_clone_model", "voice-enrollment")
+        self._api_key = api_key or _router_key or settings.cosyvoice_api_key
+        self._base_url = base_url or _router_url or settings.cosyvoice_base_url
+        self._model = model or _router_model or settings.cosyvoice_model
+        self._clone_model = clone_model or getattr(settings, "cosyvoice_clone_model", "")
         self._audio_url_signer = audio_url_signer
 
         # base_url 规范化：去掉末尾的路径残留（兼容旧版配置）

packages/application/ditto_emotion_service.py

@@ -0,0 +1,332 @@
+"""Ditto LLM 情绪分析服务 — #2076 后续：根据文案生成 emo_timeline.
+
+职责：
+1. 正则按 。！？； 初步分句
+2. 调 DoubaoClient.chat_completion 分析每句表情（emo: 0-7, intensity: 0-1）
+3. 结果 LRU 缓存（文案 hash → 情绪列表）
+4. LLM 失败/超时/格式错 → 返回空列表（降级中性表情，不阻塞生成）
+5. TTS 完成后按字数比例或 sentence_timings 对齐成秒级 timeline
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+import re
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Optional
+
+logger = logging.getLogger(__name__)
+
+# ── 表情常量 ─────────────────────────────────────────────────────
+EMO_ANGER = 0
+EMO_DISGUST = 1
+EMO_FEAR = 2
+EMO_HAPPY = 3
+EMO_NEUTRAL = 4
+EMO_SAD = 5
+EMO_SURPRISE = 6
+EMO_CONTEMPT = 7
+ALLOWED_EMOS = {EMO_HAPPY, EMO_NEUTRAL, EMO_SAD, EMO_SURPRISE}  # 营销场景白名单
+
+# ── 分句正则 ─────────────────────────────────────────────────────
+_SENT_SPLIT_RE = re.compile(r"(?<=[。！？；!?;])\s*")
+
+# ── 默认 prompt 模板文件路径 ──────────────────────────────────────
+_DEFAULT_PROMPT_PATH = Path(__file__).parent / "prompts" / "ditto_emotion.txt"
+
+
+def _load_default_prompt() -> str:
+    try:
+        return _DEFAULT_PROMPT_PATH.read_text(encoding="utf-8").strip()
+    except Exception:
+        # 文件不存在时用极简兜底
+        return (
+            "分析文案每句话表情，输出JSON数组："
+            '[{"text":"句子","emo":4,"intensity":0.2}]，emo:3开心4中性5伤心6惊讶，'
+            "禁止0/1/2/7。\n【文案】\n{文案}"
+        )
+
+
+# ── 数据结构 ─────────────────────────────────────────────────────
+class EmotionSegment:
+    """单句情绪结果（LLM 输出的原始结构）."""
+
+    __slots__ = ("text", "emo", "intensity")
+
+    def __init__(self, text: str, emo: int, intensity: float):
+        self.text = text
+        self.emo = emo
+        self.intensity = intensity
+
+    def to_dict(self) -> dict[str, Any]:
+        return {"text": self.text, "emo": self.emo, "intensity": self.intensity}
+
+
+class EmotionTimelineEntry:
+    """对齐到音频时间轴后的情绪片段（传给 Ditto）."""
+
+    __slots__ = ("start", "end", "emo", "intensity")
+
+    def __init__(self, start: float, end: float, emo: int, intensity: float):
+        self.start = round(start, 2)
+        self.end = round(end, 2)
+        self.emo = emo
+        self.intensity = round(intensity, 2)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "start": self.start,
+            "end": self.end,
+            "emo": self.emo,
+            "intensity": self.intensity,
+        }
+
+
+# ── 分句 ─────────────────────────────────────────────────────────
+def split_sentences(text: str) -> list[str]:
+    """按中文句末标点切分，过滤空串."""
+    if not text:
+        return []
+    parts = _SENT_SPLIT_RE.split(text.strip())
+    return [p.strip() for p in parts if p and p.strip()]
+
+
+# ── 解析 LLM 返回的 JSON ─────────────────────────────────────────
+def _parse_emotion_json(raw: str) -> list[EmotionSegment]:
+    """解析 LLM 返回，容错处理：
+    - 去掉 markdown 代码块包裹
+    - 只取第一个 JSON 数组
+    - 逐行校验 emo/intensity 合法性，过滤无效项
+    """
+    if not raw:
+        return []
+    text = raw.strip()
+    # 去掉 ```json ... ``` 包裹
+    if text.startswith("```"):
+        text = re.sub(r"^```(?:json)?\s*", "", text)
+        text = re.sub(r"\s*```$", "", text)
+    # 找第一个 [ 到最后一个 ]
+    lb = text.find("[")
+    rb = text.rfind("]")
+    if lb == -1 or rb == -1 or rb <= lb:
+        return []
+    try:
+        data = json.loads(text[lb : rb + 1])
+    except (json.JSONDecodeError, ValueError):
+        return []
+    if not isinstance(data, list):
+        return []
+
+    results: list[EmotionSegment] = []
+    for item in data:
+        if not isinstance(item, dict):
+            continue
+        try:
+            emo = int(item.get("emo", EMO_NEUTRAL))
+            intensity = float(item.get("intensity", 0.2))
+        except (TypeError, ValueError):
+            continue
+        if emo not in ALLOWED_EMOS:
+            emo = EMO_NEUTRAL
+        intensity = max(0.05, min(1.0, intensity))
+        sent_text = str(item.get("text", "")).strip()
+        if not sent_text:
+            continue
+        results.append(EmotionSegment(text=sent_text, emo=emo, intensity=intensity))
+    return results
+
+
+# ── 时间对齐（按字数比例）────────────────────────────────────────
+def align_timeline_by_length(
+    segments: list[EmotionSegment],
+    audio_duration: float,
+) -> list[EmotionTimelineEntry]:
+    """按各句字数占总字数比例分配 audio_duration 时长."""
+    if not segments or audio_duration <= 0:
+        return []
+    total_chars = sum(len(s.text) for s in segments)
+    if total_chars <= 0:
+        return []
+    entries: list[EmotionTimelineEntry] = []
+    pos = 0.0
+    for i, seg in enumerate(segments):
+        if i == len(segments) - 1:
+            end = audio_duration  # 最后一段到结尾，避免浮点误差
+        else:
+            end = pos + (len(seg.text) / total_chars) * audio_duration
+        if end > pos:
+            entries.append(
+                EmotionTimelineEntry(
+                    start=pos,
+                    end=end,
+                    emo=seg.emo,
+                    intensity=seg.intensity,
+                )
+            )
+        pos = end
+    return entries
+
+
+def align_timeline_by_timings(
+    segments: list[EmotionSegment],
+    sentence_timings: list[dict[str, Any]],
+    audio_duration: float,
+) -> list[EmotionTimelineEntry]:
+    """使用 TTS sentence_timings 精确对齐（优先方案）.
+
+    sentence_timings 格式：[{"start":0.0,"end":1.2,"text":"句子"}, ...]
+    按句序匹配 segments 和 timings，长度不一致时回退到按字数比例。
+    """
+    if not sentence_timings or len(sentence_timings) != len(segments):
+        return align_timeline_by_length(segments, audio_duration)
+    entries: list[EmotionTimelineEntry] = []
+    for seg, timing in zip(segments, sentence_timings, strict=False):
+        try:
+            start = float(timing.get("start", 0))
+            end = float(timing.get("end", 0))
+        except (TypeError, ValueError):
+            return align_timeline_by_length(segments, audio_duration)
+        if end <= start:
+            continue
+        entries.append(
+            EmotionTimelineEntry(
+                start=start,
+                end=end,
+                emo=seg.emo,
+                intensity=seg.intensity,
+            )
+        )
+    return entries
+
+
+# ── LLM 情绪分析服务 ─────────────────────────────────────────────
+class DittoEmotionService:
+    """Ditto 情绪分析服务（带 LRU 缓存）."""
+
+    def __init__(self, settings=None):
+        from packages.config import get_api_settings
+
+        self.settings = settings or get_api_settings()
+        self._client = None
+
+    def _cfg(self, key: str) -> Any:
+        """优先读后台 system_config，未配置则回退到 settings（env 默认）."""
+        try:
+            from packages.application.system_config_service import get_config
+
+            return get_config(key, getattr(self.settings, key, None))
+        except Exception:
+            return getattr(self.settings, key, None)
+
+    @property
+    def enabled(self) -> bool:
+        return bool(self._cfg("ditto_emotion_enabled"))
+
+    def _get_prompt_template(self) -> str:
+        """优先用配置（环境变量），否则读文件."""
+        cfg_prompt = self._cfg("ditto_emotion_prompt") or ""
+        if cfg_prompt.strip():
+            return cfg_prompt.strip()
+        return _load_default_prompt()
+
+    def _cache_key(self, text: str) -> str:
+        return hashlib.md5(text.strip().encode("utf-8")).hexdigest()
+
+    def _get_llm_client(self):
+        if self._client is None:
+            from packages.shared.ai_client import get_doubao_client
+
+            self._client = get_doubao_client()
+        return self._client
+
+    def _call_llm(self, text: str) -> list[EmotionSegment]:
+        """调 LLM 分析情绪，失败返回空列表."""
+        template = self._get_prompt_template()
+        prompt = template.replace("{文案}", text)
+        messages = [{"role": "user", "content": prompt}]
+        model = self._cfg("ditto_emotion_model") or None
+        temperature = self._cfg("ditto_emotion_temperature")
+        timeout = getattr(self.settings, "ditto_emotion_timeout", 10)
+        max_tokens = getattr(self.settings, "ditto_emotion_max_tokens", 1024)
+        try:
+            client = self._get_llm_client()
+            result = client.chat_completion(
+                messages=messages,
+                model=model,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                timeout=timeout,
+            )
+        except Exception as exc:
+            logger.warning("[ditto_emotion] LLM 调用异常: %s", exc)
+            return []
+        if not result:
+            return []
+        segments = _parse_emotion_json(result)
+        if not segments:
+            logger.warning("[ditto_emotion] LLM 返回解析失败: %s", result[:200])
+        return segments
+
+    def analyze(self, text: str) -> list[EmotionSegment]:
+        """分析文案情绪（带缓存），失败返回空列表."""
+        if not self.enabled or not text or not text.strip():
+            return []
+        key = self._cache_key(text)
+        return _cached_analyze(self, key, text)
+
+    def build_timeline(
+        self,
+        text: str,
+        audio_duration: float,
+        sentence_timings: Optional[list[dict[str, Any]]] = None,
+    ) -> str:
+        """完整流程：分句→LLM分析→时间对齐→序列化为JSON字符串.
+
+        返回: JSON 字符串（可直接传 Ditto emo_timeline 参数）；空字符串表示降级中性。
+        """
+        segments = self.analyze(text)
+        if not segments:
+            return ""
+        if sentence_timings:
+            entries = align_timeline_by_timings(segments, sentence_timings, audio_duration)
+        else:
+            entries = align_timeline_by_length(segments, audio_duration)
+        if not entries:
+            return ""
+        return json.dumps([e.to_dict() for e in entries], ensure_ascii=False)
+
+
+# ── 模块级 LRU 缓存实例 ─────────────────────────────────────────
+# 每个 service 实例共享缓存（按 cache_key 区分）
+@lru_cache(maxsize=512)
+def _cached_analyze(service: DittoEmotionService, cache_key: str, text: str) -> list[EmotionSegment]:
+    """LRU 缓存包装：cache_key 由文案 hash 生成，maxsize 从配置读."""
+    # 注意：service 参数仅用于传递调用，缓存由 cache_key 驱动
+    segments = service._call_llm(text)
+    # 如果 LLM 返回空（比如分句数量不匹配），尝试直接对预分句结果分析
+    if not segments:
+        pre_splits = split_sentences(text)
+        if len(pre_splits) > 1:
+            # 用预分句结果兜底：全中性低强度
+            segments = [EmotionSegment(text=s, emo=EMO_NEUTRAL, intensity=0.1) for s in pre_splits]
+    return segments
+
+
+_singleton: Optional[DittoEmotionService] = None
+
+
+def get_ditto_emotion_service() -> DittoEmotionService:
+    global _singleton
+    if _singleton is None:
+        _singleton = DittoEmotionService()
+    return _singleton
+
+
+def reset_ditto_emotion_service() -> None:
+    """#2246：后台配置变更后重置单例并清空 LLM 结果 LRU 缓存."""
+    global _singleton
+    _singleton = None
+    _cached_analyze.cache_clear()

packages/application/ditto_service.py

@@ -0,0 +1,308 @@
+"""蚂蚁 Ditto 数字人口型 API 客户端 — #2076.
+
+封装 Ditto FastAPI（部署在 5060Ti GPU 节点，Tailscale 内网可达）：
+- GET  /health               健康检查
+- POST /generate             生成口型视频（同步返回 MP4 流）
+
+关键特性：
+- 入参：video_url（人物驱动视频 URL，用户上传优先；未传时用 default_video_url 兜底）
+        + audio_url（TTS 音频 URL） + script（文案原文）
+        + emo_timeline（可选，LLM 情绪时间线 JSON 字符串）
+- 出参：直接返回 video/mp4 字节流（自带音频，无需二次混流）
+- 429 时指数退避重试（最多 ditto_max_retries 次）
+- 500/超时/网络不可达视为失败
+- 输出 MP4 字节流转存到自家 OSS，返回公网 URL
+
+注意：
+- 保留 MuseTalk/GPU 路径不变；本服务作为更高优先级的第三条口型路径
+- video_url 时长对齐（视频<音频时循环延长）在 Celery 任务层用 ffmpeg 预处理
+- Ditto 输出自带音视频，不需要 GFPGAN 超分，不需要 ffmpeg 音视频混流
+"""
+
+from __future__ import annotations
+
+import io
+import logging
+import time
+from dataclasses import dataclass
+from typing import Optional
+
+import httpx
+
+from packages.config import get_api_settings
+
+logger = logging.getLogger(__name__)
+
+
+class DittoError(Exception):
+    """Ditto API 调用失败."""
+
+    def __init__(self, message: str, code: str = "DittoError", status_code: int = 0):
+        self.code = code
+        self.status_code = status_code
+        super().__init__(message)
+
+
+@dataclass
+class DittoResult:
+    """Ditto 生成结果."""
+
+    video_bytes: bytes
+    video_url: str = ""  # 转存 OSS 后填充
+    elapsed_seconds: float = 0.0
+    rtf: float = 0.0  # 实时率（响应头 X-RTF）
+    frames: int = 0  # 帧数（响应头 X-Frames）
+
+
+class DittoClient:
+    """蚂蚁 Ditto 数字人口型 API 客户端."""
+
+    def __init__(
+        self,
+        base_url: Optional[str] = None,
+        default_video_url: Optional[str] = None,
+        max_retries: Optional[int] = None,
+        timeout: Optional[int] = None,
+    ):
+        s = get_api_settings()
+        self.base_url = (base_url or s.ditto_api_base_url or "").rstrip("/")
+        self.default_video_url = default_video_url or s.ditto_default_video_url or ""
+        self.max_retries = int(max_retries if max_retries is not None else s.ditto_max_retries)
+        self.timeout = int(timeout if timeout is not None else s.ditto_request_timeout)
+        try:
+            from packages.application.system_config_service import get_config
+
+            self.blend_frames = int(get_config("ditto_blend_frames", s.ditto_blend_frames))
+        except Exception:
+            self.blend_frames = int(s.ditto_blend_frames)
+
+    @property
+    def is_configured(self) -> bool:
+        """配置是否完整（base_url 必填 + 默认模板视频兜底 URL 有值）。
+
+        注意：即使 is_configured=True，实际生成时优先使用用户上传的 video_url；
+        default_video_url 仅作为用户未上传视频时的兜底。
+        """
+        return bool(self.base_url) and bool(self.default_video_url)
+
+    def health(self) -> bool:
+        """健康检查；成功返回 True，失败返回 False（不抛异常）."""
+        if not self.base_url:
+            return False
+        url = f"{self.base_url}/health"
+        try:
+            with httpx.Client(timeout=5.0) as client:
+                resp = client.get(url)
+                ok = resp.status_code == 200
+                if ok:
+                    logger.info("[ditto] health check OK: %s", url)
+                else:
+                    logger.warning("[ditto] health check status=%d: %s", resp.status_code, url)
+                return ok
+        except Exception as exc:
+            logger.warning("[ditto] health check failed: %s", exc)
+            return False
+
+    def generate(
+        self,
+        *,
+        audio_url: str,
+        script: str,
+        video_url: Optional[str] = None,
+        emo_global: int = 4,
+        use_script_emo: bool = True,
+        blend_frames: Optional[int] = None,
+        emo_timeline: str = "",
+    ) -> DittoResult:
+        """调用 Ditto /generate 接口，返回 MP4 字节流结果.
+
+        Raises DittoError on failure.
+        """
+        if not self.base_url:
+            raise DittoError("DITTO_API_BASE_URL 未配置", code="ConfigMissing")
+        driver_url = video_url or self.default_video_url
+        if not driver_url:
+            raise DittoError("Ditto 人物模板视频 URL 未配置", code="ConfigMissing")
+        if not audio_url:
+            raise DittoError("audio_url 不能为空", code="InvalidParam")
+        if not script:
+            script = " "
+
+        _blend = blend_frames if blend_frames is not None else self.blend_frames
+        payload = {
+            "video_url": driver_url,
+            "audio_url": audio_url,
+            "script": script,
+            "emo_global": emo_global,
+            "use_script_emo": use_script_emo,
+            "blend_frames": _blend,
+        }
+        if emo_timeline:
+            payload["emo_timeline"] = emo_timeline
+        url = f"{self.base_url}/generate"
+
+        last_exc: Optional[Exception] = None
+        for attempt in range(self.max_retries + 1):
+            try:
+                start = time.monotonic()
+                # 精细化超时：connect=10s（网络不通快速失败），read=120s（最长音频~45s按RTF=2.8推算）
+                _timeout = httpx.Timeout(connect=10.0, read=self.timeout, write=30.0, pool=10.0)
+                with httpx.Client(timeout=_timeout, follow_redirects=True) as client:
+                    resp = client.post(url, json=payload)
+                elapsed = time.monotonic() - start
+
+                if resp.status_code == 429:
+                    wait = min(2**attempt, 30)
+                    logger.warning(
+                        "[ditto] GPU 繁忙 (429)，%ds 后重试 (%d/%d)",
+                        wait,
+                        attempt + 1,
+                        self.max_retries,
+                    )
+                    if attempt >= self.max_retries:
+                        raise DittoError(
+                            f"Ditto GPU 繁忙，重试 {self.max_retries} 次仍失败",
+                            code="BusyRetriesExhausted",
+                            status_code=429,
+                        )
+                    time.sleep(wait)
+                    continue
+
+                if resp.status_code != 200:
+                    _text = (resp.text or "")[:300]
+                    logger.error(
+                        "[ditto] generate 失败 status=%d attempt=%d body=%s",
+                        resp.status_code,
+                        attempt + 1,
+                        _text,
+                    )
+                    if resp.status_code >= 500 and attempt < self.max_retries:
+                        time.sleep(min(2**attempt, 15))
+                        continue
+                    raise DittoError(
+                        f"Ditto 返回 {resp.status_code}: {_text}",
+                        code="DittoAPIError",
+                        status_code=resp.status_code,
+                    )
+
+                video_bytes = resp.content
+                if not video_bytes or len(video_bytes) < 1024:
+                    raise DittoError(
+                        f"Ditto 返回内容异常（size={len(video_bytes) if video_bytes else 0}）",
+                        code="EmptyResponse",
+                    )
+                try:
+                    rtf = float(resp.headers.get("X-RTF", "0") or 0)
+                except ValueError:
+                    rtf = 0.0
+                try:
+                    frames = int(resp.headers.get("X-Frames", "0") or 0)
+                except ValueError:
+                    frames = 0
+                try:
+                    x_time = float(resp.headers.get("X-Time", "0") or 0)
+                    if x_time > 0:
+                        elapsed = x_time
+                except ValueError:
+                    pass
+
+                logger.info(
+                    "[ditto] generate 成功 size=%d rtf=%.2f frames=%d elapsed=%.1fs attempt=%d",
+                    len(video_bytes),
+                    rtf,
+                    frames,
+                    elapsed,
+                    attempt + 1,
+                )
+                return DittoResult(
+                    video_bytes=video_bytes,
+                    elapsed_seconds=elapsed,
+                    rtf=rtf,
+                    frames=frames,
+                )
+
+            except DittoError:
+                raise
+            except (httpx.ConnectError, httpx.NetworkError, ConnectionError, OSError) as exc:
+                # 网络不通/连接被拒（如 GPU 断网/Tailscale 掉线），不重试，直接快速回退
+                logger.warning("[ditto] 网络不可达 attempt=%d err=%s", attempt + 1, exc)
+                raise DittoError(
+                    f"Ditto 网络不可达: {exc}",
+                    code="NetworkUnreachable",
+                ) from exc
+            except httpx.TimeoutException as exc:
+                last_exc = exc
+                logger.warning("[ditto] 请求超时 attempt=%d err=%s", attempt + 1, exc)
+                if attempt < self.max_retries:
+                    time.sleep(min(2**attempt, 15))
+                    continue
+                raise DittoError(
+                    f"Ditto 请求超时（read={self.timeout}s），重试耗尽",
+                    code="Timeout",
+                ) from exc
+            except Exception as exc:
+                last_exc = exc
+                logger.warning("[ditto] 请求异常 attempt=%d err=%s", attempt + 1, exc)
+                if attempt < self.max_retries:
+                    time.sleep(min(2**attempt, 10))
+                    continue
+                raise DittoError(f"Ditto 调用异常: {exc}", code="NetworkError") from exc
+
+        raise DittoError("Ditto 未知错误", code="Unknown") from last_exc
+
+    def generate_and_persist(
+        self,
+        *,
+        job_id: str,
+        user_id: str,
+        audio_url: str,
+        script: str,
+        video_url: Optional[str] = None,
+        emo_timeline: str = "",
+        blend_frames: Optional[int] = None,
+    ) -> DittoResult:
+        """调用 generate 并把 MP4 转存到自家 OSS，返回带 video_url 的结果."""
+        result = self.generate(
+            audio_url=audio_url,
+            script=script,
+            video_url=video_url,
+            emo_timeline=emo_timeline,
+            blend_frames=blend_frames,
+        )
+        try:
+            from packages.shared.storage import get_shared_storage_service
+
+            storage = get_shared_storage_service()
+            storage_key = f"ditto-output/{user_id}/{job_id}.mp4"
+            public_url = storage.upload_file(
+                io.BytesIO(result.video_bytes),
+                storage_key,
+                content_type="video/mp4",
+            )
+            result.video_url = public_url
+            logger.info(
+                "[ditto] 转存 OSS 完成 job=%s key=%s",
+                job_id,
+                storage_key,
+            )
+        except Exception as exc:
+            logger.error("[ditto] 转存 OSS 失败 job=%s err=%s", job_id, exc, exc_info=True)
+            raise DittoError(f"Ditto 结果转存 OSS 失败: {exc}", code="StorageError") from exc
+        return result
+
+
+_ditto_client_singleton: Optional[DittoClient] = None
+
+
+def get_ditto_client() -> DittoClient:
+    """获取 DittoClient 单例（简易工厂，便于单测 mock）."""
+    global _ditto_client_singleton
+    if _ditto_client_singleton is None:
+        _ditto_client_singleton = DittoClient()
+    return _ditto_client_singleton
+
+
+def reset_ditto_client() -> None:
+    """#2246：后台配置变更后重置 DittoClient 单例."""
+    global _ditto_client_singleton
+    _ditto_client_singleton = None

packages/application/prompts/ditto_emotion.txt

@@ -0,0 +1,27 @@
+你是一个数字人视频表情导演。给定一段口播文案，分析每句话应该用什么表情和强度，让数字人说话时表情自然有变化，不僵硬。
+【表情编号】
+0=愤怒（营销场景禁用）
+1=厌恶（禁用）
+2=害怕（禁用）
+3=开心：介绍优点、优惠、好消息、号召行动时用
+4=中性：默认表情，陈述事实、平铺直叙时用
+5=伤心：仅在共情用户痛点时低强度使用（如"是不是经常遇到…"）
+6=惊讶：惊喜、意外、强调价值时用（如"居然""只要""竟然"）
+7=轻蔑（禁用）
+【强度说明】
+0.1-0.2：几乎看不出变化，比中性多一点情绪色彩
+0.3-0.4：有明显但自然的情绪，正常说话的波动
+0.5-0.6：较强情绪，感叹句/重点强调
+0.7+：极强情绪，极少使用
+【规则】
+1. 按自然语义分句，以。！？；为主要分界，逗号不分
+2. 60-70%的句子应该用中性(4)，不要每句都标情绪
+3. 情绪和内容匹配：卖点→开心(3)，痛点共情→伤心(5)低强度，惊喜/划算→惊讶(6)，陈述→中性(4)
+4. 相邻句子情绪不要剧烈跳变
+5. 感叹号结尾强度0.4-0.6，句号结尾一般0.1-0.3
+6. 开头结尾句用中性(4)或低强度开心(3)
+7. 禁止使用0/1/2/7
+【输出格式】严格JSON数组，不要输出其他内容
+[{"text":"句子原文","emo":3,"intensity":0.4}]
+【文案】
+{文案}

packages/application/system_config_service.py

@@ -0,0 +1,189 @@
+"""系统配置应用服务 — #2246.
+
+- get_config(key, default) / set_config(...) / list_configs(category)
+- 首次访问时从 DB 加载并进程内缓存；set_config 后失效缓存
+- set_config 成功后重置 Ditto 情绪服务与 Ditto 客户端单例（含 LRU 缓存），
+  保证后台改动立即生效；worker 不直接 HTTP 读配置，统一通过本模块。
+"""
+
+from __future__ import annotations
+
+import logging
+import threading
+from typing import Any
+
+from packages.adapters.sqlalchemy_impl import session as db_session
+from packages.adapters.sqlalchemy_impl.system_setting_repository import (
+    SQLAlchemySystemSettingRepository,
+)
+from packages.domain.system_setting import (
+    SystemSetting,
+    infer_setting_type,
+    serialize_setting_value,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class SystemConfigService:
+    """进程内缓存的系统配置服务."""
+
+    def __init__(self, session_factory=None):
+        self._session_factory = session_factory
+        self._cache: dict[str, Any] | None = None
+        self._lock = threading.Lock()
+
+    # ── 会话 ─────────────────────────────────────────────────────
+    def _get_session_factory(self):
+        # 必须运行时读取模块属性：模块导入时 SessionLocal 还是 None，
+        # initialize_database() 之后才被赋值，import 时绑定会拿到旧值。
+        factory = self._session_factory or db_session.SessionLocal
+        if factory is None:
+            raise RuntimeError("数据库会话工厂未初始化")
+        return factory
+
+    # ── 缓存 ─────────────────────────────────────────────────────
+    def _load_cache(self) -> dict[str, Any]:
+        with self._lock:
+            if self._cache is not None:
+                return self._cache
+            cache: dict[str, Any] = {}
+            factory = self._get_session_factory()
+            session = factory()
+            try:
+                repo = SQLAlchemySystemSettingRepository(session)
+                for setting in repo.list_all():
+                    try:
+                        cache[setting.setting_key] = setting.get_typed_value()
+                    except Exception as exc:  # 损坏配置不阻塞启动
+                        logger.warning(
+                            "[system_config] 跳过损坏配置 %s: %s",
+                            setting.setting_key,
+                            exc,
+                        )
+            finally:
+                session.close()
+            self._cache = cache
+            return cache
+
+    def reload(self) -> dict[str, Any]:
+        """强制重新从 DB 加载配置，返回新缓存."""
+        with self._lock:
+            self._cache = None
+        return self._load_cache()
+
+    def invalidate(self) -> None:
+        with self._lock:
+            self._cache = None
+
+    # ── 读 ───────────────────────────────────────────────────────
+    def get_config(self, key: str, default: Any = None) -> Any:
+        cache = self._load_cache()
+        return cache.get(key, default)
+
+    def list_configs(self, category: str | None = None) -> list[SystemSetting]:
+        factory = self._get_session_factory()
+        session = factory()
+        try:
+            return SQLAlchemySystemSettingRepository(session).list_all(category)
+        finally:
+            session.close()
+
+    # ── 写 ───────────────────────────────────────────────────────
+    def set_config(
+        self,
+        key: str,
+        value: Any,
+        setting_type: str | None = None,
+        updated_by: str | None = None,
+        *,
+        description: str | None = None,
+        category: str = "general",
+        is_public: bool = False,
+    ) -> Any:
+        st = setting_type or infer_setting_type(value)
+        serialized = serialize_setting_value(value, st)
+
+        factory = self._get_session_factory()
+        session = factory()
+        try:
+            repo = SQLAlchemySystemSettingRepository(session)
+            existing = repo.get_by_key(key)
+            if existing is not None:
+                existing.setting_value = serialized
+                existing.setting_type = st
+                if updated_by is not None:
+                    existing.updated_by = updated_by
+                if description is not None:
+                    existing.description = description
+                setting = existing
+            else:
+                setting = SystemSetting(
+                    setting_key=key,
+                    setting_value=serialized,
+                    setting_type=st,
+                    description=description or "",
+                    is_public=is_public,
+                    category=category,
+                    updated_by=updated_by,
+                )
+            repo.upsert(setting)
+        finally:
+            session.close()
+
+        self.invalidate()
+        self._reset_ditto_singletons(key)
+        return value
+
+    def delete_config(self, key: str) -> bool:
+        factory = self._get_session_factory()
+        session = factory()
+        try:
+            deleted = SQLAlchemySystemSettingRepository(session).delete_by_key(key)
+        finally:
+            session.close()
+        if deleted:
+            self.invalidate()
+            self._reset_ditto_singletons(key)
+        return deleted
+
+    # ── Ditto 联动 ───────────────────────────────────────────────
+    @staticmethod
+    def _reset_ditto_singletons(key: str) -> None:
+        try:
+            from packages.application import ditto_emotion_service as emo_mod
+            from packages.application import ditto_service as ditto_mod
+
+            emo_mod.reset_ditto_emotion_service()
+            ditto_mod.reset_ditto_client()
+            logger.info("[system_config] %s 更新，已重置 Ditto 单例", key)
+        except Exception as exc:  # 联动失败不影响配置落库
+            logger.warning("[system_config] 重置 Ditto 单例失败: %s", exc)
+
+
+_service_singleton: SystemConfigService | None = None
+
+
+def get_system_config_service() -> SystemConfigService:
+    global _service_singleton
+    if _service_singleton is None:
+        _service_singleton = SystemConfigService()
+    return _service_singleton
+
+
+def get_config(key: str, default: Any = None) -> Any:
+    """便捷读取：优先 DB 配置，未配置时返回 default（调用方传 env 值兜底）."""
+    try:
+        return get_system_config_service().get_config(key, default)
+    except Exception as exc:
+        logger.warning("[system_config] 读取 %s 失败，使用默认值: %s", key, exc)
+        return default
+
+
+def set_config(
+    key: str,
+    value: Any,
+    setting_type: str | None = None,
+    updated_by: str | None = None,
+) -> Any:
+    return get_system_config_service().set_config(key, value, setting_type=setting_type, updated_by=updated_by)

packages/application/viral_video/prompts.py

@@ -1,16 +1,19 @@
-"""爆款视频 5 套 Prompt 模板默认值（#2040 核心资产）。
+"""爆款视频 Prompt 模板默认值（v8 / v3 叙述优先重构）。
 
-重要约定（用户明确要求）：
-- 所有 system_prompt / user_prompt_template / example_output 都是**纯文本自然语言 + XML 标签**，
-  运营可直接看懂和编辑，禁止 JSON、禁止 ```json 代码块。
-- LLM 按 XML 标签输出字段，程序用正则解析（见 xml_parser.py）。
-- user_prompt_template 中花括号占位符（如 {user_copy_text}）在运行时填充。
+设计原则（灵应 2026-10-07）：LLM 直接输出最终给用户看的文案，代码尽量薄。
+- image_analysis v8：VLM 主交付物是自然叙述风格的 summary_markdown，结构化
+  字段仅保留 type/name/brand/has_person，顶层 products 改名 images；
+- storyboard v3：口播台词口语化、画面描述有画面感，copy_display_markdown 是
+  LLM 直接写给用户看的流畅叙述文案，代码只做解析不改写；
+- intent_parsing 步骤整体删除，意图理解并入 storyboard 一次调用。
+
+模板字段与 DB 表 viral_video_prompt_templates、prompt_loader 完全对应：
+name / prompt_type / version(int) / system_prompt / user_prompt_template /
+example_output / is_active。
 """
 
 from __future__ import annotations
 
-TEMPLATE_VERSION = 1
-
 # 所有文案类 Prompt 自动注入的硬约束
 GLOBAL_CONSTRAINTS = """【必须遵守的硬约束】
 1. 不编造时间：不写“今年最新”“2024 爆款”等会过时的时间表述。
@@ -19,7 +22,7 @@ GLOBAL_CONSTRAINTS = """【必须遵守的硬约束】
 4. 符合广告法及平台社区规范。
 5. 只描述图片中真实可见的内容，看不到的不瞎猜。"""
 
-# 反套路化要求
+# 负向提示（注入 storyboard / 视频生成负面词）
 NEGATIVE_RULES = """【反套路化要求】
 禁止使用“家人们谁懂啊”“绝绝子”“宝子们”“家人们”“太绝了”“yyds”等烂大街网络词；
 禁止固定模板化开头；语言要像真人朋友之间的分享，自然、具体、有信息量。"""
@@ -27,300 +30,216 @@ NEGATIVE_RULES = """【反套路化要求】
 # 输出禁用套路词（测试会检查）
 BANNED_PHRASES = ["家人们谁懂啊", "绝绝子", "宝子们", "yyds", "太绝了"]
 
-# 文案融合三档独立指令段
+# 文案融合三档独立指令段（storyboard 一次生成，按档位注入风格指令）
 FUSION_INSTRUCTIONS = {
     "ai_full": """【本次创作模式：AI 全权创作】
-你是资深短视频编导。用户只提供了产品图片，没有给出具体文案方向。请根据图片内容和营销参数，自由发挥创作完整的爆款短视频文案。充分挖掘产品真实可见的卖点，使用爆款结构，抓人眼球。""",
+你是资深短视频编导。用户只提供了产品/门店图片，没有给出具体文案方向。请根据图片的真实观察和营销参数，自由发挥创作完整成片级方案，口播自然、画面可拍。""",
     "ai_polish": """【本次创作模式：AI 辅助润色】
-你是用户的文案助理。用户已经写了草稿/关键词/碎碎念，表达了他想讲的核心意思，但表达不完整、不够吸引人。你的任务是：以用户的意思为主，保留他想表达的所有核心信息点，在此基础上润色扩写、调整语序、增加衔接、优化表达，让文案更流畅更有吸引力。绝对不能改变用户想表达的核心意思，不能把用户的观点换成相反的，不能添加用户没提到的产品卖点。用户提到的品牌名、价格、人名、具体事实必须原样保留。""",
+用户已给出方向或碎碎念。以用户的意思为主，保留其所有核心信息，在此基础上润色、补衔接、优化表达，让口播更自然、画面更具体；绝不改变用户核心意思，不添加用户没提到的卖点，品牌名、价格、人名等事实原样保留。""",
     "user_primary": """【本次创作模式：以用户原文为主】
-你是文案润色助手。用户已经写好了明确的文案，这是他最终想表达的内容。你的任务是最小化修改：只做必要的错别字修正、标点调整、语句通顺度优化，以及添加必要的衔接词让口播更自然。用户的核心句子、关键表述、事实信息一律不改。如果用户文案本身已经很好，直接返回，不要为了改而改。personal_brands 中的事实信息必须逐字保留。""",
+最小化修改：只做必要的通顺、合规修正与衔接补全，用户的核心句子与事实一律不改；用户文案已经很好就直接用，不为改而改。""",
 }
 
-# ── 模板1：图片多模态分析（VLM）────────────────────────────────────────
-_IMAGE_ANALYSIS_SYSTEM = f"""你是电商商品视觉分析师，负责从商品图片中提取真实可见的商品信息。
+# ── 模板1：图片多模态分析 v8（叙述优先）───────────────────────────────
+_IMAGE_ANALYSIS_SYSTEM = """你是一名擅长观察和写作的品牌内容编导。面对一张真实图片，先用眼睛仔细看，再用自然、流畅、具体的中文把画面写成一段可以直接读给人听的描述。
 
-工作方式（分步骤看，不要跳步）：
-1. 先看整体：有哪些产品、什么场景、有没有人物。
-2. 再看细节：包装文字、颜色构成、人物状态、画面质感。
-3. 最后提炼卖点：只总结图片里能看到的卖点。
+## 输出格式（严格 JSON，不要输出 JSON 以外的任何内容）
+{
+  "images": [
+    {
+      "type": "store 或 product 或 person 或 scene，四选一",
+      "name": "主体名称，看不出就写“未识别”",
+      "brand": "品牌名，看不出就留空字符串",
+      "has_person": false,
+      "summary_markdown": "用 Markdown 写成的自然叙述，这是最主要的交付物"
+    }
+  ]
+}
 
-{GLOBAL_CONSTRAINTS}
+## summary_markdown 写作要求（最重要）
+1. 写成完整、通顺的句子，像在跟朋友认真描述你看到的画面；不要用分号堆砌关键词，不要罗列“核心特征：xxx”“主色调：xxx”这类填表式标签。
+2. 开头先给一句整体定性，让读者立刻明白这是什么场景、什么主体。
+3. 颜色、材质、形状、部件要具体可感，写到位置和搭配；画面里出现的文字原样读出并自然融进句子，数字、规格、价格精确引用，看不清的不要编造。
+4. 只写真实看到的内容，不脑补功能、疗效、销量或画面之外的信息。
+5. 长度控制在 200-500 字。
 
-请严格按下面的标签格式输出，标签名一个都不能改，不要输出任何解释，不要用代码块：
-<products> 下面每个产品用一个 <product> 标签，属性 name 是产品名、features 是外观特征、position 是 main 或 secondary、image_index 是第几张图（从0开始）。
-<colors> 下面每个主要颜色用一个 <color> 标签，属性 hex 是色值、name 是颜色名、coverage 是占比小数。
-<people> 用一个标签，属性 has_person、count、gender、age_range、hair（发型发色）、skin_tone（肤色）、face_shape（脸型）、outfit（穿着）、pose（姿态）、expression（表情）分别描述人物外貌。有人物时属性尽量具体（如hair="黑色长直发"、outfit="白色衬衫"），无人像时除has_person=false外其他填"无法判断"。
-<mood> 标签写画面整体情绪氛围。
-<visible_text> 下面每处可见文字用一个 <text_item> 标签，属性 text 是文字内容、position 是位置。
-<scene> 标签写场景描述。
-<quality> 用一个标签，属性 resolution、lighting、composition、blur 描述画质。
-<key_selling_points> 下面每个卖点用一个 <point> 标签。
+## 按类型组织内容
+- type=store（门店/店内环境）：用以下小标题分段，小标题下写连贯的句子而不是清单：
+###店铺主体
+###周边物品
+1.家具陈设
+2.商品与标识
+- type=product（商品）：按自然段从整体到局部描写——先说是什么、什么品牌，再写包装/外形、颜色与材质、标签文字、可见部件与规格。
+- type=person（人物）：描述人物身份感、姿态、穿着（上下装/颜色/款式）、动作与所处环境；用于品牌宣传时突出其精神状态。
+- type=scene（纯场景/风景）：描述空间或风景的构成、色彩、光线、氛围与关键物件。
 
-【人物属性硬性要求（has_person=true时必须遵守）】
-hair/skin_tone/face_shape/outfit四项绝对禁止填“无法判断”，必须基于图片可见特征给出具体中文描述：
-- hair：必须描述发型+发色，如“黑色齐肩直发”“棕色微卷中长发”“深棕色短发”
-- skin_tone：必须描述肤色，如“暖调自然肤色”“白皙肤色”“小麦色”
-- face_shape：必须描述脸型，如“鹅蛋脸”“圆脸”“瓜子脸”“方脸”
-- outfit：必须描述可见穿着，如“米色翻领衬衫”“白色T恤”“黑色连衣裙”
-即使局部被遮挡也要根据可见部分合理推断；确实看不清时按最接近的直观印象描述。
+## 判断规则
+- has_person：画面中出现可辨识的真实人物（脸或完整上半身）才为 true，海报/模特立牌/照片里的人不算。
+- 一张图只描述其本身；多张图属于同一场景时可呼应，但不编造对应关系。
+- 输出必须是严格 JSON，summary_markdown 是字符串，内部换行用 \\n 表示。"""
 
-其他非人物属性看不到或无法判断时填“无法判断”，布尔值填false，不要留空标签。
+_IMAGE_ANALYSIS_USER = """请分析这张图片。
+图片地址：{image_url}
+OCR 辅助文字（可能为空，仅供参考，不要照抄错误识别）：{ocr_text}
 
-【有人物场景输出参考（女性手持商品示例，必须写全10个属性，禁止省略）】
-<people has_person="true" count="1" gender="女" age_range="青年" hair="黑色齐肩直发" skin_tone="暖调自然肤色" face_shape="鹅蛋脸" outfit="米色翻领衬衫" pose="正面半身，手持商品" expression="面带微笑"/>"""
+严格按系统要求只输出 JSON。"""
 
-_IMAGE_ANALYSIS_USER = """请分析以下商品图片，共 {image_count} 张。
-所属行业：{industry}
-图片地址：
-{image_urls}
+_IMAGE_ANALYSIS_EXAMPLE = """{
+  "images": [
+    {
+      "type": "store",
+      "name": "御众堂门店",
+      "brand": "御众堂",
+      "has_person": false,
+      "summary_markdown": "###店铺主体\\n这是一家名为“御众堂”的线下门店内部，整体暖木色调……"
+    }
+  ]
+}"""
 
-按约定的标签格式输出分析结果。"""
+# ── 模板2：编导级分镜 v3（意图理解 + 分镜一次完成）────────────────────
+_STORYBOARD_SYSTEM = (
+    """你是一名懂短视频的编导和口播文案高手。你会拿到图片的真实观察、营销目的和用户参数，请一次性完成对营销意图的理解，并产出可直接拍摄/生成的分镜脚本。不要单独输出“意图解析”，意图要直接体现在台词和分镜里。
 
-_IMAGE_ANALYSIS_EXAMPLE = """<products>
-<product name="大公鸡头 多功能油污净 625ml" features="红色瓶盖白色瓶身，鸡头图案Logo" position="main" image_index="0"/>
-</products>
-<colors>
-<color hex="#D32F2F" name="红色" coverage="0.4"/>
-<color hex="#FFFFFF" name="白色" coverage="0.5"/>
-</colors>
-<people has_person="false" count="0" gender="无法判断" age_range="无法判断" hair="无法判断" skin_tone="无法判断" face_shape="无法判断" outfit="无法判断" pose="无法判断" expression="无法判断"/>
-<mood>干净、实用</mood>
-<visible_text>
-<text_item text="多功能油污净" position="瓶身正面"/>
-</visible_text>
-<scene>白底棚拍产品图</scene>
-<quality resolution="高清" lighting="均匀柔和" composition="主体居中" blur="false"/>
-<key_selling_points>
-<point>针对重油污设计</point>
-<point>大容量625ml</point>
-</key_selling_points>"""
+## 输出格式（XML，严格按结构输出，不要输出额外解释）
+<script>
+  <copy_display_markdown><![CDATA[直接展示给用户看的成片文案，用 Markdown 写成流畅叙述]]></copy_display_markdown>
+  <clips>
+    <clip index="1">
+      <time_range>0-3秒</time_range>
+      <voiceover>这一镜的口播台词</voiceover>
+      <visual>具体、有画面感的镜头描述（主体/动作/镜头运动/景别/光线）</visual>
+      <reference_image_index>0</reference_image_index>
+    </clip>
+  </clips>
+  <voiceover_script>把所有 clip 的 voiceover 连成完整口播稿</voiceover_script>
+  <theme>一句话主题</theme>
+  <negative>"""
+    + NEGATIVE_RULES
+    + """</negative>
+</script>
 
-# ── 模板2：用户文案意图解析（LLM）──────────────────────────────────────
-_INTENT_SYSTEM = f"""你负责理解用户的营销意图。用户给的文案可能只是几个关键词、碎碎念或者不完整的短句，你要读懂他真正想讲什么。
+## 写作要求
+1. 口播台词：像真人面对镜头说话，短句、口语化、有停顿有情绪，开头 3 秒给出钩子；不要书面腔，不要机械报参数。
+2. 画面描述：写清“观众会看到什么”，有动作、有镜头运动、有景别和光线，具体可拍；不堆砌形容词，不写无法实现的画面。
+3. copy_display_markdown：直接展示给最终用户的文案，用 Markdown 写成自然、流畅、有感染力的成片成片文案，可用小标题与短句组织；不要做字段列表，不要出现“镜头一/台词：”这类制作说明。
+4. 内容必须来自图片观察与用户给出的信息，不编造卖点、不夸大、不使用绝对化用语和虚假承诺。
+5. reference_image_index 填本镜参考图片序号（从 0 开始），没有合适参考图填 -1。
+6. 分镜数量与时长匹配总时长，节奏紧凑。
+7. 口播字数硬约束（必须严格遵守）：按每秒约 2.5~3 个中文字（正常口播语速）计算：
+   - 5秒视频：voiceover_script 总字数 12~15 字
+   - 10秒视频：voiceover_script 总字数 25~30 字
+   - 15秒视频：voiceover_script 总字数 35~45 字
+   - 20秒视频：voiceover_script 总字数 50~60 字
+   - 30秒视频：voiceover_script 总字数 75~90 字
+   - 每个 clip 的 voiceover 字数按该镜头时长比例分配
+   - 所有 clip 的 voiceover 字数之和必须等于总 voiceover_script 字数
+   - 宁可少写也不要多写，超长会导致 TTS 音频超出视频时长限制
+8. 镜头数量硬约束（必须严格遵守）：
+   - 5秒视频：1~2 个镜头
+   - 10秒视频：3 个镜头
+   - 15秒视频：3~4 个镜头
+   - 20秒视频：4~5 个镜头
+   - 30秒视频：6~8 个镜头
+9. 时间轴硬约束（必须严格遵守）：
+   - 每个 clip 的 time_range 必须写成 "X-Y秒" 格式，X 和 Y 是具体数字
+   - 第一个 clip 必须从 0 秒开始
+   - 最后一个 clip 必须结束于 total_duration 秒
+   - 相邻 clip 首尾相接，不能有间隙也不能重叠
+   - 每个 clip 的时长 = Y - X，必须 >= 2 秒
+10. 每个 clip 必须分配一个 reference_image_index（从 0 开始的图片序号），没有合适图片填 -1
+11. 必须严格按<marketing_purpose><target_audience><persona><viral_structure><language><industry>指定的参数写文案和分镜，不能忽略任何一项用户参数"""
+)
 
-{GLOBAL_CONSTRAINTS}
+_STORYBOARD_USER = """<marketing_purpose>{marketing_purpose}</marketing_purpose>
+<industry>{industry}</industry>
+<image_analysis>
+{image_summary}
+</image_analysis>
+<user_parameters>
+<theme_hint>{theme_hint}</theme_hint>
+<duration>{duration}秒</duration>
+<aspect_ratio>{aspect_ratio}</aspect_ratio>
+<tone>{tone}</tone>
+<target_audience>{target_audience}</target_audience>
+<persona>{persona_hint}</persona>
+<viral_structure>{viral_structure_hint}</viral_structure>
+<language>{language_hint}</language>
+<extra_requirements>{extra_requirements}</extra_requirements>
+</user_parameters>
+{video_style_section}
+请严格按 XML 结构输出分镜脚本。"""
 
-请严格按下面的标签格式输出，不要解释，不要用代码块：
-<intent_summary> 用用户的语言风格，一句话、30字以内概括核心意图。
-<core_messages> 下面每个核心信息点用一个 <message> 标签，属性 must_keep 为 true 或 false、confidence 为 0 到 1 的小数，标签内容写信息点。
-<personal_brands> 把用户提到的具体事实——品牌名、价格、人名、地名、时间、产品名——每条用一个 <brand> 标签，属性 category 取 brand、price、person、place、time、product 之一。这些事实必须原样引用，一个字都不能改。
-<emotion_tone> 写文案的情绪调性。
-<missing_info> 把你认为缺失、后续生成时需要合理推断的信息，每条用一个 <info> 标签；没有就输出空标签。"""
+_STORYBOARD_EXAMPLE = """<script>
+  <copy_display_markdown><![CDATA[# 在御众堂，把松弛的自己一点点找回来
+产后妈妈最懂那种力不从心，推开门，暖光和一杯热茶先接住了你……]]></copy_display_markdown>
+  <clips>
+    <clip index="1">
+      <time_range>0-3秒</time_range>
+      <voiceover>生完娃，是不是连照镜子的勇气都没了？</voiceover>
+      <visual>中近景，暖光下一位妈妈略显疲惫地看向镜中，镜头缓缓推近</visual>
+      <reference_image_index>0</reference_image_index>
+    </clip>
+  </clips>
+  <voiceover_script>生完娃，是不是连照镜子的勇气都没了？</voiceover_script>
+  <theme>产后妈妈走进御众堂重拾状态</theme>
+  <negative>模糊、畸变、夸大疗效、绝对化用语</negative>
+</script>"""
 
-_INTENT_USER = """用户原始文案：{user_copy_text}
-所属行业：{industry}
-图片分析结果（供参考）：
-{image_analysis}
+# ── 模板3：文案审核（合规/质量门禁）───────────────────────────────────
+_REVIEW_SYSTEM = """你是一名短视频广告合规审核与文案优化专家。审核待审文案：
+1) 广告法与平台合规（绝对化用语、虚假承诺、医疗功效宣称、导流违规）；
+2) 卖点是否聚焦、逻辑是否通顺、口播是否自然；
+3) 是否有机械堆砌、书面腔、标签化表述。
 
-请理解用户意图，按标签格式输出。"""
+只输出 XML，结构：
+<review>
+  <passed>true 或 false</passed>
+  <issues>
+    <issue>
+      <severity>high 或 medium 或 low</severity>
+      <field>问题所在位置/字段</field>
+      <problem>具体问题</problem>
+      <suggestion>可直接替换的修改</suggestion>
+    </issue>
+  </issues>
+  <rewrite>整体重写后的合规流畅版本（无问题时留空）</rewrite>
+</review>
+没有问题时 issues 留空、passed 为 true、rewrite 留空。"""
 
-_INTENT_EXAMPLE = """<intent_summary>一款厨房去油污神器，喷一喷油污就掉</intent_summary>
-<core_messages>
-<message must_keep="true" confidence="0.97">去油污效果好，喷上等几分钟再擦</message>
-<message must_keep="false" confidence="0.7">适合厨房重油污场景</message>
-</core_messages>
-<personal_brands>
-<brand category="product">大公鸡头多功能油污净</brand>
-<brand category="price">39块钱一瓶</brand>
-</personal_brands>
-<emotion_tone>亲切、真实、带分享感</emotion_tone>
-<missing_info>
-<info>没有说明具体容量，按图片读出的625ml处理</info>
-</missing_info>"""
+_REVIEW_USER = """<fusion_text>
+{fusion_text}
+</fusion_text>
 
-# ── 模板3：文案融合生成（LLM）──────────────────────────────────────────
-_FUSION_SYSTEM = """你负责为短视频生成营销文案。请按思维链分步完成：先定人设和目标客户，再找卖点，再搭结构，再安排情绪，最后写行动号召，不要一步到位乱写。
+请审核以上文案。"""
 
-{fusion_instruction}
-
-{global_constraints}
-
-{negative_rules}
-
-请严格按下面的标签格式输出，不要解释，不要用代码块：
-<title> 视频标题。
-<hook> 开头3秒钩子，5到15字。
-<body_points> 每个要点用一个 <point> 标签，属性 elaboration 是展开说明、image_index 是对应第几张图（从0开始），标签内容写要点。
-<cta> 口语化的行动号召。
-<script_segments> 每段配音用一个 <segment> 标签，属性 duration_sec 是秒数、image_index 是对应图片，标签内容写配音文案（纯口播文本，不加旁白标注、不加镜头标注、不加"主播："之类前缀）。
-<voiceover_script> 把所有 segment 的配音文案按顺序自然拼接成一段完整的纯口播文本（无标记、无括号、无前缀），长度要适配 {duration} 秒，约 {approx_chars} 字。
-<overview_theme> 视频主题（一句话概括）。
-<scene_and_lighting> 整体场景描述+光线设定（100-200字，要具体：在哪拍、什么光线、什么色调、什么氛围）。
-<word_count> 配音总字数，只写数字。
-<estimated_duration> 预计时长秒数，只写数字。
-
-用户在 personal_brands 中提到的品牌名、价格、人名、地名、时间、产品名等事实信息，必须原样出现在文案里，一个字都不能改。"""
-
-_FUSION_USER = """所属行业：{industry}
-目标客户：{target_customer}
-营销目的：{marketing_purpose}
-视频时长：{duration}秒
-图片分析结果：
-{image_analysis}
-用户意图解析结果：
-{intent_result}
-
-请按标签格式生成文案。"""
-
-_FUSION_EXAMPLE = """<title>厨房重油污，别再用洗洁精硬擦了</title>
-<hook>这油污，我真的忍很久了</hook>
-<body_points>
-<point elaboration="喷在油污上等几分钟，一擦就干净" image_index="0">大公鸡头油污净去油快</point>
-<point elaboration="39块钱625ml，能用很久" image_index="0">39块钱一瓶，性价比高</point>
-</body_points>
-<cta>厨房油污重的，真的可以试一瓶</cta>
-<script_segments>
-<segment duration_sec="3" image_index="0">这油污我真的忍很久了，用洗洁精擦半天都没用</segment>
-<segment duration_sec="6" image_index="0">后来换了这个大公鸡头油污净，喷上等几分钟，一擦就干净</segment>
-<segment duration_sec="4" image_index="0">39块钱625ml，厨房重油污的可以试一瓶</segment>
-</script_segments>
-<voiceover_script>这油污我真的忍很久了，用洗洁精擦半天都没用。后来换了这个大公鸡头油污净，喷上等几分钟，一擦就干净。39块钱625ml，厨房重油污的可以试一瓶。</voiceover_script>
-<overview_theme>厨房油污清洁好物分享</overview_theme>
-<scene_and_lighting>简洁明亮的厨房台面场景，自然光从窗户洒入，色调温暖柔和，突出产品白色瓶身与去油污对比效果。</scene_and_lighting>
-<word_count>58</word_count>
-<estimated_duration>13</estimated_duration>"""
-
-# ── 模板4：编导级分镜（LLM）────────────────────────────────────────────
-_STORYBOARD_SYSTEM = """你是短视频编导，负责把文案拆成可拍摄的分镜，为 Seedance 2.5 视频模型写编导分镜脚本。脚本将整体作为 prompt 一次性传给视频模型，必须让模型在连贯镜头流中清楚每段时间拍什么、画面如何、人物说什么。
-
-工作方式：
-1. 按文案的 script_segments 顺序分配镜头。
-2. 每个镜头确定景别/角度/运镜、画面场景与对白、人物动作细节、音效/BGM、转场。
-3. 检查所有镜头时长加起来接近目标时长，误差不超过2秒。
-4. image_index 必须在已上传图片范围内，第一张主图必须用在第一个镜头。
-
-{fusion_instruction}
-
-{global_constraints}
-
-{negative_rules}
-
-请严格按下面的标签格式输出，不要解释，不要用代码块：
-<clips> 下面每个镜头用一个 <clip> 标签，属性 image_index 是图片序号（从0开始）、transition 取 fade/cut/zoom_in/slide_left/dissolve/wipe 之一、zoom 取 in/out/null、duration_sec 是该镜头秒数、bgm_note 是该段BGM情绪。每个 <clip> 里面包含：
-  <voice_text> 该镜头配音文本（纯口播文本，不加旁白标注）；
-  <subtitle_text> 字幕文本，可与配音一致或更精简；
-  <shot_type_angle_movement> 景别+角度+运镜（例：近景俯拍45度，缓慢推镜；中景平视，固定镜头；特写平视，快速拉镜）；
-  <scene_and_dialogue> 画面场景描述 + 人物口播台词（对白要自然口语化，像朋友聊天，不要硬广推销腔）；
-  <action_details> 人物动作、表情、物品操作细节（手怎么动、表情变化、产品怎么展示）；
-  <audio_bgm> 环境音+BGM提示（例：轻快流行BGM，环境嘈杂咖啡店背景音）；
-  <transition> 硬切/淡入淡出/叠化（最后一镜写『结束』即可）；
-  <reference_image_index> 参考图片索引（0-based，对应第几张产品图，无则空）；
-  <ken_burns> 用一个空标签，属性 start、end 写"x,y"坐标、ease 写缓动方式；不需要运镜时坐标相同。"""
-
-_STORYBOARD_USER = """目标时长：{duration}秒
-上传图片数量：{image_count}张（第1张是主图/封面）
-文案内容：
-{fusion_result}
-图片分析结果：
-{image_analysis}
-
-请按标签格式输出分镜。"""
-
-_STORYBOARD_EXAMPLE = """<clips>
-<clip image_index="0" transition="cut" zoom="null" duration_sec="3" bgm_note="日常、轻微烦躁">
-<voice_text>这油污我真的忍很久了</voice_text>
-<subtitle_text>这油污忍很久了</subtitle_text>
-<shot_type_angle_movement>近景俯拍45度，缓慢推镜</shot_type_angle_movement>
-<scene_and_dialogue>厨房台面，主妇皱眉看着灶台油污。对白：这油污我真的忍很久了</scene_and_dialogue>
-<action_details>右手拿着脏抹布，无奈摇头</action_details>
-<audio_bgm>轻快日常BGM，带一点烦躁感</audio_bgm>
-<transition>硬切</transition>
-<reference_image_index>0</reference_image_index>
-<ken_burns start="0,0" end="0,0" ease="linear"/>
-</clip>
-<clip image_index="0" transition="zoom_in" zoom="in" duration_sec="6" bgm_note="轻快、出现转机">
-<voice_text>后来换了大公鸡头油污净，喷上等几分钟，一擦就干净</voice_text>
-<subtitle_text>喷上等几分钟，一擦就干净</subtitle_text>
-<shot_type_angle_movement>特写平视，固定镜头</shot_type_angle_movement>
-<scene_and_dialogue>手部特写，喷油污净在油污处。对白：后来换了这个大公鸡头油污净，喷上等几分钟，一擦就干净</scene_and_dialogue>
-<action_details>左手拿产品瓶身，右手按压喷头，等待片刻后用抹布轻擦</action_details>
-<audio_bgm>轻快转折BGM，带清爽感</audio_bgm>
-<transition>淡入淡出</transition>
-<reference_image_index>0</reference_image_index>
-<ken_burns start="20,20" end="80,80" ease="ease-in-out"/>
-</clip>
-<clip image_index="0" transition="fade" zoom="null" duration_sec="4" bgm_note="温暖、推荐">
-<voice_text>39块钱625ml，厨房重油污的可以试一瓶</voice_text>
-<subtitle_text>39元625ml，可以试一瓶</subtitle_text>
-<shot_type_angle_movement>中景平视，缓慢拉镜</shot_type_angle_movement>
-<scene_and_dialogue>产品正面展示，明亮背景。对白：39块钱625ml，厨房重油污的可以试一瓶</scene_and_dialogue>
-<action_details>产品置于画面中央，轻微转动展示瓶身</action_details>
-<audio_bgm>温暖收尾BGM</audio_bgm>
-<transition>结束</transition>
-<reference_image_index>0</reference_image_index>
-<ken_burns start="50,50" end="20,20" ease="ease-in-out"/>
-</clip>
-</clips>"""
-
-# ── 模板5：文案审核（LLM）──────────────────────────────────────────────
-_REVIEW_SYSTEM = f"""你是短视频文案合规审核员，从6个维度逐条检查文案：
-1. 违规词：有没有平台禁用词、敏感词。
-2. 夸大承诺：有没有“包治百病”“100%有效”“保证赚钱”等绝对化、夸大表述。
-3. 事实一致性：有没有编造价格、数据、认证，或者用户没提到的产品特性。
-4. 用户意图保留：在 ai_polish 和 user_primary 模式下，core_messages 中 must_keep=true 的点是否都保留了。
-5. 结构完整性：标题、钩子、正文、行动号召是否齐全。
-6. 语气人设：是否符合选定的人设语气，有没有“家人们谁懂啊”“绝绝子”“宝子们”等套路词。
-
-{GLOBAL_CONSTRAINTS}
-
-请严格按下面的标签格式输出，不要解释，不要用代码块：
-<passed> 整体是否通过，只写 true 或 false。
-<issues> 每个问题用一个 <issue> 标签，属性 dimension 是维度名、severity 取 error 或 warning、location 是问题所在（如 hook、body_points、cta），标签内容写问题描述；没有问题就输出空标签。
-<rewrite_suggestions> 每条具体修改建议用一个 <suggestion> 标签；没有就输出空标签。"""
-
-_REVIEW_USER = """本次创作模式：{fusion_level}
-待审核文案：
-{fusion_result}
-用户意图解析（用于核对核心信息是否保留）：
-{intent_result}
-
-请按6个维度审核，按标签格式输出。"""
-
-_REVIEW_EXAMPLE = """<passed>false</passed>
-<issues>
-<issue dimension="夸大承诺" severity="error" location="body_points">出现了“一喷100%掉光”的绝对化表述，违反广告法</issue>
-<issue dimension="用户意图保留" severity="warning" location="cta">用户强调的“39块钱”没有保留</issue>
-</issues>
-<rewrite_suggestions>
-<suggestion>把“一喷100%掉光”改为“喷上等几分钟，大部分油污能擦掉”</suggestion>
-<suggestion>在结尾补回“39块钱625ml”</suggestion>
-</rewrite_suggestions>"""
+_REVIEW_EXAMPLE = """<review>
+  <passed>false</passed>
+  <issues>
+    <issue>
+      <severity>high</severity>
+      <field>opening</field>
+      <problem>使用绝对化用语“全网第一”</problem>
+      <suggestion>改为“很多老客户回购的一款”</suggestion>
+    </issue>
+  </issues>
+  <rewrite>……</rewrite>
+</review>"""
 
 
-# 5 套模板默认数据（seed 数据源与 loader 的兜底）
 DEFAULT_TEMPLATES: list[dict] = [
     {
-        "name": "图片多模态分析",
+        "name": "图片多模态分析 v8",
         "prompt_type": "image_analysis",
-        "version": TEMPLATE_VERSION,
+        "version": 8,
         "system_prompt": _IMAGE_ANALYSIS_SYSTEM,
         "user_prompt_template": _IMAGE_ANALYSIS_USER,
         "example_output": _IMAGE_ANALYSIS_EXAMPLE,
         "is_active": True,
     },
     {
-        "name": "用户文案意图解析",
-        "prompt_type": "intent_parsing",
-        "version": TEMPLATE_VERSION,
-        "system_prompt": _INTENT_SYSTEM,
-        "user_prompt_template": _INTENT_USER,
-        "example_output": _INTENT_EXAMPLE,
-        "is_active": True,
-    },
-    {
-        "name": "文案融合生成",
-        "prompt_type": "copy_fusion",
-        "version": TEMPLATE_VERSION,
-        "system_prompt": _FUSION_SYSTEM,
-        "user_prompt_template": _FUSION_USER,
-        "example_output": _FUSION_EXAMPLE,
-        "is_active": True,
-    },
-    {
-        "name": "编导级分镜",
+        "name": "编导级分镜 v3",
         "prompt_type": "storyboard",
-        "version": TEMPLATE_VERSION,
+        "version": 3,
         "system_prompt": _STORYBOARD_SYSTEM,
         "user_prompt_template": _STORYBOARD_USER,
         "example_output": _STORYBOARD_EXAMPLE,
@@ -329,7 +248,7 @@ DEFAULT_TEMPLATES: list[dict] = [
     {
         "name": "文案审核",
         "prompt_type": "review",
-        "version": TEMPLATE_VERSION,
+        "version": 1,
         "system_prompt": _REVIEW_SYSTEM,
         "user_prompt_template": _REVIEW_USER,
         "example_output": _REVIEW_EXAMPLE,

packages/application/viral_video/reviewer.py

@@ -53,11 +53,19 @@ _LOCATIONS = ["title", "hook", "body_points", "cta", "script_segments"]
 
 
 class Reviewer:
+    # markdown展示字段不参与合规审核（避免格式字符误判）
+    _MARKDOWN_FIELDS = {"summary_markdown", "copy_display_markdown"}
+
     def __init__(self, client=None):
         if client is None:
-            from packages.shared.ai_client import get_doubao_client
+            try:
+                from packages.shared.ai_router import ai_router
 
-            client = get_doubao_client()
+                client = ai_router.get_llm_client("copy_review")
+            except Exception:
+                from packages.shared.ai_client import get_doubao_client
+
+                client = get_doubao_client()
         self.client = client
 
     # ── 审核 ────────────────────────────────────────────────────────────
@@ -65,8 +73,9 @@ class Reviewer:
         local = self._rule_check(fusion, intent, fusion_level)
         llm_result = self._llm_review(fusion, intent, fusion_level)
         if llm_result is None:
+            # LLM审核失败（超时/网络错误等），降级放行，不阻断渲染
             return ReviewResult(
-                passed=not local,
+                passed=True,
                 issues=local,
                 rewrite_suggestions=[],
                 raw="",
@@ -81,6 +90,17 @@ class Reviewer:
         )
 
     def _llm_review(self, fusion: FusionResult, intent: IntentResult, fusion_level: str) -> Optional[ReviewResult]:
+        try:
+            return self._llm_review_inner(fusion, intent, fusion_level)
+        except Exception as e:
+            import logging
+
+            logging.getLogger(__name__).warning("[Reviewer] LLM审核调用异常，降级放行: %s", e)
+            return None
+
+    def _llm_review_inner(
+        self, fusion: FusionResult, intent: IntentResult, fusion_level: str
+    ) -> Optional[ReviewResult]:
         template = get_template("review")
         system = render_system_prompt(template)
         user = render_user_prompt(
@@ -96,6 +116,7 @@ class Reviewer:
             ],
             temperature=0.2,
             max_tokens=1024,
+            timeout=25,
         )
         if not raw:
             return None
@@ -242,6 +263,7 @@ class Reviewer:
             ],
             temperature=0.5,
             max_tokens=2048,
+            timeout=25,
         )
         if not raw:
             return self._rule_fix(fusion, review)
@@ -296,10 +318,13 @@ class Reviewer:
 
     @staticmethod
     def _fusion_text(fusion: FusionResult) -> str:
+        _MARKDOWN_FIELDS = {"summary_markdown", "copy_display_markdown"}
         parts = [fusion.title, fusion.hook]
         parts += [p.text for p in fusion.body_points]
         parts += [s.text for s in fusion.script_segments]
         parts.append(fusion.cta)
+        # 过滤掉markdown展示字段，避免格式字符被误判
+        parts = [p for p in parts if not any(mk in p for mk in _MARKDOWN_FIELDS)]
         return "\n".join(p for p in parts if p)
 
     @staticmethod

packages/application/viral_video/xml_parser.py

@@ -13,6 +13,13 @@ from typing import Optional
 _OPEN_RE = re.compile(r"<(?P<tag>[\w-]+)(?P<attrs>(?:\s(?:[^>]*?\S)?)?)(?P<self>/?)>")
 _CLOSE_RE = re.compile(r"</(?P<tag>[\w-]+)\s*>")
 _ATTR_RE = re.compile(r"""([\w:-]+)\s*=\s*(?:"([^"]*)"|'([^']*)')""")
+_CDATA_RE = re.compile(r"^<!\[CDATA\[(.*)\]\]>$", re.DOTALL)
+
+
+def _strip_cdata(s: str) -> str:
+    """剥离 LLM 可能照抄示例输出的 ``<![CDATA[...]]>`` 包裹层。"""
+    m = _CDATA_RE.match(s.strip())
+    return m.group(1) if m else s
 
 
 def parse_attributes(raw: str) -> dict[str, str]:
@@ -58,6 +65,7 @@ def parse_tags(text: Optional[str]) -> list[dict]:
                 if stack[idx]["tag"] == tag:
                     node = stack[idx]
                     node["text"] = unescape(text[node["_start"] : token.start()].strip())
+                    node["text"] = _strip_cdata(node["text"])
                     node.pop("_start", None)
                     del stack[idx:]
                     break
@@ -65,6 +73,7 @@ def parse_tags(text: Optional[str]) -> list[dict]:
     for node in stack:
         if "_start" in node:
             node["text"] = unescape(text[node["_start"] :].strip())
+            node["text"] = _strip_cdata(node["text"])
             node.pop("_start", None)
     return results
 

packages/config/base.py

@@ -80,54 +80,46 @@ class SharedSettings(BaseSettings):
 
     # ── CosyVoice (阿里云百炼语音合成) ───────────────────────────────────
     cosyvoice_api_key: str = ""
-    cosyvoice_base_url: str = "https://dashscope.aliyuncs.com/api/v1"
-    cosyvoice_model: str = "cosyvoice-v3-flash"
-    cosyvoice_voice: str = "longxiaochun_v3"  # 默认音色（v3 系列系统音色带 _v3 后缀）
+    cosyvoice_base_url: str = ""
+    cosyvoice_model: str = ""
+    cosyvoice_voice: str = "longxiaochun_v3"
     cosyvoice_sample_rate: int = 22050
-    cosyvoice_format: str = "mp3"  # 输出格式：mp3/wav/pcm
+    cosyvoice_format: str = "mp3"
     # 音色克隆模型名（固定为 voice-enrollment）
-    cosyvoice_clone_model: str = "voice-enrollment"
+    cosyvoice_clone_model: str = ""
 
     # ── 豆包大模型（火山引擎方舟） ────────────────────────────────────────
+    # AI模型路由化：model/base_url 默认值清空，由 DB ai_models/ai_capability_configs 配置驱动。
+    # 环境变量仍可覆盖（兼容旧部署）；无任何配置时 ai_router fallback 提供最终默认值。
     doubao_api_key: str = ""
-    doubao_model: str = "doubao-seed-2-1-pro-260915"  # 推理模型（Seed 2.1 Pro，深度思考+多模态；原 seed-1-6 已下线）
-    doubao_fast_model: str = (
-        "doubao-seed-2-1-pro-260915"  # #2181: lite方舟侧100%超时，默认fast_model也走pro；方舟恢复lite后通过ENV DOUBAO_FAST_MODEL切回
-    )
-    doubao_base_url: str = "https://ark.cn-beijing.volces.com/api/v3"
-    doubao_timeout: int = 45  # #2180: 方舟LLM高峰期响应6-8s，原30s太紧提到45s
-    doubao_max_retries: int = 1  # #2180: timeout调大后一次调用就够，1次重试防偶发抖动；避免6次重试叠加到351s
-    doubao_vision_model: str = (
-        "doubao-seed-2-1-pro-260915"  # 高精度视觉（Seed 2.1 Pro 原生多模态；原 vision-pro-250328 已下线）
-    )
-    doubao_vision_lite_model: str = (
-        "doubao-seed-2-1-lite-260915"  # 快速视觉（Seed 2.1 Lite 原生多模态；原 vision-lite-250315 不可用）
-    )
-    doubao_vision_use_lite: bool = True  # #2188: lite恢复稳定，爆款视频默认lite-first提速（20-30s）
-    doubao_embedding_model: str = "doubao-embedding-vision-251215"  # 多模态向量化（原 large-text-240915 已 Retiring）
-    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-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
-    )
+    doubao_model: str = ""
+    doubao_fast_model: str = ""
+    doubao_base_url: str = ""
+    doubao_timeout: int = 45
+    doubao_max_retries: int = 3
+    doubao_vision_model: str = ""
+    doubao_vision_lite_model: str = ""
+    doubao_vision_use_lite: bool = True
+    doubao_embedding_model: str = ""
+    doubao_video_model: str = ""
+    doubao_video_timeout: int = 600
+    doubao_video_poll_interval: int = 10
+    doubao_image_model: str = ""
+    doubao_image_size: str = "1K"
+    doubao_image_timeout: int = 60
+    doubao_trust_chain_enabled: bool = True
 
     # ── DashScope (阿里云百炼 Wan 3.0 等) ─────────────────────────────────
     dashscope_api_key: str = ""
-    dashscope_base_url: str = "https://dashscope.aliyuncs.com/api/v1"
-    dashscope_video_timeout: int = 900  # Wan 视频任务轮询总超时（秒）
+    dashscope_base_url: str = ""
+    dashscope_video_timeout: int = 900
     dashscope_video_poll_interval: int = 10
 
     # ── MediaKit (火山引擎 AI 媒体工具) ──────────────────────────────────
     mediakit_api_key: str = ""
-    mediakit_base_url: str = "https://mediakit.cn-beijing.volces.com/api/v1"
+    mediakit_base_url: str = ""
     mediakit_timeout: int = 60
-    mediakit_cover_enabled: bool = False  # 封面抽帧是否走MediaKit(默认false走本地ffmpeg+cv2，<2s完成)
+    mediakit_cover_enabled: bool = False
 
     # ── 积分/会员系统 (#1895) ────────────────────────────────────────────
     # 积分系统总开关（产品要求 #1895：暂停积分系统但保留全部代码/表/接口）。
@@ -181,6 +173,73 @@ class SharedSettings(BaseSettings):
     # 判断 Worker 可用的心跳新鲜度窗口（秒）—— last_heartbeat_at 在窗口内视为在线
     gpu_worker_stale_seconds: int = 300
 
+    # ── Ditto 蚂蚁数字人口型 API（#2076）─────────────────────────────────
+    # 是否优先使用 Ditto（蚂蚁数字人，替代 MuseTalk）。开关开启且 base_url 配置
+    # 非空时，对口型任务优先走 Ditto；失败后回退 MuseTalk/MediaKit。
+    use_ditto_lipsync: bool = Field(
+        default=False,
+        validation_alias=AliasChoices("USE_DITTO_LIPSYNC", "use_ditto_lipsync"),
+    )
+    # Ditto FastAPI 内网地址（Tailscale），如 http://100.x.x.x:8000
+    ditto_api_base_url: str = Field(
+        default="",
+        validation_alias=AliasChoices("DITTO_API_BASE_URL", "ditto_api_base_url"),
+    )
+    # 默认人物模板视频 URL（兜底用：用户未上传视频时使用，或视频预处理失败时回退）。
+    # 正面 5-10 秒、光线均匀、半身 1080x1920 竖版；正常流程下 Ditto 优先使用用户上传的 video_url。
+    ditto_default_video_url: str = Field(
+        default="",
+        validation_alias=AliasChoices("DITTO_DEFAULT_VIDEO_URL", "ditto_default_video_url"),
+    )
+    # 429 GPU 繁忙时指数退避最大重试次数
+    ditto_max_retries: int = Field(
+        default=3,
+        validation_alias=AliasChoices("DITTO_MAX_RETRIES", "ditto_max_retries"),
+    )
+    # Ditto 单次请求 read 超时（秒）：数字人半身视频推理通常 30-120s（RTF≈2.8，40s音频约112s）
+    # connect 超时固定 10s（代码硬编码，网络不通快速失败）
+    ditto_request_timeout: int = Field(
+        default=120,
+        validation_alias=AliasChoices("DITTO_REQUEST_TIMEOUT", "ditto_request_timeout"),
+    )
+    # Ditto 句间过渡帧数（平滑表情/口型切换）
+    ditto_blend_frames: int = Field(
+        default=12,
+        validation_alias=AliasChoices("DITTO_BLEND_FRAMES", "ditto_blend_frames"),
+    )
+
+    # ── Ditto LLM 情绪分析（emo_timeline）──────────────────────────────
+    # 总开关；关闭或 LLM 失败时走 GPU 端关键词匹配兜底
+    ditto_emotion_enabled: bool = Field(
+        default=False,
+        validation_alias=AliasChoices("DITTO_EMOTION_ENABLED", "ditto_emotion_enabled"),
+    )
+    ditto_emotion_model: str = Field(
+        default="doubao-seed-2-1-lite-250915",
+        validation_alias=AliasChoices("DITTO_EMOTION_MODEL", "ditto_emotion_model"),
+    )
+    ditto_emotion_temperature: float = Field(
+        default=0.1,
+        validation_alias=AliasChoices("DITTO_EMOTION_TEMPERATURE", "ditto_emotion_temperature"),
+    )
+    ditto_emotion_timeout: int = Field(
+        default=10,
+        validation_alias=AliasChoices("DITTO_EMOTION_TIMEOUT", "ditto_emotion_timeout"),
+    )
+    ditto_emotion_max_tokens: int = Field(
+        default=1024,
+        validation_alias=AliasChoices("DITTO_EMOTION_MAX_TOKENS", "ditto_emotion_max_tokens"),
+    )
+    ditto_emotion_cache_size: int = Field(
+        default=500,
+        validation_alias=AliasChoices("DITTO_EMOTION_CACHE_SIZE", "ditto_emotion_cache_size"),
+    )
+    # 提示词模板：必须包含 {文案} 占位符；后台可通过环境变量覆盖
+    ditto_emotion_prompt: str = Field(
+        default="",
+        validation_alias=AliasChoices("DITTO_EMOTION_PROMPT", "ditto_emotion_prompt"),
+    )
+
     # ── P4000 NVENC 硬件编码 ────────────────────────────────────────────
     # GPU 编码总开关；关闭或 endpoint 为空时始终走本机 CPU libx264
     enable_gpu_encode: bool = Field(

packages/domain/asset_atom_clip.py

@@ -8,7 +8,12 @@ from __future__ import annotations
 
 import uuid
 from dataclasses import dataclass, field
-from datetime import UTC, datetime
+from datetime import datetime, timezone
+
+try:
+    from datetime import UTC
+except ImportError:
+    UTC = timezone.utc
 
 
 @dataclass

packages/domain/feature_pricing_service.py

@@ -0,0 +1,376 @@
+"""功能计费配置服务：从 feature_pricing_configs 读配置，300 秒 TTL 内存缓存。
+
+配置表由 xiaoxia-admin 侧维护（同库 PostgreSQL），本服务只读。
+DB 不可用 / 表不存在 / 无数据时自动回落到内置兜底配置，保证业务不崩。
+
+计费公式：最终积分 = (动态成本 + 固定成本) × 利润系数，price_cap 封顶。
+启用条件：全局 points_enabled 总开关 AND 功能 is_enabled 同时为 true。
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import threading
+import time
+from dataclasses import dataclass, field
+from typing import Optional
+
+import sqlalchemy as sa
+
+from packages.adapters.sqlalchemy_impl import session as _session_mod
+
+logger = logging.getLogger(__name__)
+
+CACHE_TTL_SECONDS = 300.0
+
+# ── 爆款视频兜底模型单价（与旧硬编码表/现状一致；DB 不可用时使用） ───────
+# 结构：models[model_key][resolution]["true"/"false"] = 单价
+# token 模式：元/百万输出 tokens；per_second 模式：元/秒
+# 注意：仅 seedance-2.5 配置 true（图生视频）单价；其余模型只有 false，
+# 精确 key 缺失时由 points_rules 回落到 seedance-2.5/false（与旧现状一致）。
+_FALLBACK_VIRAL_MODEL_PRICING: dict = {
+    "seedance-2.5": {
+        "480p": {"false": 70.0, "true": 42.0},
+        "720p": {"false": 70.0, "true": 42.0},
+        "1080p": {"false": 77.0, "true": 46.0},
+    },
+    "seedance-2.0": {
+        "480p": {"false": 46.0},
+        "720p": {"false": 46.0},
+        "1080p": {"false": 51.0},
+        "4k": {"false": 80.0},
+    },
+    "seedance-2.0-fast": {
+        "480p": {"false": 28.0},
+        "720p": {"false": 28.0},
+    },
+    "seedance-2.0-mini": {
+        "480p": {"false": 9.2},
+        "720p": {"false": 9.2},
+    },
+    "wan-3.0": {
+        "480p": {"false": 0.3},
+        "720p": {"false": 0.6},
+        "1080p": {"false": 1.2},
+    },
+}
+
+
+@dataclass
+class FeatureConfig:
+    """功能计费配置快照。"""
+
+    feature_key: str
+    name: str = ""
+    emoji: str = ""
+    is_enabled: bool = False
+    fixed_cost: float = 0.0
+    profit_multiplier: float = 1.0
+    dynamic_unit_cost: float = 0.0
+    billing_mode: str = "model_based"
+    price_cap: float = 0.0
+    model_pricing: dict = field(default_factory=dict)
+    description: str = ""
+
+
+# ── 进程内缓存：(loaded_monotonic, {feature_key: FeatureConfig}) ──────────
+_lock = threading.Lock()
+_cache: Optional[tuple[float, dict[str, FeatureConfig]]] = None
+
+
+def _fallback_configs() -> dict[str, FeatureConfig]:
+    """内置兜底配置：爆款启用（与现状一致），其余两个关闭。"""
+    return {
+        "viral_video": FeatureConfig(
+            feature_key="viral_video",
+            name="爆款视频",
+            emoji="🎬",
+            is_enabled=True,
+            fixed_cost=0.15,
+            profit_multiplier=1.3,
+            dynamic_unit_cost=0.0,
+            billing_mode="model_based",
+            price_cap=0.0,
+            model_pricing=json.loads(json.dumps(_FALLBACK_VIRAL_MODEL_PRICING)),
+            description="爆款视频动态定价（兜底配置）",
+        ),
+        "lip_sync": FeatureConfig(
+            feature_key="lip_sync",
+            name="对口型",
+            emoji="🎙️",
+            is_enabled=False,
+            fixed_cost=0.0,
+            profit_multiplier=1.0,
+            dynamic_unit_cost=0.0,
+            billing_mode="per_second",
+            price_cap=0.0,
+            description="对口型计费（兜底配置，默认关闭）",
+        ),
+        "smart_edit": FeatureConfig(
+            feature_key="smart_edit",
+            name="智能剪辑",
+            emoji="✂️",
+            is_enabled=False,
+            fixed_cost=0.0,
+            profit_multiplier=1.0,
+            dynamic_unit_cost=0.0,
+            billing_mode="model_based",
+            price_cap=0.0,
+            description="智能剪辑固定价计费（兜底配置，默认关闭）",
+        ),
+    }
+
+
+_lazy_session = None
+
+
+def _get_session():
+    """优先用全局 SessionLocal（worker）；否则按应用配置懒建同步引擎（api）。"""
+    global _lazy_session
+    if _session_mod.SessionLocal is not None:
+        return _session_mod.SessionLocal()
+    if _lazy_session is not None:
+        return _lazy_session()
+    try:
+        from packages.config import get_shared_settings
+
+        url = str(get_shared_settings().database_url)
+    except Exception:  # noqa: BLE001
+        return None
+    if not url:
+        return None
+    url = url.replace("postgresql+asyncpg://", "postgresql+psycopg://")
+    if url.startswith("postgresql://"):
+        url = url.replace("postgresql://", "postgresql+psycopg://")
+    engine = sa.create_engine(url, pool_pre_ping=True, pool_size=2, max_overflow=2)
+    from sqlalchemy.orm import sessionmaker
+
+    _lazy_session = sessionmaker(bind=engine)
+    return _lazy_session()
+
+
+def _parse_model_pricing(raw) -> dict:
+    """解析 model_pricing_json（Text JSON），空/失败 → {}。"""
+    if raw is None:
+        return {}
+    if isinstance(raw, dict):
+        return raw
+    text = str(raw).strip()
+    if not text:
+        return {}
+    try:
+        data = json.loads(text)
+    except (ValueError, TypeError):
+        logger.warning("model_pricing_json 解析失败，按空配置处理: %r", text[:200])
+        return {}
+    return data if isinstance(data, dict) else {}
+
+
+def _to_float(value, default: float = 0.0) -> float:
+    try:
+        if value is None:
+            return default
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _load_all() -> dict[str, FeatureConfig]:
+    """SELECT * FROM feature_pricing_configs，返回 {feature_key: FeatureConfig}。
+
+    表不存在 / DB 异常由调用方捕获并回落兜底配置。
+    """
+    session = None
+    try:
+        session = _get_session()
+        if session is None:
+            raise RuntimeError("no db session available")
+        sql = sa.text("""
+            SELECT feature_key, name, emoji, is_enabled, fixed_cost,
+                   profit_multiplier, dynamic_unit_cost, billing_mode,
+                   price_cap, model_pricing_json, description
+              FROM feature_pricing_configs
+            """)
+        rows = session.execute(sql).mappings().all()
+        configs: dict[str, FeatureConfig] = {}
+        for row in rows:
+            key = str(row["feature_key"] or "").strip()
+            if not key:
+                continue
+            configs[key] = FeatureConfig(
+                feature_key=key,
+                name=str(row["name"] or key),
+                emoji=str(row["emoji"] or ""),
+                is_enabled=bool(row["is_enabled"]),
+                fixed_cost=_to_float(row["fixed_cost"]),
+                profit_multiplier=_to_float(row["profit_multiplier"], 1.0),
+                dynamic_unit_cost=_to_float(row["dynamic_unit_cost"]),
+                billing_mode=str(row["billing_mode"] or "model_based"),
+                price_cap=_to_float(row["price_cap"]),
+                model_pricing=_parse_model_pricing(row["model_pricing_json"]),
+                description=str(row["description"] or ""),
+            )
+        return configs
+    finally:
+        if session is not None:
+            try:
+                session.close()
+            except Exception:  # noqa: BLE001
+                pass
+
+
+def _get_cache() -> dict[str, FeatureConfig]:
+    """TTL 内返回缓存，否则重新 load；DB 异常/表不存在时返回内置兜底配置。"""
+    global _cache
+    now = time.monotonic()
+    with _lock:
+        if _cache is not None and now - _cache[0] < CACHE_TTL_SECONDS:
+            return _cache[1]
+
+    try:
+        loaded = _load_all()
+    except Exception:  # noqa: BLE001 - 表不存在/DB 不可用时静默回落
+        logger.info("feature_pricing_configs 读取失败，使用内置兜底配置", exc_info=True)
+        return _fallback_configs()
+
+    # DB 可用但表为空：同样回落兜底（保证爆款现状不被改变）
+    if not loaded:
+        fallback = _fallback_configs()
+        with _lock:
+            _cache = (now, fallback)
+        return fallback
+
+    # 以兜底为底（DB 未配置的 feature_key 仍有兜底），DB 行覆盖
+    merged = _fallback_configs()
+    merged.update(loaded)
+    with _lock:
+        _cache = (now, merged)
+    return merged
+
+
+def get_feature_config(feature_key: str) -> Optional[FeatureConfig]:
+    """获取指定功能配置，未知 key 返回 None。"""
+    key = str(feature_key or "").strip()
+    if not key:
+        return None
+    return _get_cache().get(key)
+
+
+def _global_points_enabled() -> bool:
+    """全局积分总开关（兼容 api / worker 运行时），取不到时默认关闭。"""
+    try:
+        from packages.shared import get_shared_settings
+
+        return bool(get_shared_settings().points_enabled)
+    except Exception:  # noqa: BLE001
+        pass
+    try:
+        from app.config import settings
+
+        return bool(getattr(settings, "points_enabled", False))
+    except Exception:  # noqa: BLE001
+        return False
+
+
+def is_feature_enabled(feature_key: str) -> bool:
+    """功能是否启用并扣费：全局 points_enabled AND 功能 is_enabled。"""
+    cfg = get_feature_config(feature_key)
+    if cfg is None:
+        return False
+    return bool(cfg.is_enabled) and _global_points_enabled()
+
+
+def calculate_price(feature_key: str, dynamic_cost: float = 0.0) -> tuple[float, dict]:
+    """按公式计算最终积分并返回明细。
+
+    price = (dynamic_cost + fixed_cost) × profit_multiplier
+    price_cap > 0 时封顶（取 min）。
+    功能未启用 → (0.0, breakdown{is_enabled: False, charged: False})。
+    """
+    cfg = get_feature_config(feature_key)
+    dynamic = max(0.0, _to_float(dynamic_cost))
+    if cfg is None or not cfg.is_enabled:
+        return 0.0, {
+            "feature_key": feature_key,
+            "is_enabled": False,
+            "charged": False,
+            "dynamic_cost": dynamic,
+            "fixed_cost": 0.0,
+            "profit_multiplier": 1.0,
+            "price_cap": 0.0,
+            "final_price": 0.0,
+        }
+
+    fixed = max(0.0, cfg.fixed_cost)
+    multiplier = cfg.profit_multiplier if cfg.profit_multiplier > 0 else 1.0
+    raw_price = (dynamic + fixed) * multiplier
+    cap = cfg.price_cap if cfg.price_cap and cfg.price_cap > 0 else 0.0
+    final_price = min(raw_price, cap) if cap else raw_price
+    final_price = round(float(final_price), 2)
+    breakdown = {
+        "feature_key": cfg.feature_key,
+        "is_enabled": True,
+        "charged": True,
+        "dynamic_cost": round(dynamic, 4),
+        "fixed_cost": float(fixed),
+        "profit_multiplier": float(multiplier),
+        "price_cap": float(cap),
+        "raw_price": round(float(raw_price), 4),
+        "final_price": final_price,
+    }
+    return final_price, breakdown
+
+
+def lookup_model_price(
+    model_pricing: dict,
+    model_key: str,
+    resolution: str,
+    has_video_input: bool,
+) -> Optional[float]:
+    """从 model_pricing dict 取模型单价，兼容两种常见 JSON 结构。
+
+    1. 嵌套：{model: {resolution: {"true"/"false": price}}}
+       （内层 bool key 也兼容直接 bool / 省略）
+    2. 扁平：{"model|resolution|true_or_false": price}
+       （分隔符支持 | / : / , / 空格；bool 段可省略）
+    取不到返回 None。
+    """
+    if not isinstance(model_pricing, dict):
+        return None
+    model = str(model_key or "").strip()
+    res = str(resolution or "").strip()
+    flag = "true" if has_video_input else "false"
+
+    # 1. 嵌套
+    model_node = model_pricing.get(model)
+    if isinstance(model_node, dict):
+        res_node = model_node.get(res)
+        if isinstance(res_node, dict):
+            # 精确 bool key 命中才返回；不做“只有一个值就取”的模糊匹配
+            # （否则缺失 true 时会错误地取到 false 价，破坏旧版回落规则）
+            if flag in res_node:
+                return _to_float(res_node[flag]) if res_node[flag] is not None else None
+            if has_video_input in res_node:
+                val = res_node[has_video_input]
+                return _to_float(val) if val is not None else None
+        elif isinstance(res_node, (int, float)):
+            return float(res_node)
+
+    # 2. 扁平
+    for sep in ("|", ":", ",", " "):
+        for key in (
+            f"{model}{sep}{res}{sep}{flag}",
+            f"{model}{sep}{res}",
+        ):
+            if key in model_pricing:
+                value = model_pricing[key]
+                return _to_float(value) if value is not None else None
+    return None
+
+
+def refresh_feature_configs() -> None:
+    """清空缓存（下次读取重新 load DB；测试/admin 改配置后可手动调）。"""
+    global _cache
+    with _lock:
+        _cache = None

packages/domain/points_rules.py

@@ -2,17 +2,21 @@
 
 v1.6.1: 按产品决策，智能混剪/AI数字人/AI配音/抖音解析/改写/标题/封面 全部免费，
 仅保留声音克隆合成（voice_clone_synth）的扣点逻辑；声音克隆训练保持免费。
-爆款视频（viral_video）走动态定价，见本文件 VIRAL_VIDEO_MODEL_PRICES + calculate_viral_video_credits。
+爆款视频（viral_video）走动态定价，计费参数 DB 化（feature_pricing_configs，
+见 feature_pricing_service），calculate_viral_video_credits 从配置读取单价/
+固定成本/利润系数/封顶，DB 不可用时回落兜底配置。
 """
 
 from __future__ import annotations
 
 import math
 
-# ============ 爆款视频动态定价 (#2151) ============
-# key = (model_id, resolution, has_video_input)，单位：
-# - billing_mode=token: 元/百万tokens（输出）
-# - billing_mode=per_second: 元/秒（视频时长）
+from packages.domain import feature_pricing_service
+
+# ============ 爆款视频动态定价 ============
+# 单价/固定成本/利润系数已 DB 化（feature_pricing_configs，feature_key=viral_video），
+# 由 feature_pricing_service 读取（300s 缓存），DB 不可用时回落内置兜底配置。
+# 以下三个常量仅为向后兼容保留（旧引用方/兜底场景），值取自兜底配置。
 VIRAL_VIDEO_MODEL_PRICES: dict[tuple[str, str, bool], float] = {
     ("seedance-2.5", "480p", False): 70.0,
     ("seedance-2.5", "720p", False): 70.0,
@@ -33,9 +37,9 @@ VIRAL_VIDEO_MODEL_PRICES: dict[tuple[str, str, bool], float] = {
     ("wan-3.0", "1080p", False): 1.2,
 }
 
-# 固定成本（元）：VLM 分析 + LLM 文案 + TTS + OSS + 服务器
+# 固定成本（元）：VLM 分析 + LLM 文案 + TTS + OSS + 服务器（兜底默认值）
 VIRAL_VIDEO_FIXED_COST = 0.15
-# 利润系数
+# 利润系数（兜底默认值）
 VIRAL_VIDEO_PROFIT_MULTIPLIER = 1.3
 # Seedance 输出帧率
 VIRAL_VIDEO_FPS = 24
@@ -222,17 +226,22 @@ def calculate_viral_video_credits_with_breakdown(
 ) -> tuple[float, dict]:
     """计算爆款视频所需积分（1 积分 = 1 元），并返回计费公式明细。
 
+    单价/固定成本/利润系数/封顶从 feature_pricing_configs（viral_video）读取；
+    DB 不可用时回落与现状一致的内置兜底配置。
+
     公式:
         tokens = duration * width * height * fps / 1024
         video_cost = tokens / 1_000_000 * model_token_price
         total = round((video_cost + fixed_cost) * profit_multiplier, 2)
+        price_cap > 0 时封顶取 min
     若传入 actual_tokens 则用它替代计算值。
 
     Returns:
         (credits, breakdown) 二元组：
         - credits: 四舍五入保留两位小数的最终积分
         - breakdown: dict，包含 tokens / video_cost / fixed_cost / profit_multiplier /
-          model_price / width / height / fps 字段，便于前端展示计费明细。
+          model_price / width / height / fps / feature_enabled / charged / price_cap
+          字段，便于前端展示计费明细。功能关闭时 credits=0、charged=False。
     """
     w = max(1, int(width or 1))
     h = max(1, int(height or 1))
@@ -242,11 +251,36 @@ def calculate_viral_video_credits_with_breakdown(
     cfg = get_viral_video_model_config(prefix)
     res_key = _infer_resolution_key(w, h)
     billing = cfg.get("billing_mode", "token")
-    key = (prefix, res_key, bool(has_video_input))
-    price = VIRAL_VIDEO_MODEL_PRICES.get(key)
+    dur = max(1, int(duration_seconds or 15))
+
+    # ── 从 DB 配置（兜底内置）取计费参数 ──
+    feature_cfg = feature_pricing_service.get_feature_config("viral_video")
+    # 注意：此处 feature_enabled 只表示“功能自身开关”，不并入全局 points_enabled
+    # 总开关（保持与旧版计费函数行为一致：价格照常计算）。全局总开关由业务层
+    # （route/worker）通过 feature_pricing_service.is_feature_enabled 统一把关。
+    feature_enabled = bool(feature_cfg.is_enabled) if feature_cfg is not None else True
+    model_pricing = feature_cfg.model_pricing if feature_cfg is not None else {}
+    fixed_cost = float(feature_cfg.fixed_cost) if feature_cfg is not None else float(VIRAL_VIDEO_FIXED_COST)
+    multiplier = (
+        float(feature_cfg.profit_multiplier)
+        if feature_cfg is not None and feature_cfg.profit_multiplier > 0
+        else float(VIRAL_VIDEO_PROFIT_MULTIPLIER)
+    )
+    price_cap = float(feature_cfg.price_cap) if feature_cfg is not None else 0.0
+
+    # 单价：优先配置 dict；复刻旧版回落规则——精确 key 取不到时，回落
+    # seedance-2.5 同分辨率 False 单价；最终兜底 70.0。
+    price = feature_pricing_service.lookup_model_price(model_pricing, prefix, res_key, bool(has_video_input))
+    if price is None:
+        # 配置表未命中：先尝试配置里的 seedance-2.5/False
+        if prefix != "seedance-2.5" or bool(has_video_input):
+            price = feature_pricing_service.lookup_model_price(model_pricing, "seedance-2.5", res_key, False)
+    if price is None:
+        key = (prefix, res_key, bool(has_video_input))
+        price = VIRAL_VIDEO_MODEL_PRICES.get(key)
     if price is None:
         price = VIRAL_VIDEO_MODEL_PRICES.get(("seedance-2.5", res_key, False), 70.0)
-    dur = max(1, int(duration_seconds or 15))
+
     if billing == "per_second":
         tokens = 0.0
         video_cost = dur * float(price)
@@ -259,13 +293,40 @@ def calculate_viral_video_credits_with_breakdown(
         video_cost = tokens / 1_000_000.0 * float(price)
         billing_unit = "token"
 
-    total = (video_cost + VIRAL_VIDEO_FIXED_COST) * VIRAL_VIDEO_PROFIT_MULTIPLIER
+    if not feature_enabled:
+        # 功能关闭（is_enabled=false 或全局 points 关闭）：不扣费，明细照旧返回
+        credits = 0.0
+        raw_total = (video_cost + fixed_cost) * multiplier
+        breakdown = {
+            "tokens": float(tokens),
+            "video_cost": float(video_cost),
+            "fixed_cost": float(fixed_cost),
+            "profit_multiplier": float(multiplier),
+            "price_cap": float(price_cap or 0.0),
+            "model_price": float(price),
+            "model_key": prefix,
+            "billing_mode": billing,
+            "billing_unit": billing_unit,
+            "width": int(w),
+            "height": int(h),
+            "fps": int(effective_fps),
+            "duration": dur,
+            "feature_enabled": False,
+            "charged": False,
+            "raw_price": round(float(raw_total), 4),
+        }
+        return credits, breakdown
+
+    total = (video_cost + fixed_cost) * multiplier
+    if price_cap and price_cap > 0:
+        total = min(total, price_cap)
     credits = round(float(total), 2)
     breakdown = {
         "tokens": float(tokens),
         "video_cost": float(video_cost),
-        "fixed_cost": float(VIRAL_VIDEO_FIXED_COST),
-        "profit_multiplier": float(VIRAL_VIDEO_PROFIT_MULTIPLIER),
+        "fixed_cost": float(fixed_cost),
+        "profit_multiplier": float(multiplier),
+        "price_cap": float(price_cap or 0.0),
         "model_price": float(price),
         "model_key": prefix,
         "billing_mode": billing,
@@ -274,6 +335,8 @@ def calculate_viral_video_credits_with_breakdown(
         "height": int(h),
         "fps": int(effective_fps),
         "duration": dur,
+        "feature_enabled": True,
+        "charged": True,
     }
     return credits, breakdown
 

packages/domain/system_setting.py

@@ -0,0 +1,134 @@
+"""系统配置领域实体 — #2246.
+
+承载 setting_type（bool/int/float/string/json）及 setting_value 的
+序列化/反序列化规则；与 DB、框架无关。
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import Any
+from uuid import uuid4
+
+SETTING_TYPE_BOOL = "bool"
+SETTING_TYPE_INT = "int"
+SETTING_TYPE_FLOAT = "float"
+SETTING_TYPE_STRING = "string"
+SETTING_TYPE_JSON = "json"
+
+VALID_SETTING_TYPES = {
+    SETTING_TYPE_BOOL,
+    SETTING_TYPE_INT,
+    SETTING_TYPE_FLOAT,
+    SETTING_TYPE_STRING,
+    SETTING_TYPE_JSON,
+}
+
+
+class SystemSettingError(ValueError):
+    """系统配置类型或序列化错误."""
+
+
+def infer_setting_type(value: Any) -> str:
+    """根据 Python 值推断 setting_type（bool 必须先于 int 判断）."""
+    if isinstance(value, bool):
+        return SETTING_TYPE_BOOL
+    if isinstance(value, int):
+        return SETTING_TYPE_INT
+    if isinstance(value, float):
+        return SETTING_TYPE_FLOAT
+    if isinstance(value, str):
+        return SETTING_TYPE_STRING
+    return SETTING_TYPE_JSON
+
+
+def serialize_setting_value(value: Any, setting_type: str) -> str:
+    """把 Python 值按 setting_type 序列化为可入库的字符串."""
+    if setting_type == SETTING_TYPE_BOOL:
+        if not isinstance(value, bool):
+            raise SystemSettingError(f"bool 配置值必须是布尔类型，收到 {value!r}")
+        return "true" if value else "false"
+    if setting_type == SETTING_TYPE_INT:
+        if isinstance(value, bool) or not isinstance(value, int):
+            raise SystemSettingError(f"int 配置值必须是整数，收到 {value!r}")
+        return str(value)
+    if setting_type == SETTING_TYPE_FLOAT:
+        if isinstance(value, bool):
+            raise SystemSettingError(f"float 配置值不能是布尔类型，收到 {value!r}")
+        try:
+            return repr(float(value))
+        except (TypeError, ValueError) as exc:
+            raise SystemSettingError(f"float 配置值非法：{value!r}") from exc
+    if setting_type == SETTING_TYPE_STRING:
+        if not isinstance(value, str):
+            raise SystemSettingError(f"string 配置值必须是字符串，收到 {value!r}")
+        return value
+    if setting_type == SETTING_TYPE_JSON:
+        try:
+            return json.dumps(value, ensure_ascii=False)
+        except (TypeError, ValueError) as exc:
+            raise SystemSettingError(f"json 配置值无法序列化：{value!r}") from exc
+    raise SystemSettingError(f"未知 setting_type: {setting_type}")
+
+
+def deserialize_setting_value(raw: str | None, setting_type: str) -> Any:
+    """把入库字符串按 setting_type 反序列化为 Python 值."""
+    if raw is None:
+        return None
+    if setting_type == SETTING_TYPE_BOOL:
+        return str(raw).strip().lower() in {"1", "true", "yes", "on"}
+    if setting_type == SETTING_TYPE_INT:
+        try:
+            return int(str(raw).strip())
+        except ValueError as exc:
+            raise SystemSettingError(f"int 配置值损坏：{raw!r}") from exc
+    if setting_type == SETTING_TYPE_FLOAT:
+        try:
+            return float(str(raw).strip())
+        except ValueError as exc:
+            raise SystemSettingError(f"float 配置值损坏：{raw!r}") from exc
+    if setting_type == SETTING_TYPE_STRING:
+        return raw
+    if setting_type == SETTING_TYPE_JSON:
+        try:
+            return json.loads(raw)
+        except (TypeError, ValueError) as exc:
+            raise SystemSettingError(f"json 配置值损坏：{raw!r}") from exc
+    raise SystemSettingError(f"未知 setting_type: {setting_type}")
+
+
+@dataclass(slots=True)
+class SystemSetting:
+    """系统配置领域实体."""
+
+    setting_key: str
+    setting_value: str | None = None
+    setting_type: str = SETTING_TYPE_STRING
+    description: str = ""
+    is_public: bool = False
+    category: str = "general"
+    updated_by: str | None = None
+    id: str = field(default_factory=lambda: uuid4().hex)
+    created_at: datetime = field(default_factory=lambda: datetime.now(UTC))
+    updated_at: datetime = field(default_factory=lambda: datetime.now(UTC))
+
+    def get_typed_value(self) -> Any:
+        return deserialize_setting_value(self.setting_value, self.setting_type)
+
+    @classmethod
+    def from_value(
+        cls,
+        setting_key: str,
+        value: Any,
+        setting_type: str | None = None,
+        **kwargs: Any,
+    ) -> "SystemSetting":
+        st = setting_type or infer_setting_type(value)
+        return cls(
+            setting_key=setting_key,
+            setting_value=serialize_setting_value(value, st),
+            setting_type=st,
+            **kwargs,
+        )

packages/domain/viral_video.py

@@ -107,6 +107,7 @@ class ViralVideoJob:
     # v1.5.1 音频/视频参数
     voice_id: str = ""
     voice_source: str = ""
+    language: str = "zh-CN"
     video_ratio: str = "9:16"
     video_model: str = ""
     # v1.4+ 产物
@@ -191,13 +192,45 @@ class ViralVideoJob:
         self.updated_at = datetime.now(timezone.utc)
 
     def resume_from_image_analyzed(self, **kwargs) -> None:
-        if self.status not in (ViralVideoStatus.IMAGE_ANALYZED, ViralVideoStatus.PENDING):
+        """阶段2入口：允许从 IMAGE_ANALYZED/PENDING 首次进入，也允许从 COPY_GENERATED/COMPLETED/FAILED 重新生成文案。
+
+        重新生成时清空上一轮文案产物（copy_result/intent_result/storyboard/generated_copy_text），
+        并重置 completed_at/result_video_url/error_msg，确保前端轮询能看到新的阶段2进度。
+        """
+        _allowed = (
+            ViralVideoStatus.IMAGE_ANALYZED,
+            ViralVideoStatus.PENDING,
+            ViralVideoStatus.COPY_GENERATED,
+            ViralVideoStatus.COMPLETED,
+            ViralVideoStatus.FAILED,
+        )
+        if self.status not in _allowed:
             raise ValueError(f"Cannot resume from {self.status} to copy-gen")
+        _is_regen = self.status in (
+            ViralVideoStatus.COPY_GENERATED,
+            ViralVideoStatus.COMPLETED,
+            ViralVideoStatus.FAILED,
+        )
         for k, v in kwargs.items():
             if hasattr(self, k) and v not in (None, "", []):
                 setattr(self, k, v)
+        if _is_regen:
+            # 清空上一轮文案/视频产物，避免前端拿到旧数据
+            self.intent_result = None
+            self.copy_result = None
+            self.storyboard = None
+            self.generated_copy_text = ""
+            self.result_video_url = ""
+            self.current_stage = ""
+            self.phase_message = ""
+            self.error_msg = ""
+            self.completed_at = None
+            self.heartbeat_at = None
+        _now = datetime.now(timezone.utc)
+        self.started_at = _now
+        self.heartbeat_at = _now
         self.status = ViralVideoStatus.RUNNING
-        self.updated_at = datetime.now(timezone.utc)
+        self.updated_at = _now
 
     def resume_from_copy_generated(self, edited_copy: str | None = None) -> None:
         """阶段2->阶段3：用户确认/编辑口播文案，开始跑 TTS+单次Seedance渲染。"""
@@ -206,14 +239,20 @@ class ViralVideoJob:
         if edited_copy and isinstance(self.copy_result, dict):
             self.copy_result = {**self.copy_result, "voiceover_script": edited_copy}
             self.generated_copy_text = edited_copy
+        _now = datetime.now(timezone.utc)
+        self.started_at = _now
+        self.heartbeat_at = _now
         self.status = ViralVideoStatus.RUNNING
-        self.updated_at = datetime.now(timezone.utc)
+        self.updated_at = _now
 
     def resume_from_confirm(self) -> None:
         if self.status != ViralVideoStatus.WAIT_USER_CONFIRM:
             raise ValueError(f"Cannot resume from {self.status}")
+        _now = datetime.now(timezone.utc)
+        self.started_at = _now
+        self.heartbeat_at = _now
         self.status = ViralVideoStatus.RUNNING
-        self.updated_at = datetime.now(timezone.utc)
+        self.updated_at = _now
 
     def mark_completed(self, video_url: str) -> None:
         self.status = ViralVideoStatus.COMPLETED

packages/shared/ai_client.py

@@ -170,14 +170,35 @@ class DoubaoClient:
     未配置 API Key 时 is_available 为 False，调用方应降级处理。
     """
 
-    def __init__(self) -> None:
+    def __init__(
+        self,
+        api_key: str = "",
+        base_url: str = "",
+        model: str = "",
+        timeout: int = 0,
+        max_retries: int = 0,
+        max_tokens: int | None = None,
+        temperature: float | None = None,
+        extra_params: dict | None = None,
+        provider: str = "volcengine",
+    ) -> None:
         settings = get_shared_settings()
-        self.api_key: str = settings.doubao_api_key
-        self.model: str = settings.doubao_model
-        self.base_url: str = settings.doubao_base_url.rstrip("/")
-        self.timeout: int = settings.doubao_timeout
-        self.max_retries: int = settings.doubao_max_retries
+        self.provider: str = provider
+        if provider == "dashscope":
+            self.api_key: str = api_key or getattr(settings, "dashscope_api_key", "")
+            self.model: str = model or getattr(settings, "dashscope_model", "")
+            self.base_url: str = (base_url or getattr(settings, "dashscope_base_url", "")).rstrip("/")
+        else:  # volcengine (default)
+            self.api_key = api_key or settings.doubao_api_key
+            self.model = model or settings.doubao_model
+            self.base_url = (base_url or settings.doubao_base_url).rstrip("/")
+        self.timeout: int = timeout or settings.doubao_timeout
+        self.max_retries: int = max_retries or settings.doubao_max_retries
+        self.max_tokens: int | None = max_tokens
+        self.temperature: float | None = temperature
+        self.extra_params: dict = extra_params or {}
         self.vision_model: str = settings.doubao_vision_model
+        self.last_finish_reason: str = ""
         self.vision_lite_model: str = settings.doubao_vision_lite_model
         self.fast_model: str = settings.doubao_fast_model
         self.embedding_model: str = settings.doubao_embedding_model
@@ -190,6 +211,13 @@ class DoubaoClient:
         # 最近一次图片生成的详细错误，供上层读取
         self.last_image_error: dict = {}
 
+    def _resolve_timeout(self, timeout) -> "httpx.Timeout":
+        """将整数超时转为 httpx.Timeout，区分 connect/read/write/pool，避免 read 卡到 TCP 120s 默认值."""
+        if isinstance(timeout, httpx.Timeout):
+            return timeout
+        t = int(timeout) if timeout else 60
+        return httpx.Timeout(connect=10, read=max(t, 10), write=10, pool=5)
+
     def embed_text(self, text: str, timeout: int | None = None) -> list[float] | None:
         """调用豆包文本 Embedding API，返回浮点向量；失败返回 None。"""
         if not self.is_available or not text or not text.strip():
@@ -240,16 +268,17 @@ class DoubaoClient:
         self,
         messages: list[dict[str, str]],
         temperature: float = 0.7,
-        max_tokens: int = 1024,
+        max_tokens: int | None = None,
         model: str | None = None,
         timeout: int | None = None,
+        **kwargs,
     ) -> Optional[str]:
         """调用 Chat Completion 接口.
 
         Args:
             messages: 对话消息列表，[{"role": "user"/"system"/"assistant", "content": "..."}]
             temperature: 采样温度，0-2，默认0.7
-            max_tokens: 最大生成token数，默认1024
+            max_tokens: 最大生成token数，默认 None（使用实例 self.max_tokens DB 配置，兜底 1024）
 
         Returns:
             模型返回的文本内容，失败返回 None
@@ -262,18 +291,24 @@ class DoubaoClient:
             "Authorization": f"Bearer {self.api_key}",
             "Content-Type": "application/json",
         }
+        effective_max_tokens = max_tokens if max_tokens is not None else (self.max_tokens or 1024)
         payload: dict[str, Any] = {
             "model": model or self.model,
             "messages": messages,
             "temperature": temperature,
-            "max_tokens": max_tokens,
+            "max_tokens": effective_max_tokens,
         }
+        # 合并实例级额外参数和调用方传入的额外参数
+        if self.extra_params:
+            payload.update(self.extra_params)
+        if kwargs:
+            payload.update(kwargs)
 
         last_error: Optional[Exception] = None
         _t0 = time.time()
         for attempt in range(self.max_retries + 1):
             try:
-                _req_timeout = timeout if timeout is not None else self.timeout
+                _req_timeout = self._resolve_timeout(timeout if timeout is not None else self.timeout)
                 response = httpx.post(
                     url,
                     headers=headers,
@@ -282,15 +317,33 @@ class DoubaoClient:
                 )
                 response.raise_for_status()
                 data = response.json()
+                finish_reason = (data.get("choices") or [{}])[0].get("finish_reason", "")
+                if finish_reason == "length" and attempt < self.max_retries:
+                    # 输出被 max_tokens 截断：2.0x 扩容后重试（计入 max_retries，不额外增加）
+                    old_max = int(payload["max_tokens"])
+                    new_max = int(old_max * 2)
+                    payload["max_tokens"] = new_max
+                    wait = 0.5 * (2**attempt)
+                    logger.warning(
+                        "输出被max_tokens截断(%d)，扩容到%d后重试 (第%d/%d次)",
+                        old_max,
+                        new_max,
+                        attempt + 1,
+                        self.max_retries + 1,
+                    )
+                    time.sleep(wait)
+                    continue
                 content = data["choices"][0]["message"]["content"]
+                self.last_finish_reason = finish_reason
                 _elapsed = time.time() - _t0
                 logger.info(
-                    "[doubao] chat_completion 完成 model=%s tokens_in=%d tokens_out=%d elapsed=%.1fs attempt=%d timeout=%d",
+                    "[doubao] chat_completion 完成 model=%s tokens_in=%d tokens_out=%d elapsed=%.1fs attempt=%d timeout=%s",
                     payload.get("model"),
                     data.get("usage", {}).get("prompt_tokens", 0),
                     data.get("usage", {}).get("completion_tokens", 0),
                     _elapsed,
                     attempt + 1,
+                    getattr(_req_timeout, "read", _req_timeout),
                 )
                 return content.strip()
             except Exception as e:
@@ -314,20 +367,21 @@ class DoubaoClient:
         self,
         messages: list[dict],
         images: list[str] | None = None,
-        max_tokens: int = 2048,
+        max_tokens: int | None = None,
         temperature: float = 0.3,
         timeout: int | None = None,
         model: str | None = None,
+        **kwargs,
     ) -> Optional[str]:
         """调用豆包视觉理解 API（OpenAI 兼容多模态格式）.
 
         将 images 附加到最后一条 user message 的 content 中，
-        使用 vision_model（默认 doubao-1-5-vision-pro-250328）。
+        使用构造函数传入的 self.model（DB capability 绑定的视觉模型，默认 qwen-vl-plus）。
 
         Args:
             messages: 对话消息列表。最后一条 user message 会被注入图片内容。
             images: 图片列表，支持 base64 data URI 或 HTTP(S) URL。
-            max_tokens: 最大生成 token 数，默认 2048。
+            max_tokens: 最大生成 token 数，默认 None（使用实例 self.max_tokens DB 配置，兜底 2048）。
             temperature: 采样温度，默认 0.3（视觉任务偏低更稳定）。
             timeout: 单次请求超时秒数，不传则使用默认 self.timeout。
 
@@ -367,14 +421,19 @@ class DoubaoClient:
             "Authorization": f"Bearer {self.api_key}",
             "Content-Type": "application/json",
         }
+        effective_max_tokens = max_tokens if max_tokens is not None else (self.max_tokens or 2048)
         payload: dict[str, Any] = {
-            "model": model or self.vision_model,
+            "model": model or self.model,
             "messages": vision_messages,
             "temperature": temperature,
-            "max_tokens": max_tokens,
+            "max_tokens": effective_max_tokens,
         }
+        if self.extra_params:
+            payload.update(self.extra_params)
+        if kwargs:
+            payload.update(kwargs)
 
-        req_timeout = timeout or self.timeout
+        req_timeout = self._resolve_timeout(timeout or self.timeout)
         last_error: Optional[Exception] = None
         _t0 = time.time()
         for attempt in range(self.max_retries + 1):
@@ -387,7 +446,24 @@ class DoubaoClient:
                 )
                 response.raise_for_status()
                 data = response.json()
+                finish_reason = (data.get("choices") or [{}])[0].get("finish_reason", "")
+                if finish_reason == "length" and attempt < self.max_retries:
+                    # 视觉输出被 max_tokens 截断：2.0x 扩容后重试（计入 max_retries）
+                    old_max = int(payload["max_tokens"])
+                    new_max = int(old_max * 2)
+                    payload["max_tokens"] = new_max
+                    wait = 0.5 * (2**attempt)
+                    logger.warning(
+                        "视觉输出被max_tokens截断(%d)，扩容到%d后重试 (第%d/%d次)",
+                        old_max,
+                        new_max,
+                        attempt + 1,
+                        self.max_retries + 1,
+                    )
+                    time.sleep(wait)
+                    continue
                 content = data["choices"][0]["message"]["content"]
+                self.last_finish_reason = finish_reason
                 _elapsed = time.time() - _t0
                 logger.info(
                     "[doubao] vision_completion 完成 model=%s tokens_in=%d tokens_out=%d elapsed=%.1fs attempt=%d",
@@ -560,6 +636,10 @@ class DoubaoClient:
                     resolution=resolution,
                     output_dir=output_dir,
                     model=video_model,
+                    generate_audio=bool(generate_audio),
+                    reference_images=reference_images,
+                    reference_audios=reference_audios,
+                    reference_videos=reference_videos,
                 )
                 if not result and hasattr(ds, "last_video_error") and ds.last_video_error:
                     self.last_video_error = dict(ds.last_video_error)
@@ -589,28 +669,32 @@ class DoubaoClient:
         # 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:
+            # #2220: 稀疏列表模式——pre_trusted_images 与 raw_portrait_urls 等长，
+            # None 位保留原图，非 None 位用 AI 人像替换。
             raw_portrait_urls: list[str] = []
             if image_url:
                 raw_portrait_urls.append(image_url)
             for u in ref_imgs:
                 if u not in raw_portrait_urls:
                     raw_portrait_urls.append(u)
-            trusted_urls: list[str] = []
-            if len(pre_trusted_images) >= 1:
-                trusted_urls = list(pre_trusted_images)
+            _n_trusted = sum(1 for _x in pre_trusted_images if _x)
+            if _n_trusted >= 1 and len(pre_trusted_images) >= len(raw_portrait_urls):
+                merged: list[str] = []
+                for _i, _orig in enumerate(raw_portrait_urls):
+                    _ai = pre_trusted_images[_i] if _i < len(pre_trusted_images) else None
+                    merged.append(str(_ai) if _ai else _orig)
                 trust_chain_applied = True
                 logger.info(
-                    "[trust-chain] 使用预热t2i结果 %d 张，替换原参考图走 reference_image 模式（原n=%d）",
-                    len(trusted_urls),
+                    "[trust-chain] 稀疏替换 %d/%d 张为AI人像（场景/商品图保留原图），走reference_image模式",
+                    _n_trusted,
                     len(raw_portrait_urls),
                 )
-            if trust_chain_applied and trusted_urls:
-                # 替换：原 image_url 用第一张 AI 图，ref_imgs 用剩余
-                if image_url and trusted_urls:
-                    image_url = trusted_urls[0]
-                    ref_imgs = trusted_urls[1:] if len(trusted_urls) > 1 else []
+                # 替换：image_url 用第一张（可能是AI或原图），ref_imgs 用剩余
+                if image_url and merged:
+                    image_url = merged[0]
+                    ref_imgs = merged[1:] if len(merged) > 1 else []
                 else:
-                    ref_imgs = trusted_urls
+                    ref_imgs = merged
         # ─────────────────────────────────────────────────────────────────
 
         # 判断任务模式：

packages/shared/ai_config_version.py

@@ -0,0 +1,62 @@
+"""AI 配置版本号管理 — Redis 通知机制.
+
+admin 后台修改 ai_models / ai_capability_configs 后调用 bump_version()，
+SaaS 端 AIRouter 每次取配置前比对版本号，变了才重新查 DB。
+
+Redis key: xiaoxia:ai_config:version = 时间戳字符串
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+_REDIS_KEY = "xiaoxia:ai_config:version"
+
+
+def _get_redis_client():
+    """获取 Redis 客户端（复用 Celery broker 连接）."""
+    try:
+        import redis as _redis
+
+        from packages.shared.config import get_shared_settings
+
+        settings = get_shared_settings()
+        redis_url = getattr(settings, "redis_url", None) or getattr(
+            settings, "celery_broker_url", "redis://localhost:6379/0"
+        )
+        return _redis.Redis.from_url(redis_url, decode_responses=True, socket_timeout=2)
+    except Exception as e:
+        logger.warning("AI config version: Redis 客户端初始化失败: %s", e)
+        return None
+
+
+def bump_version() -> str:
+    """写入新版本号（当前时间戳），返回版本号字符串。失败返回空串。"""
+    r = _get_redis_client()
+    if r is None:
+        logger.warning("AI config bump_version: Redis 不可用，跳过版本号更新")
+        return ""
+    try:
+        ver = str(int(time.time() * 1000))
+        r.set(_REDIS_KEY, ver)
+        logger.info("AI config version bumped to %s", ver)
+        return ver
+    except Exception as e:
+        logger.warning("AI config bump_version 失败: %s", e)
+        return ""
+
+
+def get_version() -> Optional[str]:
+    """读取当前版本号。Redis 不可用或异常返回 None。"""
+    r = _get_redis_client()
+    if r is None:
+        return None
+    try:
+        return r.get(_REDIS_KEY)
+    except Exception as e:
+        logger.warning("AI config get_version 失败: %s", e)
+        return None

packages/shared/ai_router.py

@@ -0,0 +1,516 @@
+"""AI 模型路由层 — 统一模型配置读取与客户端构建.
+
+业务代码通过 AIRouter 获取客户端，不再硬编码 model/api_key/base_url。
+配置来源：DB ai_capability_configs JOIN ai_models → Redis 版本号缓存 → SharedSettings fallback。
+
+使用方式：
+    from packages.shared.ai_router import ai_router
+
+    client = ai_router.get_llm_client("intent_parsing")
+    result = client.chat_completion(messages=[...])
+"""
+
+from __future__ import annotations
+
+import logging
+import threading
+from dataclasses import dataclass
+
+from packages.shared.config import get_shared_settings
+
+logger = logging.getLogger(__name__)
+
+
+# ── 配置数据类 ──────────────────────────────────────────────────────────────
+
+
+@dataclass(frozen=True)
+class ModelConfig:
+    """单个 AI 模型配置（来自 ai_models 表）"""
+
+    id: str
+    name: str
+    provider: str
+    model_key: str
+    api_key: str
+    api_base: str
+    api_version: str | None
+    status: str
+
+
+@dataclass(frozen=True)
+class CapabilityConfig:
+    """业务能力配置（来自 ai_capability_configs JOIN ai_models）"""
+
+    capability_key: str
+    capability_name: str
+    primary_model: ModelConfig | None
+    lite_model: ModelConfig | None
+    fallback_model: ModelConfig | None
+    timeout_seconds: int
+    max_retries: int
+    max_tokens: int | None
+    temperature: float | None
+    concurrency: int
+    extra_params: dict
+    is_enabled: bool
+
+
+# ── 简单包装类（TTS / ImageGen / VideoGen）──────────────────────────────────
+
+
+class TTSClient:
+    """TTS 客户端（简单配置持有者，实际调用由 CosyVoiceService 完成）"""
+
+    def __init__(
+        self,
+        provider: str,
+        api_key: str,
+        base_url: str,
+        model: str,
+        timeout: int = 60,
+        extra_params: dict | None = None,
+    ):
+        self.provider = provider
+        self.api_key = api_key
+        self.base_url = base_url
+        self.model = model
+        self.timeout = timeout
+        self.extra_params = extra_params or {}
+
+    @property
+    def is_available(self) -> bool:
+        return bool(self.api_key and self.base_url and self.model)
+
+
+class ImageGenClient:
+    """图片生成客户端（简单配置持有者）"""
+
+    def __init__(
+        self,
+        provider: str,
+        api_key: str,
+        base_url: str,
+        model: str,
+        timeout: int = 60,
+        extra_params: dict | None = None,
+    ):
+        self.provider = provider
+        self.api_key = api_key
+        self.base_url = base_url
+        self.model = model
+        self.timeout = timeout
+        self.extra_params = extra_params or {}
+
+    @property
+    def is_available(self) -> bool:
+        return bool(self.api_key and self.base_url and self.model)
+
+
+class VideoGenClient:
+    """视频生成客户端（简单配置持有者）"""
+
+    def __init__(
+        self,
+        provider: str,
+        api_key: str,
+        base_url: str,
+        model: str,
+        timeout: int = 600,
+        extra_params: dict | None = None,
+    ):
+        self.provider = provider
+        self.api_key = api_key
+        self.base_url = base_url
+        self.model = model
+        self.timeout = timeout
+        self.extra_params = extra_params or {}
+
+    @property
+    def is_available(self) -> bool:
+        return bool(self.api_key and self.base_url and self.model)
+
+
+# ── DB Session 获取 ─────────────────────────────────────────────────────────
+
+
+def _get_session():
+    """获取 DB session，兼容 api / worker / 独立脚本场景"""
+    from packages.adapters.sqlalchemy_impl.session import SessionLocal
+
+    if SessionLocal is not None:
+        return SessionLocal()
+
+    try:
+        from worker_app.db import SessionLocal as WorkerSL
+
+        if WorkerSL is not None:
+            return WorkerSL()
+    except ImportError:
+        pass
+
+    try:
+        from app.db import SessionLocal as ApiSL
+
+        if ApiSL is not None:
+            return ApiSL()
+    except ImportError:
+        pass
+
+    return None
+
+
+# ── 核心路由类 ──────────────────────────────────────────────────────────────
+
+
+class AIRouter:
+    """AI 模型路由器 — 统一配置读取与客户端构建.
+
+    缓存策略：
+    1. 本地内存缓存 {capability_key: CapabilityConfig}
+    2. 每次读取前比对 Redis 版本号，变了则清缓存重新查 DB
+    3. DB 无配置 / Redis 不可用 → fallback 到 SharedSettings 环境变量
+    """
+
+    def __init__(self):
+        self._cache: dict[str, CapabilityConfig] = {}
+        self._local_ver: str | None = None
+        self._lock = threading.Lock()
+
+    def _check_version(self) -> bool:
+        """检查 Redis 版本号，变了返回 True（需要刷新缓存）"""
+        from packages.shared.ai_config_version import get_version
+
+        current_ver = get_version()
+        if current_ver is None:
+            return False
+        if self._local_ver != current_ver:
+            return True
+        return False
+
+    def _load_from_db(self, capability_key: str) -> CapabilityConfig | None:
+        """从 DB 加载配置（ai_capability_configs JOIN ai_models）"""
+        session = _get_session()
+        if session is None:
+            logger.warning("AI Router: 无法获取 DB session")
+            return None
+        try:
+            from sqlalchemy import text
+
+            sql = text("""
+                SELECT
+                    cc.capability_key, cc.capability_name, cc.timeout_seconds,
+                    cc.max_retries, cc.max_tokens, cc.temperature,
+                    cc.concurrency, cc.extra_params, cc.is_enabled,
+                    pm.id AS pm_id, pm.name AS pm_name, pm.provider AS pm_provider,
+                    pm.model_key AS pm_model_key, pm.api_key AS pm_api_key,
+                    pm.api_base AS pm_api_base, pm.api_version AS pm_api_version,
+                    pm.status AS pm_status,
+                    lm.id AS lm_id, lm.name AS lm_name, lm.provider AS lm_provider,
+                    lm.model_key AS lm_model_key, lm.api_key AS lm_api_key,
+                    lm.api_base AS lm_api_base, lm.api_version AS lm_api_version,
+                    lm.status AS lm_status,
+                    fm.id AS fm_id, fm.name AS fm_name, fm.provider AS fm_provider,
+                    fm.model_key AS fm_model_key, fm.api_key AS fm_api_key,
+                    fm.api_base AS fm_api_base, fm.api_version AS fm_api_version,
+                    fm.status AS fm_status
+                FROM ai_capability_configs cc
+                LEFT JOIN ai_models pm ON cc.primary_model_id = pm.id AND pm.deleted_at IS NULL
+                LEFT JOIN ai_models lm ON cc.lite_model_id = lm.id AND lm.deleted_at IS NULL
+                LEFT JOIN ai_models fm ON cc.fallback_model_id = fm.id AND fm.deleted_at IS NULL
+                WHERE cc.capability_key = :key AND cc.is_enabled = true
+            """)
+            row = session.execute(sql, {"key": capability_key}).first()
+            if not row:
+                return None
+
+            def _to_model(prefix: str) -> ModelConfig | None:
+                mid = getattr(row, f"{prefix}_id", None)
+                if not mid:
+                    return None
+                return ModelConfig(
+                    id=mid,
+                    name=getattr(row, f"{prefix}_name", "") or "",
+                    provider=getattr(row, f"{prefix}_provider", "") or "",
+                    model_key=getattr(row, f"{prefix}_model_key", "") or "",
+                    api_key=getattr(row, f"{prefix}_api_key", "") or "",
+                    api_base=getattr(row, f"{prefix}_api_base", "") or "",
+                    api_version=getattr(row, f"{prefix}_api_version", None),
+                    status=getattr(row, f"{prefix}_status", "active") or "active",
+                )
+
+            return CapabilityConfig(
+                capability_key=row.capability_key,
+                capability_name=row.capability_name,
+                primary_model=_to_model("pm"),
+                lite_model=_to_model("lm"),
+                fallback_model=_to_model("fm"),
+                timeout_seconds=row.timeout_seconds or 30,
+                max_retries=row.max_retries or 1,
+                max_tokens=row.max_tokens,
+                temperature=row.temperature,
+                concurrency=row.concurrency or 2,
+                extra_params=row.extra_params or {},
+                is_enabled=row.is_enabled,
+            )
+        except Exception as e:
+            logger.warning("AI Router: DB 查询失败 (key=%s): %s", capability_key, e)
+            return None
+        finally:
+            session.close()
+
+    def get_capability(self, key: str) -> CapabilityConfig | None:
+        """获取业务能力配置（带缓存）"""
+        with self._lock:
+            if self._check_version():
+                self._cache.clear()
+                from packages.shared.ai_config_version import get_version
+
+                self._local_ver = get_version()
+
+            if key in self._cache:
+                return self._cache[key]
+
+            config = self._load_from_db(key)
+            if config:
+                self._cache[key] = config
+            return config
+
+    def _get_model_or_fallback(self, cap: CapabilityConfig, variant: str = "primary") -> ModelConfig | None:
+        """按 variant 选择模型，不存在则降级。
+
+        - primary: primary → fallback
+        - lite: lite → primary
+        - fallback: fallback → primary（修复点：此前 fallback variant 被忽略，错误地使用了 primary 模型）
+        """
+        if variant == "fallback":
+            if cap.fallback_model:
+                return cap.fallback_model
+            if cap.primary_model:
+                return cap.primary_model
+        elif variant == "lite":
+            if cap.lite_model:
+                return cap.lite_model
+            if cap.primary_model:
+                return cap.primary_model
+        else:  # primary
+            if cap.primary_model:
+                return cap.primary_model
+            if cap.fallback_model:
+                return cap.fallback_model
+        return None
+
+    # ── 构建客户端 ─────────────────────────────────────────────────────────
+
+    def _build_llm_client(self, model: ModelConfig, cap: CapabilityConfig):
+        """构建 LLM 客户端 — 返回 DoubaoClient 实例"""
+        from packages.shared.ai_client import DoubaoClient
+
+        return DoubaoClient(
+            api_key=model.api_key,
+            base_url=model.api_base,
+            model=model.model_key,
+            timeout=cap.timeout_seconds,
+            max_retries=cap.max_retries,
+            max_tokens=cap.max_tokens,
+            temperature=cap.temperature,
+            extra_params=cap.extra_params,
+            provider=model.provider,
+        )
+
+    def _build_vision_client(self, model: ModelConfig, cap: CapabilityConfig):
+        """构建 VLM 客户端 — 返回 DoubaoClient 实例（DoubaoClient 已支持 vision_completion）"""
+        from packages.shared.ai_client import DoubaoClient
+
+        return DoubaoClient(
+            api_key=model.api_key,
+            base_url=model.api_base,
+            model=model.model_key,
+            timeout=cap.timeout_seconds,
+            max_retries=cap.max_retries,
+            max_tokens=cap.max_tokens,
+            temperature=cap.temperature,
+            extra_params=cap.extra_params,
+            provider=model.provider,
+        )
+
+    def _build_tts_client(self, model: ModelConfig, cap: CapabilityConfig) -> TTSClient:
+        return TTSClient(
+            provider=model.provider,
+            api_key=model.api_key,
+            base_url=model.api_base,
+            model=model.model_key,
+            timeout=cap.timeout_seconds,
+            extra_params=cap.extra_params,
+        )
+
+    def _build_image_gen_client(self, model: ModelConfig, cap: CapabilityConfig) -> ImageGenClient:
+        return ImageGenClient(
+            provider=model.provider,
+            api_key=model.api_key,
+            base_url=model.api_base,
+            model=model.model_key,
+            timeout=cap.timeout_seconds,
+            extra_params=cap.extra_params,
+        )
+
+    def _build_video_gen_client(self, model: ModelConfig, cap: CapabilityConfig) -> VideoGenClient:
+        return VideoGenClient(
+            provider=model.provider,
+            api_key=model.api_key,
+            base_url=model.api_base,
+            model=model.model_key,
+            timeout=cap.timeout_seconds,
+            extra_params=cap.extra_params,
+        )
+
+    # ── 公开接口 ────────────────────────────────────────────────────────────
+
+    def get_llm_client(self, key: str, variant: str = "primary"):
+        """获取 LLM 客户端（返回 DoubaoClient 实例）"""
+        cap = self.get_capability(key)
+        if cap and cap.is_enabled:
+            model = self._get_model_or_fallback(cap, variant)
+            if model and model.api_key:
+                return self._build_llm_client(model, cap)
+
+        return self._fallback_llm_client(key)
+
+    def get_vision_client(self, key: str, variant: str = "primary"):
+        """获取 VLM 客户端（返回 DoubaoClient 实例）"""
+        cap = self.get_capability(key)
+        if cap and cap.is_enabled:
+            model = self._get_model_or_fallback(cap, variant)
+            if model and model.api_key:
+                return self._build_vision_client(model, cap)
+
+        return self._fallback_vision_client(key)
+
+    def get_tts_client(self, key: str = "tts") -> TTSClient | None:
+        """获取 TTS 客户端"""
+        cap = self.get_capability(key)
+        if cap and cap.is_enabled and cap.primary_model and cap.primary_model.api_key:
+            return self._build_tts_client(cap.primary_model, cap)
+
+        return self._fallback_tts_client()
+
+    def get_image_gen_client(self, key: str = "image_generation") -> ImageGenClient | None:
+        """获取图片生成客户端"""
+        cap = self.get_capability(key)
+        if cap and cap.is_enabled and cap.primary_model and cap.primary_model.api_key:
+            return self._build_image_gen_client(cap.primary_model, cap)
+
+        return self._fallback_image_gen_client()
+
+    def get_video_gen_client(self, key: str = "video_generation") -> VideoGenClient | None:
+        """获取视频生成客户端"""
+        cap = self.get_capability(key)
+        if cap and cap.is_enabled and cap.primary_model and cap.primary_model.api_key:
+            return self._build_video_gen_client(cap.primary_model, cap)
+
+        return self._fallback_video_gen_client()
+
+    # ── Fallback 方法（读 SharedSettings 环境变量）──────────────────────────
+
+    def _fallback_llm_client(self, key: str):
+        """Fallback LLM 客户端 — 从 settings 读取配置，不硬编码"""
+        settings = get_shared_settings()
+        model_map = {
+            "intent_parsing": (settings.doubao_fast_model, settings.doubao_base_url, settings.doubao_api_key),
+            "copy_fusion": (settings.doubao_fast_model, settings.doubao_base_url, settings.doubao_api_key),
+            "storyboard": (settings.doubao_model, settings.doubao_base_url, settings.doubao_api_key),
+            "copy_review": (settings.doubao_model, settings.doubao_base_url, settings.doubao_api_key),
+            "asset_classify": (settings.doubao_model, settings.doubao_base_url, settings.doubao_api_key),
+        }
+        if key in model_map:
+            model_id, base_url, api_key = model_map[key]
+        else:
+            model_id = settings.doubao_model
+            base_url = settings.doubao_base_url
+            api_key = settings.doubao_api_key
+
+        if not api_key:
+            return None
+
+        from packages.shared.ai_client import DoubaoClient
+
+        return DoubaoClient(
+            provider="volcengine",
+            api_key=api_key,
+            base_url=base_url,
+            model=model_id,
+            timeout=settings.doubao_timeout,
+            max_retries=settings.doubao_max_retries,
+        )
+
+    def _fallback_vision_client(self, key: str):
+        """Fallback VLM 客户端 — 从 settings 读取 dashscope 配置，不硬编码"""
+        settings = get_shared_settings()
+        api_key = getattr(settings, "dashscope_api_key", "")
+        if not api_key:
+            return None
+        base_url = getattr(settings, "dashscope_base_url", "") or ""
+        model = getattr(settings, "dashscope_model", "") or getattr(settings, "doubao_vision_model", "")
+
+        from packages.shared.ai_client import DoubaoClient
+
+        return DoubaoClient(
+            provider="dashscope",
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            timeout=15,
+        )
+
+    def _fallback_tts_client(self) -> TTSClient | None:
+        settings = get_shared_settings()
+        api_key = getattr(settings, "cosyvoice_api_key", "")
+        if not api_key:
+            return None
+        base_url = getattr(settings, "cosyvoice_base_url", "")
+        model = getattr(settings, "cosyvoice_model", "")
+
+        return TTSClient(provider="dashscope", api_key=api_key, base_url=base_url, model=model)
+
+    def _fallback_image_gen_client(self) -> ImageGenClient | None:
+        settings = get_shared_settings()
+        api_key = getattr(settings, "doubao_api_key", "")
+        if not api_key:
+            return None
+        base_url = getattr(settings, "doubao_base_url", "")
+        model = getattr(settings, "doubao_image_model", "")
+
+        return ImageGenClient(
+            provider="volcengine",
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            timeout=getattr(settings, "doubao_image_timeout", 60),
+        )
+
+    def _fallback_video_gen_client(self) -> VideoGenClient | None:
+        settings = get_shared_settings()
+        api_key = getattr(settings, "doubao_api_key", "")
+        if not api_key:
+            return None
+        base_url = getattr(settings, "doubao_base_url", "")
+        model = getattr(settings, "doubao_video_model", "")
+
+        return VideoGenClient(
+            provider="volcengine",
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            timeout=getattr(settings, "doubao_video_timeout", 600),
+        )
+
+    def invalidate(self):
+        """清空本地缓存"""
+        with self._lock:
+            self._cache.clear()
+            self._local_ver = None
+
+
+# ── 全局单例 ──────────────────────────────────────────────────────────────
+
+ai_router = AIRouter()

packages/shared/celery_queues.py

@@ -51,6 +51,8 @@ task_routes = {
     "ai_avatar_render.execute": {"queue": QUEUE_GENERATION},
     # GPU MuseTalk 口型同步（用户等成片，链路子任务全部走 generation 避免跨队列阻塞）
     "lipsync_gpu_process_async": {"queue": QUEUE_GENERATION},
+    # #2076 Ditto 蚂蚁数字人口型同步（走 generation 队列，避免跨队列阻塞）
+    "lipsync_ditto_process_async": {"queue": QUEUE_GENERATION},
     "lipsync_tts.synthesize_and_submit": {"queue": QUEUE_GENERATION},
     "lipsync_tts.poll_mediakit_status": {"queue": QUEUE_GENERATION},
     "lipsync_tts.persist_output_video": {"queue": QUEUE_GENERATION},

packages/shared/dashscope_client.py

@@ -115,10 +115,20 @@ class DashScopeClient:
         watermark: bool = False,
         output_dir: str | None = None,
         model: str = "wan3.0-video",
+        generate_audio: bool = True,
+        reference_images: list[str] | None = None,
+        reference_audios: list[str] | None = None,
+        reference_videos: list[str] | None = None,
     ) -> dict | None:
-        """调用 DashScope 异步视频合成接口，轮询完成后下载到本地。
+        """调用 DashScope Wan 3.0 异步视频合成接口，轮询完成后下载到本地。
 
-        返回 {"video_path": str, "usage": dict | None}；失败返回 None，错误详情写入 self.last_video_error。
+        官方协议（input.media 数组 + parameters.audio）：
+        - 仅 1 张图且无其它参考 -> type=first_frame（首帧模式，严格从该帧起）。
+        - 有参考音频 / 多张图 -> 图片全部走 type=reference_image（全能参考模式，
+          可与 reference_audio 共存）；prompt 用"图1/图2/音频1"按 media 顺序引用。
+        - parameters.audio 控制输出是否含音轨；参考音频通过 media 传入。
+
+        返回 {"video_path": str, "usage": dict | None}；失败返回 None，错误写入 self.last_video_error。
         """
         self.last_video_error = {}
         if not self.is_available:
@@ -130,7 +140,7 @@ class DashScopeClient:
             return None
 
         # DashScope 分辨率参数：720P / 1080P / 480P（大写 P）
-        res_upper = (resolution or "720p").upper().replace("P", "P")
+        res_upper = (resolution or "720p").upper()
         if res_upper == "480P":
             ds_res = "480P"
         elif res_upper == "1080P":
@@ -138,16 +148,35 @@ class DashScopeClient:
         else:
             ds_res = "720P"
 
-        # 构造 input+parameters
+        # ── 构造官方 media 数组 ────────────────────────────────────────
+        ref_imgs = [u for u in (reference_images or [])[:10] if u]
+        ref_auds = [u for u in (reference_audios or [])[:5] if u]
+        ref_vids = [u for u in (reference_videos or [])[:5] if u]
+
+        media: list[dict[str, Any]] = []
+        all_imgs = ([image_url] if image_url else []) + [u for u in ref_imgs if u != image_url]
+        use_first_frame = bool(image_url) and len(all_imgs) == 1 and not (ref_auds or ref_vids)
+        if use_first_frame:
+            media.append({"type": "first_frame", "url": image_url})
+        else:
+            for u in all_imgs:
+                media.append({"type": "reference_image", "url": u})
+            for u in ref_vids:
+                media.append({"type": "reference_video", "url": u})
+            for u in ref_auds:
+                media.append({"type": "reference_audio", "url": u})
+
+        # ── input + parameters ─────────────────────────────────────────
         input_obj: dict[str, Any] = {"prompt": prompt.strip()}
-        if image_url:
-            input_obj["img_url"] = image_url
+        if media:
+            input_obj["media"] = media
+
         params: dict[str, Any] = {
             "resolution": ds_res,
-            "duration": str(float(duration)),
+            "duration": int(duration),
             "watermark": bool(watermark),
+            "audio": bool(generate_audio),
         }
-        # 比例透传：Wan 支持 "9:16" / "16:9" / "1:1" 等
         if ratio and ratio != "adaptive":
             params["aspect_ratio"] = ratio
 
@@ -163,14 +192,15 @@ class DashScopeClient:
         }
         create_url = f"{self.base_url}/services/aigc/video-generation/video-synthesis"
         logger.info(
-            "[dashscope] 创建任务: model=%s dur=%ds ratio=%s res=%s img=%s",
+            "[dashscope] 创建任务: model=%s dur=%ds ratio=%s res=%s media=%d audio=%s",
             model,
             duration,
             ratio,
             ds_res,
-            bool(image_url),
+            len(media),
+            generate_audio,
         )
-        logger.info("[dashscope] 创建任务 payload: model=%s params=%s", model, params)
+        logger.info("[dashscope] media types: %s", [m["type"] for m in media])
 
         # 创建任务
         task_id: str | None = None
@@ -196,27 +226,14 @@ class DashScopeClient:
                 if tid:
                     task_id = tid
                     break
-                # 部分情况下 code != 错误
                 code = data.get("code")
-                if code and code != "":
-                    err_code, user_msg = _classify_dashscope_error(400, body_text, str(code))
+                if code:
+                    logger.error("[dashscope] 创建任务返回 code=%s body=%s", code, body_text)
+                    err_code, user_msg = _classify_dashscope_error(sc, body_text)
                     self._set_error(err_code, user_msg, sc, body_text, model=model)
                     return None
-                else:
-                    self._set_error("unknown", "Wan 3.0 响应格式异常，未返回任务ID", sc, str(data)[:500], model=model)
-                    return None
-            except _HTTP_NETWORK_ERRORS as ne:
-                last_sc = 0
-                last_body = f"network error: {ne}"
-                logger.warning(
-                    "[dashscope] 网络异常 %s，重试 %d/%d", type(ne).__name__, attempt + 1, self.max_retries + 1
-                )
-                if attempt < self.max_retries:
-                    time.sleep(0.5 * (2**attempt))
-                    continue
-                self._set_error("network_error", "Wan 3.0 服务连接失败（网络超时），请稍后重试。", 0, str(ne))
-                return None
             except Exception as _e:
+                logger.warning("[dashscope] 创建任务异常(attempt=%d): %s", attempt, _e)
                 if attempt < self.max_retries:
                     time.sleep(0.5 * (2**attempt))
                     continue
@@ -258,7 +275,6 @@ class DashScopeClient:
                     video_url = out.get("video_url") or ""
                     usage = d.get("usage")
                     if not video_url:
-                        # 结果在 results 数组
                         results = out.get("results") or []
                         if results and isinstance(results, list):
                             video_url = results[0].get("url") or results[0].get("video_url")
@@ -284,7 +300,6 @@ class DashScopeClient:
                     logger.warning("[dashscope] 任务 %s 被取消", task_id)
                     self._set_error("unknown", "Wan 3.0 任务被取消。", 200, "task cancelled", task_id=task_id)
                     return None
-                # PENDING / RUNNING / SUSPENDED → 继续轮询
                 if poll_count % 5 == 0:
                     logger.info("[dashscope] 轮询中 task=%s status=%s polls=%d", task_id, task_status, poll_count)
             except Exception as e:

packages/shared/storage.py

@@ -334,7 +334,7 @@ class SharedStorageService(StoragePort):
 
         storage_key = self.normalize_storage_key(storage_key_or_url)
         try:
-            signed = sign_bucket.sign_url("GET", storage_key, expires_seconds)
+            signed = sign_bucket.sign_url("GET", storage_key, expires_seconds, slash_safe=True)
             logger.info(
                 "signed URL generated for key=%s prefix=%s",
                 storage_key[:80],

tests/unit/test_ai_router.py

@@ -0,0 +1,378 @@
+"""AI Router 单元测试 — routing/cache/fallback/client construction.
+
+本文件只做*用例级* mock：通过 autouse fixture 在每个用例内 patch
+``packages.shared.config.get_shared_settings`` / ``packages.shared.ai_router.get_shared_settings``
+并在退出时自动恢复，绝不在模块顶层替换 ``sys.modules``，因此不会污染同进程的
+其他测试模块（如 test_ai_client.py）。
+
+在 Python 3.12 且依赖齐全的 CI 环境中，直接 import 真实模块即可；Redis / DB
+会话通过 patch 隔离。
+"""
+
+from __future__ import annotations
+
+import unittest
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from packages.shared import ai_config_version as _config_version_mod
+from packages.shared import ai_router as ai_router_mod
+
+# ── 统一的假配置（等价于旧文件里的 _mock_settings）──────────────────────────
+
+
+def _make_mock_settings() -> MagicMock:
+    s = MagicMock()
+    s.doubao_model = "doubao-seed-2-1-pro-260915"
+    s.doubao_fast_model = "doubao-seed-2-1-pro-260915"
+    s.doubao_base_url = "https://ark.cn-beijing.volces.com/api/v3"
+    s.doubao_api_key = "test-key"
+    s.doubao_timeout = 45
+    s.doubao_max_retries = 1
+    s.doubao_image_model = "doubao-seedream-5-0-flash-260915"
+    s.doubao_image_timeout = 60
+    s.doubao_vision_model = "doubao-seed-1-6-vision-250615"
+    s.doubao_video_model = "doubao-seedance-2-5-260628"
+    s.doubao_video_timeout = 600
+    s.dashscope_api_key = "ds-key"
+    s.dashscope_base_url = "https://dashscope.aliyuncs.com/api/v1"
+    s.dashscope_model = "qwen-vl-max"
+    s.cosyvoice_api_key = "cv-key"
+    s.cosyvoice_base_url = "https://dashscope.aliyuncs.com/api/v1"
+    s.cosyvoice_model = "cosyvoice-v3-flash"
+    s.redis_url = "redis://localhost:6379/0"
+    s.celery_broker_url = "redis://localhost:6379/0"
+    return s
+
+
+@pytest.fixture(autouse=True)
+def _mock_settings_fixture():
+    """每个用例内 patch 配置来源，退出即恢复，不污染 sys.modules。"""
+    settings = _make_mock_settings()
+    with (
+        patch("packages.shared.config.get_shared_settings", return_value=settings),
+        patch.object(ai_router_mod, "get_shared_settings", return_value=settings),
+    ):
+        yield settings
+
+
+def _capability_row() -> MagicMock:
+    row = MagicMock()
+    row.capability_key = "intent_parsing"
+    row.capability_name = "文案意图解析"
+    row.timeout_seconds = 45
+    row.max_retries = 1
+    row.max_tokens = None
+    row.temperature = None
+    row.concurrency = 2
+    row.extra_params = {}
+    row.is_enabled = True
+    row.pm_id = "model-1"
+    row.pm_name = "豆包"
+    row.pm_provider = "volcengine"
+    row.pm_model_key = "doubao-seed-1-6-250615"
+    row.pm_api_key = "test-key"
+    row.pm_api_base = "https://ark.test.com"
+    row.pm_api_version = None
+    row.pm_status = "active"
+    row.lm_id = None
+    row.fm_id = None
+    return row
+
+
+def _model_config(**overrides):
+    kwargs = dict(
+        id="m1",
+        name="test",
+        provider="volcengine",
+        model_key="test-model",
+        api_key="key",
+        api_base="https://test.com",
+        api_version=None,
+        status="active",
+    )
+    kwargs.update(overrides)
+    return ai_router_mod.ModelConfig(**kwargs)
+
+
+def _capability_config(**overrides):
+    kwargs = dict(
+        capability_key="test",
+        capability_name="test",
+        primary_model=None,
+        lite_model=None,
+        fallback_model=None,
+        timeout_seconds=30,
+        max_retries=1,
+        max_tokens=None,
+        temperature=None,
+        concurrency=2,
+        extra_params={},
+        is_enabled=True,
+    )
+    kwargs.update(overrides)
+    return ai_router_mod.CapabilityConfig(**kwargs)
+
+
+# ── Redis 版本号机制 ────────────────────────────────────────────────────────
+
+
+class TestAIConfigVersion(unittest.TestCase):
+    """Redis 版本号机制测试"""
+
+    @patch.object(_config_version_mod, "_get_redis_client")
+    def test_bump_version_success(self, mock_redis_fn):
+        mock_r = MagicMock()
+        mock_r.set.return_value = True
+        mock_redis_fn.return_value = mock_r
+        ver = _config_version_mod.bump_version()
+        self.assertTrue(ver)
+        self.assertTrue(ver.isdigit())
+        mock_r.set.assert_called_once()
+
+    @patch.object(_config_version_mod, "_get_redis_client")
+    def test_bump_version_redis_unavailable(self, mock_redis_fn):
+        mock_redis_fn.return_value = None
+        ver = _config_version_mod.bump_version()
+        self.assertEqual(ver, "")
+
+    @patch.object(_config_version_mod, "_get_redis_client")
+    def test_get_version_success(self, mock_redis_fn):
+        mock_r = MagicMock()
+        mock_r.get.return_value = "1234567890"
+        mock_redis_fn.return_value = mock_r
+        ver = _config_version_mod.get_version()
+        self.assertEqual(ver, "1234567890")
+
+    @patch.object(_config_version_mod, "_get_redis_client")
+    def test_get_version_redis_down(self, mock_redis_fn):
+        mock_redis_fn.return_value = None
+        ver = _config_version_mod.get_version()
+        self.assertIsNone(ver)
+
+    @patch.object(_config_version_mod, "_get_redis_client")
+    def test_get_version_exception(self, mock_redis_fn):
+        mock_r = MagicMock()
+        mock_r.get.side_effect = Exception("connection refused")
+        mock_redis_fn.return_value = mock_r
+        ver = _config_version_mod.get_version()
+        self.assertIsNone(ver)
+
+
+# ── AIRouter 路由/缓存/fallback ────────────────────────────────────────────
+
+
+class TestAIRouter(unittest.TestCase):
+    """AIRouter 路由/缓存/fallback 测试"""
+
+    def setUp(self):
+        self.router = ai_router_mod.AIRouter()
+
+    def _freeze_version(self, value=None):
+        """让 get_capability 的版本比对固定，避免走 Redis。"""
+        return patch.object(_config_version_mod, "get_version", return_value=value)
+
+    def test_get_capability_db_unavailable(self):
+        with self._freeze_version(None):
+            with patch.object(ai_router_mod, "_get_session", return_value=None):
+                cap = self.router.get_capability("intent_parsing")
+                self.assertIsNone(cap)
+
+    def test_get_capability_from_db(self):
+        mock_session = MagicMock()
+        mock_session.execute.return_value.first.return_value = _capability_row()
+        with self._freeze_version(None):
+            with patch.object(ai_router_mod, "_get_session", return_value=mock_session):
+                cap = self.router.get_capability("intent_parsing")
+                self.assertIsNotNone(cap)
+                self.assertEqual(cap.capability_key, "intent_parsing")
+                self.assertEqual(cap.primary_model.model_key, "doubao-seed-1-6-250615")
+
+    def test_cache_invalidation_on_version_change(self):
+        with self._freeze_version(None):
+            with patch.object(self.router, "_load_from_db", return_value=None):
+                self.router.get_capability("test_key")
+        self.router._local_ver = "v1"
+        with patch.object(_config_version_mod, "get_version", return_value="v2"):
+            self.assertTrue(self.router._check_version())
+
+    def test_cache_hit_same_version(self):
+        cap = _capability_config(
+            primary_model=_model_config(model_key="test-model"),
+        )
+        self.router._cache["test"] = cap
+        self.router._local_ver = "same_ver"
+        with patch.object(_config_version_mod, "get_version", return_value="same_ver"):
+            result = self.router.get_capability("test")
+        self.assertEqual(result, cap)
+
+    def test_invalidate_clears_cache(self):
+        self.router._cache["x"] = MagicMock()
+        self.router._local_ver = "v1"
+        self.router.invalidate()
+        self.assertEqual(len(self.router._cache), 0)
+        self.assertIsNone(self.router._local_ver)
+
+    def test_get_llm_client_fallback(self):
+        with self._freeze_version(None):
+            with patch.object(ai_router_mod, "_get_session", return_value=None):
+                client = self.router.get_llm_client("intent_parsing")
+        self.assertIsNotNone(client)
+        self.assertEqual(client.model, "doubao-seed-2-1-pro-260915")
+        self.assertEqual(client.api_key, "test-key")
+
+    def test_get_llm_client_from_db(self):
+        cap = _capability_config(
+            capability_key="image_analysis",
+            capability_name="图片分析",
+            max_tokens=350,
+            temperature=0.1,
+            primary_model=_model_config(
+                provider="dashscope",
+                model_key="qwen3.8-flash",
+                api_key="db-key",
+                api_base="https://dashscope.test.com",
+            ),
+        )
+        with patch.object(self.router, "get_capability", return_value=cap):
+            client = self.router.get_llm_client("image_analysis")
+        self.assertIsNotNone(client)
+        self.assertEqual(client.model, "qwen3.8-flash")
+        self.assertEqual(client.provider, "dashscope")
+
+    def test_get_vision_client(self):
+        cap = _capability_config(
+            capability_key="image_analysis",
+            capability_name="图片分析",
+            primary_model=_model_config(
+                provider="dashscope",
+                model_key="qwen3.8-flash",
+                api_key="key",
+                api_base="https://dashscope.test.com",
+            ),
+        )
+        with patch.object(self.router, "get_capability", return_value=cap):
+            client = self.router.get_vision_client("image_analysis")
+        self.assertIsNotNone(client)
+        # #2220: vision client is now DoubaoClient with vision_completion
+        self.assertTrue(hasattr(client, "vision_completion"))
+
+    def test_get_tts_client(self):
+        cap = _capability_config(
+            capability_key="tts",
+            capability_name="语音合成",
+            primary_model=_model_config(
+                provider="dashscope",
+                model_key="cosyvoice-v3-flash",
+                api_key="key",
+                api_base="https://dashscope.test.com",
+            ),
+        )
+        with patch.object(self.router, "get_capability", return_value=cap):
+            client = self.router.get_tts_client()
+        self.assertIsNotNone(client)
+        self.assertEqual(client.model, "cosyvoice-v3-flash")
+
+    def test_get_image_gen_client(self):
+        cap = _capability_config(
+            capability_key="image_generation",
+            capability_name="图片生成",
+            extra_params={"size": "1K"},
+            primary_model=_model_config(
+                model_key="seedream-5.0-flash",
+                api_key="key",
+                api_base="https://ark.test.com",
+            ),
+        )
+        with patch.object(self.router, "get_capability", return_value=cap):
+            client = self.router.get_image_gen_client()
+        self.assertIsNotNone(client)
+        self.assertEqual(client.model, "seedream-5.0-flash")
+
+    def test_get_video_gen_client(self):
+        cap = _capability_config(
+            capability_key="video_generation",
+            capability_name="视频生成",
+            concurrency=1,
+            primary_model=_model_config(
+                model_key="seedance-2.5",
+                api_key="key",
+                api_base="https://ark.test.com",
+            ),
+        )
+        with patch.object(self.router, "get_capability", return_value=cap):
+            client = self.router.get_video_gen_client()
+        self.assertIsNotNone(client)
+        self.assertEqual(client.model, "seedance-2.5")
+
+    def test_lite_variant_preference(self):
+        cap = _capability_config(
+            capability_key="image_analysis",
+            capability_name="图片分析",
+            primary_model=_model_config(id="p1", name="pro", model_key="pro-model", api_key="k", api_base="u"),
+            lite_model=_model_config(id="l1", name="lite", model_key="lite-model", api_key="k", api_base="u"),
+        )
+        model = self.router._get_model_or_fallback(cap, "lite")
+        self.assertEqual(model.model_key, "lite-model")
+        model_primary = self.router._get_model_or_fallback(cap, "primary")
+        self.assertEqual(model_primary.model_key, "pro-model")
+
+    def test_disabled_capability_returns_fallback(self):
+        cap = _capability_config(is_enabled=False)
+        with patch.object(self.router, "get_capability", return_value=cap):
+            client = self.router.get_llm_client("test")
+        self.assertIsNotNone(client)
+        self.assertEqual(client.model, "doubao-seed-2-1-pro-260915")
+
+    def test_fallback_chain_primary_none(self):
+        """primary_model 为 None 时 fallback 到 fallback_model"""
+        cap = _capability_config(
+            fallback_model=_model_config(id="f1", name="fb", model_key="fb-model", api_key="k", api_base="u"),
+        )
+        model = self.router._get_model_or_fallback(cap, "primary")
+        self.assertEqual(model.model_key, "fb-model")
+
+
+# ── 数据类冻结 ──────────────────────────────────────────────────────────────
+
+
+class TestModelConfig(unittest.TestCase):
+    """数据类测试"""
+
+    def test_model_config_frozen(self):
+        m = _model_config(id="1", name="t", provider="p", model_key="k", api_key="a", api_base="b")
+        with self.assertRaises(AttributeError):
+            m.model_key = "new"
+
+    def test_capability_config_frozen(self):
+        c = _capability_config()
+        with self.assertRaises(AttributeError):
+            c.is_enabled = False
+
+
+# ── 客户端可用性 ────────────────────────────────────────────────────────────
+
+
+class TestClientAvailability(unittest.TestCase):
+    """客户端可用性测试"""
+
+    def test_tts_client_available(self):
+        c = ai_router_mod.TTSClient(provider="p", api_key="k", base_url="u", model="m")
+        self.assertTrue(c.is_available)
+
+    def test_tts_client_unavailable_no_model(self):
+        c = ai_router_mod.TTSClient(provider="p", api_key="k", base_url="u", model="")
+        self.assertFalse(c.is_available)
+
+    def test_image_gen_client_unavailable_no_url(self):
+        c = ai_router_mod.ImageGenClient(provider="p", api_key="k", base_url="", model="m")
+        self.assertFalse(c.is_available)
+
+    def test_video_gen_client_available(self):
+        c = ai_router_mod.VideoGenClient(provider="p", api_key="k", base_url="u", model="m")
+        self.assertTrue(c.is_available)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/unit/test_api_settings.py

@@ -73,15 +73,15 @@ class TestSharedSettingsDefaults:
 
     def test_default_cosyvoice_settings(self):
         s = SharedSettings()
-        assert s.cosyvoice_model == "cosyvoice-v3-flash"
+        assert s.cosyvoice_model == ""  # 零硬编码：默认值已清空
         assert s.cosyvoice_format == "mp3"
         assert s.cosyvoice_sample_rate == 22050
 
     def test_default_doubao_settings(self):
         s = SharedSettings()
-        assert "doubao" in s.doubao_model
+        assert s.doubao_model == ""  # 零硬编码：默认值已清空
         assert s.doubao_timeout == 45  # #2180 默认提到45s
-        assert s.doubao_max_retries == 1
+        assert s.doubao_max_retries == 3
 
 
 class TestAPISettingsDefaults:
@@ -321,7 +321,7 @@ class TestWorkerSettingsDefaults:
         assert s.database_url  # 继承自SharedSettings
         assert s.redis_url
         assert s.oss_endpoint
-        assert s.cosyvoice_model == "cosyvoice-v3-flash"
+        assert s.cosyvoice_model == ""  # 零硬编码：默认值已清空
 
 
 class TestGetWorkerSettings:

tests/unit/test_config_base.py

@@ -102,17 +102,17 @@ class TestSharedSettingsDefaults:
     def test_default_cosyvoice_config(self):
         """CosyVoice 默认配置"""
         s = self._make_settings()
-        assert s.cosyvoice_model == "cosyvoice-v3-flash"
+        assert s.cosyvoice_model == ""  # 零硬编码：默认值已清空
         assert s.cosyvoice_sample_rate == 22050
         assert s.cosyvoice_format == "mp3"
-        assert s.cosyvoice_clone_model == "voice-enrollment"
+        assert s.cosyvoice_clone_model == ""  # 零硬编码：默认值已清空
 
     def test_default_doubao_config(self):
         """豆包默认配置"""
         s = self._make_settings()
         assert s.doubao_timeout == 45  # #2180 默认提到45s
-        assert s.doubao_max_retries == 1
-        assert "volces.com" in s.doubao_base_url
+        assert s.doubao_max_retries == 3
+        assert s.doubao_base_url == ""  # 零硬编码：默认值已清空
 
     def test_default_empty_api_keys(self):
         """API Key 默认空字符串"""

tests/unit/test_cosyvoice_service.py

@@ -27,7 +27,10 @@ def mock_client() -> MagicMock:
 @pytest.fixture
 def service(mock_client: MagicMock) -> CosyVoiceService:
     """Create CosyVoiceService with mocked HTTP client and config."""
-    with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+    with (
+        patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+        patch("packages.shared.ai_router.ai_router") as mock_router,
+    ):
         settings = MagicMock()
         settings.cosyvoice_api_key = "sk-test-12345678"
         settings.cosyvoice_base_url = "https://dashscope.aliyuncs.com/api/v1"
@@ -37,6 +40,7 @@ def service(mock_client: MagicMock) -> CosyVoiceService:
         settings.cosyvoice_voice = "longxiaochun_v3"
         settings.cosyvoice_clone_model = "voice-enrollment"
         mock_settings.return_value = settings
+        mock_router.get_tts_client.return_value = None  # ai_router returns None in tests
         svc = CosyVoiceService(http_client=mock_client)
         svc.CLONE_POLL_INTERVAL = 0.001  # 加速测试
         svc.RETRY_BACKOFF = 0.001
@@ -48,7 +52,10 @@ class TestInitConfig:
 
     def test_base_url_with_old_text2audio_path_gets_normalized(self, mock_client: MagicMock) -> None:
         """旧版 base_url 带 text2audio 路径应自动修正."""
-        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+        with (
+            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+            patch("packages.shared.ai_router.ai_router") as mock_router,
+        ):
             settings = MagicMock()
             settings.cosyvoice_api_key = "sk-test"
             settings.cosyvoice_base_url = "https://dashscope.aliyuncs.com/api/v1/services/aigc/text2audio"
@@ -58,12 +65,16 @@ class TestInitConfig:
             settings.cosyvoice_voice = "test"
             settings.cosyvoice_clone_model = "voice-enrollment"
             mock_settings.return_value = settings
+            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             assert svc._base_url == "https://dashscope.aliyuncs.com/api/v1"
 
     def test_custom_params_override_config(self, mock_client: MagicMock) -> None:
         """显式传入参数覆盖配置."""
-        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+        with (
+            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+            patch("packages.shared.ai_router.ai_router") as mock_router,
+        ):
             settings = MagicMock()
             settings.cosyvoice_api_key = "sk-config"
             settings.cosyvoice_base_url = "https://config.example.com"
@@ -73,6 +84,7 @@ class TestInitConfig:
             settings.cosyvoice_voice = "test"
             settings.cosyvoice_clone_model = "voice-enrollment"
             mock_settings.return_value = settings
+            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(
                 api_key="sk-custom",
                 base_url="https://custom.example.com/api/v1",
@@ -87,7 +99,10 @@ class TestInitConfig:
 
     def test_context_manager(self, mock_client: MagicMock) -> None:
         """上下文管理器正常工作."""
-        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+        with (
+            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+            patch("packages.shared.ai_router.ai_router") as mock_router,
+        ):
             settings = MagicMock()
 
             settings.cosyvoice_api_key = "sk-test"
@@ -105,6 +120,7 @@ class TestInitConfig:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
+            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             with svc as s:
                 assert s is svc
@@ -113,7 +129,10 @@ class TestInitConfig:
 
     def test_owns_client_gets_closed(self) -> None:
         """自有client在close时被关闭."""
-        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+        with (
+            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+            patch("packages.shared.ai_router.ai_router") as mock_router,
+        ):
             settings = MagicMock()
 
             settings.cosyvoice_api_key = "sk-test"
@@ -131,6 +150,7 @@ class TestInitConfig:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
+            mock_router.get_tts_client.return_value = None
             with patch("packages.application.cosyvoice_service.httpx.Client") as mock_cls:
                 mock_instance = MagicMock()
                 mock_cls.return_value = mock_instance
@@ -173,7 +193,10 @@ class TestSubmitCloneTask:
 
     def test_no_api_key_raises_auth_error(self, mock_client: MagicMock) -> None:
         """无API Key抛认证错误."""
-        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+        with (
+            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+            patch("packages.shared.ai_router.ai_router") as mock_router,
+        ):
             settings = MagicMock()
 
             settings.cosyvoice_api_key = ""
@@ -191,6 +214,7 @@ class TestSubmitCloneTask:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
+            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             with pytest.raises(CosyVoiceAuthError, match="API Key 未配置"):
                 svc.submit_clone_task(audio_url="https://example.com/audio.mp3")
@@ -258,7 +282,10 @@ class TestSubmitCloneTask:
 
     def test_audio_url_signer_is_called(self, mock_client: MagicMock) -> None:
         """配置了audio_url_signer时会被调用预签名."""
-        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+        with (
+            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+            patch("packages.shared.ai_router.ai_router") as mock_router,
+        ):
             settings = MagicMock()
 
             settings.cosyvoice_api_key = "sk-test"
@@ -276,6 +303,7 @@ class TestSubmitCloneTask:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
+            mock_router.get_tts_client.return_value = None
             signer = MagicMock(return_value="https://signed.example.com/audio.mp3?token=xxx")
             svc = CosyVoiceService(http_client=mock_client, audio_url_signer=signer)
 
@@ -297,7 +325,10 @@ class TestSubmitCloneTask:
 
     def test_signer_failure_falls_back_to_original_url(self, mock_client: MagicMock) -> None:
         """预签名失败时回退到原始URL."""
-        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+        with (
+            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+            patch("packages.shared.ai_router.ai_router") as mock_router,
+        ):
             settings = MagicMock()
 
             settings.cosyvoice_api_key = "sk-test"
@@ -315,6 +346,7 @@ class TestSubmitCloneTask:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
+            mock_router.get_tts_client.return_value = None
             signer = MagicMock(side_effect=RuntimeError("sign failed"))
             svc = CosyVoiceService(http_client=mock_client, audio_url_signer=signer)
 
@@ -375,7 +407,10 @@ class TestQueryVoiceStatus:
 
     def test_no_api_key_raises(self, mock_client: MagicMock) -> None:
         """无API Key抛认证错误."""
-        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+        with (
+            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+            patch("packages.shared.ai_router.ai_router") as mock_router,
+        ):
             settings = MagicMock()
 
             settings.cosyvoice_api_key = ""
@@ -393,6 +428,7 @@ class TestQueryVoiceStatus:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
+            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             with pytest.raises(CosyVoiceAuthError):
                 svc.query_voice_status("v1")
@@ -589,7 +625,10 @@ class TestSubmitSynthesizeTask:
 
     def test_no_api_key_raises(self, mock_client: MagicMock) -> None:
         """无API Key抛认证错误."""
-        with patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings:
+        with (
+            patch("packages.application.cosyvoice_service.get_shared_settings") as mock_settings,
+            patch("packages.shared.ai_router.ai_router") as mock_router,
+        ):
             settings = MagicMock()
 
             settings.cosyvoice_api_key = ""
@@ -607,6 +646,7 @@ class TestSubmitSynthesizeTask:
             settings.cosyvoice_clone_model = "voice-enrollment"
 
             mock_settings.return_value = settings
+            mock_router.get_tts_client.return_value = None
             svc = CosyVoiceService(http_client=mock_client)
             with pytest.raises(CosyVoiceAuthError):
                 svc.submit_synthesize_task(text="你好", voice_id="v1")

tests/unit/test_ditto_emotion_service.py

@@ -0,0 +1,225 @@
+"""Ditto LLM 情绪分析服务单元测试."""
+
+from __future__ import annotations
+
+import json
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from packages.application.ditto_emotion_service import (
+    EMO_HAPPY,
+    EMO_NEUTRAL,
+    DittoEmotionService,
+    EmotionSegment,
+    _parse_emotion_json,
+    align_timeline_by_length,
+    align_timeline_by_timings,
+    split_sentences,
+)
+
+
+# ── 分句 ─────────────────────────────────────────────────────────
+class TestSplitSentences:
+    def test_empty(self):
+        assert split_sentences("") == []
+
+    def test_single(self):
+        assert split_sentences("你好。") == ["你好。"]
+
+    def test_multi(self):
+        sents = split_sentences("大家好！今天给大家推荐一款超棒的产品。它真的很好用；不信你试试？")
+        assert len(sents) == 4
+        assert "大家好！" in sents[0]
+
+    def test_english_punct(self):
+        sents = split_sentences("Hello! How are you? I'm fine.")
+        assert len(sents) == 3
+
+
+# ── JSON 解析 ────────────────────────────────────────────────────
+class TestParseEmotionJson:
+    def test_valid(self):
+        raw = json.dumps([{"text": "你好", "emo": 4, "intensity": 0.2}])
+        segs = _parse_emotion_json(raw)
+        assert len(segs) == 1
+        assert segs[0].emo == 4
+        assert segs[0].intensity == 0.2
+
+    def test_markdown_wrapped(self):
+        raw = "```json\n" + json.dumps([{"text": "好", "emo": 3, "intensity": 0.5}]) + "\n```"
+        segs = _parse_emotion_json(raw)
+        assert len(segs) == 1
+        assert segs[0].emo == 3
+
+    def test_forbidden_emo_becomes_neutral(self):
+        raw = json.dumps([{"text": "怒", "emo": 0, "intensity": 0.8}])
+        segs = _parse_emotion_json(raw)
+        assert len(segs) == 1
+        assert segs[0].emo == EMO_NEUTRAL
+
+    def test_invalid_json(self):
+        assert _parse_emotion_json("not json") == []
+
+    def test_empty(self):
+        assert _parse_emotion_json("") == []
+
+    def test_intensity_clamp(self):
+        raw = json.dumps([{"text": "a", "emo": 3, "intensity": 1.5}])
+        segs = _parse_emotion_json(raw)
+        assert segs[0].intensity == 1.0
+
+    def test_missing_text_skipped(self):
+        raw = json.dumps([{"emo": 3, "intensity": 0.4}])
+        segs = _parse_emotion_json(raw)
+        assert len(segs) == 0
+
+
+# ── 时间对齐（按字数比例）────────────────────────────────────────
+class TestAlignTimelineByLength:
+    def test_basic(self):
+        segs = [
+            EmotionSegment("ab", EMO_NEUTRAL, 0.2),
+            EmotionSegment("cd", EMO_HAPPY, 0.5),
+        ]
+        entries = align_timeline_by_length(segs, 4.0)
+        assert len(entries) == 2
+        assert entries[0].start == 0.0
+        assert entries[0].end == 2.0
+        assert entries[1].start == 2.0
+        assert entries[1].end == 4.0
+        assert entries[0].emo == EMO_NEUTRAL
+        assert entries[1].emo == EMO_HAPPY
+
+    def test_empty_segments(self):
+        assert align_timeline_by_length([], 5.0) == []
+
+    def test_zero_duration(self):
+        segs = [EmotionSegment("ab", EMO_NEUTRAL, 0.2)]
+        assert align_timeline_by_length(segs, 0) == []
+
+    def test_unequal_length(self):
+        segs = [
+            EmotionSegment("a" * 3, EMO_HAPPY, 0.5),
+            EmotionSegment("b" * 1, EMO_NEUTRAL, 0.2),
+        ]
+        entries = align_timeline_by_length(segs, 4.0)
+        assert entries[0].end == 3.0
+        assert entries[1].start == 3.0
+        assert entries[1].end == 4.0
+
+
+# ── 时间对齐（sentence_timings）──────────────────────────────────
+class TestAlignTimelineByTimings:
+    def test_exact_match(self):
+        segs = [
+            EmotionSegment("hello", EMO_HAPPY, 0.4),
+            EmotionSegment("world", EMO_NEUTRAL, 0.2),
+        ]
+        timings = [
+            {"start": 0.0, "end": 1.5},
+            {"start": 1.5, "end": 3.0},
+        ]
+        entries = align_timeline_by_timings(segs, timings, 3.0)
+        assert len(entries) == 2
+        assert entries[0].start == 0.0
+        assert entries[0].end == 1.5
+        assert entries[1].start == 1.5
+        assert entries[1].end == 3.0
+
+    def test_length_mismatch_fallback(self):
+        segs = [EmotionSegment("hello", EMO_HAPPY, 0.4)]
+        timings = [{"start": 0, "end": 1}, {"start": 1, "end": 2}]
+        entries = align_timeline_by_timings(segs, timings, 2.0)
+        assert len(entries) == 1
+        assert entries[0].end == 2.0
+
+
+# ── DittoEmotionService ──────────────────────────────────────────
+def _make_service(enabled=True, model=None, temperature=0.1, timeout=10, max_tokens=1024, prompt=""):
+    s = MagicMock()
+    s.ditto_emotion_enabled = enabled
+    s.ditto_emotion_model = model or ""
+    s.ditto_emotion_temperature = temperature
+    s.ditto_emotion_timeout = timeout
+    s.ditto_emotion_max_tokens = max_tokens
+    s.ditto_emotion_cache_size = 100
+    s.ditto_emotion_prompt = prompt
+    return DittoEmotionService(settings=s)
+
+
+class TestDittoEmotionService:
+    def test_disabled_returns_empty(self):
+        svc = _make_service(enabled=False)
+        assert svc.analyze("你好世界") == []
+
+    def test_empty_text_returns_empty(self):
+        svc = _make_service(enabled=True)
+        assert svc.analyze("") == []
+
+    def test_llm_success(self):
+        svc = _make_service(enabled=True)
+        fake_reply = json.dumps([{"text": "你好", "emo": 4, "intensity": 0.2}])
+        with patch.object(svc, "_call_llm", return_value=_parse_emotion_json(fake_reply)):
+            segs = svc.analyze("你好")
+            assert len(segs) == 1
+            assert segs[0].emo == 4
+
+    def test_cache_hit(self):
+        svc = _make_service(enabled=True)
+        fake_reply = json.dumps([{"text": "你好世界", "emo": 3, "intensity": 0.5}])
+        with patch.object(svc, "_call_llm", return_value=_parse_emotion_json(fake_reply)) as mock_call:
+            svc.analyze("你好世界")
+            svc.analyze("你好世界")
+            assert mock_call.call_count == 1
+
+    def test_build_timeline_empty_when_disabled(self):
+        svc = _make_service(enabled=False)
+        assert svc.build_timeline("test", 5.0) == ""
+
+    def test_build_timeline_returns_json(self):
+        svc = _make_service(enabled=True)
+        fake_reply = json.dumps(
+            [
+                {"text": "ab", "emo": 4, "intensity": 0.2},
+                {"text": "cd", "emo": 3, "intensity": 0.4},
+            ]
+        )
+        with patch.object(svc, "_call_llm", return_value=_parse_emotion_json(fake_reply)):
+            result = svc.build_timeline("ab。cd。", 4.0)
+            data = json.loads(result)
+            assert len(data) == 2
+            assert data[0]["emo"] == 4
+            assert data[1]["emo"] == 3
+
+    def test_build_timeline_with_sentence_timings(self):
+        svc = _make_service(enabled=True)
+        fake_reply = json.dumps(
+            [
+                {"text": "hello", "emo": 3, "intensity": 0.4},
+                {"text": "world", "emo": 4, "intensity": 0.2},
+            ]
+        )
+        timings = [
+            {"start": 0.0, "end": 1.0},
+            {"start": 1.0, "end": 3.0},
+        ]
+        with patch.object(svc, "_call_llm", return_value=_parse_emotion_json(fake_reply)):
+            result = svc.build_timeline("hello world", 3.0, sentence_timings=timings)
+            data = json.loads(result)
+            assert data[0]["start"] == 0.0
+            assert data[0]["end"] == 1.0
+            assert data[1]["end"] == 3.0
+
+
+class TestPromptLoading:
+    def test_default_prompt_contains_placeholder(self):
+        from packages.application.ditto_emotion_service import _load_default_prompt
+
+        prompt = _load_default_prompt()
+        assert "{文案}" in prompt
+
+    def test_config_prompt_override(self):
+        custom = "分析情绪: {文案}"
+        svc = _make_service(enabled=True, prompt=custom)
+        assert svc._get_prompt_template() == custom

tests/unit/test_ditto_service.py

@@ -0,0 +1,229 @@
+"""Ditto 蚂蚁数字人客户端单元测试 — #2076."""
+
+from __future__ import annotations
+
+import time
+from unittest.mock import MagicMock, patch
+
+import httpx
+import pytest
+
+from packages.application.ditto_service import DittoClient, DittoError, DittoResult
+
+
+class _FakeResponse:
+    def __init__(self, status_code=200, content=b"\x00\x01" * 1000, headers=None, text=""):
+        self.status_code = status_code
+        self.content = content
+        self.headers = headers or {}
+        self.text = text
+
+
+def _make_client(base_url="http://ditto:8000", default_video_url="http://oss/tpl.mp4", max_retries=2, timeout=60):
+    with patch("packages.application.ditto_service.get_api_settings") as mock_settings:
+        s = MagicMock()
+        s.ditto_api_base_url = base_url
+        s.ditto_default_video_url = default_video_url
+        s.ditto_max_retries = max_retries
+        s.ditto_request_timeout = timeout
+        mock_settings.return_value = s
+        return DittoClient()
+
+
+def test_is_configured_true():
+    c = _make_client()
+    assert c.is_configured is True
+
+
+def test_is_configured_false_without_base():
+    c = _make_client(base_url="")
+    assert c.is_configured is False
+
+
+def test_is_configured_false_without_template():
+    c = _make_client(default_video_url="")
+    assert c.is_configured is False
+
+
+def test_health_ok():
+    c = _make_client()
+    with patch("httpx.Client") as mock_cls:
+        client = MagicMock()
+        client.get.return_value = _FakeResponse(200)
+        mock_cls.return_value.__enter__.return_value = client
+        assert c.health() is True
+        client.get.assert_called_once()
+
+
+def test_health_fail_status():
+    c = _make_client()
+    with patch("httpx.Client") as mock_cls:
+        client = MagicMock()
+        client.get.return_value = _FakeResponse(500)
+        mock_cls.return_value.__enter__.return_value = client
+        assert c.health() is False
+
+
+def test_health_network_error():
+    c = _make_client()
+    with patch("httpx.Client") as mock_cls:
+        client = MagicMock()
+        client.get.side_effect = httpx.ConnectError("fail")
+        mock_cls.return_value.__enter__.return_value = client
+        assert c.health() is False
+
+
+def test_generate_missing_base():
+    c = _make_client(base_url="")
+    with pytest.raises(DittoError, match="DITTO_API_BASE_URL"):
+        c.generate(audio_url="http://x/a.mp3", script="你好")
+
+
+def test_generate_missing_audio():
+    c = _make_client()
+    with pytest.raises(DittoError, match="audio_url"):
+        c.generate(audio_url="", script="你好")
+
+
+def test_generate_success_with_headers():
+    c = _make_client(max_retries=0)
+    fake_resp = _FakeResponse(
+        status_code=200,
+        content=b"\x00" * 99999,
+        headers={"X-RTF": "0.35", "X-Frames": "125", "X-Time": "12.5"},
+    )
+    with patch("httpx.Client") as mock_cls, patch("time.monotonic", side_effect=[0, 1]):
+        client = MagicMock()
+        client.post.return_value = fake_resp
+        mock_cls.return_value.__enter__.return_value = client
+        result = c.generate(audio_url="http://x/a.mp3", script="你好")
+    assert isinstance(result, DittoResult)
+    assert len(result.video_bytes) == 99999
+    assert result.rtf == 0.35
+    assert result.frames == 125
+    assert result.elapsed_seconds == 12.5
+
+
+def test_generate_uses_default_template_when_video_url_empty():
+    c = _make_client(max_retries=0)
+    fake_resp = _FakeResponse(200, b"1" * 99999)
+    with patch("httpx.Client") as mock_cls:
+        client = MagicMock()
+        client.post.return_value = fake_resp
+        mock_cls.return_value.__enter__.return_value = client
+        c.generate(audio_url="http://x/a.mp3", script="你好")
+        call_kwargs = client.post.call_args
+        payload = call_kwargs.kwargs.get("json") or call_kwargs[1].get("json")
+        assert payload["video_url"] == "http://oss/tpl.mp4"
+        assert payload["audio_url"] == "http://x/a.mp3"
+        assert payload["script"] == "你好"
+        assert payload["emo_global"] == 4
+        assert payload["use_script_emo"] is True
+
+
+def test_generate_retries_on_429_then_success():
+    c = _make_client(max_retries=2)
+    busy = _FakeResponse(429, b"", text="busy")
+    ok = _FakeResponse(200, b"v" * 99999)
+    with patch("httpx.Client") as mock_cls, patch("time.sleep") as mock_sleep:
+        client = MagicMock()
+        client.post.side_effect = [busy, ok]
+        mock_cls.return_value.__enter__.return_value = client
+        result = c.generate(audio_url="http://x/a.mp3", script="你好")
+    assert len(result.video_bytes) == 99999
+    assert mock_sleep.called
+    assert client.post.call_count == 2
+
+
+def test_generate_429_exhausted():
+    c = _make_client(max_retries=1)
+    with patch("httpx.Client") as mock_cls, patch("time.sleep"):
+        client = MagicMock()
+        client.post.return_value = _FakeResponse(429, b"", text="busy")
+        mock_cls.return_value.__enter__.return_value = client
+        with pytest.raises(DittoError, match="重试"):
+            c.generate(audio_url="http://x/a.mp3", script="你好")
+
+
+def test_generate_400_no_retry():
+    c = _make_client(max_retries=2)
+    with patch("httpx.Client") as mock_cls:
+        client = MagicMock()
+        client.post.return_value = _FakeResponse(400, b"", text="bad request")
+        mock_cls.return_value.__enter__.return_value = client
+        with pytest.raises(DittoError, match="Ditto 返回 400"):
+            c.generate(audio_url="http://x/a.mp3", script="你好")
+        assert client.post.call_count == 1  # 400 不重试
+
+
+def test_generate_small_response_raises():
+    c = _make_client(max_retries=0)
+    with patch("httpx.Client") as mock_cls:
+        client = MagicMock()
+        client.post.return_value = _FakeResponse(200, b"xx")
+        mock_cls.return_value.__enter__.return_value = client
+        with pytest.raises(DittoError) as exc_info:
+            c.generate(audio_url="http://x/a.mp3", script="你好")
+        assert exc_info.value.code == "EmptyResponse"
+
+
+def test_generate_and_persist_uploads_to_storage():
+    c = _make_client(max_retries=0)
+    fake_resp = _FakeResponse(200, b"v" * 99999)
+    fake_storage = MagicMock()
+    fake_storage.upload_file.return_value = "http://oss/ditto/x.mp4"
+    with (
+        patch("httpx.Client") as mock_cls,
+        patch("packages.shared.storage.get_shared_storage_service", return_value=fake_storage),
+    ):
+        client = MagicMock()
+        client.post.return_value = fake_resp
+        mock_cls.return_value.__enter__.return_value = client
+        result = c.generate_and_persist(job_id="j1", user_id="u1", audio_url="http://x/a.mp3", script="hi")
+    assert result.video_url == "http://oss/ditto/x.mp4"
+    fake_storage.upload_file.assert_called_once()
+    call_args = fake_storage.upload_file.call_args
+    assert call_args.args[1].startswith("ditto-output/u1/j1")
+
+
+def test_empty_script_replaced_with_space():
+    c = _make_client(max_retries=0)
+    fake_resp = _FakeResponse(200, b"v" * 99999)
+    with patch("httpx.Client") as mock_cls:
+        client = MagicMock()
+        client.post.return_value = fake_resp
+        mock_cls.return_value.__enter__.return_value = client
+        c.generate(audio_url="http://x/a.mp3", script="")
+        payload = client.post.call_args.kwargs["json"]
+        assert payload["script"] == " "
+
+
+def test_generate_network_error_fails_fast(monkeypatch):
+    """网络不通（ConnectError）时不重试，直接快速抛 NetworkUnreachable，避免用户等5分钟"""
+    import httpx
+
+    from packages.application import ditto_service as ds_mod
+
+    calls = {"n": 0}
+
+    def _fake_post(self, url, json=None):
+        calls["n"] += 1
+        raise httpx.ConnectError("[Errno 113] No route to host")
+
+    monkeypatch.setattr(httpx.Client, "post", _fake_post)
+
+    client = _make_client(
+        base_url="http://100.76.80.23:8000",
+        default_video_url="http://oss/tpl.mp4",
+        max_retries=2,
+        timeout=120,
+    )
+
+    t0 = time.monotonic()
+    with pytest.raises(ds_mod.DittoError) as exc:
+        client.generate(audio_url="http://oss/a.wav", script="你好")
+    elapsed = time.monotonic() - t0
+
+    assert exc.value.code == "NetworkUnreachable"
+    assert calls["n"] == 1  # 不重试
+    assert elapsed < 5  # 快速失败<5秒

tests/unit/test_domain_entities_extended.py

@@ -521,3 +521,81 @@ class TestIngestJob:
             storage_key="k",
         )
         assert job.error_message == ""
+
+
+class TestViralVideoResumeForRegenerate:
+    """#2222: resume_from_image_analyzed 应支持 COPY_GENERATED/COMPLETED/FAILED 重新生成文案。"""
+
+    def test_regen_from_copy_generated_clears_old_copy(self):
+        from datetime import datetime, timezone
+
+        from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
+
+        job = ViralVideoJob(user_id="u1", images=["img1"])
+        # 模拟已经生成过文案和视频
+        job.status = ViralVideoStatus.COPY_GENERATED
+        job.copy_result = {"shots": [{"x": 1}], "voiceover_script": "旧文案"}
+        job.intent_result = {"intent": "旧意图"}
+        job.storyboard = [{"x": 1}]
+        job.generated_copy_text = "旧文案"
+        job.result_video_url = "http://old.mp4"
+        job.completed_at = datetime(2026, 10, 6, tzinfo=timezone.utc)
+        job.error_msg = ""
+        job.current_stage = "tts_generation"
+        job.phase_message = "TTS完成"
+
+        # 重新生成
+        job.resume_from_image_analyzed()
+
+        assert job.status == ViralVideoStatus.RUNNING
+        assert job.copy_result is None
+        assert job.intent_result is None
+        assert job.storyboard is None
+        assert job.generated_copy_text == ""
+        assert job.result_video_url == ""
+        assert job.completed_at is None
+        assert job.error_msg == ""
+        assert job.current_stage == ""
+        assert job.phase_message == ""
+
+    def test_regen_from_completed_clears_old_copy(self):
+        from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
+
+        job = ViralVideoJob(user_id="u1", images=["img1"])
+        job.status = ViralVideoStatus.COMPLETED
+        job.copy_result = {"shots": [], "voiceover_script": "xx"}
+        job.intent_result = {"intent": "x"}
+        job.result_video_url = "http://v.mp4"
+
+        job.resume_from_image_analyzed()
+
+        assert job.status == ViralVideoStatus.RUNNING
+        assert job.copy_result is None
+        assert job.intent_result is None
+        assert job.result_video_url == ""
+
+    def test_first_call_from_image_analyzed_keeps_fields(self):
+        """首次进入（IMAGE_ANALYZED）不应清空任何已有的字段。"""
+        from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
+
+        job = ViralVideoJob(user_id="u1", images=["img1"])
+        job.status = ViralVideoStatus.IMAGE_ANALYZED
+        job.image_analysis = {"products": []}
+        job.industry = "美妆"
+
+        job.resume_from_image_analyzed()
+
+        assert job.status == ViralVideoStatus.RUNNING
+        assert job.image_analysis == {"products": []}
+        assert job.industry == "美妆"
+
+    def test_wait_user_confirm_rejected(self):
+        """wait_user_confirm 中间状态应被拒绝（前端正在编辑/确认文案）。"""
+        import pytest
+
+        from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
+
+        job = ViralVideoJob(user_id="u1", images=["img1"])
+        job.status = ViralVideoStatus.WAIT_USER_CONFIRM
+        with pytest.raises(ValueError, match="Cannot resume"):
+            job.resume_from_image_analyzed()

tests/unit/test_feature_billing_integration.py

@@ -0,0 +1,221 @@
+"""功能计费改造测试：爆款读配置、对口型/智能剪辑预扣逻辑。
+
+策略：
+- 爆款：通过修改缓存中的 FeatureConfig（multiplier/model_pricing）验证价格随配置变化
+- lip_sync / smart_edit：直接测 LipsyncService 的预扣/结算/退款辅助方法，
+  PointsService 用 mock，避免依赖真实积分账户。
+"""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from packages.domain import feature_pricing_service as fps
+from packages.domain.feature_pricing_service import FeatureConfig, refresh_feature_configs
+
+
+@pytest.fixture(autouse=True)
+def _reset_cache():
+    refresh_feature_configs()
+    yield
+    refresh_feature_configs()
+
+
+def _seed_cache(configs: dict) -> None:
+    import time
+
+    fps._cache = (time.monotonic(), configs)
+
+
+class TestViralVideoReadsConfig:
+    def test_multiplier_change_changes_price(self):
+        """配置里 multiplier 改大后，爆款价格随之变大（证明不再读死常量）。"""
+        from packages.domain.points_rules import calculate_viral_video_credits
+
+        # 基线兜底
+        base = calculate_viral_video_credits(15, 1280, 720)
+        assert base == 29.68
+
+        fallback = fps._fallback_configs()
+        vv = fallback["viral_video"]
+        vv.profit_multiplier = 2.0
+        _seed_cache(fallback)
+
+        changed = calculate_viral_video_credits(15, 1280, 720)
+        assert changed > base
+        # 精确校验：video_cost 相同，仅系数从 1.3 → 2.0
+        _, bd = __import__(
+            "packages.domain.points_rules", fromlist=["calculate_viral_video_credits_with_breakdown"]
+        ).calculate_viral_video_credits_with_breakdown(15, 1280, 720)
+        assert bd["profit_multiplier"] == 2.0
+
+    def test_model_price_from_config(self):
+        """model_pricing 改单价后，token 成本按新单价计算。"""
+        from packages.domain.points_rules import calculate_viral_video_credits_with_breakdown
+
+        fallback = fps._fallback_configs()
+        vv = fallback["viral_video"]
+        # seedance-2.5/720p/false 从 70 改成 100
+        vv.model_pricing["seedance-2.5"]["720p"]["false"] = 100.0
+        _seed_cache(fallback)
+
+        _, bd = calculate_viral_video_credits_with_breakdown(15, 1280, 720)
+        assert bd["model_price"] == 100.0
+
+    def test_price_cap_from_config(self):
+        from packages.domain.points_rules import calculate_viral_video_credits_with_breakdown
+
+        fallback = fps._fallback_configs()
+        vv = fallback["viral_video"]
+        vv.price_cap = 5.0
+        _seed_cache(fallback)
+
+        credits, bd = calculate_viral_video_credits_with_breakdown(15, 1280, 720)
+        assert credits == 5.0
+        assert bd["price_cap"] == 5.0
+
+    def test_disabled_feature_returns_zero_credits(self):
+        """功能 is_enabled=false 时计费函数返回 0（纯计费层语义）。"""
+        from packages.domain.points_rules import calculate_viral_video_credits_with_breakdown
+
+        fallback = fps._fallback_configs()
+        fallback["viral_video"].is_enabled = False
+        _seed_cache(fallback)
+
+        credits, bd = calculate_viral_video_credits_with_breakdown(15, 1280, 720)
+        assert credits == 0.0
+        assert bd["feature_enabled"] is False
+        assert bd["charged"] is False
+
+
+class TestLipSyncPricing:
+    def _make_service(self):
+        from app.services.lipsync_service import LipsyncService
+
+        svc = LipsyncService.__new__(LipsyncService)
+        svc.db = MagicMock()
+        return svc
+
+    def _lip_cfg(self, **kw):
+        base = dict(
+            feature_key="lip_sync",
+            name="对口型",
+            is_enabled=True,
+            fixed_cost=0.1,
+            profit_multiplier=1.0,
+            dynamic_unit_cost=0.05,
+            billing_mode="per_second",
+            price_cap=0.0,
+            model_pricing={},
+            description="",
+        )
+        base.update(kw)
+        return FeatureConfig(**base)
+
+    def test_estimate_duration_from_script(self):
+        svc = self._make_service()
+        # 10 个字 / 5 = 2 秒，下限 1
+        assert svc._estimate_duration(script_text="一二三四五六七八九十") == 2.0
+        # 无任何信息 → 默认 10 秒
+        assert svc._estimate_duration() == 10.0
+
+    def test_calculate_lipsync_price_per_second(self):
+        _seed_cache({"lip_sync": self._lip_cfg()})
+        price, bd = fps.calculate_price("lip_sync", dynamic_cost=20.0 * 0.05)
+        # dynamic 1.0 + fixed 0.1 = 1.1
+        assert price == 1.1
+        assert bd["charged"] is True
+
+    def test_settle_refunds_overcharge(self):
+        """实际时长短 → 只退不补，退还差额。"""
+        svc = self._make_service()
+        _seed_cache({"lip_sync": self._lip_cfg()})
+
+        job = MagicMock()
+        job.credits_prepaid = 2.0
+        job.credits_cost = 0.0  # 未结算
+        job.user_id = "u1"
+        job.credits_transaction_id = "txn-old"
+
+        with patch("packages.domain.points_service.PointsService") as MockPS:
+            inst = MockPS.return_value
+            inst.refund_points.return_value = {"success": True}
+            svc._settle_lip_sync(job, actual_duration=10.0)
+
+        # final: (10*0.05 + 0.1)*1.0 = 0.6；退 2.0-0.6=1.4
+        assert round(job.credits_cost, 2) == 0.6
+        inst.refund_points.assert_called_once()
+        kwargs = inst.refund_points.call_args.kwargs
+        assert kwargs["amount"] == 1.4
+
+    def test_settle_no_refund_when_longer(self):
+        """首期只退不补：实际更贵不补扣。"""
+        svc = self._make_service()
+        _seed_cache({"lip_sync": self._lip_cfg()})
+
+        job = MagicMock()
+        job.credits_prepaid = 0.5
+        job.credits_cost = 0.0
+
+        with patch("packages.domain.points_service.PointsService") as MockPS:
+            inst = MockPS.return_value
+            svc._settle_lip_sync(job, actual_duration=60.0)
+
+        assert round(job.credits_cost, 2) > 0.5
+        inst.refund_points.assert_not_called()
+
+    def test_refund_on_failure_full(self):
+        svc = self._make_service()
+        job = MagicMock()
+        job.credits_prepaid = 3.0
+        job.credits_cost = 0.0
+        job.user_id = "u1"
+        job.credits_transaction_id = "t1"
+
+        with patch("packages.domain.points_service.PointsService") as MockPS:
+            inst = MockPS.return_value
+            inst.refund_points.return_value = {"success": True}
+            svc._refund_lip_sync(job)
+
+        kwargs = inst.refund_points.call_args.kwargs
+        assert kwargs["amount"] == 3.0
+
+
+class TestSmartEditFixedPrice:
+    def test_fixed_price_formula(self):
+        """首期固定价：dynamic=0，price=fixed*multiplier，cap 封顶。"""
+        cfg = FeatureConfig(
+            feature_key="smart_edit",
+            name="智能剪辑",
+            is_enabled=True,
+            fixed_cost=2.0,
+            profit_multiplier=1.5,
+            billing_mode="model_based",
+            price_cap=0.0,
+        )
+        _seed_cache({"smart_edit": cfg})
+        price, bd = fps.calculate_price("smart_edit", dynamic_cost=0.0)
+        # (0+2)*1.5 = 3.0
+        assert price == 3.0
+        assert bd["dynamic_cost"] == 0.0
+
+    def test_fixed_price_with_cap(self):
+        cfg = FeatureConfig(
+            feature_key="smart_edit",
+            is_enabled=True,
+            fixed_cost=10.0,
+            profit_multiplier=2.0,
+            price_cap=8.0,
+        )
+        _seed_cache({"smart_edit": cfg})
+        price, _ = fps.calculate_price("smart_edit", dynamic_cost=0.0)
+        assert price == 8.0
+
+    def test_disabled_smart_edit_free(self):
+        cfg = FeatureConfig(feature_key="smart_edit", is_enabled=False, fixed_cost=2.0)
+        _seed_cache({"smart_edit": cfg})
+        price, bd = fps.calculate_price("smart_edit", dynamic_cost=0.0)
+        assert price == 0.0
+        assert bd["charged"] is False

tests/unit/test_feature_pricing_service.py

@@ -0,0 +1,235 @@
+"""feature_pricing_service 单元测试。
+
+覆盖：
+- 300s TTL 内存缓存（命中不重复 load / 过期重新 load / refresh 强制刷新）
+- calculate_price 公式 (dynamic+fixed)*multiplier、price_cap 封顶、round
+- disabled / 未知 key 返回 0
+- DB 异常 / 空表 → 内置兜底配置（爆款启用且价格与现状一致）
+- lookup_model_price 嵌套/扁平结构与旧版回落语义
+"""
+
+from __future__ import annotations
+
+import time
+
+import pytest
+
+from packages.domain import feature_pricing_service as fps
+from packages.domain.feature_pricing_service import (
+    CACHE_TTL_SECONDS,
+    FeatureConfig,
+    calculate_price,
+    get_feature_config,
+    is_feature_enabled,
+    lookup_model_price,
+    refresh_feature_configs,
+)
+
+
+@pytest.fixture(autouse=True)
+def _reset_cache():
+    """每个用例前后清空模块缓存，避免相互污染。"""
+    refresh_feature_configs()
+    yield
+    refresh_feature_configs()
+
+
+def _cfg(key="x", **kw) -> FeatureConfig:
+    base = dict(
+        feature_key=key,
+        name=key,
+        is_enabled=True,
+        fixed_cost=0.2,
+        profit_multiplier=2.0,
+        dynamic_unit_cost=0.0,
+        billing_mode="per_second",
+        price_cap=0.0,
+        model_pricing={},
+        description="",
+    )
+    base.update(kw)
+    return FeatureConfig(**base)
+
+
+class TestCacheTTL:
+    def test_cache_hit_avoids_reload(self, monkeypatch):
+        """TTL 内第二次读取不再调 _load_all。"""
+        calls = {"n": 0}
+
+        def fake_load():
+            calls["n"] += 1
+            return {"x": _cfg()}
+
+        monkeypatch.setattr(fps, "_load_all", fake_load)
+        get_feature_config("x")
+        get_feature_config("x")
+        get_feature_config("x")
+        assert calls["n"] == 1
+
+    def test_expired_cache_reloads(self, monkeypatch):
+        """超过 TTL 后重新 load。"""
+        calls = {"n": 0}
+
+        def fake_load():
+            calls["n"] += 1
+            return {"x": _cfg()}
+
+        monkeypatch.setattr(fps, "_load_all", fake_load)
+        get_feature_config("x")
+        assert calls["n"] == 1
+
+        # 把缓存时间戳回拨到 TTL 之前
+        ts, data = fps._cache
+        fps._cache = (ts - CACHE_TTL_SECONDS - 1, data)
+        get_feature_config("x")
+        assert calls["n"] == 2
+
+    def test_refresh_forces_reload(self, monkeypatch):
+        calls = {"n": 0}
+
+        def fake_load():
+            calls["n"] += 1
+            return {"x": _cfg()}
+
+        monkeypatch.setattr(fps, "_load_all", fake_load)
+        get_feature_config("x")
+        refresh_feature_configs()
+        get_feature_config("x")
+        assert calls["n"] == 2
+
+    def test_ttl_constant_is_300(self):
+        assert CACHE_TTL_SECONDS == 300.0
+
+
+class TestCalculatePrice:
+    def test_basic_formula(self, monkeypatch):
+        # (dynamic 1.0 + fixed 0.2) * 2.0 = 2.4
+        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(dynamic_unit_cost=1.0)})
+        price, bd = calculate_price("x", dynamic_cost=1.0)
+        assert price == 2.4
+        assert bd["dynamic_cost"] == 1.0
+        assert bd["fixed_cost"] == 0.2
+        assert bd["profit_multiplier"] == 2.0
+        assert bd["final_price"] == 2.4
+        assert bd["charged"] is True
+
+    def test_price_cap_clamps(self, monkeypatch):
+        # raw = (1+0.2)*2 = 2.4，cap=1.0 → 1.0
+        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(price_cap=1.0)})
+        price, bd = calculate_price("x", dynamic_cost=1.0)
+        assert price == 1.0
+        assert bd["price_cap"] == 1.0
+
+    def test_no_cap_keeps_raw(self, monkeypatch):
+        # cap=0 视为不封顶
+        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(price_cap=0.0)})
+        price, _ = calculate_price("x", dynamic_cost=1.0)
+        assert price == 2.4
+
+    def test_rounded_two_decimals(self, monkeypatch):
+        monkeypatch.setattr(
+            fps,
+            "_load_all",
+            lambda: {"x": _cfg(fixed_cost=0.1, profit_multiplier=1.0)},
+        )
+        price, _ = calculate_price("x", dynamic_cost=1.0 / 3.0)
+        # 0.3333... + 0.1 = 0.4333 → 0.43
+        assert price == 0.43
+
+    def test_negative_dynamic_treated_as_zero(self, monkeypatch):
+        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg()})
+        price, _ = calculate_price("x", dynamic_cost=-5.0)
+        # (0 + 0.2) * 2 = 0.4
+        assert price == 0.4
+
+    def test_disabled_returns_zero(self, monkeypatch):
+        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(is_enabled=False)})
+        price, bd = calculate_price("x", dynamic_cost=1.0)
+        assert price == 0.0
+        assert bd["is_enabled"] is False
+        assert bd["charged"] is False
+
+    def test_unknown_key_returns_zero(self, monkeypatch):
+        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg()})
+        price, bd = calculate_price("nope", dynamic_cost=1.0)
+        assert price == 0.0
+        assert bd["charged"] is False
+
+
+class TestDBFailureFallback:
+    def test_load_exception_uses_fallback(self, monkeypatch):
+        def boom():
+            raise RuntimeError("table does not exist")
+
+        monkeypatch.setattr(fps, "_load_all", boom)
+        cfg = get_feature_config("viral_video")
+        assert cfg is not None
+        assert cfg.is_enabled is True
+        assert cfg.fixed_cost == 0.15
+        assert cfg.profit_multiplier == 1.3
+
+    def test_empty_table_uses_fallback(self, monkeypatch):
+        monkeypatch.setattr(fps, "_load_all", lambda: {})
+        assert get_feature_config("viral_video").is_enabled is True
+        assert get_feature_config("lip_sync").is_enabled is False
+        assert get_feature_config("smart_edit").is_enabled is False
+
+    def test_fallback_viral_price_matches_current(self, monkeypatch):
+        """兜底爆款价格与旧硬编码现状一致：seedance-2.5/720p/false=70。"""
+        monkeypatch.setattr(fps, "_load_all", lambda: {})
+        from packages.domain.points_rules import calculate_viral_video_credits
+
+        # 默认全局开关关闭，但纯计费函数价格照常算
+        assert calculate_viral_video_credits(15, 1280, 720) == 29.68
+
+    def test_db_row_overrides_fallback(self, monkeypatch):
+        monkeypatch.setattr(
+            fps,
+            "_load_all",
+            lambda: {"viral_video": _cfg("viral_video", fixed_cost=0.5, profit_multiplier=2.0, price_cap=50.0)},
+        )
+        cfg = get_feature_config("viral_video")
+        assert cfg.fixed_cost == 0.5
+        assert cfg.profit_multiplier == 2.0
+        assert cfg.price_cap == 50.0
+
+
+class TestIsFeatureEnabled:
+    def test_disabled_feature(self, monkeypatch):
+        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(is_enabled=False)})
+        assert is_feature_enabled("x") is False
+
+    def test_global_switch_off_blocks_enabled_feature(self, monkeypatch):
+        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(is_enabled=True)})
+        monkeypatch.setattr(fps, "_global_points_enabled", lambda: False)
+        assert is_feature_enabled("x") is False
+
+    def test_both_switches_on(self, monkeypatch):
+        monkeypatch.setattr(fps, "_load_all", lambda: {"x": _cfg(is_enabled=True)})
+        monkeypatch.setattr(fps, "_global_points_enabled", lambda: True)
+        assert is_feature_enabled("x") is True
+
+
+class TestLookupModelPrice:
+    NESTED = {
+        "seedance-2.5": {
+            "720p": {"false": 70.0, "true": 42.0},
+        },
+        "wan-3.0": {"480p": {"false": 0.3}},
+    }
+
+    def test_nested_exact_hit(self):
+        assert lookup_model_price(self.NESTED, "seedance-2.5", "720p", False) == 70.0
+        assert lookup_model_price(self.NESTED, "seedance-2.5", "720p", True) == 42.0
+
+    def test_missing_bool_key_returns_none(self):
+        # wan-3.0/480p 只有 false，请求 true → None（由调用方回落）
+        assert lookup_model_price(self.NESTED, "wan-3.0", "480p", True) is None
+
+    def test_unknown_model_returns_none(self):
+        assert lookup_model_price(self.NESTED, "nope", "720p", False) is None
+
+    def test_flat_structure(self):
+        flat = {"m|720p|false": 12.5}
+        assert lookup_model_price(flat, "m", "720p", False) == 12.5
+        assert lookup_model_price(flat, "m", "720p", True) is None

tests/unit/test_system_config.py

@@ -0,0 +1,287 @@
+"""#2246 system_settings / system_config_service 单元测试。
+
+覆盖：序列化反序列化、默认值兜底、DB 覆盖、缓存失效、白名单拒绝、
+Ditto 单例 reset 联动、PUT 校验。
+"""
+
+from __future__ import annotations
+
+import pytest
+from sqlalchemy import create_engine, event
+from sqlalchemy.orm import sessionmaker
+
+from packages.adapters.sqlalchemy_impl.models import Base
+from packages.application.system_config_service import SystemConfigService
+from packages.domain.system_setting import (
+    SETTING_TYPE_BOOL,
+    SETTING_TYPE_FLOAT,
+    SETTING_TYPE_INT,
+    SETTING_TYPE_JSON,
+    SETTING_TYPE_STRING,
+    SystemSettingError,
+    deserialize_setting_value,
+    infer_setting_type,
+    serialize_setting_value,
+)
+
+
+# ── domain 序列化 ──────────────────────────────────────────────
+class TestSerialization:
+    @pytest.mark.parametrize(
+        "value,st,expected",
+        [
+            (True, SETTING_TYPE_BOOL, "true"),
+            (False, SETTING_TYPE_BOOL, "false"),
+            (12, SETTING_TYPE_INT, "12"),
+            (-3, SETTING_TYPE_INT, "-3"),
+            (0.1, SETTING_TYPE_FLOAT, "0.1"),
+            ("hello", SETTING_TYPE_STRING, "hello"),
+        ],
+    )
+    def test_serialize(self, value, st, expected):
+        assert serialize_setting_value(value, st) == expected
+
+    def test_json_roundtrip(self):
+        raw = serialize_setting_value({"a": [1, 2]}, SETTING_TYPE_JSON)
+        assert deserialize_setting_value(raw, SETTING_TYPE_JSON) == {"a": [1, 2]}
+
+    def test_bool_roundtrip_variants(self):
+        for truthy in ("true", "1", "yes", "on", "TRUE"):
+            assert deserialize_setting_value(truthy, SETTING_TYPE_BOOL) is True
+        for falsy in ("false", "0", "", "no"):
+            assert deserialize_setting_value(falsy, SETTING_TYPE_BOOL) is False
+
+    def test_int_float_roundtrip(self):
+        assert deserialize_setting_value("42", SETTING_TYPE_INT) == 42
+        assert deserialize_setting_value("1.5", SETTING_TYPE_FLOAT) == 1.5
+
+    def test_type_errors(self):
+        with pytest.raises(SystemSettingError):
+            serialize_setting_value("x", SETTING_TYPE_INT)
+        with pytest.raises(SystemSettingError):
+            serialize_setting_value(1, SETTING_TYPE_BOOL)
+        with pytest.raises(SystemSettingError):
+            deserialize_setting_value("abc", SETTING_TYPE_INT)
+        with pytest.raises(SystemSettingError):
+            serialize_setting_value(1, "unknown")
+
+    def test_infer_type_bool_before_int(self):
+        assert infer_setting_type(True) == SETTING_TYPE_BOOL
+        assert infer_setting_type(5) == SETTING_TYPE_INT
+        assert infer_setting_type(0.5) == SETTING_TYPE_FLOAT
+        assert infer_setting_type("s") == SETTING_TYPE_STRING
+        assert infer_setting_type([1]) == SETTING_TYPE_JSON
+
+
+# ── DB fixture ─────────────────────────────────────────────────
+@pytest.fixture()
+def config_service():
+    engine = create_engine("sqlite://", echo=False)
+
+    @event.listens_for(engine, "connect")
+    def _noop(dbapi_conn, connection_record):
+        pass
+
+    Base.metadata.create_all(engine)
+    factory = sessionmaker(bind=engine)
+    yield SystemConfigService(session_factory=factory)
+
+
+class TestConfigService:
+    def test_default_when_missing(self, config_service):
+        assert config_service.get_config("not_exist", "fallback") == "fallback"
+
+    def test_uses_module_session_factory_assigned_after_import(self):
+        # 回归：模块导入时 session.SessionLocal 为 None，initialize_database()
+        # 之后才赋值；服务必须运行时读取模块属性，而不是 import 时绑定旧值。
+        from packages.adapters.sqlalchemy_impl import session as db_session
+
+        engine = create_engine("sqlite://")
+        Base.metadata.create_all(engine)
+        factory = sessionmaker(bind=engine)
+        writer = SystemConfigService(session_factory=factory)
+        writer.set_config("late_key", 7, setting_type=SETTING_TYPE_INT)
+
+        reader = SystemConfigService()  # 不注入工厂，依赖模块级 SessionLocal
+        old = db_session.SessionLocal
+        try:
+            db_session.SessionLocal = factory
+            assert reader.get_config("late_key", 0) == 7
+        finally:
+            db_session.SessionLocal = old
+
+    def test_raises_when_no_session_factory(self):
+        from packages.adapters.sqlalchemy_impl import session as db_session
+
+        svc = SystemConfigService()
+        old = db_session.SessionLocal
+        try:
+            db_session.SessionLocal = None
+            with pytest.raises(RuntimeError):
+                svc.get_config("anything", 1)
+        finally:
+            db_session.SessionLocal = old
+
+    def test_db_overrides_default(self, config_service):
+        config_service.set_config("k", 20, setting_type=SETTING_TYPE_INT)
+        # 再次读取应命中 DB 值，而非传入的默认
+        assert config_service.get_config("k", 12) == 20
+
+    def test_set_and_get_all_types(self, config_service):
+        config_service.set_config("b", True)
+        config_service.set_config("i", 7)
+        config_service.set_config("f", 0.3)
+        config_service.set_config("s", "文本")
+        config_service.set_config("j", {"x": 1})
+        assert config_service.get_config("b", False) is True
+        assert config_service.get_config("i", 0) == 7
+        assert abs(config_service.get_config("f", 0.0) - 0.3) < 1e-9
+        assert config_service.get_config("s", "") == "文本"
+        assert config_service.get_config("j", {}) == {"x": 1}
+
+    def test_update_existing(self, config_service):
+        config_service.set_config("k", 1, setting_type=SETTING_TYPE_INT)
+        config_service.set_config("k", 2, setting_type=SETTING_TYPE_INT)
+        assert config_service.get_config("k", 0) == 2
+        assert len(config_service.list_configs()) == 1
+
+    def test_cache_invalidation(self, config_service):
+        config_service.set_config("k", 1, setting_type=SETTING_TYPE_INT)
+        config_service.get_config("k", 0)  # 填充缓存
+        # 手工改库模拟外部写入，reload 后应可见
+        from packages.adapters.sqlalchemy_impl.models import SystemSettingModel
+
+        session = config_service._get_session_factory()()
+        session.query(SystemSettingModel).filter(SystemSettingModel.setting_key == "k").update({"setting_value": "99"})
+        session.commit()
+        session.close()
+        # 缓存未失效前仍是旧值
+        assert config_service.get_config("k", 0) == 1
+        config_service.reload()
+        assert config_service.get_config("k", 0) == 99
+
+    def test_list_by_category(self, config_service):
+        config_service.set_config("a", 1, category="ditto")
+        config_service.set_config("b", 2, category="other")
+        keys = {c.setting_key for c in config_service.list_configs("ditto")}
+        assert keys == {"a"}
+
+    def test_delete(self, config_service):
+        config_service.set_config("k", 1, setting_type=SETTING_TYPE_INT)
+        assert config_service.delete_config("k") is True
+        assert config_service.get_config("k", "d") == "d"
+        assert config_service.delete_config("k") is False
+
+    def test_reset_singletons_called(self, config_service):
+        called = {"emo": 0, "client": 0}
+
+        import packages.application.ditto_emotion_service as real_emo
+        import packages.application.ditto_service as real_client
+
+        orig_emo_reset = real_emo.reset_ditto_emotion_service
+        orig_client_reset = real_client.reset_ditto_client
+
+        def fake_emo_reset():
+            called["emo"] += 1
+
+        def fake_client_reset():
+            called["client"] += 1
+
+        real_emo.reset_ditto_emotion_service = fake_emo_reset
+        real_client.reset_ditto_client = fake_client_reset
+        try:
+            config_service.set_config("ditto_emotion_model", "m", setting_type=SETTING_TYPE_STRING)
+            assert called["emo"] == 1
+            assert called["client"] == 1
+        finally:
+            # 必须还原，否则污染同文件后续 reset 用例
+            real_emo.reset_ditto_emotion_service = orig_emo_reset
+            real_client.reset_ditto_client = orig_client_reset
+
+
+# ── Ditto 单例 reset ───────────────────────────────────────────
+class TestDittoSingletonReset:
+    def test_emotion_reset_clears_lru(self):
+        from unittest.mock import MagicMock
+
+        from packages.application import ditto_emotion_service as m
+
+        # 用一个可哈希的假 service 填 LRU，_call_llm 返回固定值，不触发真实逻辑
+        fake = MagicMock()
+        fake._call_llm.return_value = []
+        m._cached_analyze.cache_clear()
+        m._cached_analyze(fake, "k", "t")
+        assert m._cached_analyze.cache_info().currsize == 1
+
+        saved_singleton = m._singleton
+        m._singleton = fake
+        try:
+            m.reset_ditto_emotion_service()
+            assert m._singleton is None
+            assert m._cached_analyze.cache_info().currsize == 0
+        finally:
+            m._singleton = saved_singleton
+
+    def test_client_reset(self):
+        from packages.application import ditto_service as m
+
+        saved = m._ditto_client_singleton
+        m._ditto_client_singleton = object()
+        try:
+            m.reset_ditto_client()
+            assert m._ditto_client_singleton is None
+        finally:
+            m._ditto_client_singleton = saved
+
+
+# ── 路由层白名单与校验 ─────────────────────────────────────────
+class TestAdminRouteValidation:
+    def _route(self):
+        from apps.api.app.api.routes.admin import ditto_emotion as route
+
+        return route
+
+    def test_whitelist_rejects_unknown_key(self):
+        route = self._route()
+        from packages.application.system_config_service import SystemConfigService
+
+        svc = SystemConfigService(session_factory=lambda: pytest.fail("should not open session"))
+        payload = route.ConfigUpdatePayload(configs={"evil_key": 1})
+        # 直接调用 update_config，传入一个任意 key 标识
+        result = route.update_config(payload, x_api_key="k")
+        assert result["ok"] is False
+        assert "不允许修改" in result["error"]
+
+    def test_validate_blend_frames_range(self):
+        route = self._route()
+        with pytest.raises(ValueError):
+            route._validate_value("ditto_blend_frames", 3)
+        with pytest.raises(ValueError):
+            route._validate_value("ditto_blend_frames", 31)
+        assert route._validate_value("ditto_blend_frames", 12) == 12
+
+    def test_validate_temperature_range(self):
+        route = self._route()
+        with pytest.raises(ValueError):
+            route._validate_value("ditto_emotion_temperature", 1.5)
+        assert route._validate_value("ditto_emotion_temperature", "0.2") == pytest.approx(0.2)
+
+    def test_validate_prompt_placeholder(self):
+        route = self._route()
+        with pytest.raises(ValueError):
+            route._validate_value("ditto_emotion_prompt", "没有占位符的提示词")
+        assert route._validate_value("ditto_emotion_prompt", "分析：{文案}") == "分析：{文案}"
+        # 允许留空（使用默认）
+        assert route._validate_value("ditto_emotion_prompt", "") == ""
+
+    def test_validate_model_options(self):
+        route = self._route()
+        with pytest.raises(ValueError):
+            route._validate_value("ditto_emotion_model", "gpt-4")
+        assert route._validate_value("ditto_emotion_model", "deepseek-v3") == "deepseek-v3"
+
+    def test_validate_bool(self):
+        route = self._route()
+        with pytest.raises(ValueError):
+            route._validate_value("ditto_emotion_enabled", "yes")
+        assert route._validate_value("ditto_emotion_enabled", True) is True

tests/unit/test_tts_service_factory.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import os
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
 import pytest
 
@@ -108,8 +108,14 @@ class TestGetTtsService:
 
     def test_empty_env_falls_back_to_auto_detect(self):
         """环境变量为空时自动检测."""
-        with patch.dict(os.environ, {"TTS_PROVIDER": ""}):
+        with (
+            patch.dict(os.environ, {"TTS_PROVIDER": ""}),
+            patch("packages.shared.config.get_shared_settings") as mock_settings,
+        ):
             # 没有 cosyvoice_api_key 时应该用 mock
+            settings = MagicMock()
+            settings.cosyvoice_api_key = ""
+            mock_settings.return_value = settings
             service = get_tts_service(None)
             assert service.provider_name == "mock"
 

tests/unit/test_viral_video.py

@@ -350,6 +350,37 @@ class TestViralVideoRepository:
 class TestViralVideoPipeline:
     """编排器流水线测试。"""
 
+    # v3 分镜 XML（copy_display_markdown + clips + voiceover_script）
+    V3_XML = """<copy_display_markdown>今天给大家分享一支很显白的口红。</copy_display_markdown>
+<clips>
+<clip image_index="0" time_range="0-5秒">
+<voiceover>大家好，今天分享一款口红</voiceover>
+<visual>近景平视，缓慢推镜</visual>
+<action_details>手持口红特写</action_details>
+<audio_bgm>轻快流行BGM</audio_bgm>
+<transition>硬切</transition>
+<reference_image_index>0</reference_image_index>
+</clip>
+<clip image_index="1" time_range="5-10秒">
+<voiceover>颜色特别好看</voiceover>
+<visual>特写，固定镜头</visual>
+<action_details>嘴唇涂抹特写</action_details>
+<audio_bgm>轻快BGM继续</audio_bgm>
+<transition>硬切</transition>
+<reference_image_index>1</reference_image_index>
+</clip>
+<clip image_index="2" time_range="10-15秒">
+<voiceover>很显白，推荐给大家</voiceover>
+<visual>中景，微笑展示</visual>
+<action_details>口红展示</action_details>
+<audio_bgm>轻快BGM结束</audio_bgm>
+<transition>结束</transition>
+<reference_image_index>0</reference_image_index>
+</clip>
+</clips>
+<voiceover_script>大家好呀，今天来给大家分享一款超显白的口红。颜色特别好看很显气质，真心推荐给姐妹们</voiceover_script>
+<theme>口红分享</theme>"""
+
     @pytest.fixture
     def mock_job(self):
         return ViralVideoJob(
@@ -364,23 +395,27 @@ class TestViralVideoPipeline:
             video_ratio="9:16",
         )
 
-    @patch("packages.shared.ai_service.call_vision")
+    @patch("apps.worker.worker_app.tasks.vision.analyze_images_v2")
     def test_image_analysis_step(self, mock_vision, mock_job):
         from apps.worker.worker_app.tasks.viral_video import _step_image_analysis
 
-        mock_vision.return_value = {"name": "口红", "features": ["持久", "滋润"]}
+        # 每张图返回一个 v8 5 字段结果
+        mock_vision.return_value = [
+            {"type": "product", "name": "口红", "brand": "", "has_person": False, "summary_markdown": "一支口红"},
+            {"type": "product", "name": "口红", "brand": "", "has_person": False, "summary_markdown": "口红特写"},
+        ]
         result = _step_image_analysis(mock_job)
-        assert "products" in result
-        assert len(result["products"]) == 2  # 两张图片
+        assert "images" in result
+        assert len(result["images"]) == 2  # 两张图片
 
-    @patch("packages.shared.ai_service.call_vision")
+    @patch("apps.worker.worker_app.tasks.vision.analyze_images_v2")
     def test_image_analysis_fallback(self, mock_vision, mock_job):
         from apps.worker.worker_app.tasks.viral_video import _step_image_analysis
 
-        # 模拟 call_vision 不存在
-        mock_vision.side_effect = ImportError("no module")
+        # v2 分析内部异常时，每图走兜底，仍返回 images 结构
+        mock_vision.side_effect = RuntimeError("vision unavailable")
         result = _step_image_analysis(mock_job)
-        assert "products" in result
+        assert "images" in result
 
     def test_video_analysis_no_reference(self, mock_job):
         from apps.worker.worker_app.tasks.viral_video import _step_video_analysis
@@ -390,51 +425,45 @@ class TestViralVideoPipeline:
         result = _step_video_analysis(mock_job)
         assert result is None
 
-    @patch("packages.shared.ai_service.call_llm")
-    def test_intent_parsing(self, mock_llm, mock_job):
-        from apps.worker.worker_app.tasks.viral_video import _step_intent_parsing
+    def test_intent_parsing_step_removed(self, mock_job):
+        """intent_parsing 已合并进脚本生成，不再作为独立步骤/函数存在。"""
+        import apps.worker.worker_app.tasks.viral_video as vv
 
-        mock_llm.return_value = {"intent": "推广口红", "tone": "活泼"}
-        result = _step_intent_parsing(mock_job, {"products": []})
-        assert "intent" in result
+        assert not hasattr(vv, "_step_intent_parsing")
 
-    @patch("packages.shared.ai_service.call_llm")
-    def test_script_generation_returns_copy_result(self, mock_llm, mock_job):
+    def test_script_generation_returns_copy_result(self, mock_job):
         """v1.6: _step_script_generation 返回 dict 形式的 CopyResult，含 voiceover_script + shots。"""
         from apps.worker.worker_app.tasks.viral_video import _step_script_generation
+        from packages.shared.ai_router import ai_router
 
-        mock_llm.return_value = """<clips>
-<clip image_index="0" transition="cut" zoom="null" duration_sec="5" bgm_note="轻快流行BGM">
-<voice_text>大家好，今天分享一款口红</voice_text>
-<subtitle_text>大家好，今天分享一款口红</subtitle_text>
-<shot_type_angle_movement>近景平视，缓慢推镜</shot_type_angle_movement>
-<scene_and_dialogue>女主微笑展示口红：大家好，今天分享一款口红</scene_and_dialogue>
-<action_details>手持口红特写</action_details>
-<audio_bgm>轻快流行BGM</audio_bgm>
-<transition>硬切</transition>
-<reference_image_index>0</reference_image_index>
-<ken_burns start="0,0" end="0,0" ease="linear"/>
-</clip>
-<clip image_index="0" transition="fade" zoom="null" duration_sec="10" bgm_note="轻快BGM">
-<voice_text>颜色特别好看很显白</voice_text>
-<subtitle_text>颜色特别好看很显白</subtitle_text>
-<shot_type_angle_movement>特写，固定镜头</shot_type_angle_movement>
-<scene_and_dialogue>涂抹口红：颜色特别好看很显白</scene_and_dialogue>
-<action_details>嘴唇涂抹特写</action_details>
-<audio_bgm>轻快BGM继续</audio_bgm>
-<transition>结束</transition>
-<reference_image_index>1</reference_image_index>
-<ken_burns start="0,0" end="0,0" ease="linear"/>
-</clip>
-</clips>"""
-        result = _step_script_generation(
-            mock_job, {"intent": "推广口红", "key_messages": [], "tone": "亲切"}, {"products": []}
-        )
+        class _FakeClient:
+            is_available = True
+            model = "fake-storyboard"
+
+            def __init__(self, xml: str):
+                self._xml = xml
+
+            def chat_completion(self, messages, **kwargs):
+                return self._xml
+
+        fake = _FakeClient(self.V3_XML)
+        orig_get = ai_router.get_llm_client
+
+        def _get(task, variant="primary"):
+            if task == "storyboard":
+                return fake
+            return orig_get(task, variant=variant)
+
+        ai_router.get_llm_client = _get  # type: ignore
+        try:
+            result = _step_script_generation(mock_job, {"images": []})
+        finally:
+            ai_router.get_llm_client = orig_get  # type: ignore
         assert isinstance(result, dict)
         assert "voiceover_script" in result
         assert "shots" in result
         assert isinstance(result["shots"], list)
-        assert len(result["shots"]) == 2
+        assert len(result["shots"]) == 3
         assert result["overview"]["total_duration"] == 15
         # final_copy 必须 = voiceover_script（向后兼容）
         assert result.get("final_copy") == result["voiceover_script"]
@@ -483,7 +512,7 @@ class TestViralVideoPipeline:
         }
         prompt = _assemble_seedance_prompt(cr, mock_job)
         assert "【视频总览】" in prompt
-        assert "【逐镜头时间轴】" in prompt
+        assert "【分镜脚本】" in prompt
         assert "【硬性约束】" in prompt
         assert "【负面提示词】" in prompt
         assert "0-15秒" in prompt
@@ -501,7 +530,6 @@ class TestPipelineIntegration:
     @patch("apps.worker.worker_app.tasks.viral_video._step_tts")
     @patch("apps.worker.worker_app.tasks.viral_video._step_review")
     @patch("apps.worker.worker_app.tasks.viral_video._step_script_generation")
-    @patch("apps.worker.worker_app.tasks.viral_video._step_intent_parsing")
     @patch("apps.worker.worker_app.tasks.viral_video._step_video_analysis")
     @patch("apps.worker.worker_app.tasks.viral_video._step_image_analysis")
     @patch("apps.worker.worker_app.tasks.viral_video._get_repo_and_job")
@@ -512,7 +540,6 @@ class TestPipelineIntegration:
         mock_get_repo,
         mock_img_analysis,
         mock_video_analysis,
-        mock_intent,
         mock_script,
         mock_review,
         mock_tts,
@@ -539,8 +566,6 @@ class TestPipelineIntegration:
         mock_session = MagicMock()
         mock_get_repo.return_value = (mock_session, mock_repo, job)
 
-        # v1.6: 如果没有 copy_result 会现场补生成
-        mock_intent.return_value = {"intent": "推广", "key_messages": [], "tone": "亲切"}
         mock_script.return_value = {
             "overview": {"theme": "口红", "total_duration": 15, "aspect_ratio": "9:16"},
             "scene_and_lighting": "明亮化妆台",
@@ -553,6 +578,8 @@ class TestPipelineIntegration:
         mock_review.return_value = {"passed": True, "score": 90}
         mock_tts.return_value = None  # TTS 失败也能走下去（Seedance generate_audio=True 会自己合成音效）
         mock_tts_upload.return_value = None
+        # _run_render_pipeline 直接读 job.copy_result（#2218 守卫），需提前注入
+        job.copy_result = mock_script.return_value
         mock_render.return_value = ("/tmp/video.mp4", {"completion_tokens": 1000000})
         mock_upload.return_value = "https://oss.example.com/final.mp4"
 

tests/unit/test_viral_video_p0.py

@@ -129,7 +129,7 @@ class TestScriptGenerationV16:
             "negative_prompts": ["水印"],
         }
         p = _assemble_seedance_prompt(cr, mock_job)
-        for key in ("【视频总览】", "【场景与光线】", "【逐镜头时间轴】", "【硬性约束】", "【负面提示词】"):
+        for key in ("【视频总览】", "【参考素材】", "【分镜脚本】", "【硬性约束】", "【负面提示词】"):
             assert key in p
 
 
@@ -308,8 +308,7 @@ class TestResumeReadsImageAnalysis:
 
         # v1.5 改造后 resume 委托给 _run_render_pipeline，那里读取 job.image_analysis
         src = inspect.getsource(vv._run_render_pipeline)
-        assert "job.image_analysis" in src
-        assert "image_analysis" in src
+        assert "job.copy_result" in src
         # resume 本身应该调用 _run_render_pipeline
         resume_src = inspect.getsource(vv.resume_viral_video_pipeline)
         assert "_run_render_pipeline" in resume_src

tests/unit/test_viral_video_prompt_system.py

@@ -1,9 +1,12 @@
-"""#2040 爆款视频 Prompt 模板系统单测。
+"""#2040 爆款视频 Prompt 模板系统单测（v8/v3 叙述优先重构后）。
 
 不真调豆包 API，全部用 FakeClient 注入；覆盖：
-XML 标签解析 / 5 套模板纯文本 / loader 缓存热加载与回落 /
-三档融合差异 / personal_brands 保留 / 审核识别违规词夸大 / 自动重写 /
-各步 fallback / seed 幂等 / 负面词不出现。
+XML 标签解析 / 3 套模板纯文本（image_analysis/storyboard/review）/
+loader 缓存热加载与回落 / 本地规则审核识别违规词夸大 /
+各现存步 fallback / seed 幂等 / 负面词不出现。
+
+注：intent_parsing、copy_fusion 两套模板及其独立步骤已在叙述优先重构中删除，
+相关用例同步移除。
 """
 
 from __future__ import annotations
@@ -30,7 +33,6 @@ from packages.application.viral_video.prompt_loader import (  # noqa: E402
 from packages.application.viral_video.prompts import (  # noqa: E402
     BANNED_PHRASES,
     DEFAULT_TEMPLATES,
-    FUSION_INSTRUCTIONS,
 )
 from packages.application.viral_video.reviewer import Reviewer  # noqa: E402
 
@@ -45,26 +47,6 @@ IMAGE_XML = """<products>
 <quality resolution="高清" lighting="柔和" composition="居中"/>
 <key_selling_points><point>去油快</point><point>625ml大容量</point></key_selling_points>"""
 
-INTENT_XML = """<intent_summary>厨房去油污神器</intent_summary>
-<core_messages>
-<message must_keep="true" confidence="0.97">去油污效果好</message>
-<message must_keep="false" confidence="0.6">适合重油污</message>
-</core_messages>
-<personal_brands><brand category="price">39块钱一瓶</brand></personal_brands>
-<emotion_tone>亲切真实</emotion_tone>
-<missing_info><info>容量按625ml</info></missing_info>"""
-
-FUSION_XML = """<title>厨房重油污别硬擦了</title>
-<hook>这油污忍很久了</hook>
-<body_points><point elaboration="喷上等几分钟一擦就净" image_index="0">大公鸡头去油快</point></body_points>
-<cta>重油污的可以试一瓶</cta>
-<script_segments>
-<segment duration_sec="3" image_index="0">这油污忍很久了</segment>
-<segment duration_sec="6" image_index="0">大公鸡头油污净喷上等几分钟一擦就净</segment>
-<segment duration_sec="4" image_index="0">39块钱一瓶可以试一下</segment>
-</script_segments>
-<word_count>52</word_count><estimated_duration>13</estimated_duration>"""
-
 STORYBOARD_XML = """<clips>
 <clip image_index="0" transition="cut" zoom="null" duration_sec="3" bgm_note="日常">
 <voice_text>这油污忍很久了</voice_text>
@@ -86,8 +68,6 @@ REVIEW_PASS_XML = """<passed>true</passed>
 <issues></issues>
 <rewrite_suggestions></rewrite_suggestions>"""
 
-FIXED_FUSION_XML = FUSION_XML.replace("一擦就净", "大部分油污能擦掉")
-
 
 class FakeClient:
     """按 system 内容路由 canned 响应的假豆包客户端。"""
@@ -96,45 +76,20 @@ class FakeClient:
         self.chat_calls: list[list[dict]] = []
         self.vision_calls: list = []
         self.review_sequence: list[str] | None = None
-        self.rewrite_response: str = FIXED_FUSION_XML
 
     def chat_completion(self, messages, **kwargs):
         self.chat_calls.append(messages)
         system = messages[0]["content"]
-        user = messages[1]["content"]
-        if "按审核意见修正文案" in system:
-            return self.rewrite_response
-        if "文案合规审核员" in system:
+        # v3 审核 prompt 关键短语（叙述优先重构后更新）
+        if "短视频广告合规审核与文案优化专家" in system:
             if self.review_sequence:
                 return self.review_sequence.pop(0)
             return REVIEW_PASS_XML
-        if "理解用户的营销意图" in system:
-            return INTENT_XML
-        if "负责把文案拆成可拍摄" in system:
+        # v3 分镜 prompt
+        if "懂短视频的编导和口播文案高手" in system:
             return STORYBOARD_XML
-        if (
-            "短视频生成营销文案" in system
-            or "AI 全权创作" in system
-            or "AI 辅助润色" in system
-            or "用户原文为主" in system
-        ):
-            mode = (
-                "ai_full" if "AI 全权创作" in system else ("user_primary" if "用户原文为主" in system else "ai_polish")
-            )
-            if self._fusion_override is not None:
-                return self._fusion_override
-            xml = FUSION_XML
-            if mode == "ai_full":
-                xml = xml.replace("<title>厨房重油污别硬擦了</title>", "<title>我把厨房油污全搞定了</title>")
-            elif mode == "user_primary":
-                xml = xml.replace("<title>厨房重油污别硬擦了</title>", "<title>油污净使用分享</title>")
-            self._last_mode = mode
-            return xml
         return ""
 
-    _fusion_override = None
-    _last_mode = None
-
     def vision_completion(self, messages, images=None, **kwargs):
         self.vision_calls.append({"messages": messages, "images": images})
         return IMAGE_XML
@@ -171,25 +126,25 @@ class TestXmlParser:
         assert xp.text_of("乱七八糟没有标签", "intent", "默认") == "默认"
 
 
-# ── 5 套模板纯文本 ────────────────────────────────────────────────────────
+# ── 3 套模板纯文本 ────────────────────────────────────────────────────────
 class TestTemplates:
-    def test_five_templates_present(self):
+    def test_three_templates_present(self):
         types_ = {t["prompt_type"] for t in DEFAULT_TEMPLATES}
-        assert types_ == {"image_analysis", "intent_parsing", "copy_fusion", "storyboard", "review"}
+        assert types_ == {"image_analysis", "storyboard", "review"}
 
     def test_no_json_blocks_in_templates(self):
         for template in DEFAULT_TEMPLATES:
             blob = "\n".join([template["system_prompt"], template["user_prompt_template"], template["example_output"]])
-            assert "```json" not in blob
-            assert "JSON schema" not in blob
+            # image_analysis 模板明确要求输出 JSON，故只对非 image_analysis 模板校验
+            if template["prompt_type"] != "image_analysis":
+                assert "```json" not in blob
+                assert "JSON schema" not in blob
 
     def test_placeholders_render_and_missing_key_kept(self):
-        template = get_template("intent_parsing")
-        rendered = render_user_prompt(template, user_copy_text="去油快", industry="家居")
+        # 现存模板里选取 storyboard 做占位符渲染校验
+        template = get_template("storyboard")
+        rendered = render_user_prompt(template, marketing_purpose="去油快", industry="家居")
         assert "去油快" in rendered
-        assert "去油快" in render_user_prompt(template, image_analysis="产品图", user_copy_text="去油快")
-        partial = render_user_prompt(template, user_copy_text="x")
-        assert "{industry}" not in partial or "{" in partial
 
 
 # ── loader：DB 加载/缓存/回落 ─────────────────────────────────────────────
@@ -200,7 +155,8 @@ class TestPromptLoader:
         monkeypatch.setattr(session_mod, "SessionLocal", None, raising=False)
         template = get_template("review")
         assert template is not None
-        assert "6个维度" in template.system_prompt
+        # v3 审核 prompt 实际内容断言
+        assert "合规审核" in template.system_prompt
 
     def test_db_row_takes_precedence(self, tmp_path, monkeypatch):
         import packages.adapters.sqlalchemy_impl.session as session_mod
@@ -240,50 +196,8 @@ class TestPromptLoader:
             get_template("not_exist")
 
 
-# ── 5 步编排与 fallback ──────────────────────────────────────────────────
+# ── 现存步编排与 fallback ────────────────────────────────────────────────
 class TestGenerator:
-    def test_full_pipeline_xml_parseable(self):
-        client = FakeClient()
-        gen = CopyGenerator(client=client)
-        result = gen.generate(["https://x/1.jpg"], industry="家居", user_copy_text="去油快", fusion_level="ai_polish")
-        analysis = result["image_analysis"]
-        assert analysis.products[0].name == "大公鸡头油污净"
-        assert analysis.key_selling_points == ["去油快", "625ml大容量"]
-        assert analysis.has_person is False
-
-        intent = result["intent_result"]
-        assert intent.intent_summary == "厨房去油污神器"
-        assert intent.core_messages[0].must_keep is True
-        assert intent.personal_brands[0].text == "39块钱一瓶"
-
-        fusion = result["fusion_result"]
-        assert fusion.title == "厨房重油污别硬擦了"
-        assert len(fusion.script_segments) == 3
-
-        board = result["storyboard"]
-        assert len(board.clips) == 2
-        assert board.clips[1].transition == "zoom_in"
-        assert board.clips[1].ken_burns.end == "80,80"
-        # vision 确实被调用且带图
-        assert client.vision_calls[0]["images"] == ["https://x/1.jpg"]
-
-    def test_three_fusion_levels_distinct(self):
-        client = FakeClient()
-        gen = CopyGenerator(client=client)
-        analysis = gen.analyze_images(["https://x/1.jpg"])
-        intent = gen.parse_intent("去油快", analysis)
-
-        titles = {}
-        for level in ["ai_full", "ai_polish", "user_primary"]:
-            client._fusion_override = None
-            fusion = gen.fuse(level, analysis, intent, duration=15)
-            titles[level] = fusion.title
-            # system 里注入了对应档位指令
-            system = client.chat_calls[-1][0]["content"]
-            assert FUSION_INSTRUCTIONS[level][:12] in system
-        assert titles["ai_full"] != titles["ai_polish"]
-        assert titles["user_primary"] != titles["ai_polish"]
-
     def test_image_fallback_on_garbage(self):
         client = FakeClient()
         client.vision_completion = lambda *a, **k: "完全无法解析的内容"  # type: ignore
@@ -291,29 +205,6 @@ class TestGenerator:
         analysis = gen.analyze_images(["https://x/1.jpg"])
         assert analysis.products[0].name.startswith("无法判断")
 
-    def test_intent_fallback_on_garbage(self):
-        client = FakeClient()
-        client.chat_completion = lambda *a, **k: "乱码"  # type: ignore
-        gen = CopyGenerator(client=client)
-        from packages.application.viral_video.schemas import ImageAnalysis
-
-        intent = gen.parse_intent("这是我的原意", ImageAnalysis())
-        assert intent.intent_summary == "这是我的原意"
-        assert intent.core_messages[0].must_keep is True
-
-    def test_fusion_fallback_on_garbage_levels(self):
-        client = FakeClient()
-        client.chat_completion = lambda *a, **k: "标签全无"  # type: ignore
-        gen = CopyGenerator(client=client)
-        from packages.application.viral_video.schemas import ImageAnalysis, IntentResult
-
-        analysis = ImageAnalysis(products=[])
-        intent = IntentResult(intent_summary="用户的意思")
-        full = gen._fallback_fusion("ai_full", analysis, intent, 15, "")
-        user = gen._fallback_fusion("user_primary", analysis, intent, 15, "")
-        assert "回购" in full.title
-        assert user.title == "用户的意思"
-
     def test_storyboard_fallback_on_garbage(self):
         client = FakeClient()
         client.chat_completion = lambda *a, **k: "啥都没有"  # type: ignore
@@ -329,7 +220,7 @@ class TestGenerator:
         assert board.clips[0].voice_text == "a"
 
 
-# ── 审核与自动重写 ────────────────────────────────────────────────────────
+# ── 审核本地规则（LLM 降级放行时本地规则仍应识别红线）────────────────────
 class TestReview:
     def test_rule_check_catches_exaggeration_even_if_llm_passes(self):
         client = FakeClient()  # LLM 默认返回 passed
@@ -338,6 +229,7 @@ class TestReview:
 
         fusion = FusionResult(title="一喷100%掉光", hook="x", cta="买")
         result = reviewer.review(fusion, IntentResult(), "ai_full")
+        # LLM 返回 passed，且本地规则命中夸大 → 整体不通过
         assert result.passed is False
         dims = {i.dimension for i in result.issues}
         assert "夸大承诺" in dims
@@ -381,18 +273,6 @@ class TestReview:
         result = reviewer.review(fusion, intent, "user_primary")
         assert any(i.dimension == "用户意图保留" for i in result.issues)
 
-    def test_auto_rewrite_once_then_pass(self):
-        client = FakeClient()
-        client.review_sequence = [REVIEW_FAIL_XML, REVIEW_PASS_XML]
-        gen = CopyGenerator(client=client)
-        from packages.application.viral_video.schemas import FusionResult, IntentResult
-
-        fusion = gen._parse_fusion(FUSION_XML)
-        final, review, rewrites = gen.review_and_rewrite(fusion, IntentResult(), "ai_polish")
-        assert rewrites == 1
-        assert review.passed is True
-        assert "大部分油污能擦掉" in client.chat_calls[-2][1]["content"] or True
-
     def test_rule_fix_local(self):
         reviewer = Reviewer(client=FakeClient())
         from packages.application.viral_video.schemas import (
@@ -442,18 +322,17 @@ class TestSeed:
                     "UNIQUE(prompt_type, version))"
                 )
             )
-        assert seed_mod.seed(engine) == 5
-        assert seed_mod.seed(engine) == 5  # 再来一次不报错
+        assert seed_mod.seed(engine) == 3
+        assert seed_mod.seed(engine) == 3  # 再来一次不报错
         with engine.begin() as conn:
             count = conn.execute(sa.text("SELECT COUNT(*) FROM viral_video_prompt_templates")).scalar()
-        assert count == 5
-        active_types = conn.execute  # noqa: B018
+        assert count == 3
         with engine.begin() as conn:
             types_ = {
                 r[0]
                 for r in conn.execute(sa.text("SELECT prompt_type FROM viral_video_prompt_templates WHERE is_active=1"))
             }
-        assert types_ == {"image_analysis", "intent_parsing", "copy_fusion", "storyboard", "review"}
+        assert types_ == {"image_analysis", "storyboard", "review"}
 
 
 # ── 负面词不出现于程序产出 ────────────────────────────────────────────────

tests/unit/test_viral_video_routes.py

@@ -29,6 +29,15 @@ def _make_job(job_id: str = "job-1", user_id: str = "u1", status: str = "pending
     job.user_id = user_id
     job.status = ViralVideoStatus(status) if isinstance(status, str) else status
     job.images = kwargs.pop("images", ["img-1"])
+    # confirm_copy 新增 copy_result 完整性校验：默认提供合法文案数据
+    job.copy_result = kwargs.pop(
+        "copy_result",
+        {
+            "theme": "测试主题",
+            "voiceover_script": "这是一段测试口播文案内容。",
+            "shots": [{"time_range": "0-15秒", "voiceover": "这是一段测试口播文案内容。"}],
+        },
+    )
     job.industry = kwargs.pop("industry", "电商")
     job.target_customer = kwargs.pop("target_customer", "年轻人")
     for k, v in {
@@ -55,7 +64,6 @@ def _make_job(job_id: str = "job-1", user_id: str = "u1", status: str = "pending
         "intent_result": None,
         "image_analysis": None,
         "storyboard": None,
-        "copy_result": None,
         "generated_copy_text": "",
         "voice_id": "",
         "voice_source": "",
@@ -147,9 +155,16 @@ class TestRetryViralVideo:
         user = _auth_user("u1")
         session = MagicMock()
         job = _make_job(
-            job_id="job-retry2", user_id="u1", status=ViralVideoStatus.FAILED,
-            duration=15, video_ratio="9:16", video_resolution="720p", video_model="seedance-2.5",
-            credits_prepaid=5.0, credits_transaction_id="txn1", retry_count=0,
+            job_id="job-retry2",
+            user_id="u1",
+            status=ViralVideoStatus.FAILED,
+            duration=15,
+            video_ratio="9:16",
+            video_resolution="720p",
+            video_model="seedance-2.5",
+            credits_prepaid=5.0,
+            credits_transaction_id="txn1",
+            retry_count=0,
         )
         repo = MagicMock()
         repo.get.return_value = job
@@ -180,24 +195,32 @@ class TestRetryViralVideo:
         user = _auth_user("u1")
         session = MagicMock()
         job = _make_job(
-            job_id="job-retry3a", user_id="u1", status=ViralVideoStatus.FAILED,
-            duration=15, video_ratio="9:16", video_resolution="720p", video_model="seedance-2.5",
-            credits_prepaid=5.0, credits_transaction_id="txn-old", retry_count=0,
+            job_id="job-retry3a",
+            user_id="u1",
+            status=ViralVideoStatus.FAILED,
+            duration=15,
+            video_ratio="9:16",
+            video_resolution="720p",
+            video_model="seedance-2.5",
+            credits_prepaid=5.0,
+            credits_transaction_id="txn-old",
+            retry_count=0,
         )
         repo = MagicMock()
         repo.get.return_value = job
 
         fake_svc = MagicMock()
         fake_svc.deduct_viral_video.return_value = {"success": False, "balance": 1.0}
-        req = RetryViralVideoRequest(duration=30, video_resolution="1080p", video_ratio="16:9", video_model="seedance-2.5")
+        req = RetryViralVideoRequest(
+            duration=30, video_resolution="1080p", video_ratio="16:9", video_model="seedance-2.5"
+        )
 
         with (
             patch.object(vv_mod, "_get_job_repo", return_value=repo),
             patch("app.config.settings") as mock_settings,
             patch("packages.domain.points_service.PointsService", return_value=fake_svc),
             patch("packages.domain.points_rules.resolve_video_dimensions", return_value=(1920, 1080)),
-            patch("packages.domain.points_rules.calculate_viral_video_credits_with_breakdown",
-                  return_value=(15.0, {})),
+            patch("packages.domain.points_rules.calculate_viral_video_credits_with_breakdown", return_value=(15.0, {})),
             patch.object(vv_mod.celery_app, "send_task"),
         ):
             mock_settings.points_enabled = True
@@ -216,19 +239,29 @@ class TestRetryViralVideo:
         user = _auth_user("u1")
         session = MagicMock()
         job = _make_job(
-            job_id="job-retry3b", user_id="u1", status=ViralVideoStatus.FAILED,
-            duration=15, video_ratio="9:16", video_resolution="720p", video_model="seedance-2.5",
-            credits_prepaid=5.0, credits_transaction_id="txn-old", retry_count=0,
+            job_id="job-retry3b",
+            user_id="u1",
+            status=ViralVideoStatus.FAILED,
+            duration=15,
+            video_ratio="9:16",
+            video_resolution="720p",
+            video_model="seedance-2.5",
+            credits_prepaid=5.0,
+            credits_transaction_id="txn-old",
+            retry_count=0,
         )
         # 用 SimpleNamespace 让属性真正可写
         from types import SimpleNamespace
+
         job.credits_prepaid = 5.0
         repo = MagicMock()
         repo.get.return_value = job
 
         fake_svc = MagicMock()
         fake_svc.deduct_viral_video.return_value = {"success": True, "balance": 50.0, "transaction_id": "txn-new"}
-        req = RetryViralVideoRequest(duration=30, video_resolution="1080p", video_ratio="16:9", video_model="seedance-2.5")
+        req = RetryViralVideoRequest(
+            duration=30, video_resolution="1080p", video_ratio="16:9", video_model="seedance-2.5"
+        )
 
         new_est = 15.0
         with (
@@ -236,8 +269,9 @@ class TestRetryViralVideo:
             patch("app.config.settings") as mock_settings,
             patch("packages.domain.points_service.PointsService", return_value=fake_svc),
             patch("packages.domain.points_rules.resolve_video_dimensions", return_value=(1920, 1080)),
-            patch("packages.domain.points_rules.calculate_viral_video_credits_with_breakdown",
-                  return_value=(new_est, {})),
+            patch(
+                "packages.domain.points_rules.calculate_viral_video_credits_with_breakdown", return_value=(new_est, {})
+            ),
             patch.object(vv_mod.celery_app, "send_task"),
         ):
             mock_settings.points_enabled = True
@@ -262,9 +296,16 @@ class TestRetryViralVideo:
         user = _auth_user("u1")
         session = MagicMock()
         job = _make_job(
-            job_id="job-retry4", user_id="u1", status=ViralVideoStatus.FAILED,
-            duration=20, video_ratio="16:9", video_resolution="1080p", video_model="seedance-2.5",
-            credits_prepaid=10.0, credits_transaction_id="txn-old", retry_count=0,
+            job_id="job-retry4",
+            user_id="u1",
+            status=ViralVideoStatus.FAILED,
+            duration=20,
+            video_ratio="16:9",
+            video_resolution="1080p",
+            video_model="seedance-2.5",
+            credits_prepaid=10.0,
+            credits_transaction_id="txn-old",
+            retry_count=0,
         )
         job.credits_prepaid = 10.0
         repo = MagicMock()
@@ -280,8 +321,9 @@ class TestRetryViralVideo:
             patch("app.config.settings") as mock_settings,
             patch("packages.domain.points_service.PointsService", return_value=fake_svc),
             patch("packages.domain.points_rules.resolve_video_dimensions", return_value=(270, 480)),
-            patch("packages.domain.points_rules.calculate_viral_video_credits_with_breakdown",
-                  return_value=(new_est, {})),
+            patch(
+                "packages.domain.points_rules.calculate_viral_video_credits_with_breakdown", return_value=(new_est, {})
+            ),
             patch.object(vv_mod.celery_app, "send_task"),
         ):
             mock_settings.points_enabled = True
@@ -431,7 +473,7 @@ class TestGenerateCopy:
         assert resp.id == "job-gc"
 
     def test_generate_copy_rejects_wrong_status(self):
-        """任务在 copy_generated/completed 时不能再 generate-copy（状态保护）。"""
+        """wait_user_confirm 等中间状态不允许调用 generate-copy（状态保护）。"""
         import pytest
         from app.api.routes import viral_video as vv_mod
         from app.schemas.viral_video import GenerateCopyRequest
@@ -441,7 +483,8 @@ class TestGenerateCopy:
 
         user = _auth_user("u1")
         session = MagicMock()
-        job = _make_job(job_id="job-gc2", user_id="u1", status=ViralVideoStatus.COPY_GENERATED)
+        # wait_user_confirm 属于前端在编辑/确认文案的中间状态，应拒绝重新触发生成
+        job = _make_job(job_id="job-gc2", user_id="u1", status=ViralVideoStatus.WAIT_USER_CONFIRM)
         repo = MagicMock()
         repo.get.return_value = job
 
@@ -450,6 +493,33 @@ class TestGenerateCopy:
                 vv_mod.generate_copy("job-gc2", GenerateCopyRequest(), authenticated_user=user, session=session)
             assert exc.value.status_code == 409
 
+    def test_generate_copy_allows_regenerate_from_copy_generated(self):
+        """#2222: COPY_GENERATED/COMPLETED 状态下点「重新生成文案」应放行入队，不返回 409。"""
+        from unittest.mock import patch
+
+        from app.api.routes import viral_video as vv_mod
+        from app.schemas.viral_video import GenerateCopyRequest
+
+        from packages.domain.viral_video import ViralVideoStatus
+
+        user = _auth_user("u1")
+        session = MagicMock()
+        for regen_status in (ViralVideoStatus.COPY_GENERATED, ViralVideoStatus.COMPLETED):
+            job = _make_job(job_id=f"job-regen-{regen_status}", user_id="u1", status=regen_status)
+            repo = MagicMock()
+            repo.get.return_value = job
+            with (
+                patch.object(vv_mod, "_get_job_repo", return_value=repo),
+                patch.object(vv_mod.celery_app, "send_task") as mock_send,
+            ):
+                resp = vv_mod.generate_copy(
+                    f"job-regen-{regen_status}", GenerateCopyRequest(), authenticated_user=user, session=session
+                )
+            mock_send.assert_called_once()
+            job.resume_from_image_analyzed.assert_called()
+            assert job.retry_count >= 1
+            assert resp.id == f"job-regen-{regen_status}"
+
     def test_generate_copy_persists_voice_and_ratio(self):
         """generate-copy 应把 voice_id/voice_source/video_ratio 写入 job。"""
         from unittest.mock import patch
@@ -604,7 +674,7 @@ class TestConfirmCopyPointsDeduction:
         mock_svc.deduct_viral_video.assert_called_once()
         call_args = mock_svc.deduct_viral_video.call_args
         assert call_args.args[0] == "u1"  # user_id
-        assert call_args.args[1] == 5.2   # credits
+        assert call_args.args[1] == 5.2  # credits
         assert call_args.args[2] == "job-pay"  # job_id
         # credits_prepaid / credits_transaction_id 被写入
         assert job.credits_prepaid == 5.2
@@ -781,9 +851,14 @@ class TestEstimateCredits:
         req = EstimateCreditsRequest(model="seedance-2.5", resolution="1080p", ratio="16:9", duration=20)
         user = _auth_user("u1")
         fake_bd = {
-            "tokens": 1000.0, "video_cost": 1.0, "fixed_cost": 0.15,
-            "profit_multiplier": 1.3, "model_price": 70.0,
-            "width": 1920, "height": 1080, "fps": 24,
+            "tokens": 1000.0,
+            "video_cost": 1.0,
+            "fixed_cost": 0.15,
+            "profit_multiplier": 1.3,
+            "model_price": 70.0,
+            "width": 1920,
+            "height": 1080,
+            "fps": 24,
         }
 
         with (
@@ -815,9 +890,14 @@ class TestEstimateCredits:
         req = EstimateCreditsRequest(model="", resolution="720p", ratio="9:16", duration=10)
         user = _auth_user("u1")
         fake_bd = {
-            "tokens": 500.0, "video_cost": 0.5, "fixed_cost": 0.15,
-            "profit_multiplier": 1.3, "model_price": 70.0,
-            "width": 720, "height": 1280, "fps": 24,
+            "tokens": 500.0,
+            "video_cost": 0.5,
+            "fixed_cost": 0.15,
+            "profit_multiplier": 1.3,
+            "model_price": 70.0,
+            "width": 720,
+            "height": 1280,
+            "fps": 24,
         }
 
         with (
@@ -844,9 +924,14 @@ class TestEstimateCredits:
         )
         user = _auth_user("u1")
         fake_bd = {
-            "tokens": 100.0, "video_cost": 0.1, "fixed_cost": 0.15,
-            "profit_multiplier": 1.3, "model_price": 46.0,
-            "width": 480, "height": 480, "fps": 24,
+            "tokens": 100.0,
+            "video_cost": 0.1,
+            "fixed_cost": 0.15,
+            "profit_multiplier": 1.3,
+            "model_price": 46.0,
+            "width": 480,
+            "height": 480,
+            "fps": 24,
         }
 
         with (

tests/unit/test_viral_video_wiring.py

@@ -1,11 +1,13 @@
-"""#2040 接线集成测试：验证运行中的 viral_video 任务使用 prompt_loader 从 DB 读取模板。
+"""#2040 接线集成测试（v8/v3 叙述优先重构后）：
 
+验证运行中的 viral_video 任务使用 prompt_loader 从 DB 读取模板。
 mock LLM/Vision 调用，验证：
-1. image_analysis 走 loader 模板 + XML 解析
-2. intent_parsing 走 loader 模板 + XML 解析
-3. script_generation 走 storyboard 模板 + XML 解析，输出兼容 Seedance 的 copy_result
-4. review 走 Reviewer（review 模板）带自动重写
-5. 三档融合（ai_full / ai_polish / user_primary）注入不同 FUSION_INSTRUCTIONS
+1. image_analysis 走 V2 批处理路径，输出 {"images": [...]}
+2. script_generation 走 storyboard 模板 + v3 XML 解析，输出兼容 Seedance 的 copy_result
+3. review 走 Reviewer（review 模板）带自动重写
+4. 三档融合（ai_full / ai_polish / user_primary）的风格指令随 job.fusion_level 体现
+
+注：intent_parsing 独立步骤已删除，相关用例同步移除。
 """
 
 from __future__ import annotations
@@ -17,7 +19,7 @@ _WORKER_ROOT = _Path(__file__).resolve().parents[2] / "apps" / "worker"
 if str(_WORKER_ROOT) not in sys.path:
     sys.path.insert(0, str(_WORKER_ROOT))
 
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch
 
 import pytest
 
@@ -31,57 +33,35 @@ def job():
         images=["https://img/1.jpg", "https://img/2.jpg"],
         industry="美妆",
         duration=15,
-        user_copy_text="这款口红真的太绝了，显白又持久，姐妹们冲！",
+        user_copy_text="这款口红真的显白又持久，姐妹们冲！",
         fusion_level="ai_polish",
     )
     return j
 
 
-# ── Mock LLM/Vision 返回的 XML 文本 ─────────────────────────────────
+# ── v3 分镜 XML（与新 storyboard 模板 schema 对齐）──────────────────
 
-IMAGE_XML = """
-<analysis>
-<scene>室内桌面拍摄，柔和自然光</scene>
-<mood>清新温暖</mood>
-<product name="lipstick" brand="品牌X" category="唇部彩妆"
-         appearance="管状红色膏体" packaging="黑色金属管"
-         features="显白,持久,滋润" portrait_prompt="无人像"
-         summary="品牌X红色口红">
-  <text_on_package>品牌X,211</text_on_package>
-</product>
-</analysis>
-""".strip()
-
-INTENT_XML = """
-<intent>
-<intent_summary>推广显白持久口红</intent_summary>
-<core_messages>
-  <message must_keep="true">显白</message>
-  <message must_keep="true">持久</message>
-</core_messages>
-<personal_brands>
-  <brand text="品牌X" category="brand"/>
-</personal_brands>
-<emotion_tone>亲切自然</emotion_tone>
-<suggested_title>显白持久口红推荐</suggested_title>
-</intent>
-""".strip()
-
-STORYBOARD_XML = """
+V3_XML = """<copy_display_markdown>今天给大家分享一支很显白的口红。</copy_display_markdown>
 <clips>
-<clip image_index="0" transition="cut" zoom="null" duration_sec="5" bgm_note="轻快BGM">
-  <voice_text>这款口红真的太绝了</voice_text>
-  <subtitle_text>显白又持久</subtitle_text>
-  <shot_type_angle_movement>近景俯拍45度，缓慢推镜</shot_type_angle_movement>
-  <scene_and_dialogue>厨房台面，主妇展示口红。对白：这款口红真的太绝了</scene_and_dialogue>
-  <action_details>右手持口红展示膏体</action_details>
-  <audio_bgm>轻快BGM</audio_bgm>
-  <transition>硬切</transition>
-  <reference_image_index>0</reference_image_index>
-  <ken_burns start="0,0" end="0,0" ease="linear"/>
+<clip image_index="0" time_range="0-5秒">
+<voiceover>大家好，今天分享一款口红</voiceover>
+<visual>近景平视，缓慢推镜</visual>
+<action_details>手持口红特写</action_details>
+<audio_bgm>轻快流行BGM</audio_bgm>
+<transition>硬切</transition>
+<reference_image_index>0</reference_image_index>
+</clip>
+<clip image_index="1" time_range="5-15秒">
+<voiceover>颜色特别好看很显白</voiceover>
+<visual>特写，固定镜头</visual>
+<action_details>嘴唇涂抹特写</action_details>
+<audio_bgm>轻快BGM继续</audio_bgm>
+<transition>结束</transition>
+<reference_image_index>1</reference_image_index>
 </clip>
 </clips>
-""".strip()
+<voiceover_script>大家好，今天分享一款口红。颜色特别好看很显白</voiceover_script>
+<theme>口红分享</theme>"""
 
 
 @pytest.fixture(autouse=True)
@@ -93,89 +73,129 @@ def invalidate_loader_cache():
     pl.invalidate()
 
 
-# ── 1) 图片分析走模板 ───────────────────────────────────────────────
+class _FakeClient:
+    """替代 ai_router 返回的假 LLM 客户端，固定返回 v3 XML。"""
+
+    is_available = True
+    model = "fake-storyboard"
+
+    def __init__(self, xml: str = V3_XML):
+        self._xml = xml
+        self.captured: list[list[dict]] = []
+
+    def chat_completion(self, messages, **kwargs):
+        self.captured.append(messages)
+        return self._xml
+
+
+@pytest.fixture
+def patch_router(job):
+    """把 ai_router 单例的 get_llm_client 替换为返回 _FakeClient。"""
+    from packages.shared.ai_router import ai_router as _router
+
+    fake = _FakeClient()
+
+    def _get(_key, variant=None):
+        return fake
+
+    orig = _router.get_llm_client
+    _router.get_llm_client = _get  # type: ignore
+    job.image_analysis = {"images": []}
+    yield fake
+    _router.get_llm_client = orig  # type: ignore
+
+
+# ── 1) 图片分析走 V2 批处理 ──────────────────────────────────────────
 
 
 class TestImageAnalysisWiring:
-    def test_uses_loader_template_and_xml_parse(self, job):
+    def test_step_image_analysis_uses_v2_batch_path(self, job):
+        """图片分析走 V2 批处理，_step_image_analysis 归一化 URL 后调用 analyze_images_v2。"""
         from apps.worker.worker_app.tasks import viral_video as vv
 
-        with patch("packages.shared.ai_service.call_vision", return_value=IMAGE_XML) as mock_v:
-            result = vv._analyze_single_image(0, "https://img/1.jpg", "vlm-lite", 15)
+        fake_image = {
+            "type": "product",
+            "name": "lipstick",
+            "brand": "品牌X",
+            "has_person": False,
+            "summary_markdown": "一支品牌X的红色口红。",
+            "_source": "v2",
+        }
+        with patch.object(vv, "_normalize_image_url", side_effect=lambda raw, idx: raw):
+            with patch(
+                "worker_app.tasks.vision.analyze_images_v2",
+                return_value=[fake_image, fake_image],
+                create=True,
+            ) as mock_v2:
+                result = vv._step_image_analysis(job)
 
-        mock_v.assert_called_once()
-        # 验证调用时传入了 system_prompt（说明走了 loader 渲染的模板）
-        call_kwargs = mock_v.call_args.kwargs
-        assert "system_prompt" in call_kwargs and call_kwargs["system_prompt"]
-        # 结果包含从 XML 解析出的产品信息
-        assert result["name"] == "lipstick"
-        assert result["brand"] == "品牌X"
-        assert "显白" in result["key_features"]
-        assert result["text_on_package"] == ["品牌X", "211"]
+        mock_v2.assert_called_once()
+        # 传入的是归一化后的图片 URL 列表
+        assert mock_v2.call_args.args[0] == job.images
+        images = result["images"]
+        assert len(images) == 2
+        assert images[0]["name"] == "lipstick"
+        assert images[0]["brand"] == "品牌X"
+        assert images[0]["type"] == "product"
+        assert images[0]["summary_markdown"] == "一支品牌X的红色口红。"
 
-
-# ── 2) 意图解析走模板 ───────────────────────────────────────────────
-
-
-class TestIntentParsingWiring:
-    def test_uses_loader_and_parses_xml(self, job):
+    def test_step_image_analysis_empty_images(self, job):
         from apps.worker.worker_app.tasks import viral_video as vv
 
-        img_result = {"products": [{"name": "lipstick", "brand": "品牌X", "key_features": ["显白", "持久"]}]}
-        with patch("packages.shared.ai_service.call_llm", return_value=INTENT_XML) as mock_llm:
-            result = vv._step_intent_parsing(job, img_result)
-
-        mock_llm.assert_called_once()
-        assert result["intent"] == "推广显白持久口红"
-        assert "显白" in result["key_messages"]
-        assert result["suggested_title"] == "显白持久口红推荐"
+        job.images = []
+        result = vv._step_image_analysis(job)
+        assert result == {"images": []}
 
 
-# ── 3) 脚本生成：storyboard 模板 + XML 解析 + fusion_level 注入 ────
+# ── 2) 脚本生成：storyboard 模板 + v3 XML 解析 + fusion_level ───────
 
 
 class TestScriptGenerationWiring:
     @pytest.mark.parametrize("level", ["ai_full", "ai_polish", "user_primary"])
-    def test_fusion_level_injected(self, job, level):
-        """三档融合水平被注入到 storyboard 模板的 system_prompt"""
+    def test_fusion_level_injected(self, job, patch_router, level):
+        """不同 fusion_level 下脚本生成走通，输出 Seedance 兼容结构。
+
+        叙述优先后，三档差异由 v3 storyboard 系统提示统一承载，这里验证调用成功
+        且输出结构完整（保留三档参数化以确保各档位都能跑通）。
+        """
         from apps.worker.worker_app.tasks import viral_video as vv
-        from packages.application.viral_video.prompts import FUSION_INSTRUCTIONS
 
         job.fusion_level = level
-        intent = {"intent": "推广", "key_messages": ["显白"], "tone": "亲切"}
+        result = vv._step_script_generation(job, {"images": []})
 
-        captured_system = {}
-
-        def fake_call_llm(messages, **kw):
-            captured_system["final"] = messages[0]["content"]
-            return STORYBOARD_XML
-
-        with patch("packages.shared.ai_service.call_llm", side_effect=fake_call_llm):
-            result = vv._step_script_generation(job, intent, {})
-
-        # fusion_level 对应的指令文本被注入到 system prompt 中
-        assert FUSION_INSTRUCTIONS[level] in captured_system["final"], f"fusion_level {level} 指令未注入 system_prompt"
         # 输出保持 Seedance 兼容结构
         assert "overview" in result
         assert "shots" in result
         assert len(result["shots"]) >= 1
         assert result["shots"][0]["shot_type_angle_movement"]
         assert result["voiceover_script"]
+        # 系统提示确实被发送
+        assert patch_router.captured[0][0]["role"] == "system"
 
     def test_fallback_when_xml_and_json_unparseable(self, job):
-        """XML 解析失败且无法解析为 JSON 时，回退到兜底脚本"""
-        from apps.worker.worker_app.tasks import viral_video as vv
+        """XML 与 JSON 均无法解析时回退到兜底脚本。"""
+        from packages.shared.ai_router import ai_router as _router
 
-        job.fusion_level = "ai_polish"
-        intent = {"intent": "推广", "key_messages": [], "tone": "亲切"}
-        with patch("packages.shared.ai_service.call_llm", return_value="not xml not json"):
-            result = vv._step_script_generation(job, intent, {})
+        fake = _FakeClient(xml="not xml not json")
+
+        def _get(_key, variant=None):
+            return fake
+
+        orig = _router.get_llm_client
+        _router.get_llm_client = _get  # type: ignore
+        job.image_analysis = {"images": []}
+        try:
+            from apps.worker.worker_app.tasks import viral_video as vv
+
+            result = vv._step_script_generation(job, {"images": []})
+        finally:
+            _router.get_llm_client = orig  # type: ignore
         assert isinstance(result, dict)
         assert "voiceover_script" in result
         assert "shots" in result
 
 
-# ── 4) Review 使用 Reviewer + 自动重写 ─────────────────────────────
+# ── 3) Review 使用 Reviewer + 自动重写 ─────────────────────────────
 
 
 class TestReviewWiring:
@@ -197,7 +217,7 @@ class TestReviewWiring:
         assert out["passed"] is True
 
     def test_rewrite_path(self, job):
-        """审核不通过时触发自动重写，并更新 job.copy_result"""
+        """审核不通过时触发自动重写，并更新 job.copy_result。"""
         from apps.worker.worker_app.tasks import viral_video as vv
         from packages.application.viral_video.reviewer import Reviewer, ReviewResult
         from packages.application.viral_video.schemas import FusionResult, ReviewIssue, ScriptSegment
@@ -233,70 +253,65 @@ class TestReviewWiring:
             out = vv._step_review(job, copy_result)
 
         assert out["passed"] is True
-        assert "rewritten_copy" in out
-        assert job.generated_copy_text == "修改后口播正文"
 
 
-# ── 5) 端到端：每个 step 调用 loader 对应 prompt_type ──────────────
+# ── 4) 端到端：image 走 V2、script 走 storyboard loader ─────────────
 
 
 class TestEndToEndLoaderUsed:
-    def test_each_step_calls_loader(self, job):
+    def test_image_v2_and_script_uses_storyboard(self, job):
         from apps.worker.worker_app.tasks import viral_video as vv
         from packages.application.viral_video import prompt_loader as pl
+        from packages.shared.ai_router import ai_router as _router
 
-        called_types = []
+        called_types: list[str] = []
         real_get = pl.get_template
 
         def spy_get(prompt_type, **kwargs):
             called_types.append(prompt_type)
             return real_get(prompt_type, **kwargs)
 
-        with (
-            patch.object(pl, "get_template", side_effect=spy_get),
-            patch("packages.shared.ai_service.call_vision", return_value=IMAGE_XML),
-            patch("packages.shared.ai_service.call_llm", return_value=INTENT_XML),
-        ):
-            # 1) image
-            img_res = vv._analyze_single_image(0, "https://img/1.jpg", "vlm", 15)
-            # 2) intent
-            intent_res = vv._step_intent_parsing(job, {"products": [img_res]})
+        v2_image = {
+            "type": "product",
+            "name": "lipstick",
+            "brand": "品牌X",
+            "has_person": False,
+            "summary_markdown": "一支品牌X口红。",
+        }
+        fake = _FakeClient()
 
-        # 前两步分别调用了 image_analysis 和 intent_parsing
-        assert "image_analysis" in called_types
-        assert "intent_parsing" in called_types
+        def _get(_key, variant=None):
+            return fake
 
-        # script 和 review 单独验证（需要不同的 LLM 返回）
-        called_types_2 = []
+        orig = _router.get_llm_client
+        _router.get_llm_client = _get  # type: ignore
+        job.image_analysis = {"images": []}
+        try:
+            with (
+                patch.object(pl, "get_template", side_effect=spy_get),
+                patch.object(vv, "_normalize_image_url", side_effect=lambda raw, idx: raw),
+                patch(
+                    "worker_app.tasks.vision.analyze_images_v2",
+                    return_value=[v2_image],
+                    create=True,
+                ),
+            ):
+                img_step = vv._step_image_analysis(job)
+                img_res = img_step["images"][0]
+                copy_res = vv._step_script_generation(job, {"images": [img_res]})
+        finally:
+            _router.get_llm_client = orig  # type: ignore
 
-        def spy_get_2(prompt_type, **kwargs):
-            called_types_2.append(prompt_type)
-            return real_get(prompt_type, **kwargs)
-
-        with (
-            patch.object(pl, "get_template", side_effect=spy_get_2),
-            patch("packages.shared.ai_service.call_llm", return_value=STORYBOARD_XML),
-        ):
-            copy_res = vv._step_script_generation(job, intent_res, {"products": [img_res]})
-        assert "storyboard" in called_types_2
-
-        called_types_3 = []
-
-        def spy_get_3(prompt_type, **kwargs):
-            called_types_3.append(prompt_type)
-            return real_get(prompt_type, **kwargs)
+        # V2 图片分析不经过 prompt_loader；脚本生成调用 storyboard 模板
+        assert "image_analysis" not in called_types
+        assert "storyboard" in called_types
+        assert copy_res["voiceover_script"]
 
+        # review 走 Reviewer.review
         from packages.application.viral_video.reviewer import Reviewer, ReviewResult
 
         pass_result = ReviewResult(passed=True, score=90, issues=[], rewrite_suggestions=[])
-        job.intent_result = intent_res
-        job.copy_result = copy_res
-        with (
-            patch.object(pl, "get_template", side_effect=spy_get_3),
-            patch.object(Reviewer, "review", return_value=pass_result) as mock_review,
-        ):
+        with patch.object(Reviewer, "review", return_value=pass_result) as mock_review:
             review_res = vv._step_review(job, copy_res)
-        # review 步骤内部直接调用 Reviewer.review，该方法被 mock，因此 get_template 不会被调用；
-        # 此处验证 Reviewer.review 被调用即可说明 review 步骤走通了。
-        assert mock_review.called, "_step_review 未调用 Reviewer.review"
+        assert mock_review.called
         assert isinstance(review_res, dict) and "passed" in review_res

tests/unit/test_viral_video_ws.py

@@ -430,3 +430,56 @@ class TestWSInitialSnapshot:
             for p in patches:
                 p.stop()
         assert any(m["type"] == "forwarder_reached" for m in received)
+
+    def test_image_analyzed_initial_snapshot_contains_image_analysis(self):
+        """P0: image_analyzed 状态时初始快照必须带 image_analysis。"""
+        ia = {"images": [{"type": "product", "name": "X", "summary_markdown": "# X\nhello"}]}
+        job = _make_job(
+            status="image_analyzed",
+            user_id="user-a",
+            is_terminal=False,
+            image_analysis=ia,
+            copy_result=None,
+            generated_copy_text="",
+            storyboard=[],
+        )
+        received, _, _ = _run_ws_handshake(job=job)
+        assert received[0]["data"]["status"] == "image_analyzed"
+        assert received[0]["data"]["image_analysis"] == ia
+
+    def test_copy_generated_initial_snapshot_contains_copy_result(self):
+        """P0: copy_generated 状态时初始快照必须带 copy_result/storyboard。"""
+        cr = {"shots": [{"time_range": "0-3s", "voiceover": "hi"}], "voiceover_script": "hi"}
+        sb = [{"order": 1, "text": "hi", "duration": 3.0}]
+        job = _make_job(
+            status="copy_generated",
+            user_id="user-a",
+            is_terminal=False,
+            image_analysis={"images": []},
+            copy_result=cr,
+            generated_copy_text="hi",
+            storyboard=sb,
+        )
+        received, _, _ = _run_ws_handshake(job=job)
+        data = received[0]["data"]
+        assert data["status"] == "copy_generated"
+        # _build_copy_result 会补 final_copy/suggested_copy/title 兜底
+        assert data["copy_result"]["shots"] == cr["shots"]
+        assert data["storyboard"] == sb
+        assert data["generated_copy_text"] == "hi"
+        assert data["image_analysis"] == {"images": []}
+
+    def test_initial_snapshot_without_business_fields_only_has_status(self):
+        """running/pending 等中间态，无业务字段时不应塞空 dict/list。"""
+        job = _make_job(
+            status="running",
+            user_id="user-a",
+            is_terminal=False,
+            image_analysis=None,
+            copy_result=None,
+            generated_copy_text="",
+            storyboard=[],
+        )
+        received, _, _ = _run_ws_handshake(job=job)
+        data = received[0]["data"]
+        assert data == {"status": "running"}

tests/unit/test_vision_v4_prompt.py

@@ -0,0 +1,191 @@
+# -*- coding: utf-8 -*-
+"""vision v8 叙述优先 assembler / prompt 单元测试。
+
+- assembler 输出仅 5 字段（type/name/brand/has_person/summary_markdown）
+- images / 老 products 两种顶层键都能解析
+- summary_markdown 正常时原样透传，不改写
+- summary_markdown 缺失时才用一句话基础兜底
+- _prompt：DB 有 active 模板原样使用，无记录回落到 prompts.py 默认 v8
+"""
+
+from __future__ import annotations
+
+import sys
+import types
+from typing import Any
+
+import pytest
+from worker_app.tasks.vision import _prompt, assembler
+
+# packages 层依赖 datetime.UTC（Python 3.11+）。开发机若为旧版本，prompt 相关用例
+# 在 CI（3.11）上正常执行，本地直接跳过，避免污染基线。
+_PY311 = sys.version_info >= (3, 11)
+requires_packages = pytest.mark.skipif(not _PY311, reason="packages 需要 Python 3.11+")
+
+REQUIRED_KEYS = {"type", "name", "brand", "has_person", "summary_markdown"}
+
+
+# ---------- 正常 v8：叙述原样透传 ----------
+
+
+def test_assemble_v8_store_passthrough() -> None:
+    md = "###店铺主体\n这是一家名为“御众堂”的线下门店内部，整体暖木色调……"
+    fj = {
+        "images": [
+            {
+                "type": "store",
+                "name": "御众堂门店",
+                "brand": "御众堂",
+                "has_person": False,
+                "summary_markdown": md,
+            }
+        ]
+    }
+    r = assembler.assemble_result(0, fj, [])
+    assert REQUIRED_KEYS <= set(r.keys())
+    assert r["type"] == "store"
+    assert r["name"] == "御众堂门店"
+    assert r["brand"] == "御众堂"
+    assert r["has_person"] is False
+    assert r["summary_markdown"] == md
+    assert "_source" not in r
+
+
+def test_assemble_v8_product() -> None:
+    md = "这是一瓶洗衣液，亮红色瓶身配白色按压泵头，瓶身正面印着品牌标识……"
+    fj = {
+        "images": [{"type": "product", "name": "洗衣液", "brand": "OMO", "has_person": False, "summary_markdown": md}]
+    }
+    r = assembler.assemble_result(0, fj, ["OMO"])
+    assert r["type"] == "product"
+    assert r["summary_markdown"] == md
+
+
+def test_assemble_v8_person() -> None:
+    md = "画面里是一位年轻女性，穿白色T恤、黑色阔腿裤，神情自信……"
+    fj = {"images": [{"type": "person", "name": "年轻女性", "brand": "", "has_person": True, "summary_markdown": md}]}
+    r = assembler.assemble_result(0, fj, [])
+    assert r["type"] == "person"
+    assert r["has_person"] is True
+    assert r["summary_markdown"] == md
+
+
+def test_assemble_v8_scene() -> None:
+    fj = {
+        "images": [
+            {"type": "scene", "name": "海边日落", "brand": "", "has_person": False, "summary_markdown": "海边……"}
+        ]
+    }
+    r = assembler.assemble_result(0, fj, [])
+    assert r["type"] == "scene"
+
+
+# ---------- 顶层 products 老键兼容（assembler 层）----------
+
+
+def test_assemble_top_level_products_key() -> None:
+    fj = {"products": [{"type": "store", "name": "门店", "brand": "御众堂", "summary_markdown": "门店……"}]}
+    r = assembler.assemble_result(0, fj, [])
+    assert r["brand"] == "御众堂"
+    assert r["type"] == "store"
+
+
+# ---------- 字段缺失的异常兜底 ----------
+
+
+def test_assemble_missing_summary_uses_basic_fallback() -> None:
+    fj = {"images": [{"type": "store", "name": "御众堂门店", "brand": "御众堂", "has_person": False}]}
+    r = assembler.assemble_result(0, fj, [])
+    assert REQUIRED_KEYS <= set(r.keys())
+    assert r["summary_markdown"]
+    assert "御众堂" in r["summary_markdown"]
+    assert r.get("_source") == "summary_missing"
+
+
+def test_assemble_invalid_type_defaults_scene() -> None:
+    fj = {"images": [{"type": "weird", "name": "x", "summary_markdown": ""}]}
+    r = assembler.assemble_result(0, fj, [])
+    assert r["type"] == "scene"
+    assert r["summary_markdown"]  # basic fallback
+
+
+def test_assemble_empty_fast_json_uses_ocr_hint() -> None:
+    r = assembler.assemble_result(0, {}, ["御众堂"])
+    assert REQUIRED_KEYS <= set(r.keys())
+    assert "御众堂" in r["name"]
+    assert r.get("_source") == "empty_fast_json"
+
+
+def test_assemble_none_input() -> None:
+    r = assembler.assemble_result(0, None, [])
+    assert REQUIRED_KEYS <= set(r.keys())
+    assert r["type"] == "scene"
+
+
+# ---------- 布尔归一化 ----------
+
+
+def test_coerce_bool() -> None:
+    assert assembler._coerce_bool(True) is True
+    assert assembler._coerce_bool(1) is True
+    assert assembler._coerce_bool("true") is True
+    assert assembler._coerce_bool(False) is False
+    assert assembler._coerce_bool(0) is False
+    assert assembler._coerce_bool("否") is False
+
+
+# ---------- _prompt 解析 ----------
+
+
+@pytest.fixture(autouse=True)
+def _clear_prompt_cache() -> Any:
+    _prompt.invalidate_cache()
+    yield
+    _prompt.invalidate_cache()
+
+
+def _fake_tpl(system_prompt: str = "DB_V8_PROMPT_XYZ") -> Any:
+    return types.SimpleNamespace(
+        system_prompt=system_prompt,
+        user_prompt_template="地址：{image_url}，OCR：{ocr_text}",
+        version=8,
+    )
+
+
+@requires_packages
+def test_resolve_uses_db_prompt(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(_prompt, "_load_db_template", lambda: _fake_tpl())
+    sys_prompt, user_prompt = _prompt.resolve_fast_prompt("http://img", "御众堂")
+    assert sys_prompt == "DB_V8_PROMPT_XYZ"
+    assert "http://img" in user_prompt
+    assert "御众堂" in user_prompt
+
+
+@requires_packages
+def test_resolve_pro_uses_db_prompt(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(_prompt, "_load_db_template", lambda: _fake_tpl("DB_PRO_PROMPT"))
+    sys_prompt, _ = _prompt.resolve_pro_prompt()
+    assert sys_prompt == "DB_PRO_PROMPT"
+
+
+@requires_packages
+def test_resolve_falls_back_to_default(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(_prompt, "_load_db_template", lambda: None)
+    default = _prompt._default_template()
+    sys_prompt, _ = _prompt.resolve_fast_prompt()
+    assert sys_prompt == default["system_prompt"]
+
+
+@requires_packages
+def test_resolve_caches(monkeypatch: pytest.MonkeyPatch) -> None:
+    calls = {"n": 0}
+
+    def _load() -> Any:
+        calls["n"] += 1
+        return _fake_tpl("CACHED")
+
+    monkeypatch.setattr(_prompt, "_load_db_template", _load)
+    s1, _ = _prompt.resolve_fast_prompt()
+    s2, _ = _prompt.resolve_fast_prompt()
+    assert s1 == s2 == "CACHED"
+    assert calls["n"] == 1

tests/unit/test_xml_parser.py

@@ -0,0 +1,49 @@
+"""xml_parser CDATA 剥离单元测试。"""
+
+from packages.application.viral_video.xml_parser import find_all, text_of
+
+XML = """<script>
+<copy_display_markdown><![CDATA[# 标题
+
+这是第一段，含**加粗**和[链接](https://a.com)。
+
+第二行，保留换行。]]></copy_display_markdown>
+<voiceover>口播不带 CDATA，保持原样。</voiceover>
+<visual><![CDATA[画面：产品特写，光线柔和]]></visual>
+<action_details><![CDATA[未闭合标签里的 CDATA 也要剥离]]></action_details>
+</script>"""
+
+
+def test_text_of_strips_cdata_with_markdown_newlines():
+    text = text_of(XML, "copy_display_markdown")
+    assert not text.startswith("<![CDATA[")
+    assert not text.endswith("]]>")
+    assert "# 标题" in text
+    assert "**加粗**" in text
+    assert "[链接](https://a.com)" in text
+    # markdown 换行被保留
+    assert "\n\n第二行" in text
+
+
+def test_plain_text_unchanged():
+    assert text_of(XML, "voiceover") == "口播不带 CDATA，保持原样。"
+
+
+def test_other_cdata_fields_stripped():
+    assert text_of(XML, "visual") == "画面：产品特写，光线柔和"
+
+
+def test_unclosed_tag_cdata_stripped():
+    # action_details 没有闭合标签，走未闭合兜底分支
+    node = find_all(XML, "action_details")[0]
+    assert node["text"] == "未闭合标签里的 CDATA 也要剥离"
+
+
+def test_no_cdata_returns_original():
+    xml = "<copy_display_markdown>普通内容]]> 残留结尾</copy_display_markdown>"
+    # 非完整 CDATA 包裹不应被误剥离
+    assert text_of(xml, "copy_display_markdown") == "普通内容]]> 残留结尾"
+
+
+def test_missing_tag_default():
+    assert text_of(XML, "nope", default="缺省") == "缺省"