fix(viral-video): xml_parser 剥离 CDATA 包裹，修复 copy_display_markdown 前端泄露 (#2238)

Co-authored-by: xiaoxia <dev@xiaoxiajianji.com>
Co-committed-by: xiaoxia <dev@xiaoxiajianji.com>

CHANGES.md

@@ -0,0 +1,2 @@
+Mon Oct  5 04:09:11 PM CST 2026
+2198 lite/pro并行竞速 (commit 9699a1f) — CI rebuild trigger Mon Oct  5 08:09:11 AM UTC 2026

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

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+# -*- 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"
+        )
+    )

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

@@ -280,11 +280,18 @@ 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 = ""
 
@@ -341,6 +348,9 @@ 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」")
 
     # 积分预扣（已扣过/重试任务跳过）
     from app.config import settings as _settings

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()
@@ -677,6 +866,8 @@ 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 +893,8 @@ 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 +912,8 @@ 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 +1007,8 @@ 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 +1,318 @@
+"""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. 调 DittoClient.generate_and_persist(video_url=默认模板, audio_url=job.audio_url, script=job.script_text)
+3. 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）
+4. 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 使用预置的人物模板视频（settings.ditto_default_video_url），不用用户上传的 video_url
+- 不传 GFPGAN 超分，不需要 ffmpeg 音视频混流
+"""
+
+from __future__ import annotations
+
+import logging
+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
+
+
+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_video_duration(video_bytes: bytes) -> float:
+    """用 ffprobe 探测视频时长（秒）；失败返回 0。"""
+    try:
+        import os
+        import subprocess
+        import tempfile
+
+        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp:
+            tmp.write(video_bytes)
+            tmp_path = tmp.name
+        try:
+            out = subprocess.check_output(
+                [
+                    "ffprobe",
+                    "-v",
+                    "error",
+                    "-show_entries",
+                    "format=duration",
+                    "-of",
+                    "default=noprint_wrappers=1:nokey=1",
+                    tmp_path,
+                ],
+                stderr=subprocess.STDOUT,
+                timeout=10,
+            )
+            return float(out.decode().strip() or 0)
+        finally:
+            os.unlink(tmp_path)
+    except Exception as exc:
+        logger.warning("[ditto_task] ffprobe 失败: %s", exc)
+        return 0.0
+
+
+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)
+            # 复用 lipsync_service._submit_to_gpu_create 逻辑
+            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_service import DittoError, get_ditto_client
+
+    db: Session = _get_db_session()
+    job: Optional[LipsyncJobModel] = None
+    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 ""
+        if not audio_url:
+            raise DittoError("job.audio_url 为空，无法调用 Ditto", code="InvalidParam")
+
+        logger.info(
+            "[ditto_task] 开始 Ditto 生成: job_id=%s audio=%s script_len=%d",
+            job_id,
+            audio_url[:100],
+            len(script),
+        )
+        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 不传则用默认模板
+        )
+
+        # Ditto 返回的 MP4 自带音频，直接标记完成
+        job.output_video_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（1秒≈1秒）
+            try:
+                from packages.domain.sentence_timings import probe_audio_duration
+                from packages.shared.url_security import safe_download_bytes
+
+                audio_data = safe_download_bytes(
+                    audio_url, allowed_mime_types=("audio/mpeg", "audio/wav", "audio/x-wav"), timeout=30
+                )
+                duration = probe_audio_duration(audio_data)
+            except Exception:
+                duration = 0.0
+        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
+                # 回退 GPU/MediaKit
+                _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/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/components/common/DurationWheelPicker/DurationWheelPicker.css

@@ -0,0 +1,182 @@
+/* DurationWheelPicker —— 弹层式滚轮选择器（样式与表单一致） */
+
+/* 触发按钮：外观复用 .vv-select 风格 */
+.dw-trigger {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  width: 100%;
+  height: 36px;
+  padding: 0 12px;
+  background: #fff;
+  border: 1px solid #e0e0e8;
+  border-radius: 8px;
+  font-size: 13px;
+  color: #1f2937;
+  cursor: pointer;
+  box-sizing: border-box;
+  transition: all 0.15s;
+  user-select: none;
+}
+.dw-trigger:hover {
+  border-color: #c0c0d0;
+}
+.dw-trigger-open,
+.dw-trigger:focus-within {
+  border-color: #7c3aed !important;
+  box-shadow: 0 0 0 2px rgba(124, 58, 237, 0.12);
+}
+.dw-trigger-disabled {
+  opacity: 0.5;
+  pointer-events: none;
+  cursor: not-allowed;
+}
+.dw-trigger-val {
+  flex: 1;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+.dw-trigger-placeholder {
+  color: #9ca3af;
+}
+.dw-trigger-arrow {
+  font-size: 10px;
+  color: #9ca3af;
+  margin-left: 8px;
+  transition: transform 0.2s;
+}
+.dw-trigger-arrow-up {
+  transform: rotate(180deg);
+}
+
+/* 弹层容器 */
+.dw-popup {
+  padding: 8px;
+  min-width: 140px;
+}
+
+/* 滚轮 */
+.dw-picker {
+  position: relative;
+  width: 100%;
+  overflow: hidden;
+  border-radius: 8px;
+  background: #fafafe;
+  border: 1px solid #e5e7eb;
+}
+.dw-picker-list {
+  margin: 0;
+  padding: 0;
+  list-style: none;
+  height: 100%;
+  overflow-y: scroll;
+  scroll-snap-type: y mandatory;
+  -webkit-overflow-scrolling: touch;
+  scrollbar-width: none;
+}
+.dw-picker-list::-webkit-scrollbar {
+  display: none;
+}
+.dw-picker-item {
+  display: flex;
+  align-items: baseline;
+  justify-content: center;
+  gap: 3px;
+  scroll-snap-align: center;
+  cursor: pointer;
+  font-size: 15px;
+  color: #9ca3af;
+  font-weight: 400;
+  transition:
+    color 0.15s,
+    transform 0.15s,
+    font-weight 0.15s;
+}
+.dw-picker-item-val {
+  font-variant-numeric: tabular-nums;
+}
+.dw-picker-item-unit {
+  font-size: 13px;
+  color: inherit;
+}
+.dw-picker-item-active {
+  color: #7c3aed;
+  font-weight: 600;
+}
+.dw-picker-item-active .dw-picker-item-val {
+  font-size: 18px;
+}
+.dw-picker-item-active .dw-picker-item-unit {
+  font-size: 14px;
+}
+
+/* 中心选中条 */
+.dw-picker-mask {
+  position: absolute;
+  left: 6px;
+  right: 6px;
+  pointer-events: none;
+  background: #f5f0ff;
+  border-radius: 6px;
+  z-index: 1;
+}
+.dw-picker-mask::before,
+.dw-picker-mask::after {
+  content: "";
+  position: absolute;
+  left: 0;
+  right: 0;
+  height: 1px;
+  background: #d8c4ff;
+}
+.dw-picker-mask::before {
+  top: 0;
+}
+.dw-picker-mask::after {
+  bottom: 0;
+}
+
+/* 上下渐变 */
+.dw-picker-fade {
+  position: absolute;
+  left: 0;
+  right: 0;
+  height: 40%;
+  pointer-events: none;
+  z-index: 2;
+}
+.dw-picker-fade-top {
+  top: 0;
+  background: linear-gradient(to bottom, #fafafe 25%, rgba(250, 250, 254, 0));
+}
+.dw-picker-fade-bottom {
+  bottom: 0;
+  background: linear-gradient(to top, #fafafe 25%, rgba(250, 250, 254, 0));
+}
+
+/* 弹层按钮区 */
+.dw-popup-actions {
+  display: flex;
+  gap: 8px;
+  justify-content: flex-end;
+  margin-top: 8px;
+}
+.dw-popup-actions .ant-btn {
+  border-radius: 6px;
+}
+.dw-popup-actions .ant-btn-primary {
+  background: #7c3aed;
+}
+.dw-popup-actions .ant-btn-primary:hover {
+  background: #6d28d9 !important;
+}
+
+/* 覆盖 antd Popover 默认内边距 */
+.dw-popover .ant-popover-inner {
+  padding: 0 !important;
+  overflow: hidden;
+}
+.dw-popover .ant-popover-arrow {
+  display: none;
+}

apps/web/src/components/common/DurationWheelPicker/index.tsx

@@ -0,0 +1,180 @@
+/**
+ * DurationWheelPicker —— 竖屏滚轮式时长选择器（弹层版）
+ *
+ * 设计：
+ * - 外观是和其他表单 Select 一致的输入框（白色底+1px灰边+紫色focus ring）
+ * - 点击输入框弹出 Popover，内部是滚轮 picker（原生 scroll-snap，零依赖）
+ * - 滚轮样式：白底容器，选中行 #7c3aed 紫字加粗+浅紫背景条
+ * - 支持触摸/鼠标滚轮/点击；松手吸附；底部"确认/取消"按钮
+ * - 默认范围 15–30 秒，步长 1 秒
+ */
+import React, { useEffect, useMemo, useRef, useState, useCallback } from "react"
+import { Popover, Button } from "antd"
+import { DownOutlined } from "@ant-design/icons"
+import "./DurationWheelPicker.css"
+
+export interface DurationWheelPickerProps {
+  value?: number
+  min?: number
+  max?: number
+  step?: number
+  unit?: string
+  onChange?: (value: number) => void
+  placeholder?: string
+  disabled?: boolean
+  /** 弹层宽度，默认 160px */
+  popupWidth?: number
+  /** 弹层内滚轮高度，默认 180px */
+  wheelHeight?: number
+}
+
+const ITEM_HEIGHT = 36
+
+const DurationWheelPicker: React.FC<DurationWheelPickerProps> = ({
+  value = 20,
+  min = 15,
+  max = 30,
+  step = 1,
+  unit = "秒",
+  onChange,
+  placeholder = "请选择时长",
+  disabled = false,
+  popupWidth = 160,
+  wheelHeight = 180,
+}) => {
+  const options = useMemo(() => {
+    const arr: number[] = []
+    for (let v = min; v <= max; v += step) arr.push(v)
+    return arr
+  }, [min, max, step])
+
+  const [open, setOpen] = useState(false)
+  // 弹层内暂存值，点确认才提交
+  const [draft, setDraft] = useState<number>(value)
+  const listRef = useRef<HTMLUListElement>(null)
+  const scrollTimerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
+
+  useEffect(() => {
+    if (open) {
+      setDraft(value)
+      // 下一帧滚到当前值
+      requestAnimationFrame(() => scrollToValue(value, false))
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [open])
+
+  const scrollToValue = useCallback(
+    (v: number, smooth = true) => {
+      const list = listRef.current
+      if (!list) return
+      const idx = options.indexOf(v)
+      if (idx < 0) return
+      list.scrollTo({ top: idx * ITEM_HEIGHT, behavior: smooth ? "smooth" : "auto" })
+    },
+    [options],
+  )
+
+  const handleScroll = () => {
+    if (scrollTimerRef.current) clearTimeout(scrollTimerRef.current)
+    scrollTimerRef.current = setTimeout(() => {
+      const list = listRef.current
+      if (!list) return
+      const idx = Math.round(list.scrollTop / ITEM_HEIGHT)
+      const clamped = Math.max(0, Math.min(options.length - 1, idx))
+      const targetTop = clamped * ITEM_HEIGHT
+      if (Math.abs(list.scrollTop - targetTop) > 1) {
+        list.scrollTo({ top: targetTop, behavior: "smooth" })
+      }
+      setDraft(options[clamped])
+    }, 100)
+  }
+
+  const handleConfirm = () => {
+    onChange?.(draft)
+    setOpen(false)
+  }
+
+  const handleCancel = () => {
+    setOpen(false)
+  }
+
+  const handleItemClick = (v: number) => {
+    setDraft(v)
+    scrollToValue(v, true)
+  }
+
+  const maskTop = wheelHeight / 2 - ITEM_HEIGHT / 2
+
+  const wheel = (
+    <div className="dw-popup">
+      <div
+        className="dw-picker"
+        style={{ height: wheelHeight, width: popupWidth - 24 /* padding */ }}
+      >
+        <div className="dw-picker-mask" style={{ top: maskTop, height: ITEM_HEIGHT }} aria-hidden />
+        <div className="dw-picker-fade dw-picker-fade-top" aria-hidden />
+        <div className="dw-picker-fade dw-picker-fade-bottom" aria-hidden />
+        <ul
+          ref={listRef}
+          className="dw-picker-list"
+          onScroll={handleScroll}
+          style={{
+            paddingTop: wheelHeight / 2 - ITEM_HEIGHT / 2,
+            paddingBottom: wheelHeight / 2 - ITEM_HEIGHT / 2,
+          }}
+        >
+          {options.map((v) => {
+            const isActive = v === draft
+            return (
+              <li
+                key={v}
+                className={`dw-picker-item${isActive ? " dw-picker-item-active" : ""}`}
+                style={{ height: ITEM_HEIGHT, lineHeight: `${ITEM_HEIGHT}px` }}
+                onClick={() => handleItemClick(v)}
+                aria-selected={isActive}
+                role="option"
+              >
+                <span className="dw-picker-item-val">{v}</span>
+                <span className="dw-picker-item-unit">{unit}</span>
+              </li>
+            )
+          })}
+        </ul>
+      </div>
+      <div className="dw-popup-actions">
+        <Button size="small" onClick={handleCancel}>
+          取消
+        </Button>
+        <Button size="small" type="primary" onClick={handleConfirm}>
+          确认
+        </Button>
+      </div>
+    </div>
+  )
+
+  return (
+    <Popover
+      open={!disabled && open}
+      onOpenChange={(v) => setOpen(v)}
+      content={wheel}
+      trigger="click"
+      placement="bottomLeft"
+      overlayClassName="dw-popover"
+      overlayStyle={{ padding: 0 }}
+      overlayInnerStyle={{ padding: 0, borderRadius: 10 }}
+      destroyTooltipOnHide
+    >
+      <div
+        className={`dw-trigger${disabled ? " dw-trigger-disabled" : ""}${open ? " dw-trigger-open" : ""}`}
+        style={{ height: 36 }}
+      >
+        <span className={`dw-trigger-val${value != null ? "" : " dw-trigger-placeholder"}`}>
+          {value != null ? `${value}${unit}` : placeholder}
+        </span>
+        <DownOutlined className={`dw-trigger-arrow${open ? " dw-trigger-arrow-up" : ""}`} />
+      </div>
+    </Popover>
+  )
+}
+
+export default DurationWheelPicker

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"]
@@ -218,12 +229,12 @@ const PURPOSES = [
   "悬念短剧",
   "情绪短片",
 ]
-const DURATIONS = [15, 20, 30, 45, 60]
 const RATIOS = [
   { v: "9:16", label: "9:16 竖屏（抖音/视频号）" },
   { v: "16:9", label: "16:9 横屏（B站/YouTube）" },
   { v: "1:1", label: "1:1 方形（小红书）" },
 ]
+const DURATIONS = Array.from({ length: 16 }, (_, i) => 15 + i)
 /** 兜底模型列表（接口未返回时使用，字段与 ViralVideoModel 对齐；后端返回后自动覆盖） */
 const FALLBACK_VIDEO_MODELS: ViralVideoModel[] = [
   {
@@ -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) => {
@@ -437,7 +453,7 @@ const emptyTask = (id: string, title: string): TabTask => ({
   language: "中文(普通话)",
   viralStructure: STRUCTURES[0],
   marketingPurpose: "",
-  duration: 15,
+  duration: 20,
   persona: "",
   videoRatio: "9:16",
   videoModel: "seedance-2.5",
@@ -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">
@@ -2431,7 +2415,7 @@ const ViralVideoPage: React.FC = () => {
                     options={PURPOSES.map((i) => ({ value: i, label: i }))}
                   />
                 </div>
-                <div className="vv-form-row" style={{ gridColumn: "1 / -1" }}>
+                <div className="vv-form-row">
                   <label className="vv-label">文案视频时长</label>
                   <Select
                     className="vv-select"

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/__init__.py

@@ -0,0 +1,4 @@
+# -*- coding: utf-8 -*-
+"""V2 图片分析：火山OCR专用API + doubao-lite强约束JSON并行，单次pro VLM兜底。"""
+
+from .fast_path import analyze_image_v2, analyze_images_v2  # noqa: F401

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

@@ -0,0 +1,106 @@
+"""V2 结果组装（v8 叙述优先，大幅精简）。
+
+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
+
+logger = logging.getLogger(__name__)
+
+_VALID_TYPES = ("store", "product", "person", "scene")
+
+
+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
+
+
+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 _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"
+
+    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
+
+    summary = raw.get("summary_markdown")
+    summary = summary.strip() if isinstance(summary, str) else ""
+
+    image: dict[str, Any] = {
+        "type": typ,
+        "name": name,
+        "brand": brand,
+        "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 _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

@@ -0,0 +1,124 @@
+# -*- coding: utf-8 -*-
+"""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。
+
+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
+
+import logging
+import os
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Any
+
+from . import assembler, ocr_volc, vlm_fallback, vlm_fast_json
+
+logger = logging.getLogger(__name__)
+
+_IMG_WORKERS = int(os.environ.get("VISION_V2_IMG_WORKERS", "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"))
+
+
+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 _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]:
+    t0 = time.time()
+
+    fj_result: dict[str, Any] | None = None
+    ocr_result: list[str] = []
+    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)
+
+    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", idx, fast_elapsed)
+            return assembled
+
+    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)
+        logger.info("[vision.v2] 图片 #%d pro兜底命中 total=%.2fs", idx, time.time() - t0)
+        return pro_result
+
+    logger.warning("[vision.v2] 图片 #%d 全路径失败 elapsed=%.2fs", idx, time.time() - t0)
+    return _basic_failure(ocr_result, fast_elapsed, "v2_all_failed")
+
+
+def analyze_images_v2(img_urls: list[str]) -> list[dict[str, Any]]:
+    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 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)}
+        for fut in as_completed(future_to_idx):
+            idx = future_to_idx[fut]
+            try:
+                results[idx] = fut.result()
+            except Exception as e:  # noqa: BLE001
+                logger.warning("[vision.v2] 图片 #%d future异常: %s", idx, e, exc_info=True)
+                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 _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]  # 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/ocr_volc.py

@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+"""火山引擎 AI MediaKit OCR（同步）调用封装。
+
+接口：POST {mediakit_base_url}/tools-sync/ocr
+鉴权：Bearer {mediakit_api_key}
+请求体：{"image_url": "<公网可访问URL>"}  （部分版本也支持 image_base64）
+响应：{"code":0,"data":{"texts":[{"text":"...","bbox":[x,y,w,h],...},...],...}}
+
+目标：识别商品包装/Logo/水印上的文字，作为 fast_json VLM 的补充。
+返回值：识别到的文本字符串列表（失败返回 []）。
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_TIMEOUT = 8  # OCR 秒级返回，8s 绰绰有余
+
+
+def call_ocr(img_url: str, *, timeout: int = DEFAULT_TIMEOUT) -> list[str]:
+    """调用 MediaKit 同步 OCR，返回去重后的纯文本列表。
+
+    不做重试（外层降级逻辑负责）。失败/未配置返回空列表，不抛异常。
+    """
+    t0 = time.time()
+    try:
+        import httpx
+
+        from packages.shared.mediakit_client import get_mediakit_client
+
+        client = get_mediakit_client()
+        if not client.is_available:
+            logger.info("[vision.v2] mediakit 未配置，跳过 OCR")
+            return []
+
+        url = f"{client.base_url}/tools-sync/ocr"
+        headers = {
+            "Authorization": f"Bearer {client.api_key}",
+            "Content-Type": "application/json",
+        }
+        payload: dict[str, Any] = {"image_url": img_url}
+        # 部分文档版本用 image_base64，但公网 URL 场景下 image_url 最简
+        resp = httpx.post(url, headers=headers, json=payload, timeout=timeout)
+        elapsed = time.time() - t0
+        if resp.status_code != 200:
+            logger.warning(
+                "[vision.v2] OCR HTTP %d elapsed=%.1fs body=%s",
+                resp.status_code,
+                elapsed,
+                resp.text[:200],
+            )
+            return []
+        data = resp.json()
+        # 兼容几种可能的响应结构
+        code = data.get("code", data.get("status", 0))
+        if code not in (0, "OK", "success", 200):
+            logger.warning("[vision.v2] OCR 业务错误 code=%s elapsed=%.1fs resp=%s", code, elapsed, str(data)[:200])
+            return []
+        texts = _extract_texts(data)
+        # 去重 + 过滤空
+        seen: set[str] = set()
+        out: list[str] = []
+        for t in texts:
+            t = (t or "").strip()
+            if t and t not in seen and len(t) <= 100:  # 过滤过长的误识别
+                seen.add(t)
+                out.append(t)
+        logger.info("[vision.v2] OCR 完成 elapsed=%.1fs n=%d texts=%s", elapsed, len(out), out[:5])
+        return out
+    except Exception as e:
+        elapsed = time.time() - t0
+        logger.warning("[vision.v2] OCR 异常 elapsed=%.1fs err=%s", elapsed, e, exc_info=True)
+        return []
+
+
+def _extract_texts(data: dict) -> list[str]:
+    """从 OCR 响应中抽取文本，兼容多种结构。"""
+    out: list[str] = []
+    # 常见结构1: data.texts = [{"text": "..."}, ...]
+    d = data.get("data") or data
+    if isinstance(d, dict):
+        for key in ("texts", "lines", "words", "items", "result"):
+            items = d.get(key)
+            if isinstance(items, list):
+                for it in items:
+                    if isinstance(it, dict):
+                        txt = it.get("text") or it.get("content") or it.get("word")
+                        if txt:
+                            out.append(str(txt))
+                    elif isinstance(it, str):
+                        out.append(it)
+                break
+        # 结构2: data.text = "..."
+        if not out:
+            t = d.get("text")
+            if isinstance(t, str):
+                out.append(t)
+        # 结构3: data.ocr_text / data.content
+        if not out:
+            for key in ("ocr_text", "content", "raw_text"):
+                v = d.get(key)
+                if isinstance(v, str) and v.strip():
+                    out.append(v)
+                    break
+    return out

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

@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+"""V2 兜底路径：vision client（fallback 变体）单图调用，走 v8 叙述优先 prompt。
+
+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 logging
+import time
+from typing import Any
+
+from . import _prompt, assembler
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_TIMEOUT = 45
+
+
+def call_pro_vlm(
+    img_url: str,
+    idx: int,
+    *,
+    ocr_hint: list[str] | None = None,
+    timeout: int = _DEFAULT_TIMEOUT,
+    max_tokens: int | None = None,
+) -> dict[str, Any] | None:
+    t0 = time.time()
+    ocr_text = "、".join(t for t in (ocr_hint or []) if t)[:200]
+
+    try:
+        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
+
+    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},
+            ],
+        },
+    ]
+
+    try:
+        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,
+        }
+
+        from .json_utils import extract_json_object
+
+        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] pro 非JSON(100字) outer=%s: %s", _outer, raw[:100])
+
+        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
+        logger.info("[vision.v2] pro 完成 model=%s elapsed=%.1fs", client.model, elapsed)
+        return result
+    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

@@ -0,0 +1,111 @@
+# -*- coding: utf-8 -*-
+"""V2 快速路径：vision client（默认 image_analysis 能力）强约束 JSON-only 调用。
+
+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 logging
+import time
+from typing import Any
+
+from . import _prompt
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_TIMEOUT = 20
+
+
+def call_fast_json(
+    img_url: str,
+    *,
+    timeout: int = _DEFAULT_TIMEOUT,
+    max_tokens: int | None = None,
+) -> dict[str, Any] | None:
+    t0 = time.time()
+
+    try:
+        from packages.shared.ai_router import ai_router
+
+        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
+
+    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,
+            "timeout": timeout,
+            "enable_thinking": False,
+            "response_format": {"type": "json_object"},
+        }
+        if max_tokens is not None:
+            call_kwargs["max_tokens"] = max_tokens
+
+        from .json_utils import extract_json_object
+
+        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 type=%s",
+            client.model,
+            elapsed,
+            obj.get("type"),
+        )
+        return obj
+    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

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."""

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_service.py

@@ -0,0 +1,269 @@
+"""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（人物模板视频 URL） + audio_url（TTS 音频 URL） + script（文案原文）
+- 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
+- 不传 emotion/表情精细控制，使用默认 emo_global=4（中性）+ use_script_emo=true（关键词驱动表情）
+- 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)
+
+    @property
+    def is_configured(self) -> bool:
+        """配置是否完整（base_url + 默认模板视频都有值）."""
+        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: int = 6,
+    ) -> 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 = " "
+
+        payload = {
+            "video_url": driver_url,
+            "audio_url": audio_url,
+            "script": script,
+            "emo_global": emo_global,
+            "use_script_emo": use_script_emo,
+            "blend_frames": blend_frames,
+        }
+        url = f"{self.base_url}/generate"
+
+        last_exc: Optional[Exception] = None
+        for attempt in range(self.max_retries + 1):
+            try:
+                start = time.monotonic()
+                with httpx.Client(timeout=self.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.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（{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,
+    ) -> 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)
+        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

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 +30,189 @@ 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。"""
+)
 
-{GLOBAL_CONSTRAINTS}
+_STORYBOARD_USER = """<marketing_purpose>{marketing_purpose}</marketing_purpose>
+<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>
+<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 +221,7 @@ 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 +173,35 @@ 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（正面 5-10 秒循环、光线均匀、半身）。Ditto 模式下忽略
+    # 用户上传的驱动视频/图片，统一用该模板；后续可扩展为多模板让用户选择。
+    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 单次请求超时（秒）：数字人半身视频推理通常 30-120s
+    ditto_request_timeout: int = Field(
+        default=300,
+        validation_alias=AliasChoices("DITTO_REQUEST_TIMEOUT", "ditto_request_timeout"),
+    )
+
     # ── 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/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/viral_video.py

@@ -191,13 +191,45 @@ 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 +238,20 @@ 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",
@@ -589,28 +665,32 @@ 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},

tests/unit/test_ai_router.py

@@ -0,0 +1,395 @@
+"""AI Router 单元测试 — 23 cases covering routing/cache/fallback/client construction."""
+
+from __future__ import annotations
+
+import sys
+import unittest
+from dataclasses import dataclass
+from typing import Optional
+from unittest.mock import MagicMock, patch
+
+# ── Pre-mock heavy import chain to avoid pulling in full app ──
+_mock_config = MagicMock()
+_mock_settings = MagicMock()
+_mock_settings.doubao_model = "doubao-seed-2-1-pro-260915"
+_mock_settings.doubao_fast_model = "doubao-seed-2-1-pro-260915"
+_mock_settings.doubao_base_url = "https://ark.cn-beijing.volces.com/api/v3"
+_mock_settings.doubao_api_key = "test-key"
+_mock_settings.doubao_timeout = 45
+_mock_settings.doubao_max_retries = 1
+_mock_settings.doubao_image_model = "doubao-seedream-5-0-flash-260915"
+_mock_settings.doubao_image_timeout = 60
+_mock_settings.doubao_video_model = "doubao-seedance-2-5-260628"
+_mock_settings.doubao_video_timeout = 600
+_mock_settings.dashscope_api_key = "ds-key"
+_mock_settings.cosyvoice_api_key = "cv-key"
+_mock_settings.cosyvoice_base_url = "https://dashscope.aliyuncs.com/api/v1"
+_mock_settings.cosyvoice_model = "cosyvoice-v3-flash"
+_mock_settings.redis_url = "redis://localhost:6379/0"
+_mock_settings.celery_broker_url = "redis://localhost:6379/0"
+_mock_config.get_shared_settings.return_value = _mock_settings
+
+# Prevent the full packages.shared from loading
+for mod_name in list(sys.modules.keys()):
+    if "packages.shared" in mod_name and "ai_router" not in mod_name and "ai_config_version" not in mod_name:
+        pass  # don't remove, just prevent new imports
+
+# Direct import of our modules (bypassing __init__.py)
+import importlib.util
+import os
+
+
+def _load_module_from_file(name, path):
+    spec = importlib.util.spec_from_file_location(name, path)
+    mod = importlib.util.module_from_spec(spec)
+    sys.modules[name] = mod
+    spec.loader.exec_module(mod)
+    return mod
+
+
+# Load ai_config_version
+_ai_config_version = _load_module_from_file(
+    "packages.shared.ai_config_version",
+    os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "packages", "shared", "ai_config_version.py"),
+)
+# Patch get_shared_settings in the loaded module
+_ai_config_version.get_shared_settings = lambda: _mock_settings
+
+# Load ai_router - needs packages.shared.config to be available
+sys.modules["packages.shared.config"] = MagicMock()
+sys.modules["packages.shared.config"].get_shared_settings = lambda: _mock_settings
+
+# Mock packages.shared.ai_client to avoid triggering packages.shared.__init__ chain
+# (which fails on Python 3.10 due to datetime.UTC import in packages.domain)
+_mock_ai_client = MagicMock()
+
+class _FakeDoubaoClient:
+    """Fake DoubaoClient for testing - mimics the real interface."""
+    def __init__(self, api_key="", base_url="", model="", timeout=0, max_retries=0,
+                 max_tokens=None, temperature=None, extra_params=None, provider="volcengine"):
+        self.api_key = api_key
+        self.base_url = base_url
+        self.model = model
+        self.timeout = timeout
+        self.max_retries = max_retries
+        self.max_tokens = max_tokens
+        self.temperature = temperature
+        self.extra_params = extra_params or {}
+        self.provider = provider
+        self.vision_model = model
+
+    @property
+    def is_available(self):
+        return bool(self.api_key)
+
+    def chat_completion(self, messages, **kwargs):
+        return None
+
+    def vision_completion(self, messages, **kwargs):
+        return None
+
+_mock_ai_client.DoubaoClient = _FakeDoubaoClient
+sys.modules["packages.shared.ai_client"] = _mock_ai_client
+
+_ai_router = _load_module_from_file(
+    "packages.shared.ai_router",
+    os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "packages", "shared", "ai_router.py"),
+)
+
+
+class TestAIConfigVersion(unittest.TestCase):
+    """Redis 版本号机制测试"""
+
+    @patch.object(_ai_config_version, "_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 = _ai_config_version.bump_version()
+        self.assertTrue(ver)
+        self.assertTrue(ver.isdigit())
+        mock_r.set.assert_called_once()
+
+    @patch.object(_ai_config_version, "_get_redis_client")
+    def test_bump_version_redis_unavailable(self, mock_redis_fn):
+        mock_redis_fn.return_value = None
+        ver = _ai_config_version.bump_version()
+        self.assertEqual(ver, "")
+
+    @patch.object(_ai_config_version, "_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 = _ai_config_version.get_version()
+        self.assertEqual(ver, "1234567890")
+
+    @patch.object(_ai_config_version, "_get_redis_client")
+    def test_get_version_redis_down(self, mock_redis_fn):
+        mock_redis_fn.return_value = None
+        ver = _ai_config_version.get_version()
+        self.assertIsNone(ver)
+
+    @patch.object(_ai_config_version, "_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 = _ai_config_version.get_version()
+        self.assertIsNone(ver)
+
+
+class TestAIRouter(unittest.TestCase):
+    """AIRouter 路由/缓存/fallback 测试"""
+
+    def setUp(self):
+        self.router = _ai_router.AIRouter()
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_get_capability_db_unavailable(self, mock_ver):
+        with patch.object(_ai_router, "_get_session", return_value=None):
+            cap = self.router.get_capability("intent_parsing")
+            self.assertIsNone(cap)
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_get_capability_from_db(self, mock_ver):
+        mock_session = MagicMock()
+        mock_row = MagicMock()
+        mock_row.capability_key = "intent_parsing"
+        mock_row.capability_name = "文案意图解析"
+        mock_row.timeout_seconds = 45
+        mock_row.max_retries = 1
+        mock_row.max_tokens = None
+        mock_row.temperature = None
+        mock_row.concurrency = 2
+        mock_row.extra_params = {}
+        mock_row.is_enabled = True
+        mock_row.pm_id = "model-1"
+        mock_row.pm_name = "豆包"
+        mock_row.pm_provider = "volcengine"
+        mock_row.pm_model_key = "doubao-seed-1-6-250615"
+        mock_row.pm_api_key = "test-key"
+        mock_row.pm_api_base = "https://ark.test.com"
+        mock_row.pm_api_version = None
+        mock_row.pm_status = "active"
+        mock_row.lm_id = None
+        mock_row.fm_id = None
+        mock_session.execute.return_value.first.return_value = mock_row
+
+        with patch.object(_ai_router, "_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")
+
+    @patch.object(_ai_config_version, "get_version", side_effect=[None, "v2"])
+    def test_cache_invalidation_on_version_change(self, mock_ver):
+        with patch.object(self.router, "_load_from_db", return_value=None):
+            self.router.get_capability("test_key")
+        self.router._local_ver = "v1"
+        self.assertTrue(self.router._check_version())
+
+    @patch.object(_ai_config_version, "get_version", return_value="same_ver")
+    def test_cache_hit_same_version(self, mock_ver):
+        model = _ai_router.ModelConfig(
+            id="m1", name="test", provider="volcengine", model_key="test-model",
+            api_key="key", api_base="https://test.com", api_version=None, status="active",
+        )
+        cap = _ai_router.CapabilityConfig(
+            capability_key="test", capability_name="test", primary_model=model,
+            lite_model=None, fallback_model=None, timeout_seconds=30,
+            max_retries=1, max_tokens=None, temperature=None, concurrency=2,
+            extra_params={}, is_enabled=True,
+        )
+        self.router._cache["test"] = cap
+        self.router._local_ver = "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)
+
+    @patch.object(_ai_router, "_get_session", return_value=None)
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_get_llm_client_fallback(self, mock_ver, mock_session):
+        _ai_router.get_shared_settings = lambda: _mock_settings
+        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")
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_get_llm_client_from_db(self, mock_ver):
+        model = _ai_router.ModelConfig(
+            id="m1", name="test", provider="dashscope", model_key="qwen3.8-flash",
+            api_key="db-key", api_base="https://dashscope.test.com", api_version=None, status="active",
+        )
+        cap = _ai_router.CapabilityConfig(
+            capability_key="image_analysis", capability_name="图片分析",
+            primary_model=model, lite_model=None, fallback_model=None,
+            timeout_seconds=15, max_retries=1, max_tokens=350, temperature=0.1,
+            concurrency=2, extra_params={}, is_enabled=True,
+        )
+        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")
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_get_vision_client(self, mock_ver):
+        model = _ai_router.ModelConfig(
+            id="m1", name="test", provider="dashscope", model_key="qwen3.8-flash",
+            api_key="key", api_base="https://dashscope.test.com", api_version=None, status="active",
+        )
+        cap = _ai_router.CapabilityConfig(
+            capability_key="image_analysis", capability_name="图片分析",
+            primary_model=model, lite_model=None, fallback_model=None,
+            timeout_seconds=15, max_retries=1, max_tokens=None, temperature=None,
+            concurrency=2, extra_params={}, is_enabled=True,
+        )
+        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"))
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_get_tts_client(self, mock_ver):
+        model = _ai_router.ModelConfig(
+            id="m1", name="test", provider="dashscope", model_key="cosyvoice-v3-flash",
+            api_key="key", api_base="https://dashscope.test.com", api_version=None, status="active",
+        )
+        cap = _ai_router.CapabilityConfig(
+            capability_key="tts", capability_name="语音合成",
+            primary_model=model, lite_model=None, fallback_model=None,
+            timeout_seconds=60, max_retries=1, max_tokens=None, temperature=None,
+            concurrency=2, extra_params={}, is_enabled=True,
+        )
+        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")
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_get_image_gen_client(self, mock_ver):
+        model = _ai_router.ModelConfig(
+            id="m1", name="test", provider="volcengine", model_key="seedream-5.0-flash",
+            api_key="key", api_base="https://ark.test.com", api_version=None, status="active",
+        )
+        cap = _ai_router.CapabilityConfig(
+            capability_key="image_generation", capability_name="图片生成",
+            primary_model=model, lite_model=None, fallback_model=None,
+            timeout_seconds=60, max_retries=1, max_tokens=None, temperature=None,
+            concurrency=2, extra_params={"size": "1K"}, is_enabled=True,
+        )
+        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")
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_get_video_gen_client(self, mock_ver):
+        model = _ai_router.ModelConfig(
+            id="m1", name="test", provider="volcengine", model_key="seedance-2.5",
+            api_key="key", api_base="https://ark.test.com", api_version=None, status="active",
+        )
+        cap = _ai_router.CapabilityConfig(
+            capability_key="video_generation", capability_name="视频生成",
+            primary_model=model, lite_model=None, fallback_model=None,
+            timeout_seconds=600, max_retries=1, max_tokens=None, temperature=None,
+            concurrency=1, extra_params={}, is_enabled=True,
+        )
+        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")
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_lite_variant_preference(self, mock_ver):
+        primary = _ai_router.ModelConfig(id="p1", name="pro", provider="volcengine", model_key="pro-model", api_key="k", api_base="u", api_version=None, status="active")
+        lite = _ai_router.ModelConfig(id="l1", name="lite", provider="volcengine", model_key="lite-model", api_key="k", api_base="u", api_version=None, status="active")
+        cap = _ai_router.CapabilityConfig(
+            capability_key="image_analysis", capability_name="图片分析",
+            primary_model=primary, lite_model=lite, fallback_model=None,
+            timeout_seconds=15, max_retries=1, max_tokens=None, temperature=None,
+            concurrency=2, extra_params={}, is_enabled=True,
+        )
+        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")
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_disabled_capability_returns_fallback(self, mock_ver):
+        cap = _ai_router.CapabilityConfig(
+            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=False,
+        )
+        _ai_router.get_shared_settings = lambda: _mock_settings
+        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")
+
+    @patch.object(_ai_config_version, "get_version", return_value=None)
+    def test_fallback_chain_primary_none(self, mock_ver):
+        """primary_model 为 None 时 fallback 到 fallback_model"""
+        fb = _ai_router.ModelConfig(id="f1", name="fb", provider="volcengine", model_key="fb-model", api_key="k", api_base="u", api_version=None, status="active")
+        cap = _ai_router.CapabilityConfig(
+            capability_key="test", capability_name="test",
+            primary_model=None, lite_model=None, fallback_model=fb,
+            timeout_seconds=30, max_retries=1, max_tokens=None, temperature=None,
+            concurrency=2, extra_params={}, is_enabled=True,
+        )
+        model = self.router._get_model_or_fallback(cap, "primary")
+        self.assertEqual(model.model_key, "fb-model")
+
+
+class TestModelConfig(unittest.TestCase):
+    """数据类测试"""
+
+    def test_model_config_frozen(self):
+        m = _ai_router.ModelConfig(id="1", name="t", provider="p", model_key="k", api_key="a", api_base="b", api_version=None, status="active")
+        with self.assertRaises(AttributeError):
+            m.model_key = "new"
+
+    def test_capability_config_frozen(self):
+        c = _ai_router.CapabilityConfig(
+            capability_key="k", capability_name="n", 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,
+        )
+        with self.assertRaises(AttributeError):
+            c.is_enabled = False
+
+
+class TestClientAvailability(unittest.TestCase):
+    """客户端可用性测试"""
+
+    def test_tts_client_available(self):
+        c = _ai_router.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.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.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.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_service.py

@@ -0,0 +1,197 @@
+"""Ditto 蚂蚁数字人客户端单元测试 — #2076."""
+
+from __future__ import annotations
+
+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"] == " "

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_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 +350,29 @@ 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="5-15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>
+<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
     def mock_job(self):
         return ViralVideoJob(
@@ -364,23 +387,27 @@ 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
@@ -390,46 +417,40 @@ 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
@@ -501,7 +522,6 @@ 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 +532,6 @@ class TestPipelineIntegration:
         mock_get_repo,
         mock_img_analysis,
         mock_video_analysis,
-        mock_intent,
         mock_script,
         mock_review,
         mock_tts,
@@ -539,8 +558,6 @@ 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 +570,8 @@ 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_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

@@ -431,7 +431,7 @@ 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 +441,8 @@ 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 +451,31 @@ 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

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_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"