style: auto-format with black + isort + ruff + prettier [skip ci-format-check]

- 新增 _resolve_tts_voice_id(job)：voice_source 为 my_voice/clone 时，
  前端传的 voice_id 是 VoiceCloneProfile.id，查 DB 取 profile.voice_id（CosyVoice 真正的克隆音色ID）
- profile 不存在/未就绪/voice_id为空时回退默认 longxiaochun_v3，任务不崩
- preset 或 voice_source 为空时，voice_id 直接当 CosyVoice 音色名透传（与原有行为一致）
- _step_tts 改用 _resolve_tts_voice_id 解析后的 voice_id 调 synthesize
- 不改 DB schema，不加字段；voice_source/voice_id schema 已在之前合入

.env.example

@@ -211,10 +211,24 @@ COSYVOICE_CLONE_MODEL=voice-enrollment
 # 用于 AI 文案生成、智能剪辑等需要大模型能力的场景
 
 DOUBAO_API_KEY=your-doubao-api-key
-DOUBAO_MODEL=doubao-seed-1-6-250615
+DOUBAO_MODEL=doubao-seed-2-1-pro-260915
+DOUBAO_FAST_MODEL=doubao-seed-2-1-lite-260915
 DOUBAO_BASE_URL=https://ark.cn-beijing.volces.com/api/v3
 DOUBAO_TIMEOUT=30
 DOUBAO_MAX_RETRIES=2
+# 视觉模型：pro 精度高，lite 速度快（viral-video 商品识别默认用 lite 提速）
+DOUBAO_VISION_MODEL=doubao-seed-2-1-pro-260915
+DOUBAO_VISION_LITE_MODEL=doubao-seed-2-1-lite-260915
+DOUBAO_VISION_USE_LITE=true
+# Embedding 向量化模型
+DOUBAO_EMBEDDING_MODEL=doubao-embedding-vision-251215
+# 视频模型（Seedance 2.5，统一走方舟；真人参考图通过信任链自动 AI 化）
+DOUBAO_VIDEO_MODEL=doubao-seedance-2-5-260628
+DOUBAO_VIDEO_TIMEOUT=480
+DOUBAO_VIDEO_POLL_INTERVAL=10
+# 图片模型（Seedream 5.0 Pro，用于信任链真人 AI 化 + 文生图）
+DOUBAO_IMAGE_MODEL=doubao-seedream-5-0-pro-260628
+DOUBAO_IMAGE_TIMEOUT=120
 
 # ==================== 积分/会员系统 (#1895) ====================
 # 积分系统总开关：默认 false（暂停积分系统）。

.gitea/workflows/ci-pipeline.yml

@@ -1187,6 +1187,14 @@ jobs:
         DOUBAO_MODEL: "${{ secrets.DOUBAO_MODEL }}"
         DOUBAO_BASE_URL: "${{ secrets.DOUBAO_BASE_URL }}"
         DOUBAO_VISION_MODEL: "${{ secrets.DOUBAO_VISION_MODEL }}"
+        DOUBAO_VISION_LITE_MODEL: "${{ secrets.DOUBAO_VISION_LITE_MODEL }}"
+        DOUBAO_VISION_USE_LITE: "${{ secrets.DOUBAO_VISION_USE_LITE }}"
+        DOUBAO_IMAGE_MODEL: "${{ secrets.DOUBAO_IMAGE_MODEL }}"
+        DOUBAO_IMAGE_SIZE: "${{ secrets.DOUBAO_IMAGE_SIZE }}"
+        DOUBAO_IMAGE_TIMEOUT: "${{ secrets.DOUBAO_IMAGE_TIMEOUT }}"
+        DOUBAO_FAST_MODEL: "${{ secrets.DOUBAO_FAST_MODEL }}"
+        DOUBAO_TIMEOUT: "${{ secrets.DOUBAO_TIMEOUT }}"
+        DOUBAO_MAX_RETRIES: "${{ secrets.DOUBAO_MAX_RETRIES }}"
         WECHAT_APP_ID: "${{ secrets.WECHAT_APP_ID }}"
         WECHAT_APP_SECRET: "${{ secrets.WECHAT_APP_SECRET }}"
         TIKHUB_API_KEY: "${{ secrets.TIKHUB_API_KEY }}"

alembic/versions/086_add_viral_video_tables.py

@@ -0,0 +1,100 @@
+"""add viral video tables
+
+Revision ID: 086_add_viral_video_tables
+Revises: 085_atom_clip_caption_embedding
+Create Date: 2026-09-28
+
+新增爆款视频相关表：
+- viral_video_jobs: 爆款视频任务
+- viral_video_style_templates: 风格模板配置
+- viral_video_prompt_templates: Prompt 模板（由 #2040 seed）
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "086_add_viral_video_tables"
+down_revision = "085_atom_clip_caption_embedding"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # viral_video_jobs
+    op.create_table(
+        "viral_video_jobs",
+        sa.Column("id", sa.String(36), primary_key=True),
+        sa.Column("user_id", sa.String(36), nullable=False, index=True),
+        sa.Column("images", sa.JSON(), nullable=False, server_default="[]"),
+        sa.Column("industry", sa.String(100), nullable=False, server_default=""),
+        sa.Column("target_customer", sa.String(500), nullable=False, server_default=""),
+        sa.Column("persona_id", sa.String(36), nullable=False, server_default=""),
+        sa.Column("viral_structure", sa.String(50), nullable=False, server_default=""),
+        sa.Column("marketing_purpose", sa.String(100), nullable=False, server_default=""),
+        sa.Column("bgm_preference", sa.String(50), nullable=False, server_default=""),
+        sa.Column("duration", sa.Integer(), nullable=False, server_default="30"),
+        sa.Column("user_copy_text", sa.Text(), nullable=False, server_default=""),
+        sa.Column("fusion_level", sa.String(20), nullable=False, server_default="ai_polish"),
+        sa.Column("reference_audio_path", sa.String(1000), nullable=False, server_default=""),
+        # v1.3 新增
+        sa.Column("reference_video_url", sa.String(1000), nullable=False, server_default=""),
+        sa.Column("style_strength", sa.String(20), nullable=False, server_default="medium"),
+        sa.Column("style_guide", sa.JSON(), nullable=True),
+        sa.Column("style_template_id", sa.String(36), nullable=False, server_default="", index=True),
+        # 状态与结果
+        sa.Column("status", sa.String(30), nullable=False, server_default="pending", index=True),
+        sa.Column("intent_result", sa.JSON(), nullable=True),
+        sa.Column("result_video_url", sa.String(1000), nullable=False, server_default=""),
+        sa.Column("credits_cost", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("error_msg", sa.Text(), nullable=False, server_default=""),
+        sa.Column("retry_count", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("started_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column("completed_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.func.now()),
+        sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.func.now()),
+    )
+
+    # viral_video_style_templates
+    op.create_table(
+        "viral_video_style_templates",
+        sa.Column("id", sa.String(36), primary_key=True),
+        sa.Column("name", sa.String(200), nullable=False),
+        sa.Column("description", sa.Text(), nullable=False, server_default=""),
+        sa.Column("thumbnail_url", sa.String(1000), nullable=False, server_default=""),
+        sa.Column("style_config", sa.JSON(), nullable=False, server_default="{}"),
+        sa.Column("is_system", sa.Boolean(), nullable=False, server_default=sa.text("true"), index=True),
+        sa.Column("sort_order", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.func.now()),
+        sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.func.now()),
+    )
+
+    # viral_video_prompt_templates
+    op.create_table(
+        "viral_video_prompt_templates",
+        sa.Column("id", sa.String(36), primary_key=True),
+        sa.Column("prompt_type", sa.String(50), nullable=False, index=True),
+        sa.Column("name", sa.String(200), nullable=False),
+        sa.Column("content", sa.Text(), nullable=False, server_default=""),
+        sa.Column("variables", sa.JSON(), nullable=False, server_default="[]"),
+        sa.Column("version", sa.Integer(), nullable=False, server_default="1"),
+        sa.Column("is_active", sa.Boolean(), nullable=False, server_default=sa.text("true"), index=True),
+        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.func.now()),
+        sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False, server_default=sa.func.now()),
+    )
+
+    # Seed 默认风格模板
+    op.execute("""
+        INSERT INTO viral_video_style_templates (id, name, description, style_config, is_system, sort_order)
+        VALUES
+            ('style-tpl-001', '快节奏冲击', '高频切镜+动感BGM，适合食品饮料等快消品', '{"cut_speed": "fast", "transition": "jump_cut", "energy": "high"}', true, 1),
+            ('style-tpl-002', '质感慢镜', '慢节奏+电影感调色，适合美妆护肤珠宝', '{"cut_speed": "slow", "transition": "dissolve", "energy": "low", "color_grade": "cinematic"}', true, 2),
+            ('style-tpl-003', '口播种草', '数字人口播+产品特写穿插', '{"cut_speed": "medium", "transition": "cross_dissolve", "has_talking_head": true}', true, 3),
+            ('style-tpl-004', '场景叙事', '多场景切换+故事线叙述', '{"cut_speed": "medium", "transition": "wipe", "narrative": true}', true, 4)
+        """)
+
+
+def downgrade() -> None:
+    op.drop_table("viral_video_prompt_templates")
+    op.drop_table("viral_video_style_templates")
+    op.drop_table("viral_video_jobs")

alembic/versions/087_viral_video_image_analysis.py

@@ -0,0 +1,25 @@
+"""viral video add image_analysis column
+
+Revision ID: 087_viral_video_image_analysis
+Revises: 086_add_viral_video_tables
+Create Date: 2026-09-30
+
+#2106 爆款视频 P0：持久化图片分析结果（image_analysis JSON），供 resume 阶段使用。
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "087_viral_video_image_analysis"
+down_revision = "086_add_viral_video_tables"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column("viral_video_jobs", sa.Column("image_analysis", sa.JSON(), nullable=True))
+
+
+def downgrade() -> None:
+    op.drop_column("viral_video_jobs", "image_analysis")

alembic/versions/088_viral_video_copy_result.py

@@ -0,0 +1,51 @@
+"""viral video add copy_result + voice/video columns
+
+Revision ID: 088_viral_video_copy_result
+Revises: 087_viral_video_image_analysis
+Create Date: 2026-10-01
+
+v1.6 爆款视频字段补齐：
+- copy_result JSON: 编导分镜脚本完整结构（overview/scene_and_lighting/shots/hard_constraints/negative_prompts/voiceover_script）
+- voice_id/voice_source: TTS 音色参数
+- video_ratio/video_model: Seedance 视频比例/模型
+注意：线上启动也有幂等 ADD COLUMN 补列逻辑 (_ensure_viral_video_columns)，本 migration 提供标准 Alembic 路径，
+两套机制互不冲突（IF NOT EXISTS 等价行为）。
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "088_viral_video_copy_result"
+down_revision = "087_viral_video_image_analysis"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # 幂等添加列（通过单独执行 + 异常忽略兼容已由 backfill 补上的环境）
+    cols = [
+        ("voice_id", "VARCHAR(200) NOT NULL DEFAULT ''"),
+        ("voice_source", "VARCHAR(20) NOT NULL DEFAULT ''"),
+        ("video_ratio", "VARCHAR(10) NOT NULL DEFAULT '9:16'"),
+        ("video_model", "VARCHAR(100) NOT NULL DEFAULT ''"),
+        ("copy_result", "JSON"),
+    ]
+    conn = op.get_bind()
+    for name, ddl in cols:
+        try:
+            conn.execute(sa.text(f"ALTER TABLE viral_video_jobs ADD COLUMN IF NOT EXISTS {name} {ddl}"))
+        except Exception:
+            # 不支持 IF NOT EXISTS 的库（如老版本 SQLite）直接尝试 ADD COLUMN，失败则忽略
+            try:
+                conn.execute(sa.text(f"ALTER TABLE viral_video_jobs ADD COLUMN {name} {ddl}"))
+            except Exception:
+                pass
+
+
+def downgrade() -> None:
+    for name in ("copy_result", "video_model", "video_ratio", "voice_source", "voice_id"):
+        try:
+            op.drop_column("viral_video_jobs", name)
+        except Exception:
+            pass

alembic/versions/089_viral_video_cols.py

@@ -0,0 +1,62 @@
+"""viral video add storyboard + generated_copy_text (complement 088)
+
+Revision ID: 089_viral_video_cols
+Revises: 088_viral_video_copy_result
+Create Date: 2026-10-01
+
+#2129 兜底迁移：补齐 _VIRAL_VIDEO_BACKFILL_COLS 中所有列，覆盖
+# watchtower 自动部署未跑历史 migration、且 AUTO_CREATE_SCHEMA=false 时
+# _ensure_viral_video_columns 未执行的场景。
+# 幂等 ADD COLUMN IF NOT EXISTS，已存在则跳过。
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "089_viral_video_cols"
+down_revision = "088_viral_video_copy_result"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # 扩展 alembic_version.version_num 字段长度（原来 VARCHAR(32) 装不下长 revision id）
+    conn = op.get_bind()
+    try:
+        conn.execute(sa.text("ALTER TABLE alembic_version ALTER COLUMN version_num TYPE VARCHAR(256)"))
+    except Exception:
+        pass
+    cols = [
+        ("storyboard", "JSON"),
+        ("generated_copy_text", "TEXT NOT NULL DEFAULT ''"),
+        ("voice_id", "VARCHAR(200) NOT NULL DEFAULT ''"),
+        ("voice_source", "VARCHAR(20) NOT NULL DEFAULT ''"),
+        ("video_ratio", "VARCHAR(10) NOT NULL DEFAULT '9:16'"),
+        ("video_model", "VARCHAR(100) NOT NULL DEFAULT ''"),
+        ("copy_result", "JSON"),
+    ]
+    for name, ddl in cols:
+        try:
+            conn.execute(sa.text(f"ALTER TABLE viral_video_jobs ADD COLUMN IF NOT EXISTS {name} {ddl}"))
+        except Exception:
+            try:
+                conn.execute(sa.text(f"ALTER TABLE viral_video_jobs ADD COLUMN {name} {ddl}"))
+            except Exception:
+                pass
+
+
+def downgrade() -> None:
+    for name in (
+        "copy_result",
+        "video_model",
+        "video_ratio",
+        "voice_source",
+        "voice_id",
+        "generated_copy_text",
+        "storyboard",
+    ):
+        try:
+            op.drop_column("viral_video_jobs", name)
+        except Exception:
+            pass

alembic/versions/090_viral_video_phase_msg.py

@@ -0,0 +1,35 @@
+"""viral video add phase_message column (#2134)
+
+Revision ID: 090_viral_video_phase_msg
+Revises: 089_viral_video_cols
+Create Date: 2026-10-02
+
+#2134 阶段细粒度提示：viral_video 表新增 phase_message 列（中文阶段提示文案）。
+current_stage 列已在之前版本存在，本迁移只补 phase_message。
+幂等 ADD COLUMN IF NOT EXISTS。
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "090_viral_video_phase_msg"
+down_revision = "089_viral_video_cols"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # SQLite/PostgreSQL 兼容的幂等添加列
+    conn = op.get_bind()
+    inspector = sa.inspect(conn)
+    cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
+    if "phase_message" not in cols:
+        op.add_column(
+            "viral_video_jobs",
+            sa.Column("phase_message", sa.String(length=500), nullable=False, server_default=""),
+        )
+
+
+def downgrade() -> None:
+    op.drop_column("viral_video_jobs", "phase_message")

alembic/versions/091_viral_video_stage.py

@@ -0,0 +1,49 @@
+"""viral video add current_stage column (#2137 follow-up)
+
+Revision ID: 091_viral_video_stage
+Revises: 090_viral_video_phase_msg
+Create Date: 2026-10-02
+
+#2137 follow-up fix: 090 migration missed current_stage column on viral_video_jobs,
+causing UndefinedColumn errors and 500s on all authenticated viral-video endpoints.
+Idempotently add current_stage and double-check phase_message.
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "091_viral_video_stage"
+down_revision = "090_viral_video_phase_msg"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    inspector = sa.inspect(conn)
+    cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
+    if "current_stage" not in cols:
+        op.add_column(
+            "viral_video_jobs",
+            sa.Column(
+                "current_stage",
+                sa.String(length=200),
+                nullable=False,
+                server_default="",
+            ),
+        )
+    if "phase_message" not in cols:
+        op.add_column(
+            "viral_video_jobs",
+            sa.Column(
+                "phase_message",
+                sa.String(length=500),
+                nullable=False,
+                server_default="",
+            ),
+        )
+
+
+def downgrade() -> None:
+    op.drop_column("viral_video_jobs", "current_stage")

alembic/versions/092_viral_video_heartbeat.py

@@ -0,0 +1,42 @@
+"""viral_video_jobs 增加 heartbeat_at 列（worker 心跳，用于僵尸任务超时回收）
+
+Revision ID: 092_viral_video_heartbeat
+Revises: 091_viral_video_stage
+Create Date: 2026-10-02
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "092_viral_video_heartbeat"
+down_revision = "091_viral_video_stage"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    inspector = sa.inspect(conn)
+    cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
+    if "heartbeat_at" not in cols:
+        op.add_column("viral_video_jobs", sa.Column("heartbeat_at", sa.DateTime(), nullable=True))
+        op.execute(
+            "UPDATE viral_video_jobs SET heartbeat_at = updated_at " "WHERE status = 'running' AND heartbeat_at IS NULL"
+        )
+        try:
+            op.create_index("ix_viral_video_jobs_heartbeat_at", "viral_video_jobs", ["heartbeat_at"])
+        except Exception:
+            pass
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+    inspector = sa.inspect(conn)
+    cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
+    if "heartbeat_at" in cols:
+        try:
+            op.drop_index("ix_viral_video_jobs_heartbeat_at", table_name="viral_video_jobs")
+        except Exception:
+            pass
+        op.drop_column("viral_video_jobs", "heartbeat_at")

alembic/versions/093_viral_video_pricing_points_float.py

@@ -0,0 +1,87 @@
+"""viral_video 动态积分定价 + 积分字段从 Integer 改为 Float (#2151)
+
+Revision ID: 093
+Revises: 092_viral_video_heartbeat
+Create Date: 2026-10-02
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "093"
+down_revision = "092_viral_video_heartbeat"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    inspector = sa.inspect(conn)
+
+    # 1) points_accounts 三列 Integer -> Float
+    pa_cols = {c["name"]: c for c in inspector.get_columns("points_accounts")}
+    for col in ("balance", "total_earned", "total_spent"):
+        if col in pa_cols:
+            op.alter_column(
+                "points_accounts",
+                col,
+                existing_type=sa.Integer(),
+                type_=sa.Float(),
+                existing_nullable=False,
+            )
+
+    # 2) points_transactions amount/balance_after Integer -> Float
+    pt_cols = {c["name"]: c for c in inspector.get_columns("points_transactions")}
+    for col in ("amount", "balance_after"):
+        if col in pt_cols:
+            op.alter_column(
+                "points_transactions",
+                col,
+                existing_type=sa.Integer(),
+                type_=sa.Float(),
+                existing_nullable=False,
+            )
+
+    # 3) users.points_balance Integer -> Float
+    user_cols = {c["name"]: c for c in inspector.get_columns("users")}
+    if "points_balance" in user_cols:
+        op.alter_column(
+            "users",
+            "points_balance",
+            existing_type=sa.Integer(),
+            type_=sa.Float(),
+            existing_nullable=False,
+        )
+
+    # 4) viral_video_jobs.credits_cost Integer -> Float
+    vv_cols = {c["name"]: c for c in inspector.get_columns("viral_video_jobs")}
+    if "credits_cost" in vv_cols:
+        op.alter_column(
+            "viral_video_jobs",
+            "credits_cost",
+            existing_type=sa.Integer(),
+            type_=sa.Float(),
+            existing_nullable=False,
+        )
+
+    # 5) viral_video_jobs 新增列
+    if "video_resolution" not in vv_cols:
+        op.add_column(
+            "viral_video_jobs",
+            sa.Column("video_resolution", sa.String(20), nullable=False, server_default="720p"),
+        )
+    if "credits_prepaid" not in vv_cols:
+        op.add_column(
+            "viral_video_jobs",
+            sa.Column("credits_prepaid", sa.Float(), nullable=False, server_default="0"),
+        )
+    if "credits_transaction_id" not in vv_cols:
+        op.add_column(
+            "viral_video_jobs",
+            sa.Column("credits_transaction_id", sa.String(36), nullable=False, server_default=""),
+        )
+
+
+def downgrade() -> None:
+    pass

alembic/versions/094_viral_video_pre_trusted.py

@@ -0,0 +1,31 @@
+"""viral_video_jobs 增加 pre_trusted_images 列（信任链Seedream预热结果）
+
+Revision ID: 094_viral_video_pre_trusted
+Revises: 093_viral_video_pricing_points_float
+Create Date: 2026-10-04
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "094_viral_video_pre_trusted"
+down_revision = "093"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    inspector = sa.inspect(conn)
+    cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
+    if "pre_trusted_images" not in cols:
+        op.add_column("viral_video_jobs", sa.Column("pre_trusted_images", sa.Text(), nullable=True))
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+    inspector = sa.inspect(conn)
+    cols = {c["name"] for c in inspector.get_columns("viral_video_jobs")}
+    if "pre_trusted_images" in cols:
+        op.drop_column("viral_video_jobs", "pre_trusted_images")

alembic/versions/095_viral_video_prompt_templates.py

@@ -0,0 +1,102 @@
+"""爆款视频 Prompt 模板配置表（#2040）。
+
+086 曾预留同名旧表（id varchar / content / variables json），从未被业务使用；
+本迁移将其替换为 #2040 新结构。
+
+Revision ID: 095_viral_video_prompt_templates
+Revises: 094_viral_video_pre_trusted
+Create Date: 2026-10-04
+"""
+
+import sqlalchemy as sa
+
+from alembic import op
+
+revision = "095_viral_video_prompt_templates"
+down_revision = "094_viral_video_pre_trusted"
+branch_labels = None
+depends_on = None
+
+
+def _table_exists(conn, name: str) -> bool:
+    return name in sa.inspect(conn).get_table_names()
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    # 086 预留的旧结构表：先删除（无业务数据、无任何引用）
+    if _table_exists(conn, "viral_video_prompt_templates"):
+        op.drop_table("viral_video_prompt_templates")
+
+    op.create_table(
+        "viral_video_prompt_templates",
+        sa.Column("id", sa.Integer, primary_key=True, autoincrement=True),
+        sa.Column("name", sa.String(128), nullable=False),
+        sa.Column("prompt_type", sa.String(32), nullable=False),
+        sa.Column("version", sa.Integer, nullable=False, server_default="1"),
+        sa.Column("system_prompt", sa.Text, nullable=False),
+        sa.Column("user_prompt_template", sa.Text, nullable=False),
+        sa.Column("example_output", sa.Text, nullable=True),
+        sa.Column("is_active", sa.Boolean, nullable=False, server_default=sa.text("true")),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.func.now(),
+            nullable=False,
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.func.now(),
+            nullable=False,
+        ),
+    )
+    op.create_index(
+        "ix_vvpt_type_active",
+        "viral_video_prompt_templates",
+        ["prompt_type", "is_active"],
+    )
+    op.create_index(
+        "uq_vvpt_type_version",
+        "viral_video_prompt_templates",
+        ["prompt_type", "version"],
+        unique=True,
+    )
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+    if _table_exists(conn, "viral_video_prompt_templates"):
+        op.drop_index("uq_vvpt_type_version", table_name="viral_video_prompt_templates")
+        op.drop_index("ix_vvpt_type_active", table_name="viral_video_prompt_templates")
+        op.drop_table("viral_video_prompt_templates")
+
+    # 恢复 086 的旧预留结构
+    op.create_table(
+        "viral_video_prompt_templates",
+        sa.Column("id", sa.String(36), primary_key=True),
+        sa.Column("prompt_type", sa.String(50), nullable=False, index=True),
+        sa.Column("name", sa.String(200), nullable=False),
+        sa.Column("content", sa.Text, nullable=False, server_default=""),
+        sa.Column("variables", sa.JSON, nullable=False, server_default="[]"),
+        sa.Column("version", sa.Integer, nullable=False, server_default="1"),
+        sa.Column(
+            "is_active",
+            sa.Boolean,
+            nullable=False,
+            server_default=sa.text("true"),
+            index=True,
+        ),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.func.now(),
+        ),
+    )

apps/api/app/api/router.py

@@ -36,6 +36,7 @@ from app.api.routes.titles import router as titles_router
 from app.api.routes.tts import router as tts_router
 from app.api.routes.upload import router as upload_router
 from app.api.routes.videos import router as videos_router
+from app.api.routes.viral_video import router as viral_video_router
 from app.api.routes.voice_clones import router as voice_clones_router
 from app.api.routes.voices import router as voices_router
 from fastapi import APIRouter
@@ -240,3 +241,4 @@ api_router.include_router(
     prefix="/gpu",
     tags=["GPU Worker"],
 )
+api_router.include_router(viral_video_router, prefix="/viral-video", tags=["爆款视频"])

apps/api/app/api/routes/ai_avatar_render.py

@@ -29,8 +29,6 @@ from app.services.ai_avatar_render_service import (
 from fastapi import APIRouter, Depends, HTTPException, Query
 from sqlalchemy.orm import Session
 
-from packages.middleware.points_gate import points_gate
-
 logger = logging.getLogger(__name__)
 
 router = APIRouter()
@@ -44,7 +42,6 @@ def _get_service(db: Session = Depends(get_db_session)) -> AiAvatarRenderService
 
 
 @router.post("", response_model=AiAvatarRenderJobResponse, status_code=201)
-@points_gate("ai_digital_human", per_unit=15)
 def create_render_job(
     body: CreateAiAvatarRenderRequest,
     current_user: AuthenticatedUser = Depends(get_current_user),

apps/api/app/api/routes/generation_cover.py

@@ -27,7 +27,6 @@ from packages.adapters.sqlalchemy_impl.generation_task_repository import (
 )
 from packages.application import ListGeneratedVideosByTaskUseCase
 from packages.domain.config_schemas import normalize_plan_config
-from packages.middleware.points_gate import points_gate
 from packages.shared.storage import get_shared_storage_service
 
 from .templates_editor.dependencies import get_draft_plan_id, get_editor_services
@@ -346,7 +345,6 @@ def _is_trusted_media_url(url: str) -> bool:
 
 
 @router.post("/generate-cover", response_model=GenerateCoverResponse)
-@points_gate("ai_cover")
 def generate_cover(
     body: GenerateCoverRequest,
     template_id: str = Query(..., description="模板 ID"),

apps/api/app/api/routes/generation_preview.py

@@ -11,9 +11,7 @@ from app.auth import AuthenticatedUser, get_current_user
 from app.core.storage import get_storage_service
 from app.core.task_enqueue import (
     GLOBAL_PENDING_LIMIT,
-    USER_PENDING_LIMIT,
     GlobalQueueFull,
-    UserPendingLimitExceeded,
     build_rate_limit_detail,
     safe_enqueue_generation_task,
 )
@@ -43,7 +41,6 @@ from packages.application import (
     GetGenerationTaskUseCase,
     ListGeneratedVideosByTaskUseCase,
 )
-from packages.middleware.points_gate import points_gate
 
 logger = logging.getLogger(__name__)
 
@@ -272,7 +269,6 @@ def _variant_value(values: list[str], index: int, fallback: str = "") -> str:
 
 
 @router.post("/preview", response_model=BatchPreviewGenerationTaskResponse, status_code=201)
-@points_gate("ai_video", quantity_field="preview_count")
 def create_preview_generation_task(
     request: CreatePreviewGenerationTaskRequest,
     authenticated_user: AuthenticatedUser = Depends(get_current_user),
@@ -302,26 +298,17 @@ def create_preview_generation_task(
         count,
     )
 
-    # 预检查队列限流（按变体总数计）
-    try:
-        user_pending = generation_task_repository.count_pending_by_user(user_id)
-        global_pending = generation_task_repository.count_pending_total()
-        if user_pending + count > USER_PENDING_LIMIT:
-            raise UserPendingLimitExceeded(
-                user_id=user_id, pending_count=user_pending + count, limit=USER_PENDING_LIMIT
-            )
-        if global_pending + count > GLOBAL_PENDING_LIMIT:
-            raise GlobalQueueFull(pending_count=global_pending + count, limit=GLOBAL_PENDING_LIMIT)
-    except UserPendingLimitExceeded as e:
-        raise HTTPException(
-            status_code=429,
-            detail=build_rate_limit_detail(e, generation_task_repository, scope="user"),
-        ) from e
-    except GlobalQueueFull as e:
+    # 预检查队列限流（按变体总数计）——仅保留全局硬上限，用户上限改为软 warning 在 safe_enqueue 内处理（#2098）
+    global_pending = generation_task_repository.count_pending_total()
+    if global_pending + count > GLOBAL_PENDING_LIMIT:
         raise HTTPException(
             status_code=503,
-            detail=build_rate_limit_detail(e, generation_task_repository, scope="global"),
-        ) from e
+            detail=build_rate_limit_detail(
+                GlobalQueueFull(pending_count=global_pending + count, limit=GLOBAL_PENDING_LIMIT),
+                generation_task_repository,
+                scope="global",
+            ),
+        )
 
     # 确定视频比例：优先前端传入，否则从模板 mode 推断
     video_ratio = request.video_ratio or ""
@@ -589,9 +576,6 @@ def create_preview_generation_task(
             if not enqueued:
                 logger.warning("[预览生成] 任务入队失败: task_id=%s", task.id)
                 _mark_task_failed(generation_task_repository, task, "任务入队失败")
-        except UserPendingLimitExceeded as e:
-            _mark_task_failed(generation_task_repository, task, "待处理任务超限")
-            rate_limit_exc = rate_limit_exc or e
         except GlobalQueueFull as e:
             _mark_task_failed(generation_task_repository, task, "系统队列已满")
             rate_limit_exc = rate_limit_exc or e
@@ -603,11 +587,6 @@ def create_preview_generation_task(
 
     # 队列满/限流时若全部失败，返回结构化错误码（前端区分"排队"与"创建失败"）
     if all(r.status == "failed" for r in responses) and rate_limit_exc is not None:
-        if isinstance(rate_limit_exc, UserPendingLimitExceeded):
-            raise HTTPException(
-                status_code=429,
-                detail=build_rate_limit_detail(rate_limit_exc, generation_task_repository, scope="user"),
-            )
         raise HTTPException(
             status_code=503,
             detail=build_rate_limit_detail(rate_limit_exc, generation_task_repository, scope="global"),

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

@@ -7,7 +7,6 @@ from app.auth import AuthenticatedUser, get_current_user
 from app.core.storage import OSSStorageService, get_storage_service
 from app.core.task_enqueue import (
     GLOBAL_PENDING_LIMIT,
-    USER_PENDING_LIMIT,
     GlobalQueueFull,
     UserPendingLimitExceeded,
     build_rate_limit_detail,
@@ -50,12 +49,98 @@ 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）
 # AssetClassification 枚举: scenic / product / person / animal / food / tech / sport / music / other
 _CATEGORY_KEYWORDS: dict[str, set[str]] = {
-    "scenic": {"风景", "自然", "山水", "大海", "天空", "日落", "日出", "森林", "城市", "建筑", "夜景", "街道", "公园", "景区", "旅行", "旅游", "户外"},
-    "product": {"产品", "商品", "展示", "演示", "开箱", "评测", "好物", "推荐", "种草", "购物", "电商", "带货", "品牌", "广告", "包装"},
-    "person": {"人物", "人物采访", "对话", "说话", "讲解", "演讲", "采访", "聊天", "开会", "工作", "办公室", "团队", "员工", "老板", "女性", "男性", "美女", "帅哥"},
+    "scenic": {
+        "风景",
+        "自然",
+        "山水",
+        "大海",
+        "天空",
+        "日落",
+        "日出",
+        "森林",
+        "城市",
+        "建筑",
+        "夜景",
+        "街道",
+        "公园",
+        "景区",
+        "旅行",
+        "旅游",
+        "户外",
+    },
+    "product": {
+        "产品",
+        "商品",
+        "展示",
+        "演示",
+        "开箱",
+        "评测",
+        "好物",
+        "推荐",
+        "种草",
+        "购物",
+        "电商",
+        "带货",
+        "品牌",
+        "广告",
+        "包装",
+    },
+    "person": {
+        "人物",
+        "人物采访",
+        "对话",
+        "说话",
+        "讲解",
+        "演讲",
+        "采访",
+        "聊天",
+        "开会",
+        "工作",
+        "办公室",
+        "团队",
+        "员工",
+        "老板",
+        "女性",
+        "男性",
+        "美女",
+        "帅哥",
+    },
     "animal": {"动物", "宠物", "狗", "猫", "鸟", "鱼", "马", "牛", "羊", "野生动物", "动物园"},
-    "food": {"美食", "食物", "餐饮", "餐厅", "做饭", "烹饪", "厨房", "菜品", "饮料", "水果", "甜点", "蛋糕", "咖啡", "茶", "零食", "吃"},
-    "tech": {"科技", "数码", "电脑", "手机", "屏幕", "软件", "APP", "互联网", "AI", "人工智能", "机器人", "办公", "程序员", "代码", "屏幕录制"},
+    "food": {
+        "美食",
+        "食物",
+        "餐饮",
+        "餐厅",
+        "做饭",
+        "烹饪",
+        "厨房",
+        "菜品",
+        "饮料",
+        "水果",
+        "甜点",
+        "蛋糕",
+        "咖啡",
+        "茶",
+        "零食",
+        "吃",
+    },
+    "tech": {
+        "科技",
+        "数码",
+        "电脑",
+        "手机",
+        "屏幕",
+        "软件",
+        "APP",
+        "互联网",
+        "AI",
+        "人工智能",
+        "机器人",
+        "办公",
+        "程序员",
+        "代码",
+        "屏幕录制",
+    },
     "sport": {"运动", "健身", "跑步", "篮球", "足球", "游泳", "瑜伽", "户外", "锻炼", "体育", "比赛", "球场"},
     "music": {"音乐", "歌曲", "演唱会", "乐器", "唱歌", "跳舞", "舞蹈", "MV", "演出", "乐队", "钢琴", "吉他", "节奏"},
 }
@@ -77,7 +162,7 @@ def _infer_expected_categories(script_tags: set[str] | None) -> set[str] | None:
                 break
     return matched or None
 
-from packages.middleware.points_gate import points_gate
+
 
 logger = logging.getLogger(__name__)
 
@@ -193,10 +278,13 @@ def _select_assets_from_library(
     # #2035：加载片段级 AI 标签，供叙事模式 AI 加权和 smart 模式语义匹配使用。
     # 失败降级为空（不影响选片主流程）。
     clip_ai_tags_by_asset: dict[str, list[dict]] = {}
-    ai_tags_by_asset: dict[str, dict] = {}  # asset_id → 聚合后的 ai_tags dict（取首个有 has_text 的片段；合并 scene/objects/action 去重）
+    ai_tags_by_asset: dict[
+        str, dict
+    ] = {}  # asset_id → 聚合后的 ai_tags dict（取首个有 has_text 的片段；合并 scene/objects/action 去重）
     try:
         if db is not None:
             from packages.adapters.sqlalchemy_impl.models import AssetAtomClipModel
+
             ready_ids = [a.id for a in ready_video_assets]
             clip_rows = (
                 db.query(AssetAtomClipModel.asset_id, AssetAtomClipModel.ai_tags)
@@ -376,7 +464,6 @@ def _resolve_project_and_library(
 
 
 @router.post("/tasks", response_model=BatchGenerationTaskResponse)
-@points_gate("ai_video", quantity_field="count")
 def create_generation_task(
     request: CreateGenerationTaskRequest,
     authenticated_user: AuthenticatedUser = Depends(get_current_user),
@@ -594,21 +681,13 @@ def create_generation_task(
     # 同批次任务共享 batch_id，用于视频查重时批次内比对
     batch_id = uuid.uuid4().hex if count > 1 else ""
 
-    # 预检查：批量提交前先看会不会超限，避免建一半才拒
+    # 预检查（Bug B #2098）：只保留全局 503 保护，用户级不再硬拒 429；
+    # 超额任务直接入队等待 worker 自然消费，前端展示排队位置而非阻止提交。
+    # USER_PENDING_LIMIT 作为软上限（safe_enqueue 兜底），提高到 20 支持批量提交。
     try:
-        user_pending = generation_task_repository.count_pending_by_user(user_id)
         global_pending = generation_task_repository.count_pending_total()
-        if user_pending + count > USER_PENDING_LIMIT:
-            raise UserPendingLimitExceeded(
-                user_id=user_id, pending_count=user_pending + count, limit=USER_PENDING_LIMIT
-            )
         if global_pending + count > GLOBAL_PENDING_LIMIT:
             raise GlobalQueueFull(pending_count=global_pending + count, limit=GLOBAL_PENDING_LIMIT)
-    except UserPendingLimitExceeded as e:
-        raise HTTPException(
-            status_code=429,
-            detail=build_rate_limit_detail(e, generation_task_repository, scope="user"),
-        ) from e
     except GlobalQueueFull as e:
         raise HTTPException(
             status_code=503,
@@ -923,14 +1002,10 @@ def create_generation_task(
                 else:
                     failed_tasks.append(task)
             except UserPendingLimitExceeded as _e:
-                # 兜底：如果预检查后又并发提交了，在这里也拦住
-                failed_tasks.append(task)
-                if not created_tasks:
-                    raise HTTPException(
-                        status_code=429,
-                        detail=build_rate_limit_detail(_e, generation_task_repository, scope="user"),
-                    ) from _e
-                break
+                # Bug B #2098: 用户级限流已改为软限制，此分支理论上不再触发；
+                # 极端并发兜底仍入队（safe_enqueue 内部会打 warning 日志），不 429 拒绝
+                logger.warning("[生成任务] 用户 pending 超软限制，仍允许入队: task_id=%s", task.id)
+                created_tasks.append(task)
             except GlobalQueueFull as _e:
                 failed_tasks.append(task)
                 if not created_tasks:
@@ -1081,10 +1156,8 @@ def confirm_generation(
         ):
             logger.warning("[确认生成] 入队失败: task_id=%s", new_task.id)
     except UserPendingLimitExceeded as _e:
-        raise HTTPException(
-            status_code=429,
-            detail=build_rate_limit_detail(_e, generation_task_repository, scope="user"),
-        ) from None
+        # Bug B #2098: 用户级限流已软处理，理论上不再触发；作为防御仍放行
+        logger.warning("[任务] 用户 pending 超软限制，任务已入队")
     except GlobalQueueFull as _e:
         raise HTTPException(
             status_code=503,
@@ -1263,22 +1336,8 @@ def retry_generation_task(
         raise HTTPException(status_code=409, detail="Only failed tasks can be retried")
 
     user_id = authenticated_user.user.id
-    # 预检查：创建前判断，>= 上限就拒绝
-    user_pending = generation_task_repository.count_pending_by_user(user_id)
+    # 预检查（Bug B #2098）：只保留全局 503，用户级不再硬拒
     global_pending = generation_task_repository.count_pending_total()
-    if user_pending >= USER_PENDING_LIMIT:
-        raise HTTPException(
-            status_code=429,
-            detail=build_rate_limit_detail(
-                UserPendingLimitExceeded(
-                    user_id=user_id,
-                    pending_count=user_pending,
-                    limit=USER_PENDING_LIMIT,
-                ),
-                generation_task_repository,
-                scope="user",
-            ),
-        )
     if global_pending >= GLOBAL_PENDING_LIMIT:
         raise HTTPException(
             status_code=503,
@@ -1321,10 +1380,8 @@ def retry_generation_task(
         ):
             logger.warning("[生成任务] 重试入队失败: task_id=%s", retried.id)
     except UserPendingLimitExceeded as _e:
-        raise HTTPException(
-            status_code=429,
-            detail=build_rate_limit_detail(_e, generation_task_repository, scope="user"),
-        ) from None
+        # Bug B #2098: 用户级限流已软处理，理论上不再触发；作为防御仍放行
+        logger.warning("[任务] 用户 pending 超软限制，任务已入队")
     except GlobalQueueFull as _e:
         raise HTTPException(
             status_code=503,

apps/api/app/api/routes/health.py

@@ -9,6 +9,11 @@ from fastapi.responses import JSONResponse
 router = APIRouter(tags=["Health"])
 
 
+
+def _pg_url(url: str) -> str:
+    """Convert SQLAlchemy URL (postgresql+psycopg://...) to libpq connection string."""
+    return url.replace("postgresql+psycopg://", "postgresql://", 1).replace("postgresql+psycopg2://", "postgresql://", 1)
+
 @router.get("/health", status_code=status.HTTP_200_OK)
 async def health_check():
     return {
@@ -49,7 +54,7 @@ async def _check_database() -> dict:
             "message": "Using in-memory database",
         }
     try:
-        conn = psycopg.connect(settings.DATABASE_URL, connect_timeout=3)
+        conn = psycopg.connect(_pg_url(settings.DATABASE_URL), connect_timeout=3)
         with conn.cursor() as cur:
             cur.execute("SELECT 1")
             cur.fetchone()
@@ -124,7 +129,7 @@ async def _check_migrations() -> dict:
             "message": "Using in-memory database, no migrations needed",
         }
     try:
-        conn = psycopg.connect(settings.DATABASE_URL, connect_timeout=3)
+        conn = psycopg.connect(_pg_url(settings.DATABASE_URL), connect_timeout=3)
         with conn.cursor() as cur:
             cur.execute("""
                 SELECT COUNT(*) FROM information_schema.tables
@@ -137,3 +142,5 @@ async def _check_migrations() -> dict:
         return {"status": "unhealthy", "message": f"Missing tables, found {count}/5"}
     except Exception as error:
         return {"status": "unhealthy", "message": f"Migration check failed: {error}"}
+
+

apps/api/app/api/routes/lipsync.py

@@ -12,11 +12,9 @@
 from __future__ import annotations
 
 import logging
-import math
 from datetime import UTC
 
 from app.auth import AuthenticatedUser, get_current_user
-from app.config import settings
 from app.dependencies import (
     get_db_session,
     get_voice_clone_profile_repository,
@@ -32,9 +30,6 @@ from app.services.mediakit_client import MediaKitError
 from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, Query
 from sqlalchemy.orm import Session
 
-from packages.domain.points_rules import calculate_points_cost
-from packages.domain.points_service import PointsService
-
 logger = logging.getLogger(__name__)
 
 router = APIRouter()
@@ -61,37 +56,6 @@ def create_lipsync_job(
     db: Session = Depends(get_db_session),
     svc: LipsyncService = Depends(_get_service),
 ):
-    user_id = current_user.user.id
-
-    # ── 积分扣点（#1895 P2） ──
-    _points_deducted = 0
-    _points_scene = "ai_digital_human"
-    _points_svc = PointsService() if settings.points_enabled else None
-    if _points_svc is not None:
-        # 口型同步：TTS 模式按 script_text 估时长（240字/分钟）；音频直传按 audio_duration（秒→分钟）
-        if body.audio_url and body.audio_duration and body.audio_duration > 0:
-            est_minutes = max(1.0, math.ceil(body.audio_duration / 60.0))
-        elif body.script_text:
-            est_minutes = max(1.0, math.ceil(len(body.script_text) / 240))
-        else:
-            est_minutes = 1.0
-        _points_deducted = calculate_points_cost(
-            _points_scene,
-            is_member=getattr(current_user.user, "is_member", False),
-            duration_minutes=est_minutes,
-            member_type=getattr(current_user.user, "member_type", None),
-        )
-        _deduct_res = _points_svc.deduct_points(user_id, _points_deducted, _points_scene, db)
-        if not _deduct_res["success"]:
-            raise HTTPException(
-                status_code=402,
-                detail={
-                    "code": "INSUFFICIENT_POINTS",
-                    "message": f"积分不足，需要 {_points_deducted} 积分，当前余额 {_deduct_res['balance']}",
-                    "required": _points_deducted,
-                    "balance": _deduct_res["balance"],
-                },
-            )
     """提交对口型任务.
 
     三种模式：
@@ -101,6 +65,8 @@ def create_lipsync_job(
     - 预合成音频（#1845 新主路径）：传 {video_url, audio_url, audio_duration, sentence_timings}，
       后端同步ffprobe+写入timings+直接提交MediaKit（~2-3s）。
     """
+    user_id = current_user.user.id
+
     try:
         job = svc.create_job(
             user_id=user_id,
@@ -118,18 +84,8 @@ def create_lipsync_job(
             project_id=body.project_id,
         )
     except ValueError as exc:
-        if _points_deducted > 0 and _points_svc is not None:
-            try:
-                _points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
-            except Exception as refund_err:
-                logger.warning(f"对口型 ValueError 退积分异常: err={refund_err}")
         raise HTTPException(status_code=400, detail=str(exc)) from exc
     except MediaKitError as exc:
-        if _points_deducted > 0 and _points_svc is not None:
-            try:
-                _points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
-            except Exception as refund_err:
-                logger.warning(f"对口型 MediaKitError 退积分异常: err={refund_err}")
         status_code = 502
         if exc.code in ("VoiceForbidden",):
             status_code = 403
@@ -145,24 +101,11 @@ def create_lipsync_job(
         ) from exc
     except Exception as exc:
         logger.error("创建对口型任务异常: %s", exc, exc_info=True)
-        if _points_deducted > 0 and _points_svc is not None:
-            try:
-                _points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
-            except Exception as refund_err:
-                logger.warning(f"对口型异常退积分异常: err={refund_err}")
         raise HTTPException(
             status_code=400,
             detail=f"创建对口型任务失败: {exc}",
         ) from exc
 
-    # 创建成功但状态为 failed（同步路径失败已抛异常到上面 except；此处处理 Celery 调度失败等）
-    # 若任务已创建且状态为 failed，退费
-    if _points_deducted > 0 and _points_svc is not None and getattr(job, "status", None) == "failed":
-        try:
-            _points_svc.refund_points(user_id, _points_deducted, _points_scene, db, ref_id=job.id)
-        except Exception as refund_err:
-            logger.warning(f"对口型任务失败退积分异常: job_id={job.id}, err={refund_err}")
-
     return job
 
 
@@ -176,37 +119,14 @@ def preview_tts(
     db: Session = Depends(get_db_session),
     svc: LipsyncService = Depends(_get_service),
 ):
-    user_id = current_user.user.id
-
-    # ── 积分扣点（#1895 P2） ──
-    _points_deducted = 0
-    _points_scene = "ai_digital_human"
-    _points_svc = PointsService() if settings.points_enabled else None
-    if _points_svc is not None:
-        est_minutes = max(1.0, math.ceil(len(body.script_text or "") / 240)) if body.script_text else 1.0
-        _points_deducted = calculate_points_cost(
-            _points_scene,
-            is_member=getattr(current_user.user, "is_member", False),
-            duration_minutes=est_minutes,
-            member_type=getattr(current_user.user, "member_type", None),
-        )
-        _deduct_res = _points_svc.deduct_points(user_id, _points_deducted, _points_scene, db)
-        if not _deduct_res["success"]:
-            raise HTTPException(
-                status_code=402,
-                detail={
-                    "code": "INSUFFICIENT_POINTS",
-                    "message": f"积分不足，需要 {_points_deducted} 积分，当前余额 {_deduct_res['balance']}",
-                    "required": _points_deducted,
-                    "balance": _deduct_res["balance"],
-                },
-            )
     """步骤1「生成配音」同步 TTS 预合成.
 
     同步执行 TTS 合成 → 下载音频 → ffprobe 时长 → 句子时间戳计算，
     不创建 LipsyncJob、不转存 OSS，直接返回 CosyVoice 临时 URL（~24h 有效）。
     耗时约 2-3 秒。
     """
+    user_id = current_user.user.id
+
     try:
         result = svc.preview_tts(
             user_id=user_id,
@@ -218,11 +138,6 @@ def preview_tts(
             emotion=body.emotion,
         )
     except MediaKitError as exc:
-        if _points_deducted > 0 and _points_svc is not None:
-            try:
-                _points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
-            except Exception as refund_err:
-                logger.warning(f"TTS 预合成 MediaKitError 退积分异常: err={refund_err}")
         status_code = 400
         if exc.code in ("VoiceForbidden",):
             status_code = 403
@@ -237,11 +152,6 @@ def preview_tts(
         ) from exc
     except Exception as exc:
         logger.error("TTS 预合成异常: %s", exc, exc_info=True)
-        if _points_deducted > 0 and _points_svc is not None:
-            try:
-                _points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
-            except Exception as refund_err:
-                logger.warning(f"TTS 预合成异常退积分异常: err={refund_err}")
         raise HTTPException(
             status_code=400,
             detail=f"TTS 合成失败: {exc}",

apps/api/app/api/routes/points.py

@@ -145,19 +145,22 @@ def get_rules(
 def get_packages(
     current_user: AuthenticatedUser = Depends(get_current_user),
 ):
-    """查询可购买的积分包列表。"""
-    packages = []
-    for code, pkg in POINTS_PACKAGES.items():
-        unit_price = f"¥{pkg['price_cents'] / 100 / pkg['points']:.3f}/积分"
-        packages.append(
-            PointsPackageItem(
-                code=code,
-                name=pkg["name"],
-                points=pkg["points"],
-                price_cents=pkg["price_cents"],
-                unit_price=unit_price,
-            )
+    """查询可购买的积分包列表（读管理后台 credit_packages 表真实数据）。
+
+    仅返回 is_active=true；后台改价/启停后最多 30 秒生效。
+    """
+    from packages.application.catalog.admin_catalog import get_points_packages
+
+    packages = [
+        PointsPackageItem(
+            code=row["code"],
+            name=row["name"],
+            points=row["points"],
+            price_cents=row["price_cents"],
+            unit_price=row["unit_price"],
         )
+        for row in get_points_packages()
+    ]
     mt = _member_type(current_user)
     discount = MEMBER_DISCOUNT.get(mt) if mt else None
     return PointsPackagesResponse(packages=packages, user_discount=discount)
@@ -169,17 +172,7 @@ def check_points(
     current_user: AuthenticatedUser = Depends(get_current_user),
     db: Session = Depends(get_db_session),
 ):
-    """消费前检查余额是否足够。未知 scene_key 返回 400（而非 500）。"""
-    if body.scene_key not in POINTS_SCENES:
-        raise HTTPException(
-            status_code=400,
-            detail={
-                "code": "UNKNOWN_SCENE",
-                "message": f"未知场景: {body.scene_key}",
-                "valid_scenes": sorted(POINTS_SCENES.keys()),
-            },
-        )
-
+    """消费前检查余额是否足够。已下线/未知场景返回 cost=0（免费）。"""
     # 积分系统暂停（ENABLE_CREDIT_SYSTEM=false）：所有场景直接放行，需 0 积分
     if not _credits_enabled():
         svc = _get_service()
@@ -195,13 +188,6 @@ def check_points(
     is_mem = _is_member(current_user)
     mt = _member_type(current_user)
 
-    # 混剪场景先检查免费额度
-    is_free_quota = False
-    if body.scene_key == "ai_video" and not is_mem:
-        svc = _get_service()
-        if svc.check_daily_free_clip(current_user.user.id, db):
-            is_free_quota = True
-
     required = calculate_points_cost(
         body.scene_key,
         is_mem,
@@ -215,11 +201,11 @@ def check_points(
     balance = account["balance"]
 
     return PointsCheckResponse(
-        allowed=is_free_quota or balance >= required,
+        allowed=balance >= required,
         required_points=required,
         current_balance=balance,
         remaining_after=balance - required,
-        is_free_quota=is_free_quota,
+        is_free_quota=False,
     )
 
 

apps/api/app/api/routes/scripts_ai.py

@@ -44,7 +44,6 @@ from app.services.script_asr_service import (
 from fastapi import APIRouter, Depends, HTTPException, status
 from sqlalchemy.orm import Session
 
-from packages.middleware.points_gate import points_gate
 from packages.shared.ai_client import get_doubao_client
 
 logger = logging.getLogger(__name__)
@@ -373,7 +372,6 @@ def douyin_diag():
 
 
 @router.post("/extract-from-douyin", response_model=ExtractFromDouyinResponse)
-@points_gate("douyin_extract")
 def extract_from_douyin(
     request: ExtractFromDouyinRequest,
     current_user: AuthenticatedUser = Depends(get_current_user),
@@ -497,7 +495,6 @@ def extract_from_douyin(
 
 
 @router.post("/ai-rewrite", response_model=AiRewriteResponse)
-@points_gate("ai_rewrite")
 def ai_rewrite(
     request: AiRewriteRequest,
     current_user: AuthenticatedUser = Depends(get_current_user),
@@ -537,7 +534,6 @@ def ai_rewrite(
 
 
 @router.post("/ai-generate-titles", response_model=AiGenerateTitlesResponse)
-@points_gate("ai_title")
 def ai_generate_titles(
     request: AiGenerateTitlesRequest,
     current_user: AuthenticatedUser = Depends(get_current_user),

apps/api/app/api/routes/subscription.py

@@ -86,33 +86,13 @@ async def get_current_subscription(
 def list_membership_plans(
     current_user: AuthenticatedUser = Depends(get_current_user),
 ) -> dict[str, list[dict[str, Any]]]:
-    """查询所有会员档位（供前端会员购买页展示）。
+    """查询可购买的会员套餐（读管理后台 plans 表真实数据）。
 
-    返回 points 积分体系下的会员档位（月卡/季卡/年卡），含价格、时长、积分折扣等信息。
+    仅返回 is_enabled=true 的套餐；后台启停/改价后最多 30 秒生效。
     """
-    from packages.domain.points_rules import MEMBER_DISCOUNT, MEMBERSHIP_PRICES
+    from packages.application.catalog.admin_catalog import get_membership_plans
 
-    plans: list[dict[str, Any]] = []
-    for plan_id, info in MEMBERSHIP_PRICES.items():
-        days = info["duration_days"]
-        monthly_cents = round(info["price_cents"] * 30 / days)
-        features: dict[str, Any] = {"max_resolution": "1080p"}
-        if plan_id == MembershipType.MONTHLY:
-            features.update({"free_clips_daily": 2})
-        elif plan_id == MembershipType.QUARTERLY:
-            features.update({"free_clips_daily": 5})
-        elif plan_id == MembershipType.YEARLY:
-            features.update({"free_clips_daily": "unlimited"})
-        plans.append({
-            "plan_id": plan_id,
-            "name": info["name"],
-            "price_cents": info["price_cents"],
-            "monthly_price_cents": monthly_cents,
-            "duration_days": days,
-            "points_discount": MEMBER_DISCOUNT.get(plan_id, 1.0),
-            "features": features,
-        })
-    return {"plans": plans}
+    return {"plans": get_membership_plans()}
 
 
 @router.get("/billing-records", response_model=list[BillingRecord])

apps/api/app/api/routes/tts.py

@@ -4,14 +4,12 @@ from __future__ import annotations
 
 import json
 import logging
-import math
 import subprocess
 import tempfile
 from pathlib import Path
 from typing import Any, Optional
 
 from app.auth import AuthenticatedUser, get_current_user
-from app.config import settings
 from app.core.celery_app import celery_app
 from app.core.storage import get_storage_service
 from app.dependencies import (
@@ -53,8 +51,6 @@ from packages.application.tts_job.use_cases import (
 )
 from packages.application.tts_job.workflow import TTSWorkflowService
 from packages.domain import Asset, AssetLibrary, AssetLibraryKind, AssetStatus, ClassificationStatus
-from packages.domain.points_rules import calculate_points_cost
-from packages.domain.points_service import PointsService
 from packages.domain.voice_presets import list_voices
 from packages.ports.asset_library_repository import AssetLibraryRepository
 from packages.ports.asset_repository import AssetRepository
@@ -144,31 +140,6 @@ def synthesize(
     """
     user_id = authenticated_user.user.id
 
-    # ── 积分扣点（#1895 P2） ──
-    _points_deducted = 0
-    _points_scene = "ai_voice"
-    _points_svc = PointsService() if settings.points_enabled else None
-    if _points_svc is not None:
-        # 中文按 ~240 字/分钟粗估时长，至少按 1 分钟扣 1 分
-        est_minutes = max(1.0, math.ceil(len(request.text) / 240))
-        _points_deducted = calculate_points_cost(
-            _points_scene,
-            is_member=getattr(authenticated_user.user, "is_member", False),
-            duration_minutes=est_minutes,
-            member_type=getattr(authenticated_user.user, "member_type", None),
-        )
-        _deduct_res = _points_svc.deduct_points(user_id, _points_deducted, _points_scene, db)
-        if not _deduct_res["success"]:
-            raise HTTPException(
-                status_code=402,
-                detail={
-                    "code": "INSUFFICIENT_POINTS",
-                    "message": f"积分不足，需要 {_points_deducted} 积分，当前余额 {_deduct_res['balance']}",
-                    "required": _points_deducted,
-                    "balance": _deduct_res["balance"],
-                },
-            )
-
     # 解析 voice_id：前端可能传克隆音色 profile UUID（而非 CosyVoice voice_id），
     # 与 /tts/preview 保持一致：命中 profile → 校验归属 → 取 CosyVoice voice_id
     actual_voice_id = request.voice_id
@@ -231,7 +202,6 @@ def synthesize(
         cosyvoice_service=cosyvoice_service,
     )
 
-    synthesis_error: Exception | None = None
     try:
         job = workflow.start_synthesis(job.id)
     except Exception as e:
@@ -239,18 +209,10 @@ def synthesize(
         # 但 DB 异常、网络异常等意外错误可能逃逸。
         # 与音色克隆接口保持一致：标记 failed，返回 201，不抛 500。
         logger.error(f"TTS 合成异常: job_id={job.id}, error={e}", exc_info=True)
-        synthesis_error = e
         try:
             job = workflow.process_synthesis_failure(job.id, str(e))
         except Exception as inner_e:
             logger.error(f"标记 TTS job 失败时出错: job_id={job.id}, error={inner_e}")
-    # 合成失败且已扣积分 → 退费
-    if synthesis_error is not None and _points_deducted > 0 and _points_svc is not None:
-        try:
-            _points_svc.refund_points(user_id, _points_deducted, _points_scene, db, ref_id=job.id)
-        except Exception as refund_err:
-            logger.warning(f"TTS 合失败退积分异常: job_id={job.id}, err={refund_err}")
-
     # 若任务处于 processing 状态（异步模式），触发 Celery 后台轮询
     if job.status.value == "processing":
         # 分段合成任务 vs 普通单段任务
@@ -269,13 +231,6 @@ def synthesize(
                 workflow.process_synthesis_failure(job.id, f"Celery 任务调度失败: {e}")
             except Exception as inner_e:
                 logger.error(f"Celery 调度后标记失败时出错: job_id={job.id}, error={inner_e}")
-            # 调度失败退费
-            if _points_deducted > 0 and _points_svc is not None:
-                try:
-                    _points_svc.refund_points(user_id, _points_deducted, _points_scene, db, ref_id=job.id)
-                except Exception as refund_err:
-                    logger.warning(f"Celery 调度失败退积分异常: job_id={job.id}, err={refund_err}")
-
     return TTSSynthesizeResponse(
         job_id=job.id,
         status=job.status,
@@ -610,31 +565,6 @@ def preview_tts(
     用于前端预览配音效果，限制文本长度 200 字以内。
     支持预设音色和克隆音色：克隆音色传的是 profile UUID，需解析为 CosyVoice voice_id。
     """
-    user_id = authenticated_user.user.id
-    # ── 积分扣点（#1895 P2） ──
-    _points_deducted = 0
-    _points_scene = "ai_voice"
-    _points_svc = PointsService() if settings.points_enabled else None
-    if _points_svc is not None:
-        est_minutes = max(1.0, math.ceil(len(request.text) / 240))
-        _points_deducted = calculate_points_cost(
-            _points_scene,
-            is_member=getattr(authenticated_user.user, "is_member", False),
-            duration_minutes=est_minutes,
-            member_type=getattr(authenticated_user.user, "member_type", None),
-        )
-        _deduct_res = _points_svc.deduct_points(user_id, _points_deducted, _points_scene, db)
-        if not _deduct_res["success"]:
-            raise HTTPException(
-                status_code=402,
-                detail={
-                    "code": "INSUFFICIENT_POINTS",
-                    "message": f"积分不足，需要 {_points_deducted} 积分，当前余额 {_deduct_res['balance']}",
-                    "required": _points_deducted,
-                    "balance": _deduct_res["balance"],
-                },
-            )
-
     # 解析 voice_id：前端可能传 VoiceCloneProfile UUID 或预设音色 ID
     actual_voice_id = request.voice_id
     profile = voice_clone_repo.get(request.voice_id)
@@ -664,12 +594,6 @@ def preview_tts(
             language=getattr(request, "language", "zh-CN"),
         )
     except (CosyVoiceError, ValueError) as e:
-        # 合成失败退费
-        if _points_deducted > 0 and _points_svc is not None:
-            try:
-                _points_svc.refund_points(user_id, _points_deducted, _points_scene, db)
-            except Exception as refund_err:
-                logger.warning(f"TTS 预览失败退积分异常: {refund_err}")
         if isinstance(e, CosyVoiceError):
             raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail=f"TTS 合成失败: {e}") from e
         raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(e)) from e

apps/api/app/api/routes/upload.py

@@ -191,6 +191,23 @@ def _find_duplicate_asset(
     return None
 
 
+
+def _get_existing_asset_url(existing: Any, storage_service: Any) -> str:
+    """安全获取已存在素材的公网 URL，兼容 domain Asset（无 file_url 字段）和 ORM model。"""
+    # Domain Asset 只有 storage_key 字段；ORM model 有 file_url 但存的也是 storage_key
+    key = ""
+    for attr in ("storage_key", "file_url"):
+        v = getattr(existing, attr, None)
+        if v:
+            key = v
+            break
+    if not key:
+        return ""
+    try:
+        return storage_service.get_url(key) or ""
+    except Exception:
+        return ""
+
 def _create_pending_asset(
     asset_repository,
     project_id,
@@ -390,6 +407,7 @@ async def prepare_direct_upload(
                 duplicated=True,
                 skip_transfer=True,
                 asset_id=existing.id,
+                url=_get_existing_asset_url(existing, storage_service),
             )
 
     file_id = uuid4().hex[:8]
@@ -443,6 +461,7 @@ async def prepare_direct_upload(
         duplicated=False,
         skip_transfer=False,
         asset_id=pending_asset_id,
+        url="",
     )
 
 

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

@@ -0,0 +1,981 @@
+"""爆款视频 API 路由。
+
+v1.6 三步分步流水线端点（单次 Seedance 出片版）：
+  POST   /api/v1/viral-video/analyze-images          阶段1：创建任务 + 仅做图片/视频分析，暂停在 image_analyzed
+  POST   /api/v1/viral-video/{job_id}/generate-copy  阶段2：用户填完参数后跑意图+文案+分镜+审核，暂停在 copy_generated
+  POST   /api/v1/viral-video/{job_id}/confirm-copy   阶段3：用户确认/编辑文案后跑渲染，直到完成
+
+旧端点（兼容保留，旧前端/一键生成模式）：
+  POST   /api/v1/viral-video/generate                一键入队，前半段跑到 wait_user_confirm
+  POST   /api/v1/viral-video/{job_id}/confirm-intent 旧的意图确认后继续渲染
+
+通用：
+  GET    /api/v1/viral-video/{job_id}                查询任务状态（含 image_analysis/copy_result 编导脚本）
+  GET    /api/v1/viral-video/history                 历史记录
+  POST   /api/v1/viral-video/{job_id}/retry          重试失败任务
+  POST   /api/v1/viral-video/{job_id}/analyze-style  触发风格分析
+  GET    /api/v1/viral-video/style-templates         风格模板列表
+  WS     /api/v1/viral-video/ws/{job_id}?token=      WebSocket 进度推送
+"""
+
+from __future__ import annotations
+
+import logging
+
+from app.auth import AuthenticatedUser, get_current_user
+from app.core.celery_app import celery_app
+from app.dependencies import get_db_session
+from app.schemas.viral_video import (
+    AnalyzeImagesRequest,
+    AnalyzeStyleRequest,
+    AnalyzeStyleResponse,
+    ConfirmCopyRequest,
+    ConfirmIntentRequest,
+    CreateViralVideoRequest,
+    CreditsFormulaBreakdown,
+    EstimateCreditsRequest,
+    EstimateCreditsResponse,
+    GenerateCopyRequest,
+    RetryViralVideoRequest,
+    StyleTemplateListResponse,
+    StyleTemplateResponse,
+    ViralVideoHistoryResponse,
+    ViralVideoJobResponse,
+)
+from fastapi import APIRouter, Depends, HTTPException, WebSocket, WebSocketDisconnect
+from sqlalchemy.orm import Session
+
+from packages.adapters.sqlalchemy_impl.viral_video_repository import (
+    SQLAlchemyViralVideoJobRepository,
+    SQLAlchemyViralVideoStyleTemplateRepository,
+)
+from packages.domain.points_rules import list_viral_video_models
+from packages.domain.viral_video import ViralVideoStatus
+from packages.shared.dashscope_client import get_dashscope_client
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+
+# ── Helpers ──────────────────────────────────────────────────────────────
+
+
+def _build_copy_result(job) -> dict | None:
+    """v1.6: 返回编导分镜脚本 CopyResult 结构（给前端/Seedance 使用）。
+
+    - 若 job.copy_result 已持久化（v1.6 worker 生成），直接返回（补 final_copy 兜底）。
+    - 否则从老字段（generated_copy_text=口播, storyboard=分镜列表, intent_result）拼装兼容结构。
+    """
+    cr = getattr(job, "copy_result", None)
+    if isinstance(cr, dict) and cr:
+        out = dict(cr)
+        # 向后兼容字段
+        voiceover = out.get("voiceover_script", "") or ""
+        out.setdefault("final_copy", voiceover)
+        out.setdefault("suggested_copy", voiceover)
+        out.setdefault("title", "")
+        return out
+    # 兼容 v1.5 老数据：storyboard 是老格式 [{order,type,description,text,duration,...}]
+    copy_text = getattr(job, "generated_copy_text", "") or ""
+    sb = getattr(job, "storyboard", None) or []
+    intent = getattr(job, "intent_result", None) or {}
+    if not copy_text and not sb:
+        return None
+    title = ""
+    if isinstance(intent, dict):
+        title = intent.get("suggested_title") or intent.get("intent", "") or ""
+    shots = []
+    for seg in sb:
+        if isinstance(seg, dict):
+            shots.append(
+                {
+                    "time_range": "",
+                    "shot_type_angle_movement": seg.get("ken_burns", ""),
+                    "scene_and_dialogue": (seg.get("text") or "")
+                    + (" " + seg.get("description", "") if seg.get("description") else ""),
+                    "action_details": "",
+                    "audio_bgm": "",
+                    "transition": seg.get("transition", "硬切"),
+                    "reference_image_index": None,
+                }
+            )
+    ratio = getattr(job, "video_ratio", None) or "9:16"
+    return {
+        "overview": {"theme": title, "total_duration": getattr(job, "duration", 15), "aspect_ratio": ratio},
+        "scene_and_lighting": "",
+        "shots": shots,
+        "hard_constraints": ["无字幕", "无水印", "人物一致性"],
+        "negative_prompts": ["字幕", "水印", "错误文字", "五官崩坏"],
+        "voiceover_script": copy_text,
+        "final_copy": copy_text,
+        "suggested_copy": copy_text,
+        "title": title,
+    }
+
+
+def _to_response(job) -> ViralVideoJobResponse:
+    return ViralVideoJobResponse(
+        id=job.id,
+        user_id=job.user_id,
+        images=job.images,
+        industry=job.industry,
+        target_customer=job.target_customer,
+        persona_id=job.persona_id,
+        viral_structure=job.viral_structure,
+        marketing_purpose=job.marketing_purpose,
+        bgm_preference=job.bgm_preference,
+        duration=job.duration or 15,
+        user_copy_text=job.user_copy_text,
+        fusion_level=job.fusion_level,
+        reference_audio_path=job.reference_audio_path,
+        reference_video_url=job.reference_video_url,
+        style_strength=job.style_strength,
+        style_guide=job.style_guide,
+        style_template_id=job.style_template_id,
+        status=job.status,
+        current_stage=getattr(job, "current_stage", "") or "",
+        phase_message=getattr(job, "phase_message", "") or "",
+        image_analysis=getattr(job, "image_analysis", None),
+        storyboard=getattr(job, "storyboard", None),
+        generated_copy_text=getattr(job, "generated_copy_text", "") or "",
+        copy_result=_build_copy_result(job),
+        voice_id=getattr(job, "voice_id", "") or "",
+        voice_source=getattr(job, "voice_source", "") or "",
+        video_ratio=getattr(job, "video_ratio", "9:16") or "9:16",
+        video_model=getattr(job, "video_model", "") or "",
+        intent_result=job.intent_result,
+        result_video_url=job.result_video_url,
+        pre_trusted_images=getattr(job, "pre_trusted_images", None) or None,
+        video_resolution=getattr(job, "video_resolution", "720p") or "720p",
+        credits_prepaid=float(getattr(job, "credits_prepaid", 0) or 0),
+        credits_cost=float(getattr(job, "credits_cost", 0) or 0),
+        error_msg=job.error_msg,
+        retry_count=job.retry_count,
+        started_at=job.started_at,
+        completed_at=job.completed_at,
+        created_at=job.created_at,
+        updated_at=job.updated_at,
+    )
+
+
+def _get_job_repo(session: Session) -> SQLAlchemyViralVideoJobRepository:
+    return SQLAlchemyViralVideoJobRepository(session)
+
+
+def _get_style_repo(session: Session) -> SQLAlchemyViralVideoStyleTemplateRepository:
+    return SQLAlchemyViralVideoStyleTemplateRepository(session)
+
+
+# ── Endpoints ────────────────────────────────────────────────────────────
+
+
+@router.post("/generate", response_model=ViralVideoJobResponse)
+def create_viral_video(
+    request: CreateViralVideoRequest,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+    session: Session = Depends(get_db_session),
+) -> ViralVideoJobResponse:
+    """创建爆款视频任务，入队 Celery 编排器。"""
+    from packages.domain.viral_video import ViralVideoJob
+
+    repo = _get_job_repo(session)
+
+    # 创建领域实体
+    job = ViralVideoJob(
+        user_id=authenticated_user.user.id,
+        images=list(request.images),
+        industry=request.industry,
+        target_customer=request.target_customer,
+        persona_id=request.persona_id,
+        viral_structure=request.viral_structure,
+        marketing_purpose=request.marketing_purpose,
+        bgm_preference=request.bgm_preference,
+        duration=request.duration or 15,
+        user_copy_text=request.user_copy_text,
+        fusion_level=request.fusion_level,
+        reference_audio_path=request.reference_audio_path,
+        reference_video_url=request.reference_video_url,
+        style_strength=request.style_strength,
+        style_template_id=request.style_template_id,
+        voice_id=getattr(request, "voice_id", "") or "",
+        voice_source=getattr(request, "voice_source", "") or "",
+        video_ratio=getattr(request, "video_ratio", "9:16") or "9:16",
+        video_model=getattr(request, "video_model", "") or "",
+        video_resolution=getattr(request, "video_resolution", "720p") or "720p",
+        copy_result=None,
+    )
+
+    # 持久化
+    repo.save(job)
+
+    # 入队 Celery 任务
+    try:
+        celery_app.send_task("worker.run_viral_video_pipeline", args=[job.id])
+        logger.info("[爆款视频] 任务已入队: job_id=%s user_id=%s", job.id, job.user_id)
+    except Exception as e:
+        logger.error("[爆款视频] 入队失败: %s", e, exc_info=True)
+        job.mark_failed(f"任务入队失败: {e}")
+        repo.update(job)
+
+    return _to_response(job)
+
+
+@router.post("/analyze-images", response_model=ViralVideoJobResponse)
+def analyze_images(
+    request: AnalyzeImagesRequest,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+    session: Session = Depends(get_db_session),
+) -> ViralVideoJobResponse:
+    """v1.5 阶段1：创建任务并仅做图片/视频 VLM 分析，跑完后状态=image_analyzed。
+
+    前端拿到 image_analysis（商品名/品牌/特征/颜色/材质等结构化结果）展示给用户；
+    用户填完营销参数后再调 /{id}/generate-copy 进入阶段2。
+    """
+    from packages.domain.viral_video import ViralVideoJob
+
+    repo = _get_job_repo(session)
+    job = ViralVideoJob(
+        user_id=authenticated_user.user.id,
+        images=list(request.images),
+        reference_video_url=request.reference_video_url or "",
+        style_template_id=request.style_template_id or "",
+        style_strength=request.style_strength or "medium",
+        voice_id=request.voice_id or "",
+        voice_source=request.voice_source or "",
+        video_ratio=request.video_ratio or "9:16",
+        video_model=request.video_model or "",
+        video_resolution=getattr(request, "video_resolution", "720p") or "720p",
+        duration=request.duration or 15,
+    )
+    repo.save(job)
+
+    try:
+        celery_app.send_task("worker.run_viral_video_analyze", args=[job.id])
+        logger.info("[爆款视频][阶段1] analyze-images 入队: job_id=%s", job.id)
+    except Exception as e:
+        logger.error("[爆款视频][阶段1] analyze-images 入队失败: %s", e, exc_info=True)
+        job.mark_failed(f"任务入队失败: {e}")
+        repo.update(job)
+
+    return _to_response(job)
+
+
+@router.post("/{job_id}/generate-copy", response_model=ViralVideoJobResponse)
+def generate_copy(
+    job_id: str,
+    request: GenerateCopyRequest,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+    session: Session = Depends(get_db_session),
+) -> ViralVideoJobResponse:
+    """v1.6 阶段2：用户填完营销参数后，跑 意图解析 → 编导分镜脚本生成 → 合规审核。
+
+    跑完后状态=copy_generated，响应 copy_result（含 overview/scene_and_lighting/shots/
+    hard_constraints/negative_prompts/voiceover_script），前端展示脚本与口播供用户编辑；
+    确认/编辑后调 /{id}/confirm-copy 进入阶段3（TTS + 单次 Seedance 出片）。
+    """
+    repo = _get_job_repo(session)
+    job = repo.get(job_id)
+    if job is None:
+        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):
+        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:
+        job.retry_count += 1
+        job.error_msg = ""
+
+    # 把用户填的营销参数写到 job 上
+    job.industry = request.industry or job.industry
+    job.target_customer = request.target_customer or job.target_customer
+    job.persona_id = request.persona_id or job.persona_id
+    job.viral_structure = request.viral_structure or job.viral_structure
+    job.marketing_purpose = request.marketing_purpose or job.marketing_purpose
+    job.bgm_preference = request.bgm_preference or job.bgm_preference
+    if request.duration:
+        job.duration = max(5, min(30, int(request.duration)))
+    job.user_copy_text = request.user_copy_text if request.user_copy_text else job.user_copy_text
+    job.fusion_level = request.fusion_level or job.fusion_level
+    job.reference_audio_path = request.reference_audio_path or job.reference_audio_path
+    job.reference_video_url = request.reference_video_url or job.reference_video_url
+    job.style_strength = request.style_strength or job.style_strength
+    job.style_template_id = request.style_template_id or job.style_template_id
+    if request.style_guide is not None:
+        job.style_guide = request.style_guide
+    job.voice_id = request.voice_id or job.voice_id
+    job.voice_source = request.voice_source or job.voice_source
+    job.video_ratio = request.video_ratio or job.video_ratio or "9:16"
+    job.video_model = request.video_model or job.video_model or ""
+    job.video_resolution = getattr(request, "video_resolution", "") or job.video_resolution or "720p"
+
+    job.resume_from_image_analyzed()
+    repo.update(job)
+
+    try:
+        celery_app.send_task("worker.run_viral_video_generate_copy", args=[job.id])
+        logger.info("[爆款视频][阶段2] generate-copy 入队: job_id=%s", job.id)
+    except Exception as e:
+        logger.error("[爆款视频][阶段2] generate-copy 入队失败: %s", e, exc_info=True)
+        job.mark_failed(f"任务入队失败: {e}")
+        repo.update(job)
+
+    return _to_response(job)
+
+
+@router.post("/{job_id}/confirm-copy", response_model=ViralVideoJobResponse)
+def confirm_copy(
+    job_id: str,
+    request: ConfirmCopyRequest,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+    session: Session = Depends(get_db_session),
+) -> ViralVideoJobResponse:
+    """v1.6 阶段3：用户确认/编辑口播后开始 TTS + 单次 Seedance 生成 + 上传。"""
+    repo = _get_job_repo(session)
+    job = repo.get(job_id)
+    if job is None:
+        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 != 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）")
+
+    # 积分预扣（已扣过/重试任务跳过）
+    from app.config import settings as _settings
+
+    if _settings.points_enabled:
+        already_paid = (float(getattr(job, "credits_prepaid", 0) or 0) > 0) or (
+            float(getattr(job, "credits_cost", 0) or 0) > 0
+        )
+        if not already_paid:
+            from packages.domain.points_rules import calculate_viral_video_credits, resolve_video_dimensions
+            from packages.domain.points_service import PointsService
+
+            w, h = resolve_video_dimensions(
+                getattr(job, "video_resolution", "720p") or "720p",
+                job.video_ratio or "9:16",
+            )
+            est_credits = calculate_viral_video_credits(
+                int(job.duration or 15), w, h, job.video_model or "seedance-2.5"
+            )
+            svc = PointsService()
+            res = svc.deduct_viral_video(authenticated_user.user.id, est_credits, job.id, session)
+            if not res.get("success"):
+                balance = res.get("balance", 0)
+                raise HTTPException(
+                    status_code=402,
+                    detail={
+                        "code": "INSUFFICIENT_POINTS",
+                        "message": f"积分不足，需要 {est_credits} 积分，当前余额 {balance}",
+                        "required": est_credits,
+                        "balance": balance,
+                    },
+                )
+            job.credits_prepaid = est_credits
+            job.credits_transaction_id = res.get("transaction_id", "") or ""
+            repo.update(job)
+
+    job.resume_from_copy_generated(edited_copy=request.edited_copy or None)
+    repo.update(job)
+
+    try:
+        celery_app.send_task("worker.run_viral_video_render", args=[job.id])
+        logger.info("[爆款视频][阶段3] confirm-copy 入队: job_id=%s", job.id)
+    except Exception as e:
+        logger.error("[爆款视频][阶段3] confirm-copy 入队失败: %s", e, exc_info=True)
+        job.mark_failed(f"任务入队失败: {e}")
+        repo.update(job)
+
+    return _to_response(job)
+
+
+@router.post("/estimate-credits", response_model=EstimateCreditsResponse)
+def estimate_credits(
+    request: EstimateCreditsRequest,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+) -> EstimateCreditsResponse:
+    """爆款视频积分预估（纯计算，不扣费、不创建任务）。
+
+    返回 estimated_credits 与 formula_breakdown（tokens / video_cost / fixed_cost /
+    profit_multiplier / model_price / width / height / fps），便于前端展示计费明细。
+    同时兼容前端传 model 或 video_model、resolution 或 video_resolution、ratio 或 video_ratio。
+    """
+    from packages.domain.points_rules import (
+        calculate_viral_video_credits_with_breakdown,
+        resolve_video_dimensions,
+    )
+
+    model = (request.model or "").strip() or "seedance-2.5"
+    resolution = (request.resolution or "").strip() or "720p"
+    ratio = (request.ratio or "").strip() or "9:16"
+    duration = int(request.duration or 15)
+
+    w, h = resolve_video_dimensions(resolution, ratio)
+    credits, bd = calculate_viral_video_credits_with_breakdown(
+        duration,
+        w,
+        h,
+        model,
+    )
+    breakdown = CreditsFormulaBreakdown(**bd)
+    return EstimateCreditsResponse(estimated_credits=credits, formula_breakdown=breakdown)
+
+
+@router.get("/history", response_model=ViralVideoHistoryResponse)
+def list_viral_video_history(
+    limit: int = 50,
+    offset: int = 0,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+    session: Session = Depends(get_db_session),
+) -> ViralVideoHistoryResponse:
+    """获取用户的爆款视频历史列表。"""
+    repo = _get_job_repo(session)
+    jobs = repo.list_by_user(authenticated_user.user.id, limit=limit, offset=offset)
+    items = [_to_response(j) for j in jobs]
+    return ViralVideoHistoryResponse(items=items, total=len(items))
+
+
+@router.get("/style-templates", response_model=StyleTemplateListResponse)
+def list_style_templates(
+    session: Session = Depends(get_db_session),
+) -> StyleTemplateListResponse:
+    """获取风格模板列表。"""
+    repo = _get_style_repo(session)
+    templates = repo.list_all()
+    items = [
+        StyleTemplateResponse(
+            id=t["id"],
+            name=t["name"],
+            description=t["description"],
+            thumbnail_url=t["thumbnail_url"],
+            style_config=t["style_config"],
+        )
+        for t in templates
+    ]
+    return StyleTemplateListResponse(items=items)
+
+
+@router.get("/models")
+def list_available_models() -> dict:
+    """返回爆款视频可用模型列表（供前端模型选择器使用）。"""
+    dashscope_available = get_dashscope_client() is not None
+    models = list_viral_video_models(
+        include_placeholder=False,
+        dashscope_available=dashscope_available,
+    )
+    return {"models": models}
+
+
+@router.get("/{job_id}", response_model=ViralVideoJobResponse)
+def get_viral_video_job(
+    job_id: str,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+    session: Session = Depends(get_db_session),
+) -> ViralVideoJobResponse:
+    """查询爆款视频任务状态。"""
+    repo = _get_job_repo(session)
+    job = repo.get(job_id)
+    if job is None:
+        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="无权查看此任务")
+    return _to_response(job)
+
+
+@router.post("/{job_id}/retry", response_model=ViralVideoJobResponse)
+def retry_viral_video_job(
+    job_id: str,
+    request: RetryViralVideoRequest | None = None,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+    session: Session = Depends(get_db_session),
+) -> ViralVideoJobResponse:
+    """重试失败的爆款视频任务（也支持对僵尸/超时 running 任务强制重置后重试）。
+
+    可选 body (RetryViralVideoRequest)：若传入新的 duration/video_resolution/video_ratio/
+    video_model，会重新预估积分并与原 credits_prepaid 做差额多退少补（不足抛 402 阻止重试）；
+    不传 body 或参数无变化时，保持原参数、原预扣金额不变，仅重置状态并入队。
+    credits_prepaid 为 0 的老任务首次重试会走预扣流程（与 confirm-copy 一致）。
+    """
+    from datetime import datetime, timezone
+
+    repo = _get_job_repo(session)
+    job = repo.get(job_id)
+    if job is None:
+        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")
+
+    # 判定是否为僵尸 running 任务：running 超过 10 分钟且心跳停止超过 2 分钟
+    now = datetime.now(timezone.utc)
+    is_stale_running = False
+    if job.status == ViralVideoStatus.RUNNING and job.started_at is not None:
+        hb = getattr(job, "heartbeat_at", None) or job.updated_at
+        if (now - job.started_at).total_seconds() > 10 * 60 and hb is not None and (now - hb).total_seconds() > 2 * 60:
+            is_stale_running = True
+
+    if job.status != ViralVideoStatus.FAILED and not is_stale_running:
+        raise HTTPException(status_code=409, detail="只有失败或超时的任务可以重试")
+
+    # ── 参数变更检测 + 积分多退少补 ──────────────────────────────────────
+    req = request or RetryViralVideoRequest()
+    new_duration = req.duration
+    new_resolution = (req.video_resolution or "").strip() or None
+    new_ratio = (req.video_ratio or "").strip() or None
+    new_model = (req.video_model or "").strip() or None
+
+    old_duration = int(getattr(job, "duration", 15) or 15)
+    old_resolution = (getattr(job, "video_resolution", "720p") or "720p").strip() or "720p"
+    old_ratio = (getattr(job, "video_ratio", "9:16") or "9:16").strip() or "9:16"
+    old_model = (getattr(job, "video_model", "") or "").strip()
+
+    # 仅当有任意字段传入且值不同才算"参数变更"
+    param_changed = bool(
+        (new_duration is not None and int(new_duration) != old_duration)
+        or (new_resolution is not None and new_resolution != old_resolution)
+        or (new_ratio is not None and new_ratio != old_ratio)
+        or (new_model is not None and new_model != old_model)
+    )
+
+    from app.config import settings as _settings
+
+    need_points_settle = False
+    new_est = 0.0
+    if _settings.points_enabled and param_changed:
+        from packages.domain.points_rules import (
+            calculate_viral_video_credits_with_breakdown,
+            resolve_video_dimensions,
+        )
+
+        eff_dur = int(new_duration if new_duration is not None else old_duration)
+        eff_res = new_resolution if new_resolution is not None else old_resolution
+        eff_ratio = new_ratio if new_ratio is not None else old_ratio
+        eff_model = new_model if new_model is not None else (old_model or "seedance-2.5")
+        w, h = resolve_video_dimensions(eff_res, eff_ratio)
+        new_est, _ = calculate_viral_video_credits_with_breakdown(eff_dur, w, h, eff_model or "seedance-2.5")
+        need_points_settle = True
+
+    # 写入新参数（即使不开 points 也要允许用户重试时改参数）
+    if new_duration is not None:
+        job.duration = max(5, min(30, int(new_duration)))
+    if new_resolution is not None:
+        job.video_resolution = new_resolution
+    if new_ratio is not None:
+        job.video_ratio = new_ratio
+    if new_model is not None:
+        job.video_model = new_model
+
+    if need_points_settle:
+        from packages.domain.points_service import PointsService
+
+        old_prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
+        svc = PointsService()
+        diff = round(new_est - old_prepaid, 2)
+        if abs(diff) >= 0.01:
+            if diff > 0:
+                # 新预扣更多：补扣差额
+                res = svc.deduct_viral_video(authenticated_user.user.id, diff, job.id, session)
+                if not res.get("success"):
+                    balance = res.get("balance", 0)
+                    raise HTTPException(
+                        status_code=402,
+                        detail={
+                            "code": "INSUFFICIENT_POINTS",
+                            "message": f"重试参数变更后需补扣 {diff} 积分，余额不足（当前 {balance}，需 {new_est}）",
+                            "required": new_est,
+                            "balance": balance,
+                            "delta": diff,
+                        },
+                    )
+                job.credits_prepaid = round(old_prepaid + diff, 2)
+                logger.info(
+                    "[爆款视频][retry] 补扣差额 job_id=%s diff=%.2f new_prepaid=%.2f",
+                    job.id,
+                    diff,
+                    job.credits_prepaid,
+                )
+            else:
+                # 新预扣更少：退还差额
+                refund = round(-diff, 2)
+                txn_id = getattr(job, "credits_transaction_id", "") or ""
+                svc.refund_points(
+                    user_id=authenticated_user.user.id,
+                    amount=refund,
+                    source="viral_video",
+                    db=session,
+                    ref_id=txn_id or job.id,
+                    description="爆款视频重试参数变更退费",
+                )
+                job.credits_prepaid = round(old_prepaid - refund, 2)
+                logger.info(
+                    "[爆款视频][retry] 退还差额 job_id=%s refund=%.2f new_prepaid=%.2f",
+                    job.id,
+                    refund,
+                    job.credits_prepaid,
+                )
+        # 差额为 0 则不调整
+
+    # 重置状态
+    job.retry_count += 1
+    job.status = ViralVideoStatus.PENDING
+    job.error_msg = "" if not is_stale_running else "任务执行超时，已重置重试"
+    job.started_at = None
+    job.completed_at = None
+    job.current_stage = ""
+    job.phase_message = ""
+    job.heartbeat_at = None
+    repo.update(job)
+
+    # 重新入队
+    try:
+        celery_app.send_task("worker.run_viral_video_pipeline", args=[job.id])
+        logger.info(
+            "[爆款视频] 重试入队: job_id=%s retry_count=%d stale=%s params_changed=%s",
+            job.id,
+            job.retry_count,
+            is_stale_running,
+            param_changed,
+        )
+    except Exception as e:
+        logger.error("[爆款视频] 重试入队失败: %s", e, exc_info=True)
+        job.mark_failed(f"重试入队失败: {e}")
+        repo.update(job)
+
+    return _to_response(job)
+
+
+@router.post("/{job_id}/confirm-intent", response_model=ViralVideoJobResponse)
+def confirm_intent(
+    job_id: str,
+    request: ConfirmIntentRequest,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+    session: Session = Depends(get_db_session),
+) -> ViralVideoJobResponse:
+    """用户确认/修改 AI 生成的意图文案，恢复流水线。"""
+    repo = _get_job_repo(session)
+    job = repo.get(job_id)
+    if job is None:
+        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 != ViralVideoStatus.WAIT_USER_CONFIRM:
+        raise HTTPException(status_code=409, detail="任务当前不在等待确认状态")
+
+    # 更新文案
+    if request.confirmed_copy:
+        job.user_copy_text = request.confirmed_copy
+
+    # 恢复流水线
+    job.resume_from_confirm()
+    repo.update(job)
+
+    # 从断点恢复 Celery 任务
+    try:
+        celery_app.send_task("worker.resume_viral_video_pipeline", args=[job.id])
+        logger.info("[爆款视频] 意图确认，恢复流水线: job_id=%s", job.id)
+    except Exception as e:
+        logger.error("[爆款视频] 恢复流水线失败: %s", e, exc_info=True)
+        job.mark_failed(f"恢复流水线失败: {e}")
+        repo.update(job)
+
+    return _to_response(job)
+
+
+@router.post("/{job_id}/analyze-style", response_model=AnalyzeStyleResponse)
+def analyze_style(
+    job_id: str,
+    request: AnalyzeStyleRequest,
+    authenticated_user: AuthenticatedUser = Depends(get_current_user),
+    session: Session = Depends(get_db_session),
+) -> AnalyzeStyleResponse:
+    """触发参考视频风格分析（独立步骤，可在生成前单独调用）。"""
+    repo = _get_job_repo(session)
+    job = repo.get(job_id)
+    if job is None:
+        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")
+
+    # 更新参考视频 URL
+    job.reference_video_url = request.reference_video_url
+    if request.style_template_id:
+        job.style_template_id = request.style_template_id
+    repo.update(job)
+
+    # 入队风格分析任务
+    try:
+        celery_app.send_task("worker.run_video_style_analysis", args=[job.id])
+        logger.info("[爆款视频] 风格分析入队: job_id=%s", job.id)
+    except Exception as e:
+        logger.error("[爆款视频] 风格分析入队失败: %s", e, exc_info=True)
+
+    return AnalyzeStyleResponse(
+        job_id=job.id,
+        status="analyzing",
+        style_guide=None,
+    )
+
+
+# ── WebSocket 进度推送 ──────────────────────────────────────────────────
+
+
+def _ws_authenticate_user(token: str):
+    """从 token 字符串解析用户（复用 HTTP Bearer 的解码 + 黑名单逻辑）。
+
+    WebSocket 握手阶段不能发自定义 Authorization header，
+    因此统一通过 query 参数 ``?token=...`` 传 JWT。
+    """
+    from app.auth import _decode_user_token
+    from app.dependencies import get_user_repository
+
+    if not token:
+        return None
+    try:
+        payload = _decode_user_token(token)
+    except Exception:
+        return None
+    user_id = payload.get("sub")
+    if not isinstance(user_id, str) or not user_id:
+        return None
+    # 同步场景下手动拉 repository 实例
+    from app.db import SessionLocal
+
+    session = SessionLocal()
+    try:
+        user_repo = get_user_repository(session)
+        user = user_repo.find_by_id(user_id)
+        return user
+    finally:
+        session.close()
+
+
+async def _run_pubsub_forwarder(
+    websocket, redis_lib, settings, job_id: str
+) -> None:  # pragma: no cover - integration tested (real Redis + thread)
+    """订阅 Redis 频道并把消息桥接到 WebSocket，终态消息后自动关闭。
+
+    该函数封装了线程 + asyncio.Queue 桥接逻辑，在单测中可被整体替换为桩，
+    避免引入真实 Redis 与线程调度的不确定性。
+    """
+    import asyncio
+    import json
+    import threading
+
+    r = redis_lib.from_url(settings.REDIS_URL, decode_responses=True)
+    pubsub = r.pubsub(ignore_subscribe_messages=True)
+    channel = f"viral_video:{job_id}"
+    pubsub.subscribe(channel)
+
+    loop = asyncio.get_running_loop()
+    queue: asyncio.Queue = asyncio.Queue(maxsize=64)
+    stop_event = asyncio.Event()
+
+    def _reader() -> None:
+        try:
+            while not stop_event.is_set():
+                msg = pubsub.get_message(timeout=0.5)
+                if msg is None or msg.get("type") != "message":
+                    continue
+                raw = msg.get("data")
+                if not isinstance(raw, str):
+                    continue
+                try:
+                    payload = json.loads(raw)
+                except Exception:
+                    payload = {"type": "viral_video:progress", "data": {"raw": raw}}
+                loop.call_soon_threadsafe(queue.put_nowait, payload)
+                if payload.get("type") in ("viral_video:completed", "viral_video:failed"):
+                    loop.call_soon_threadsafe(stop_event.set)
+                    break
+        except Exception as e:
+            logger.warning("[爆款视频WS] pubsub reader 异常退出: %s", e)
+            loop.call_soon_threadsafe(stop_event.set)
+
+    try:
+        reader_thread = threading.Thread(target=_reader, name=f"viral-video-ws-{job_id}", daemon=True)
+        reader_thread.start()
+
+        while not stop_event.is_set():
+            try:
+                payload = await asyncio.wait_for(queue.get(), timeout=1.0)
+            except asyncio.TimeoutError:
+                continue
+            try:
+                await websocket.send_json(payload)
+            except Exception:
+                break
+            if payload.get("type") in ("viral_video:completed", "viral_video:failed"):
+                break
+    except WebSocketDisconnect:
+        logger.info("[爆款视频WS] 客户端断开: job_id=%s", job_id)
+    except Exception as e:
+        logger.error("[爆款视频WS] 转发异常: %s", e, exc_info=True)
+        try:
+            await websocket.send_json({"type": "viral_video:error", "message": f"服务异常: {e}"})
+        except Exception:
+            pass
+    finally:
+        stop_event.set()
+        try:
+            pubsub.unsubscribe(channel)
+            pubsub.close()
+        except Exception:
+            pass
+        try:
+            r.close()
+        except Exception:
+            pass
+        try:
+            await websocket.close()
+        except Exception:
+            pass
+
+
+@router.websocket("/ws/{job_id}")
+async def viral_video_websocket(websocket: WebSocket, job_id: str) -> None:
+    """WebSocket 桥接：订阅 Redis `viral_video:{job_id}` 频道并转发给前端。
+
+    认证：通过 ``?token=<jwt>`` query 参数传 JWT（浏览器 WS 握手不支持自定义 header）。
+    事件类型：
+      - viral_video:progress    中间进度（progress: 0-100）
+      - viral_video:wait_user   等待用户确认意图文案
+      - viral_video:completed   任务完成（data.video_url）
+      - viral_video:failed      任务失败（data.error）
+      - viral_video:error       服务端错误（如鉴权失败 / job 不存在 / 无权限）
+    """
+
+    import redis as redis_lib
+    from app.config import settings
+
+    # ── 1. 鉴权 ──────────────────────────────────────────────────────
+    token = websocket.query_params.get("token", "")
+    user = _ws_authenticate_user(token)
+    if user is None:
+        await websocket.close(code=4401, reason="Unauthorized")
+        return
+
+    # ── 2. 校验 job 归属 ─────────────────────────────────────────────
+    from app.db import SessionLocal
+
+    session = SessionLocal()
+    try:
+        job_repo = SQLAlchemyViralVideoJobRepository(session)
+        job = job_repo.get(job_id)
+        if job is None:
+            await websocket.close(code=4404, reason="Job not found")
+            return
+        if job.user_id != user.id:
+            await websocket.close(code=4403, reason="Forbidden")
+            return
+    finally:
+        session.close()
+
+    await websocket.accept()
+
+    # ── 3. 发送一条初始状态（前端连接后立即拿到当前进度） ────────────
+    try:
+        session = SessionLocal()
+        job_repo = SQLAlchemyViralVideoJobRepository(session)
+        job = job_repo.get(job_id)
+        if job is not None:
+            status_val = job.status.value if hasattr(job.status, "value") else str(job.status)
+            initial = {
+                "type": "viral_video:progress",
+                "job_id": job_id,
+                "stage": _stage_from_status(job),
+                "progress": _estimate_progress(job),
+                "message": _initial_message(job),
+                "data": {"status": status_val},
+            }
+            await websocket.send_json(initial)
+            # 已经终态 → 再发一条终态事件后立即关闭，避免占连接
+            if job.is_terminal:
+                is_completed = status_val == "completed"
+                terminal_type = "viral_video:completed" if is_completed else "viral_video:failed"
+                terminal_data = (
+                    {"video_url": job.result_video_url or ""} if is_completed else {"error": job.error_msg or ""}
+                )
+                await websocket.send_json(
+                    {
+                        "type": terminal_type,
+                        "job_id": job_id,
+                        "stage": "",
+                        "progress": 100 if is_completed else 0,
+                        "message": "视频生成完成" if is_completed else "任务失败",
+                        "data": terminal_data,
+                    }
+                )
+                await websocket.close()
+                return
+        session.close()
+    except Exception as e:
+        logger.warning("[爆款视频WS] 发送初始状态失败: %s", e)
+        try:
+            session.close()
+        except Exception:
+            pass
+
+    # ── 4. 订阅 Redis 频道并转发 ─────────────────────────────────────
+    # redis-py 的 pubsub 是同步阻塞的，放到线程里跑，通过 asyncio.Queue 桥接到 event loop。
+    # 该段依赖真实 Redis + 线程调度，属于集成测试范围，单测通过桩替换。
+    await _run_pubsub_forwarder(websocket, redis_lib, settings, job_id)
+
+
+def _job_status(job) -> str:
+    return job.status.value if hasattr(job.status, "value") else str(job.status)
+
+
+# 初始快照的 stage 推断：领域对象不持久化 stage，
+# 只能根据 status 给一个占位，后续 worker 推送的真实进度事件会覆盖。
+_STATUS_STAGE = {
+    "pending": "",
+    "running": "",
+    "image_analyzed": "image_analysis",
+    "copy_generated": "review",
+    "wait_user_confirm": "intent_parsing",
+    "completed": "uploading",
+    "failed": "",
+    "cancelled": "",
+}
+
+_STATUS_PROGRESS = {
+    "pending": 0.0,
+    "running": 5.0,
+    "image_analyzed": 15.0,
+    "copy_generated": 70.0,
+    "wait_user_confirm": 35.0,
+    "completed": 100.0,
+    "failed": 0.0,
+    "cancelled": 0.0,
+}
+
+_STATUS_MESSAGE = {
+    "pending": "任务已创建，等待执行",
+    "running": "任务执行中",
+    "image_analyzed": "图片分析完成，等待填写营销参数",
+    "copy_generated": "文案与分镜已生成，等待确认文案",
+    "wait_user_confirm": "等待用户确认意图文案",
+    "completed": "视频生成完成",
+    "failed": "任务失败",
+    "cancelled": "任务已取消",
+}
+
+
+def _stage_from_status(job) -> str:
+    return _STATUS_STAGE.get(_job_status(job), "")
+
+
+def _estimate_progress(job) -> float:
+    """根据 status 粗略估算百分比（0-100），用于连接初始快照；
+    连接建立后由 Redis 推送的真实事件持续更新。
+    """
+    return _STATUS_PROGRESS.get(_job_status(job), 5.0)
+
+
+def _initial_message(job) -> str:
+    """给新连接的前端一个可读的初始状态文案。"""
+    status_val = _job_status(job)
+    if status_val == "failed" and job.error_msg:
+        return f"任务失败: {job.error_msg}"
+    return _STATUS_MESSAGE.get(status_val, "任务准备中")

apps/api/app/core/task_enqueue.py

@@ -6,7 +6,7 @@ from app.core.celery_app import celery_app
 logger = logging.getLogger(__name__)
 
 # ── 限流阈值常量（全系统统一管理，不要在业务代码里硬编码） ──
-USER_PENDING_LIMIT = 3  # 单用户 pending 上限
+USER_PENDING_LIMIT = 20  # 单用户 pending 上限（#2098: 从 3 提到 20，支持批量任务自动排队）
 GLOBAL_PENDING_LIMIT = 20  # 全局 pending 上限
 WORKER_CONCURRENCY = 4  # worker 渲染并发数（infra/docker/compose.yml WORKER_CONCURRENCY 默认值）
 
@@ -154,19 +154,18 @@ def check_queue_limits(
     user_pending_limit: int = USER_PENDING_LIMIT,
     global_pending_limit: int = GLOBAL_PENDING_LIMIT,
 ) -> None:
-    """检查队列限流（预检查用，任务创建前调用），超限抛对应异常。
+    """检查队列限流（预检查用，任务创建前调用）。
 
-    边界语义：>= 上限即拒绝（达到上限就不能再加新任务）。
+    #2098 语义变更：用户级限流改为软提示，不再抛异常拒绝；仅全局硬上限抛 GlobalQueueFull。
 
     Args:
-        user_id: 用户 ID
+        user_id: 用户 ID（保留参数，当前不做用户级硬拒）
         generation_task_repository: 任务仓储
-        user_pending_limit: 单用户 pending 上限，默认 USER_PENDING_LIMIT
+        user_pending_limit: 单用户 pending 上限（保留，当前未硬拒）
         global_pending_limit: 全局 pending 上限，默认 GLOBAL_PENDING_LIMIT
 
     Raises:
-        GlobalQueueFull: 全局超限时抛出（优先级更高，先查全局）
-        UserPendingLimitExceeded: 用户超限时抛出
+        GlobalQueueFull: 全局超限时抛出
     """
     # 先查全局（系统级保护优先级更高）
     global_pending = generation_task_repository.count_pending_total()
@@ -179,17 +178,9 @@ def check_queue_limits(
         )
         raise GlobalQueueFull(pending_count=global_pending, limit=global_pending_limit)
 
-    # 再查用户级
-    if user_id:
-        user_pending = generation_task_repository.count_pending_by_user(user_id)
-        if user_pending >= user_pending_limit:
-            logger.warning(
-                "[队列限流] 用户 pending 任务数超限: user_id=%s, count=%d/%d",
-                user_id,
-                user_pending,
-                user_pending_limit,
-            )
-            raise UserPendingLimitExceeded(user_id=user_id, pending_count=user_pending, limit=user_pending_limit)
+    # #2098: 用户级限流改为软提示，不在预检查阶段拒绝（超额任务仍入队排队）。
+    # 真正的系统保护由全局 GLOBAL_PENDING_LIMIT 硬上限承担。
+    # UserPendingLimitExceeded 保留以兼容历史 import/except，但预检查与 safe_enqueue 均不再 raise。
 
 
 def _mark_task_failed_safely(
@@ -246,7 +237,6 @@ def safe_enqueue_generation_task(
 
     Raises:
         GlobalQueueFull: 全局 pending 超限时抛出，任务会被标记为 failed
-        UserPendingLimitExceeded: 用户 pending 超限时抛出，任务会被标记为 failed
     """
     # ── 入队前检查：任务已是 pending，用 > 判断（包含当前任务） ──
 
@@ -263,19 +253,18 @@ def safe_enqueue_generation_task(
         _mark_task_failed_safely(task, generation_task_repository, log_prefix, str(exc))
         raise exc
 
-    # 用户级限流检查（传了 user_id 才做）
+    # Bug B #2098: 用户级限流改为软提示，不再硬拒；所有任务都入队等待 worker 自然消费。
+    # user_pending_limit 作为兜底阈值保留（默认 20），达到时打 warning 日志但仍入队，
+    # 避免极端情况下恶意用户无限堆积任务。真正的系统保护由全局 GLOBAL_PENDING_LIMIT 承担。
     if user_id:
         user_pending = generation_task_repository.count_pending_by_user(user_id)
         if user_pending > user_pending_limit:
             logger.warning(
-                "[队列限流] 用户 pending 任务数超限(入队前): user_id=%s, count=%d/%d",
+                "[队列限流] 用户 pending 任务数超过软上限(入队): user_id=%s, count=%d/%d, 仍允许入队排队",
                 user_id,
                 user_pending,
                 user_pending_limit,
             )
-            exc = UserPendingLimitExceeded(user_id=user_id, pending_count=user_pending, limit=user_pending_limit)
-            _mark_task_failed_safely(task, generation_task_repository, log_prefix, str(exc))
-            raise exc
 
     # ── 发送 Celery 任务 ──
     try:
@@ -317,16 +306,18 @@ def safe_enqueue_generation_task(
     user_after = generation_task_repository.count_pending_by_user(user_id) if user_id else 0
 
     global_over = global_after > global_pending_limit
-    user_over = bool(user_id and user_after > user_pending_limit)
-
-    if global_over or user_over:
-        if global_over:
-            reason = f"全局 pending 超限(入队后): {global_after}/{global_pending_limit}"
-            exc = GlobalQueueFull(pending_count=global_after, limit=global_pending_limit)
-        else:
-            reason = f"用户 pending 超限(入队后): {user_after}/{user_pending_limit}"
-            exc = UserPendingLimitExceeded(user_id=user_id, pending_count=user_after, limit=user_pending_limit)
+    # Bug B #2098: 用户超限仅日志警告，不回滚任务
+    if user_id and user_after > user_pending_limit:
+        logger.warning(
+            "[队列限流] 用户 pending 超软上限(入队后): user_id=%s, count=%d/%d",
+            user_id,
+            user_after,
+            user_pending_limit,
+        )
 
+    if global_over:
+        reason = f"全局 pending 超限(入队后): {global_after}/{global_pending_limit}"
+        exc = GlobalQueueFull(pending_count=global_after, limit=global_pending_limit)
         logger.warning(
             "[队列限流] %s, task_id=%s, user_id=%s — 回滚状态为 failed",
             reason,

apps/api/app/db.py

@@ -7,9 +7,9 @@ from packages.adapters.sqlalchemy_impl import (
 )
 from packages.adapters.sqlalchemy_impl.schema_guard import assert_auto_create_schema_allowed
 
-ensure_database_exists(settings.DATABASE_URL)
+ensure_database_exists(settings.effective_database_url)
 engine, SessionLocal = build_session_factory(
-    settings.DATABASE_URL,
+    settings.effective_database_url,
     pool_size=settings.DATABASE_POOL_SIZE,
     max_overflow=settings.DATABASE_MAX_OVERFLOW,
     pool_timeout=settings.DATABASE_POOL_TIMEOUT,

apps/api/app/dependencies.py

@@ -56,7 +56,7 @@ from packages.adapters.sqlalchemy_impl.voice_library_repository import (
 from packages.ports.tag_repository import TagRepository
 from packages.ports.user_repository import UserRepository
 
-_engine, _SessionLocal = build_session_factory(settings.DATABASE_URL)
+_engine, _SessionLocal = build_session_factory(settings.effective_database_url)
 
 
 def get_db_session() -> Generator[Session, None, None]:

apps/api/app/schemas/points.py

@@ -13,9 +13,9 @@ from pydantic import BaseModel, Field
 class PointsBalanceResponse(BaseModel):
     """积分余额 + 会员状态"""
 
-    balance: int = Field(..., description="当前积分余额")
-    total_earned: int = Field(..., description="累计获得积分")
-    total_spent: int = Field(..., description="累计消耗积分")
+    balance: float = Field(..., description="当前积分余额")
+    total_earned: float = Field(..., description="累计获得积分")
+    total_spent: float = Field(..., description="累计消耗积分")
     is_member: bool = Field(default=False, description="是否付费会员")
     member_type: Optional[str] = Field(None, description="会员类型: monthly/quarterly/yearly")
     member_expires_at: Optional[datetime] = Field(None, description="会员到期时间")
@@ -30,8 +30,8 @@ class PointsTransactionItem(BaseModel):
     id: str
     type: str = Field(..., description="类型: add/deduct")
     source: str = Field(..., description="来源场景")
-    amount: int
-    balance_after: int
+    amount: float
+    balance_after: float
     description: str = ""
     ref_id: str = ""
     created_at: Optional[str] = None
@@ -99,9 +99,9 @@ class PointsCheckResponse(BaseModel):
     """消费前余额检查响应"""
 
     allowed: bool
-    required_points: int
-    current_balance: int
-    remaining_after: int
+    required_points: float
+    current_balance: float
+    remaining_after: float
     is_free_quota: bool = False
 
 
@@ -112,7 +112,7 @@ class PointsDeductRequest(BaseModel):
     """积分扣减请求"""
 
     scene_key: str
-    amount: int
+    amount: float
     description: Optional[str] = ""
     ref_id: Optional[str] = ""
 
@@ -170,7 +170,7 @@ class MembershipStatusResponse(BaseModel):
     is_member: bool
     member_type: Optional[str] = None
     member_expires_at: Optional[datetime] = None
-    points_balance: int
+    points_balance: float
     max_resolution: str = Field(
         default="1080p",
         description="可用最高分辨率: 720p(free) / 1080p(paid)",

apps/api/app/schemas/upload.py

@@ -29,6 +29,8 @@ class DirectUploadPrepareResponse(BaseModel):
     duplicated: bool = False
     skip_transfer: bool = False
     asset_id: str = ""
+    # duplicated=true 时填充已存在素材的公网 URL，前端可直接用而不必再调 complete
+    url: str = Field(default="", description="duplicated=true 时已存在素材的公网 URL")
 
 
 class DirectUploadCompleteRequest(BaseModel):

apps/api/app/schemas/viral_video.py

@@ -0,0 +1,320 @@
+"""爆款视频 API schemas (v1.6 单次 Seedance 出片版)。"""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from pydantic import BaseModel, Field, field_validator
+
+# -- 枚举常量 --
+
+VALID_FUSION_LEVELS = ("ai_full", "full_ai", "ai_polish", "user_primary")
+VALID_STYLE_STRENGTHS = ("light", "medium", "strict")
+VALID_STAGES = (
+    "image_analysis",
+    "video_analysis",
+    "intent_parsing",
+    "script_generation",
+    "review",
+    "tts",
+    "rendering",
+    "uploading",
+)
+VALID_VIDEO_RATIOS = ("9:16", "16:9", "1:1", "4:3", "3:4", "21:9")
+VALID_DURATIONS = (5, 10, 15, 20, 25, 30)
+VALID_VIDEO_RESOLUTIONS = ("480p", "720p", "1080p", "普清", "高清", "超清")
+
+
+# -- 编导脚本结构（v1.6） --
+
+
+class ShotScript(BaseModel):
+    """逐镜头分镜。"""
+
+    time_range: str = Field(default="", description="时间区间，如 0-3秒")
+    shot_type_angle_movement: str = Field(default="", description="景别/角度/运镜，如『近景俯拍45度，缓慢推镜』")
+    scene_and_dialogue: str = Field(default="", description="场景描述+口播台词")
+    action_details: str = Field(default="", description="人物动作、表情、物品操作细节")
+    audio_bgm: str = Field(default="", description="环境音+BGM提示")
+    transition: str = Field(default="硬切", description="转场方式：硬切/淡入淡出/叠化")
+    reference_image_index: int | None = Field(
+        default=None, description="参考图片索引（0-based，对应上传的第几张产品图）"
+    )
+
+
+class CopyResultOverview(BaseModel):
+    theme: str = ""
+    total_duration: int = 15
+    aspect_ratio: str = "9:16"
+
+
+class CopyResult(BaseModel):
+    """v1.6 编导分镜脚本结构（给前端 + Seedance 用）。"""
+
+    overview: CopyResultOverview = Field(default_factory=CopyResultOverview)
+    scene_and_lighting: str = ""
+    shots: list[ShotScript] = Field(default_factory=list)
+    hard_constraints: list[str] = Field(default_factory=list)
+    negative_prompts: list[str] = Field(default_factory=list)
+    voiceover_script: str = Field(
+        default="", description="纯口播对白，从各镜 scene_and_dialogue 的对白部分拼接，供 TTS 使用"
+    )
+    # 向后兼容：final_copy = voiceover_script
+    final_copy: str = ""
+    suggested_copy: str = ""
+    title: str = ""
+
+
+# -- Request Schemas --
+
+
+class CreateViralVideoRequest(BaseModel):
+    """旧接口：一键创建（保留兼容）。"""
+
+    images: list[str] = Field(..., min_length=1, max_length=20)
+    industry: str = ""
+    target_customer: str = ""
+    persona_id: str = ""
+    viral_structure: str = ""
+    marketing_purpose: str = ""
+    bgm_preference: str = ""
+    duration: int = Field(default=15, ge=5, le=30, description="视频时长（秒），5-30")
+    user_copy_text: str = ""
+    fusion_level: str = "ai_polish"
+    reference_audio_path: str = ""
+    reference_video_url: str = ""
+    style_strength: str = "medium"
+    style_template_id: str = ""
+    voice_id: str = ""
+    voice_source: str = ""
+    video_ratio: str = "9:16"
+    video_model: str = ""
+    video_resolution: str = "720p"
+
+    @field_validator("fusion_level")
+    @classmethod
+    def _v_fl(cls, v: str) -> str:
+        if v == "full_ai":
+            return "ai_full"
+        if v not in VALID_FUSION_LEVELS:
+            raise ValueError(f"fusion_level must be one of {VALID_FUSION_LEVELS}")
+        return v
+
+    @field_validator("style_strength")
+    @classmethod
+    def _v_ss(cls, v: str) -> str:
+        if v not in VALID_STYLE_STRENGTHS:
+            raise ValueError(f"style_strength must be one of {VALID_STYLE_STRENGTHS}")
+        return v
+
+
+class AnalyzeImagesRequest(BaseModel):
+    """v1.5+ 阶段1：创建任务 + 图片/视频分析。"""
+
+    images: list[str] = Field(..., min_length=1, max_length=30)
+    reference_video_url: str = ""
+    style_template_id: str = ""
+    style_strength: str = "medium"
+    voice_id: str = ""
+    voice_source: str = ""
+    video_ratio: str = "9:16"
+    video_model: str = ""
+    video_resolution: str = "720p"
+    duration: int = Field(default=15, ge=5, le=30)
+
+
+class GenerateCopyRequest(BaseModel):
+    """v1.5+ 阶段2：填完营销参数，生成编导脚本。"""
+
+    industry: str = ""
+    target_customer: str = ""
+    persona_id: str = ""
+    viral_structure: str = ""
+    marketing_purpose: str = ""
+    bgm_preference: str = ""
+    duration: int = Field(default=15, ge=5, le=30)
+    user_copy_text: str = ""
+    fusion_level: str = "ai_polish"
+    reference_audio_path: str = ""
+    reference_video_url: str = ""
+    style_strength: str = "medium"
+    style_template_id: str = ""
+    style_guide: dict | None = None
+    voice_id: str = ""
+    voice_source: str = ""
+    video_ratio: str = "9:16"
+    video_model: str = ""
+    video_resolution: str = "720p"
+
+    @field_validator("fusion_level")
+    @classmethod
+    def _v_fl(cls, v: str) -> str:
+        if v == "full_ai":
+            return "ai_full"
+        if v not in VALID_FUSION_LEVELS:
+            raise ValueError(f"fusion_level must be one of {VALID_FUSION_LEVELS}")
+        return v
+
+    @field_validator("style_strength")
+    @classmethod
+    def _v_ss(cls, v: str) -> str:
+        if v not in VALID_STYLE_STRENGTHS:
+            raise ValueError(f"style_strength must be one of {VALID_STYLE_STRENGTHS}")
+        return v
+
+
+class ConfirmCopyRequest(BaseModel):
+    """v1.5+ 阶段3：用户确认/编辑口播后开始渲染（TTS+单次Seedance）。"""
+
+    edited_copy: str = Field(default="", description="用户编辑后的口播文案；为空则用 AI 生成的 voiceover_script")
+
+
+class ConfirmIntentRequest(BaseModel):
+    """旧 confirm-intent（兼容）。"""
+
+    confirmed_copy: str = ""
+    adjustments: str = ""
+
+
+class AnalyzeStyleRequest(BaseModel):
+    reference_video_url: str = Field(..., description="参考视频 URL")
+    style_template_id: str = ""
+
+
+# -- Response Schemas --
+
+
+class ViralVideoJobResponse(BaseModel):
+    """爆款视频任务响应（v1.6 包含 copy_result 编导脚本结构）。"""
+
+    id: str
+    user_id: str
+    images: list[str] = Field(default_factory=list)
+    industry: str = ""
+    target_customer: str = ""
+    persona_id: str = ""
+    viral_structure: str = ""
+    marketing_purpose: str = ""
+    bgm_preference: str = ""
+    duration: int = 15
+    user_copy_text: str = ""
+    fusion_level: str = "ai_polish"
+    reference_audio_path: str = ""
+    reference_video_url: str = ""
+    style_strength: str = "medium"
+    style_guide: dict | None = None
+    style_template_id: str = ""
+    status: str
+    current_stage: str = (
+        ""  # 细粒度阶段 snake_case（analyzing_images/parsing_intent/generating_script/reviewing/tts_synthesizing/rendering_video/uploading）
+    )
+    phase_message: str = ""  # 中文阶段提示文案（前端轮询/SSE 直接展示）
+    image_analysis: dict | None = None
+    # v1.6 编导脚本（推荐前端使用）
+    copy_result: dict | None = None
+    # v1.5 兼容字段
+    storyboard: list | None = None
+    generated_copy_text: str = ""
+    # 音色/视频参数
+    voice_id: str = ""
+    voice_source: str = ""
+    video_ratio: str = "9:16"
+    video_model: str = ""
+    intent_result: dict | None = None
+    result_video_url: str = ""
+    pre_trusted_images: list[str] | None = None
+    video_resolution: str = "720p"
+    credits_prepaid: float = 0.0
+    credits_cost: float = 0.0
+    error_msg: str = ""
+    retry_count: int = 0
+    started_at: datetime | None = None
+    completed_at: datetime | None = None
+    created_at: datetime | None = None
+    updated_at: datetime | None = None
+
+
+class ViralVideoHistoryResponse(BaseModel):
+    items: list[ViralVideoJobResponse]
+    total: int
+
+
+class StyleTemplateResponse(BaseModel):
+    id: str
+    name: str
+    description: str = ""
+    thumbnail_url: str = ""
+    style_config: dict = Field(default_factory=dict)
+
+
+class StyleTemplateListResponse(BaseModel):
+    items: list[StyleTemplateResponse]
+
+
+class AnalyzeStyleResponse(BaseModel):
+    job_id: str
+    status: str
+    style_guide: dict | None = None
+
+
+# -- 积分预估 --
+
+
+class EstimateCreditsRequest(BaseModel):
+    """爆款视频积分预估请求。
+
+    前端可传 model 或 video_model（兼容老字段）；resolution/ratio/duration 为预估所需参数。
+    """
+
+    model: str = Field(default="", alias="video_model")
+    resolution: str = Field(default="720p", alias="video_resolution")
+    ratio: str = Field(default="9:16", alias="video_ratio")
+    duration: int = Field(default=15, ge=5, le=30)
+
+    model_config = {"populate_by_name": True}
+
+
+class CreditsFormulaBreakdown(BaseModel):
+    """爆款视频积分计费公式明细（前端展示用）。"""
+
+    tokens: float = Field(..., description="估算视频 tokens 数 (duration*width*height*fps/1024)")
+    video_cost: float = Field(..., description="视频生成成本（元）= tokens/1e6 * model_price")
+    fixed_cost: float = Field(..., description="固定成本（元），含 VLM/LLM/TTS/OSS/服务器")
+    profit_multiplier: float = Field(..., description="利润系数（默认 1.3）")
+    model_price: float = Field(..., description="模型单价（元/百万 tokens）")
+    width: int = Field(..., description="视频宽度像素")
+    height: int = Field(..., description="视频高度像素")
+    fps: int = Field(..., description="视频帧率")
+
+
+class EstimateCreditsResponse(BaseModel):
+    """爆款视频积分预估响应。"""
+
+    estimated_credits: float
+    formula_breakdown: CreditsFormulaBreakdown = Field(..., description="计费公式明细")
+
+
+class RetryViralVideoRequest(BaseModel):
+    """重试爆款视频任务的请求体（可选，允许改参数重新预估积分多退少补）。
+
+    不传 body 或字段全缺省：保持原参数、不重新扣点，走默认重置+入队逻辑。
+    传入新的 duration/video_resolution/video_ratio/video_model：重新预估积分，
+    与原 credits_prepaid 比较后多退少补（差额补扣不足抛 402）。
+    """
+
+    duration: int | None = Field(default=None, ge=5, le=30, description="重试时新的视频时长（秒）")
+    video_resolution: str | None = Field(default=None, description="重试时新的分辨率，如 720p/1080p")
+    video_ratio: str | None = Field(default=None, description="重试时新的画幅比，如 9:16/16:9")
+    video_model: str | None = Field(default=None, description="重试时新的视频模型，如 seedance-2.5")
+
+
+# -- WebSocket 事件 Schema --
+
+
+class WSProgressEvent(BaseModel):
+    type: str = "viral_video:progress"
+    job_id: str
+    stage: str
+    progress: float = Field(ge=0.0, le=100.0)
+    message: str = ""
+    data: dict = Field(default_factory=dict)

apps/api/app/services/narrative_service.py

@@ -11,14 +11,12 @@
    存储路径与元信息约定），返回 asset_id —— 下游仍以 voice_library_id（实为
    audio asset id）消费，渲染链路零改动。
 
-积分扣点与 /tts 合成端点保持一致（ai_voice 场景），失败退费。
 """
 
 from __future__ import annotations
 
 import json
 import logging
-import math
 import subprocess
 import tempfile
 from dataclasses import dataclass
@@ -32,13 +30,10 @@ from packages.application.cosyvoice_service import CosyVoiceService
 from packages.application.tts_job.use_cases import CreateTTSJobUseCase
 from packages.application.tts_job.workflow import TTSWorkflowService
 from packages.domain import Asset, AssetLibrary, AssetLibraryKind, AssetStatus, ClassificationStatus
-from packages.domain.points_rules import calculate_points_cost
-from packages.domain.points_service import PointsService
 from packages.shared.storage import SharedStorageService
 
 logger = logging.getLogger(__name__)
 
-_POINTS_SCENE = "ai_voice"
 _SYNTH_TIMEOUT = 180.0  # 叙事配音在 HTTP 请求内同步等待，长文案分段合成时留出余量
 _CONTENT_TYPE_MAP = {"mp3": "audio/mpeg", "wav": "audio/wav", "pcm": "audio/pcm", "opus": "audio/opus"}
 
@@ -273,24 +268,6 @@ def prepare_narrative_voice(
         voice_clone_repository=voice_clone_repository,
     )
 
-    # 积分扣点（与 /tts 合成端点同口径），失败时在合成失败分支退费
-    points_svc = PointsService() if points_enabled else None
-    points_deducted = 0
-    if points_svc is not None:
-        est_minutes = max(1.0, math.ceil(len(content) / 240))
-        points_deducted = calculate_points_cost(
-            _POINTS_SCENE,
-            is_member=is_member,
-            duration_minutes=est_minutes,
-            member_type=member_type,
-        )
-        deduct_res = points_svc.deduct_points(user_id, points_deducted, _POINTS_SCENE, db)
-        if not deduct_res["success"]:
-            raise NarrativeError(
-                f"积分不足，需要 {points_deducted} 积分，当前余额 {deduct_res['balance']}",
-                status_code=402,
-            )
-
     use_case = CreateTTSJobUseCase(tts_repository)
     job = use_case.execute(
         user_id=user_id,
@@ -311,19 +288,9 @@ def prepare_narrative_voice(
             workflow.process_synthesis_failure(job.id, str(e))
         except Exception:  # noqa: BLE001
             logger.warning("标记叙事 TTS job 失败出错: job_id=%s", job.id, exc_info=True)
-        if points_deducted and points_svc is not None:
-            try:
-                points_svc.refund_points(user_id, points_deducted, _POINTS_SCENE, db, ref_id=job.id)
-            except Exception:  # noqa: BLE001
-                logger.warning("叙事 TTS 失败退积分异常: job_id=%s", job.id, exc_info=True)
         raise NarrativeError(f"配音合成失败：{e}", status_code=502) from e
 
     if not job.is_completed:
-        if points_deducted and points_svc is not None:
-            try:
-                points_svc.refund_points(user_id, points_deducted, _POINTS_SCENE, db, ref_id=job.id)
-            except Exception:  # noqa: BLE001
-                logger.warning("叙事 TTS 未完成退积分异常: job_id=%s", job.id, exc_info=True)
         raise NarrativeError("配音合成未完成，请稍后重试", status_code=504)
 
     asset = _save_tts_job_as_voice_asset(

apps/web/src/api/assets/types.ts

@@ -150,6 +150,8 @@ export interface DirectUploadPrepareResult {
    * 两个字段是同一语义的别名（后端可能只返回其一），前端任意为 true 即视为命中去重。
    */
   skip_transfer?: boolean
+  /** duplicated=true 时后端返回已存在素材的公网 URL，前端直接用而不必再调 complete */
+  url?: string
 }
 
 /** 直传完成确认返回 */

apps/web/src/api/assets/upload.ts

@@ -3,9 +3,24 @@
  */
 import apiClient from "../client"
 import { getOrCreateDefaultProject } from "../projects"
+import { ensureDefaultLibrary } from "./libraries"
 import type { DirectUploadPrepareResult, DirectUploadCompleteResult } from "./types"
 import { computeFileHash, makeClientUploadId } from "./uploadDedup"
 
+/** 根据 File.type 推断素材库 kind（image/video/voice）；无法推断时默认 image */
+function inferKindFromFile(file: File): "image" | "video" | "voice" {
+  const t = (file.type || "").toLowerCase()
+  if (t.startsWith("image/")) return "image"
+  if (t.startsWith("video/")) return "video"
+  if (t.startsWith("audio/")) return "voice"
+  // 兜底：按扩展名再判一次
+  const name = file.name.toLowerCase()
+  if (/\.(png|jpe?g|gif|webp|bmp|svg|avif)$/.test(name)) return "image"
+  if (/\.(mp4|mov|webm|avi|mkv|flv|wmv|m4v)$/.test(name)) return "video"
+  if (/\.(mp3|wav|m4a|aac|ogg|flac|opus|webm)$/.test(name)) return "voice"
+  return "image"
+}
+
 /** 预签名直传准备 */
 export const prepareDirectUpload = async (data: {
   project_id: string
@@ -108,6 +123,8 @@ const putToOSS = (
 
 /** 单个文件的上传阶段信息（供批量上传队列做状态绑定） */
 export interface DirectUploadHandle {
+  /** 实际使用的素材库（内部解析出来，便于调用方做后续 UI/缓存操作） */
+  library: { id: string; kind: "image" | "video" | "voice" }
   /** prepare 返回（含可能的预建 asset_id） */
   prepared: DirectUploadPrepareResult
   /** 直传 OSS（可重复调用用于重试） */
@@ -119,10 +136,17 @@ export interface DirectUploadHandle {
 /**
  * 准备一次直传：调 prepare 拿到签名表单（后端可能同时预建 uploading 态 asset），
  * 返回分段执行的 handle，调用方自行控制 transfer/complete 时机（便于队列并发与重试）。
+ *
+ * 修复 P0 404：library_id 改为可选；未传时自动根据文件类型在默认项目下确保对应素材库存在，
+ * 避免调用方从「全部素材库列表」里挑一个 library_id、但与默认项目 project_id 不匹配，
+ * 导致后端返回 "Asset library not found" 404。
  */
 export const prepareDirectUploadHandle = async (data: {
   file: File
-  library_id: string
+  /** 素材库 ID；未传时按文件类型自动在默认项目下 ensure-default */
+  library_id?: string
+  /** 显式指定素材库 kind；未传时按 MIME/扩展名推断 */
+  kind?: "image" | "video" | "voice"
   /** 前端算好的文件内容哈希（SHA-256 hex），prepare/complete 均携带 */
   fileHash?: string
   /** 本次逻辑上传的幂等 token，prepare/complete 一致、重试复用 */
@@ -138,9 +162,17 @@ export const prepareDirectUploadHandle = async (data: {
     throw new Error(`初始化默认项目失败，无法开始上传：${reason}`)
   }
 
+  // 解析 library_id：调用方传了就用，没传就按 kind 自动 ensure-default
+  let resolvedLibraryId = data.library_id
+  const resolvedKind = data.kind ?? inferKindFromFile(data.file)
+  if (!resolvedLibraryId) {
+    const lib = await ensureDefaultLibrary({ project_id: project.id, kind: resolvedKind })
+    resolvedLibraryId = lib.id
+  }
+
   const prepared = await prepareDirectUpload({
     project_id: project.id,
-    library_id: data.library_id,
+    library_id: resolvedLibraryId,
     filename: data.file.name,
     content_type: data.file.type || "application/octet-stream",
     file_size: data.file.size,
@@ -149,12 +181,13 @@ export const prepareDirectUploadHandle = async (data: {
   })
 
   return {
+    library: { id: resolvedLibraryId, kind: resolvedKind },
     prepared,
     transfer: (onProgress) => putToOSS(prepared, data.file, onProgress),
     complete: () =>
       completeDirectUpload({
         project_id: project.id,
-        library_id: data.library_id,
+        library_id: resolvedLibraryId,
         storage_key: prepared.storage_key,
         file_hash: data.fileHash,
         client_upload_id: data.clientUploadId,
@@ -164,10 +197,17 @@ export const prepareDirectUploadHandle = async (data: {
   }
 }
 
-/** 直传上传（大文件推荐），支持可选进度回调；一次性完成 prepare→transfer→complete */
+/** 直传上传（大文件推荐），支持可选进度回调；一次性完成 prepare→transfer→complete
+ *
+ * P0 404 修复：library_id 可选；不传时内部按文件类型自动匹配正确项目下的素材库，
+ * 保证 project_id 与 library_id 必然一致。
+ */
 export const uploadAssetDirect = async (data: {
   file: File
-  library_id: string
+  /** 素材库 ID；可选，不传按文件类型自动解析默认项目下的对应素材库（推荐用法） */
+  library_id?: string
+  /** 显式指定素材库 kind；未传时按文件 MIME/扩展名推断 */
+  kind?: "image" | "video" | "voice"
   onProgress?: (percent: number) => void
   /** 文件内容哈希；未传时自动补算（配音/封面/克隆等非队列链路统一受益） */
   fileHash?: string
@@ -180,6 +220,7 @@ export const uploadAssetDirect = async (data: {
   const handle = await prepareDirectUploadHandle({
     file: data.file,
     library_id: data.library_id,
+    kind: data.kind,
     fileHash,
     clientUploadId,
   })
@@ -188,7 +229,7 @@ export const uploadAssetDirect = async (data: {
     return {
       storage_key: handle.prepared.storage_key,
       ingest_job_id: "",
-      url: "",
+      url: handle.prepared.url || "",
       duplicated: true,
       asset_id: handle.prepared.asset_id,
     }

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

@@ -0,0 +1,156 @@
+import apiClient from "@/api/client"
+import type {
+  GenerateViralVideoRequest,
+  HistoryResponse,
+  StyleTemplate,
+  ViralVideoJob,
+  ImageAnalysisResult,
+  CopyResult,
+  AnalyzeImagesRequest,
+  GenerateCopyRequest,
+  ConfirmCopyRequest,
+  ViralVideoModel,
+  ViralVideoModelsResponse,
+} from "./types"
+
+/** 创建爆款视频任务 */
+export function generateViralVideo(payload: GenerateViralVideoRequest) {
+  return apiClient.post<ViralVideoJob>("/viral-video/generate", payload).then((r) => r.data)
+}
+
+/** 查询单个任务 */
+export function getViralVideoJob(id: string) {
+  return apiClient.get<ViralVideoJob>(`/viral-video/${id}`).then((r) => r.data)
+}
+
+/** 用户确认/修改 AI 理解的意图后继续 */
+export function confirmViralVideoIntent(
+  id: string,
+  payload: { confirmed_copy?: string; edits?: Record<string, unknown> },
+) {
+  return apiClient
+    .post<ViralVideoJob>(`/viral-video/${id}/confirm-intent`, payload)
+    .then((r) => r.data)
+}
+
+/** 重试失败任务 */
+export function retryViralVideo(id: string) {
+  return apiClient.post<ViralVideoJob>(`/viral-video/${id}/retry`).then((r) => r.data)
+}
+
+/** 历史记录（分页） */
+export function getViralVideoHistory(params?: { page?: number; page_size?: number }) {
+  return apiClient.get<HistoryResponse>("/viral-video/history", { params }).then((r) => r.data)
+}
+
+/** 预设风格模板 */
+export function getViralStyleTemplates() {
+  return apiClient.get<StyleTemplate[]>("/viral-video/style-templates").then((r) => r.data)
+}
+
+/** 上传参考视频后触发风格分析 */
+export function analyzeViralStyle(id: string) {
+  return apiClient.post<ViralVideoJob>(`/viral-video/${id}/analyze-style`).then((r) => r.data)
+}
+
+/** 动态预估积分消耗（STEP3 参数变化时调用） */
+export function estimateViralVideoCredits(params: {
+  video_model: string
+  resolution: string
+  video_ratio: string
+  duration: number
+}) {
+  return apiClient
+    .post<{ estimated_credits: number }>("/viral-video/estimate-credits", params)
+    .then((r) => r.data)
+}
+
+/** 获取支持的视频模型列表（GET /viral-video/models）。后端返回 {models: [...]} 包装 */
+export function getViralVideoModels() {
+  return apiClient.get<ViralVideoModelsResponse>("/viral-video/models").then((r) => {
+    const data = r.data as ViralVideoModelsResponse | ViralVideoModel[] | null | undefined
+    if (Array.isArray(data)) return data
+    if (data && Array.isArray((data as ViralVideoModelsResponse).models)) {
+      return (data as ViralVideoModelsResponse).models
+    }
+    return []
+  })
+}
+/** ── 三步拆分：前端 mock 辅助函数（后端新接口上线后可替换） ── */
+
+/**
+ * 客户端图片分析 mock（后端未提供 analyze-only 端点前的占位方案）：
+ * 基于已上传图片生成一份示例识别汇览，让 STEP1→STEP2 交互可走通。
+ * 后端上线后改为调用真实接口。
+ */
+export function mockImageAnalysis(images: { name: string }[]): Promise<ImageAnalysisResult> {
+  return new Promise((resolve) => {
+    setTimeout(() => {
+      const products = images.slice(0, 3).map((img, i) => {
+        const n = img.name.replace(/\.[^.]+$/, "")
+        return {
+          name: n || `商品 ${i + 1}`,
+          spec: i === 0 ? "500ml/瓶" : i === 1 ? "300g/盒" : undefined,
+          brand: i === 0 ? "示例品牌" : undefined,
+          features:
+            i === 0
+              ? "瓶身透明、蓝色标签、白色瓶盖；标签上印有品牌Logo和产品名称；光线均匀，主体居中"
+              : i === 1
+                ? "盒装包装、主色调为米白+暖黄；正面有产品实物图；文字清晰可辨"
+                : "产品主体清晰、背景干净、色彩鲜艳，突出核心卖点",
+          label_text: i === 0 ? "包装正面印有产品名称、净含量、品牌Logo" : undefined,
+          image_index: i,
+        }
+      })
+      resolve({ products })
+    }, 1800)
+  })
+}
+
+/**
+ * 客户端文案生成 mock（后端未提供 generate-copy 端点前的占位方案）：
+ * 后端上线后改为调用真实接口。
+ */
+export function mockGenerateCopy(params: {
+  product: string
+  sellingPoints?: string[]
+  tone?: string
+  duration?: number
+  marketingPurpose?: string
+  industry?: string
+  targetCustomer?: string
+}): Promise<CopyResult> {
+  return new Promise((resolve) => {
+    setTimeout(() => {
+      const product = params.product || "这款产品"
+      const tone = params.tone || "亲切务实"
+      const purpose = params.marketingPurpose || "品牌种草"
+      resolve({
+        title: `【${purpose}】${product}，用过的人都说好！`,
+        final_copy: `你有没有发现，选对一款${params.industry || "好物"}真的能让生活省心很多？\n\n今天给大家推荐这款${product}。${tone.includes("亲切") ? "说实话，" : ""}我自己用了一段时间，最直观的感受就是——好用、省心、值得回购。\n\n✅ 亮点一：品质到位，用料扎实，细节处见用心\n✅ 亮点二：使用体验舒服，日常高频场景都能打\n✅ 亮点三：性价比很能打，这个价位真的没什么可挑的\n\n如果你也在找一款靠谱的${params.industry || "日常好物"}，真的建议试试${product}，不会让你失望。点击左下角，直接入手！`,
+        suggested_copy: `你有没有发现，选对一款${params.industry || "好物"}真的能让生活省心很多？\n\n今天给大家推荐这款${product}。${tone.includes("亲切") ? "说实话，" : ""}我自己用了一段时间，最直观的感受就是——好用、省心、值得回购。\n\n✅ 亮点一：品质到位，用料扎实，细节处见用心\n✅ 亮点二：使用体验舒服，日常高频场景都能打\n✅ 亮点三：性价比很能打，这个价位真的没什么可挑的\n\n如果你也在找一款靠谱的${params.industry || "日常好物"}，真的建议试试${product}，不会让你失望。点击左下角，直接入手！`,
+      })
+    }, 2200)
+  })
+}
+
+/** ── 三步拆分 v1.5 真实后端 API（PR #2117 合入后启用，前端可替换 mock 调用） ── */
+
+/** 阶段1：上传图片后仅做 VLM 图片分析 + 可选参考视频风格分析，完成后状态=image_analyzed */
+export function analyzeViralImages(payload: AnalyzeImagesRequest) {
+  return apiClient.post<ViralVideoJob>("/viral-video/analyze-images", payload).then((r) => r.data)
+}
+
+/** 阶段2：用户填完营销参数后生成文案+分镜+合规审核，完成后状态=copy_generated，返回 copy_result */
+export function generateViralCopy(id: string, payload: GenerateCopyRequest) {
+  return apiClient
+    .post<ViralVideoJob>(`/viral-video/${id}/generate-copy`, payload)
+    .then((r) => r.data)
+}
+
+/** 阶段3：用户确认/编辑文案后开始 TTS→渲染→上传，完成后状态=completed */
+export function confirmViralCopy(id: string, payload: ConfirmCopyRequest = {}) {
+  return apiClient
+    .post<ViralVideoJob>(`/viral-video/${id}/confirm-copy`, payload)
+    .then((r) => r.data)
+}

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

@@ -0,0 +1,317 @@
+export type FusionLevel = "ai_full" | "ai_polish" | "user_primary"
+export const FUSION_LEVELS: { value: FusionLevel; label: string; desc: string }[] = [
+  { value: "ai_full", label: "AI 全写", desc: "给我方向，全由AI创作" },
+  { value: "ai_polish", label: "AI润色", desc: "我写草稿，AI帮我润色" },
+  { value: "user_primary", label: "按我写的来", desc: "几乎不改我的文案" },
+]
+
+export type StyleStrength = "light" | "medium" | "strict"
+export const STYLE_STRENGTHS: { value: StyleStrength; label: string }[] = [
+  { value: "light", label: "轻度借鉴" },
+  { value: "medium", label: "中度参考" },
+  { value: "strict", label: "像素级复刻" },
+]
+
+/** v1.6 前端时长下拉选项（5/10/15/20/25/30秒） */
+export const VALID_DURATIONS = [5, 10, 15, 20, 25, 30] as const
+export type VideoDuration = (typeof VALID_DURATIONS)[number]
+
+/** v1.6 支持的画幅比例 */
+export const VALID_RATIOS = ["9:16", "16:9", "1:1"] as const
+export type VideoRatio = (typeof VALID_RATIOS)[number]
+
+export type ViralVideoStatus =
+  | "pending"
+  | "running"
+  | "wait_user_confirm"
+  | "image_analyzed"
+  | "copy_generated"
+  | "completed"
+  | "failed"
+  | "cancelled"
+
+/**
+ * v1.6 后端流水线阶段。单次 Seedance 出片版：
+ * image_analysis → video_analysis(可选) → intent_parsing → script_generation → review → tts → rendering → uploading
+ */
+export type ViralVideoStage =
+  | "image_analysis"
+  | "video_analysis"
+  | "intent_parsing"
+  | "script_generation"
+  | "review"
+  | "tts"
+  | "rendering"
+  | "uploading"
+
+/** 图片+视频分析阶段：属于「分析图片」按钮的范围 */
+const IMAGE_ANALYSIS_STAGES = new Set<ViralVideoStage>(["image_analysis", "video_analysis"])
+/** 编导脚本阶段：属于「生成文案」按钮的范围 */
+const COPY_STAGES = new Set<ViralVideoStage>(["intent_parsing", "script_generation", "review"])
+/** 视频生成阶段：属于「开始生成视频」按钮的范围（v1.6: TTS+单次Seedance+上传） */
+const VIDEO_STAGES = new Set<ViralVideoStage>(["tts", "rendering", "uploading"])
+
+export function isImageAnalysisStage(stage: ViralVideoStage | undefined): boolean {
+  return !!stage && IMAGE_ANALYSIS_STAGES.has(stage)
+}
+export function isCopyStage(stage: ViralVideoStage | undefined): boolean {
+  return !!stage && COPY_STAGES.has(stage)
+}
+export function isVideoStage(stage: ViralVideoStage | undefined): boolean {
+  return !!stage && VIDEO_STAGES.has(stage)
+}
+/** 兼容旧调用：分析图片+生成文案 的所有前置阶段 */
+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 */
+export interface ImageProductAnalysis {
+  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
+}
+
+export interface ImageAnalysisResult {
+  products?: ImageProductAnalysis[]
+}
+
+/** v1.6 编导分镜脚本 - 单镜头 */
+export interface ShotScript {
+  /** 时间区间，如 "0-3秒" */
+  time_range?: string
+  /** 景别/角度/运镜，如 "近景俯拍45度，缓慢推镜" */
+  shot_type_angle_movement?: string
+  /** 场景描述+对白 */
+  scene_and_dialogue?: string
+  /** 人物动作/表情/物品操作细节 */
+  action_details?: string
+  /** 环境音+BGM提示 */
+  audio_bgm?: string
+  /** 转场方式（硬切/淡入淡出/叠化/结束） */
+  transition?: string
+  /** 参考图片索引（0-based，对应上传产品图数组） */
+  reference_image_index?: number | null
+}
+
+/** v1.6 编导分镜脚本 - 总览 */
+export interface CopyResultOverview {
+  theme?: string
+  total_duration?: number
+  aspect_ratio?: string
+}
+
+/** v1.6 编导分镜脚本（核心输出结构，给 Seedance 做 prompt，给 TTS 取 voiceover_script） */
+export interface CopyResult {
+  overview?: CopyResultOverview
+  /** 整体场景+光线描述 */
+  scene_and_lighting?: string
+  /** 逐镜头时间轴 */
+  shots?: ShotScript[]
+  /** 硬性约束（禁止字幕/水印/变形等） */
+  hard_constraints?: string[]
+  /** 负面提示词 */
+  negative_prompts?: string[]
+  /** 完整口播稿（纯文本，用于 TTS 合成） */
+  voiceover_script?: string
+  /** 向后兼容：= voiceover_script */
+  final_copy?: string
+  /** 向后兼容：= voiceover_script */
+  suggested_copy?: string
+  title?: 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 }>
+}
+
+export interface StyleTemplate {
+  id: string
+  name: string
+  description?: string
+  thumbnail_url?: string
+  style_config?: Record<string, unknown>
+  tags?: string[]
+}
+
+export interface IntentResult {
+  intent?: string
+  key_messages?: string[]
+  tone?: string
+  target_emotion?: string
+  call_to_action?: string
+  suggested_title?: string
+  /** v1.5 旧字段兼容 */
+  product?: string
+  selling_points?: string[]
+  target_audience?: string
+  structure?: string
+  duration?: number
+  suggested_copy?: string
+}
+
+export interface ViralVideoJob {
+  id: string
+  status: ViralVideoStatus
+  images: string[]
+  reference_video_url?: string
+  style_strength?: StyleStrength
+  style_template_id?: string
+  style_guide?: string | Record<string, unknown>
+  user_copy_text?: string
+  /** v1.6: = copy_result.voiceover_script（从 copy_result 派生，向后兼容） */
+  final_copy_text?: string
+  generated_copy_text?: string
+  fusion_level?: FusionLevel
+  voice_id?: string
+  voice_mode?: "global" | "per_video"
+  voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
+  bgm_preference?: string
+  intent_result?: IntentResult
+  intent_text?: string
+  /** v1.6 编导分镜脚本（核心产物） */
+  copy_result?: CopyResult
+  /** 向后兼容：= copy_result.shots */
+  storyboard?: ShotScript[]
+  image_analysis?: ImageAnalysisResult
+  /** 视频比例：9:16 / 16:9 / 1:1，默认 9:16 */
+  video_ratio?: string
+  /** Seedance 模型 ID（空=后端默认） */
+  video_model?: string
+  /** 视频时长（秒，5-30，默认15） */
+  duration?: number
+  progress_stage?: ViralVideoStage
+  progress_percent?: number
+  progress_message?: string
+  output_url?: string
+  result_video_url?: string
+  error_message?: string
+  error_msg?: string
+  credits_cost?: number
+  created_at?: string
+  updated_at?: string
+}
+
+export interface GenerateViralVideoRequest {
+  images: string[]
+  reference_video_url?: string
+  douyin_url?: string
+  style_strength?: StyleStrength
+  style_template_id?: string
+  user_copy_text?: string
+  fusion_level?: FusionLevel
+  voice_id?: string
+  voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
+  bgm_preference?: string
+  industry?: string
+  target_customer?: string
+  language?: string
+  persona_id?: string
+  viral_structure?: string
+  marketing_purpose?: string
+  /** 视频时长（5-30秒，默认15） */
+  duration?: number
+  video_model?: string
+  video_ratio?: string
+  /** 三步拆分：step 控制后端执行到哪一步暂停 */
+  step?: "analyze" | "generate_copy" | "generate_video"
+}
+
+export interface HistoryResponse {
+  items: ViralVideoJob[]
+  total: number
+  page: number
+  page_size: number
+}
+
+/** v1.6 阶段1请求：图片/视频分析（POST /viral-video/analyze-images） */
+export interface AnalyzeImagesRequest {
+  images: string[]
+  reference_video_url?: string
+  style_template_id?: string
+  style_strength?: StyleStrength
+  /** TTS 音色 ID（STEP1 已选音色时传） */
+  voice_id?: string
+  /** 音色来源：preset | library | clone | upload */
+  voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
+  /** Seedance 视频比例：9:16 | 16:9 | 1:1 */
+  video_ratio?: string
+  /** Seedance 模型 ID（空则使用服务端默认） */
+  video_model?: string
+  /** 视频时长（秒，5-30，默认15） */
+  duration?: number
+}
+
+/** v1.6 阶段2请求：填完营销参数后生成编导分镜脚本（POST /viral-video/{id}/generate-copy） */
+export interface GenerateCopyRequest {
+  industry?: string
+  target_customer?: string
+  persona_id?: string
+  viral_structure?: string
+  marketing_purpose?: string
+  bgm_preference?: string
+  /** 视频时长（秒，5-30，默认15） */
+  duration?: number
+  user_copy_text?: string
+  fusion_level?: FusionLevel
+  reference_audio_path?: string
+  reference_video_url?: string
+  style_strength?: StyleStrength
+  style_template_id?: string
+  style_guide?: string | Record<string, unknown>
+  /** TTS 音色 ID（优先级高于 persona_id） */
+  voice_id?: string
+  /** 音色来源：preset | library | clone | upload */
+  voice_source?: "preset" | "library" | "clone" | "upload" | "my_voice"
+  /** Seedance 视频比例（9:16/16:9/1:1 等） */
+  video_ratio?: string
+  /** Seedance 模型 ID（空则使用服务端默认） */
+  video_model?: string
+}
+
+/** 视频模型描述（GET /viral-video/models） */
+export interface ViralVideoModel {
+  key: string
+  display_name: string
+  supports_audio: boolean
+  supported_resolutions: string[]
+  max_duration: number
+  /** 计费模式（可选）：per_second / per_video / token 等 */
+  billing_mode?: string
+  is_default?: boolean
+}
+
+/** GET /viral-video/models 响应包装 */
+export interface ViralVideoModelsResponse {
+  models: ViralVideoModel[]
+}
+
+/** v1.6 阶段3请求：用户确认/编辑口播文案后开始单次 Seedance 出片（POST /viral-video/{id}/confirm-copy） */
+export interface ConfirmCopyRequest {
+  /** 用户编辑后的口播文案；为空则使用 AI 生成的 voiceover_script */
+  edited_copy?: string
+  /** 视频模型 key，覆盖默认 */
+  video_model?: string
+}
+
+/** 旧分镜片段结构（保留兼容；新代码请使用 ShotScript） */
+export interface StoryboardSegment {
+  order: number
+  type: string
+  description: string
+  text: string
+  duration: number
+  ken_burns?: string
+  transition?: string
+}

apps/web/src/api/voice-clone/types.ts

@@ -18,6 +18,8 @@ export interface VoiceClone {
   language: string
   gender: string
   error_message: string | null
+  /** CosyVoice 实际使用的音色 ID（status=ready 时由后端填充，用于 TTS 调用） */
+  voice_id?: string | null
   created_at: string
   updated_at: string
 }

apps/web/src/api/voice-clone/utils.ts

@@ -18,6 +18,7 @@ export const toVoiceClone = (profile: VoiceCloneProfile): VoiceClone => ({
   language: profile.language || "",
   gender: profile.gender || "",
   error_message: profile.error_message || null,
+  voice_id: profile.voice_id,
   created_at: profile.created_at,
   updated_at: profile.updated_at,
 })

apps/web/src/components/layout/PageHead/constants.ts

@@ -2,6 +2,7 @@
 export const ROUTE_TITLE_MAP: Record<string, string> = {
   "/app/dashboard": "首页",
   "/app/generate": "智能剪辑",
+  "/app/viral-video": "爆款视频",
   "/app/assets": "视频库",
   "/app/voices": "配音库",
   "/app/products": "成片库",

apps/web/src/config/navigation.ts

@@ -18,6 +18,7 @@ import {
   ThunderboltOutlined,
   UnorderedListOutlined,
   UserOutlined,
+  FireOutlined,
 } from "@ant-design/icons"
 
 /** 导航项类型 */
@@ -76,6 +77,12 @@ export const NAV_ITEMS: NavItem[] = [
     path: "/app/ai-avatar",
     icon: React.createElement(UserOutlined),
   },
+  {
+    key: "viral-video",
+    label: "爆款视频",
+    path: "/app/viral-video",
+    icon: React.createElement(FireOutlined),
+  },
   {
     key: "history",
     label: "任务历史",
@@ -142,6 +149,12 @@ export const NAV_GROUPS: NavGroup[] = [
         path: "/app/ai-avatar",
         icon: React.createElement(UserOutlined),
       },
+      {
+        key: "viral-video",
+        label: "爆款视频",
+        path: "/app/viral-video",
+        icon: React.createElement(FireOutlined),
+      },
     ],
   },
   {

apps/web/src/pages/ai-avatar/AiAvatarPage.tsx

@@ -80,6 +80,8 @@ const AiAvatarPage: React.FC = () => {
   const [finalizeLoading, setFinalizeLoading] = useState(false)
 
   /* ── 对口型轮询 ── */
+  /** 对口型轮询总时长上限（10分钟）：超过后停止轮询并提示去历史记录查看 */
+  const LIPSYNC_POLL_MAX_MS = 10 * 60 * 1000
   const lipsyncTimerRef = useRef<ReturnType<typeof setInterval> | null>(null)
   /* ── 渲染进度轮询 ── */
   const renderTimerRef = useRef<ReturnType<typeof setInterval> | null>(null)
@@ -270,7 +272,24 @@ const AiAvatarPage: React.FC = () => {
       // 如果是预合成模式，后端会同步把状态置为 submitted（甚至可能已返回 running），
       // 但仍需轮询等 completed
       if (lipsyncTimerRef.current) clearInterval(lipsyncTimerRef.current)
+      // 轮询间隔 5 秒；单请求超时 5 分钟（见 api/aiAvatar.ts）；总轮询上限 10 分钟
+      // 单次请求失败/超时不中断轮询，继续下一轮；超过总上限后停止并提示用户去历史记录查看
       lipsyncTimerRef.current = setInterval(async () => {
+        // 总时长保护：超过 10 分钟停止轮询
+        if (Date.now() - lipsyncStartAtRef.current > LIPSYNC_POLL_MAX_MS) {
+          if (lipsyncTimerRef.current) {
+            clearInterval(lipsyncTimerRef.current)
+            lipsyncTimerRef.current = null
+          }
+          if (lipsyncTickRef.current) {
+            clearInterval(lipsyncTickRef.current)
+            lipsyncTickRef.current = null
+          }
+          setLipsyncStatus("failed")
+          setLipsyncErrorMessage("渲染时间较长，请稍后在历史记录中查看")
+          message.warning("对口型渲染时间较长，已停止自动刷新，请稍后在历史记录中查看")
+          return
+        }
         try {
           const updated = await getLipsyncJob(job.id)
           state.setLipsyncJob(updated)
@@ -296,9 +315,10 @@ const AiAvatarPage: React.FC = () => {
             setLipsyncErrorMessage(updated.error_message || "对口型生成失败")
           }
         } catch (err) {
-          console.error("[对口型] 轮询错误:", err)
+          // 单次轮询失败（含 timeout）：不中断轮询，打印日志后等下一轮
+          console.warn("[对口型] 轮询请求失败，将继续下一轮:", err)
         }
-      }, 3000)
+      }, 5000)
     } catch (err) {
       console.error("[对口型] 创建失败:", {
         status: (err as { response?: { status?: number } })?.response?.status,
@@ -580,20 +600,6 @@ const AiAvatarPage: React.FC = () => {
 
   return (
     <div className="aa-page">
-      <div className="aa-page-header">
-        <h1>AI数字人</h1>
-      </div>
-
-      {/* 步骤切换导航条 */}
-      <div className="aa-step-nav">
-        <span className={`aa-step-nav__item${currentStep === 1 ? " active" : ""}`}>
-          1. 视频 / 配音 / 文案
-        </span>
-        <span className={`aa-step-nav__item${currentStep === 2 ? " active" : ""}`}>
-          2. 对口型 / 标题 / 封面 / 生成
-        </span>
-      </div>
-
       <div className="aa-page-body">
         {/* ════ 步骤 1：出镜视频 / 配音库 / 文案 ════ */}
         {currentStep === 1 && (

apps/web/src/pages/ai-avatar/api/aiAvatar.ts

@@ -72,7 +72,8 @@ export const previewTts = async (data: {
 }
 
 export const getLipsyncJob = async (id: string): Promise<LipsyncJob> => {
-  const response = await apiClient.get<LipsyncJob>(`/lipsync/jobs/${id}`, { timeout: 60000 })
+  // MuseTalk 渲染 8s 视频约 54s + 排队时间，给足 5 分钟超时避免单次轮询 AxiosError 中断
+  const response = await apiClient.get<LipsyncJob>(`/lipsync/jobs/${id}`, { timeout: 300_000 })
   return response.data
 }
 
@@ -91,7 +92,10 @@ export const submitRender = async (data: {
 }
 
 export const getRenderJob = async (jobId: string): Promise<RenderJob> => {
-  const response = await apiClient.get<RenderJob>(`/ai-avatar/render/${jobId}`, { timeout: 60000 })
+  // 渲染链路（对口型+B-roll+标题+合成+上传）耗时较长，给足 5 分钟超时
+  const response = await apiClient.get<RenderJob>(`/ai-avatar/render/${jobId}`, {
+    timeout: 300_000,
+  })
   return response.data
 }
 

apps/web/src/pages/generate/GeneratePage.tsx

@@ -13,8 +13,6 @@ import CloneModal from "@/components/voice/CloneModal"
 import VoiceSelectModal from "./components/VoiceSelectModal"
 import ScriptSelectModal from "./components/ScriptSelectModal"
 import TtsVoiceModal from "./components/TtsVoiceModal"
-import GenerateHeader from "./components/GenerateHeader"
-import GenerateStepsBar from "./components/GenerateStepsBar"
 import GenerateStepContent from "./components/GenerateStepContent"
 import GenerateStepActions from "./components/GenerateStepActions"
 import { useGenerateFormState } from "./hooks/useGenerateFormState"
@@ -88,7 +86,6 @@ const GeneratePage: React.FC = () => {
     style,
     autoSubtitles,
     bgm,
-    editPlanId,
     sourceEditPlanId,
     previewTaskId,
     setPreviewTaskId,
@@ -523,10 +520,6 @@ const GeneratePage: React.FC = () => {
 
   return (
     <div className="xx-generate-page">
-      <GenerateHeader fromEditPlan={!!editPlanId} />
-
-      <GenerateStepsBar currentStep={currentStep} onStepClick={setCurrentStep} />
-
       <div className={layoutClassName}>
         {/* ════ 步骤1~2 表单 / 步骤3 标题设置 / 步骤4 确认生成进度 / 步骤5 封面 ════ */}
         <div className="xx-generate-form">

apps/web/src/pages/generate/components/Step6CoverSettings.tsx

@@ -17,7 +17,7 @@ import CoverSettingsModal from "./cover-settings/CoverSettingsModal"
 import CoverEditorModal from "./cover-settings/CoverEditorModal"
 import { useSharedCover } from "@/components/cover/useSharedCover"
 import { generateCover as apiGenerateCover } from "@/api/generation"
-import { uploadAssetDirect, getAssetLibraries } from "@/api/assets"
+import { uploadAssetDirect } from "@/api/assets"
 
 interface Step6CoverSettingsProps {
   coverSettings: CoverConfig
@@ -176,15 +176,8 @@ const Step6CoverSettings: React.FC<Step6CoverSettingsProps> = (props) => {
           thumbnail_url: previewUrl,
           mode: "upload",
         })
-        // 查找图片素材库（复用批量封面的逻辑）
-        const libs = await getAssetLibraries()
-        const imageLib = libs.find((l) => l.kind === "image") || libs[0]
-        if (!imageLib) {
-          hide()
-          message.error("未找到素材库，请先创建图片素材库")
-          return previewUrl
-        }
-        const result = await uploadAssetDirect({ file, library_id: imageLib.id })
+        // 后端自动在默认项目下确保图片素材库存在（P0 404 修复）
+        const result = await uploadAssetDirect({ file, kind: "image" })
         const realUrl = result?.url || ""
         if (!realUrl) {
           hide()

apps/web/src/pages/generate/hooks/useBatchCovers.ts

@@ -12,7 +12,7 @@
 import { useCallback, useState } from "react"
 import { message } from "antd"
 import { generateCover } from "@/api/generation"
-import { uploadAssetDirect, getAssetLibraries } from "@/api/assets"
+import { uploadAssetDirect } from "@/api/assets"
 import type { GeneratedVideo } from "@/api/template-editor"
 
 /** onCoversChange 支持直接传值或函数式 updater（函数式用于串行回写避免闭包覆盖） */
@@ -182,15 +182,9 @@ export function useBatchCovers({
     async (index: number, file: File) => {
       addUploading(index)
       try {
-        const libs = await getAssetLibraries()
-        const imageLib = libs.find((l) => l.kind === "image") || libs[0]
-        if (!imageLib) {
-          message.error("未找到素材库，请先创建")
-          return
-        }
         const result = await uploadAssetDirect({
           file,
-          library_id: imageLib.id,
+          kind: "image",
         })
         const url = result?.url || ""
         if (url) {

apps/web/src/pages/viral-video/components/AssetPickerModal.tsx

@@ -0,0 +1,263 @@
+/**
+ * 爆款视频素材选择弹窗（通用版，支持 image/video/voice）
+ * 基于 ai-avatar 的 ModalAssetPicker 改造：
+ *  - kind 可传 "image" | "video" | "voice"
+ *  - 多图场景 multiple=true 时底部"确认选择"
+ *  - 单选场景点击即回调关闭
+ */
+import { useEffect, useState } from "react"
+import { getAssets, getAssetLibraries, type AssetItem, type AssetLibraryItem } from "@/api/assets"
+
+export interface AssetPickerModalProps {
+  open: boolean
+  kind: "image" | "video" | "voice"
+  multiple?: boolean
+  title?: string
+  onClose: () => void
+  onSelect: (assets: AssetItem[]) => void
+}
+
+const KIND_LABEL: Record<AssetPickerModalProps["kind"], string> = {
+  image: "图片",
+  video: "视频",
+  voice: "音频",
+}
+
+const MIME_KIND: Record<AssetPickerModalProps["kind"], string> = {
+  image: "image",
+  video: "video",
+  voice: "audio",
+}
+
+export default function AssetPickerModal({
+  open,
+  kind,
+  multiple = false,
+  title,
+  onClose,
+  onSelect,
+}: AssetPickerModalProps) {
+  const [keyword, setKeyword] = useState("")
+  const [libraries, setLibraries] = useState<AssetLibraryItem[]>([])
+  const [libraryId, setLibraryId] = useState<string>("")
+  const [assets, setAssets] = useState<AssetItem[]>([])
+  const [picked, setPicked] = useState<Set<string>>(new Set())
+  const [loadingLibs, setLoadingLibs] = useState(false)
+  const [loadingAssets, setLoadingAssets] = useState(false)
+  const [error, setError] = useState("")
+
+  useEffect(() => {
+    if (!open) return
+    setKeyword("")
+    setLibraries([])
+    setLibraryId("")
+    setAssets([])
+    setError("")
+    setPicked(new Set())
+  }, [open])
+
+  useEffect(() => {
+    if (!open) return
+    let cancelled = false
+    setLoadingLibs(true)
+    getAssetLibraries(kind)
+      .then((libs) => {
+        if (cancelled) return
+        const list = Array.isArray(libs) ? libs : []
+        setLibraries(list)
+        if (list.length > 0) setLibraryId(list[0].id)
+      })
+      .catch(() => {
+        if (!cancelled) setError("素材库加载失败，请重试")
+      })
+      .finally(() => {
+        if (!cancelled) setLoadingLibs(false)
+      })
+    return () => {
+      cancelled = true
+    }
+  }, [open, kind])
+
+  useEffect(() => {
+    if (!open || !libraryId) return
+    let cancelled = false
+    setLoadingAssets(true)
+    const load = async () => {
+      try {
+        const { items } = await getAssets(libraryId, { page_size: 100 })
+        if (cancelled) return
+        let list = Array.isArray(items) ? items : []
+        const mimePrefix = MIME_KIND[kind]
+        list = list.filter((a) => !a.mime_type || a.mime_type.startsWith(mimePrefix))
+        const kw = keyword.trim()
+        if (kw) list = list.filter((a) => a.name?.includes(kw))
+        setAssets(list)
+        setError("")
+      } catch {
+        if (!cancelled) {
+          setError("素材加载失败，请重试")
+          setAssets([])
+        }
+      } finally {
+        if (!cancelled) setLoadingAssets(false)
+      }
+    }
+    const timer = window.setTimeout(load, 250)
+    return () => {
+      cancelled = true
+      window.clearTimeout(timer)
+    }
+  }, [open, libraryId, keyword, kind])
+
+  const thumbFor = (a: AssetItem) => {
+    if (kind === "image") return a.thumbnail_url || a.file_url
+    if (kind === "video") return a.thumbnail_url
+    return ""
+  }
+
+  const togglePick = (id: string) => {
+    if (multiple) {
+      setPicked((prev) => {
+        const n = new Set(prev)
+        if (n.has(id)) n.delete(id)
+        else n.add(id)
+        return n
+      })
+    } else {
+      const asset = assets.find((a) => a.id === id)
+      if (asset) {
+        onSelect([asset])
+        onClose()
+      }
+    }
+  }
+
+  const handleConfirm = () => {
+    const list = assets.filter((a) => picked.has(a.id))
+    if (list.length > 0) onSelect(list)
+    onClose()
+  }
+
+  if (!open) return null
+
+  return (
+    <div className="vv-modal-mask" onClick={onClose}>
+      <div className="vv-modal" onClick={(e) => e.stopPropagation()}>
+        <div className="vv-modal-head">
+          <span className="vv-modal-title">{title || `选择${KIND_LABEL[kind]}素材`}</span>
+          <button className="vv-modal-close" onClick={onClose} aria-label="关闭">
+            ×
+          </button>
+        </div>
+        <div className="vv-modal-body">
+          <div className="vv-asset-search">
+            <select
+              className="vv-input"
+              style={{ width: 170, flex: "0 0 auto" }}
+              value={libraryId}
+              onChange={(e) => setLibraryId(e.target.value)}
+              disabled={loadingLibs || libraries.length === 0}
+            >
+              {libraries.length === 0 ? (
+                <option value="">
+                  {loadingLibs ? "加载中…" : `暂无${KIND_LABEL[kind]}素材库`}
+                </option>
+              ) : (
+                libraries.map((lib) => (
+                  <option key={lib.id} value={lib.id}>
+                    📁 {lib.name}
+                  </option>
+                ))
+              )}
+            </select>
+            <input
+              className="vv-input"
+              type="text"
+              placeholder={`搜索${KIND_LABEL[kind]}名称…`}
+              value={keyword}
+              onChange={(e) => setKeyword(e.target.value)}
+            />
+          </div>
+
+          {libraries.length === 0 && !loadingLibs ? (
+            <div className="vv-modal-empty">
+              <div className="vv-empty-icon">📁</div>
+              暂无{KIND_LABEL[kind]}素材库，请先在「素材库」中创建并上传
+            </div>
+          ) : loadingAssets ? (
+            <div className="vv-modal-empty">
+              <div className="vv-empty-icon">⏳</div>
+              素材加载中…
+            </div>
+          ) : error ? (
+            <div className="vv-modal-empty">
+              <div className="vv-empty-icon">⚠️</div>
+              {error}
+            </div>
+          ) : assets.length === 0 ? (
+            <div className="vv-modal-empty">
+              <div className="vv-empty-icon">
+                {kind === "image" ? "🖼️" : kind === "video" ? "🎬" : "🎵"}
+              </div>
+              {kind === "voice" ? (
+                <>
+                  <div style={{ marginTop: 8, fontSize: 13 }}>暂无配音素材</div>
+                  <div style={{ marginTop: 4, fontSize: 12, color: "#9ca3af" }}>
+                    请先在「配音/我的音色」中上传音频文件，或在素材库管理中添加
+                  </div>
+                </>
+              ) : (
+                <>该素材库暂无{KIND_LABEL[kind]}素材</>
+              )}
+            </div>
+          ) : (
+            <div className={`vv-asset-thumbs vv-asset-${kind}`}>
+              {assets.map((asset) => {
+                const active = picked.has(asset.id)
+                const thumb = thumbFor(asset)
+                return (
+                  <div
+                    key={asset.id}
+                    className={`vv-thumb-card${active ? " selected" : ""}`}
+                    onClick={() => togglePick(asset.id)}
+                  >
+                    {thumb ? (
+                      <img src={thumb} alt={asset.name} />
+                    ) : kind === "video" ? (
+                      <video src={asset.file_url} muted preload="metadata" />
+                    ) : (
+                      <div className="vv-thumb-ph">{kind === "voice" ? "🎵" : "📄"}</div>
+                    )}
+                    {active && <div className="vv-thumb-check">✓</div>}
+                    <div className="vv-thumb-name" title={asset.name}>
+                      <span className="vv-thumb-name-txt">{asset.name}</span>
+                      {kind === "voice" &&
+                        typeof asset.duration === "number" &&
+                        asset.duration > 0 && (
+                          <span className="vv-thumb-dur">{Math.round(asset.duration)}s</span>
+                        )}
+                    </div>
+                  </div>
+                )
+              })}
+            </div>
+          )}
+        </div>
+        {multiple && (
+          <div className="vv-modal-foot">
+            <button className="vv-btn vv-btn-ghost" onClick={onClose}>
+              取消
+            </button>
+            <button
+              className="vv-btn vv-btn-primary"
+              onClick={handleConfirm}
+              disabled={picked.size === 0}
+            >
+              确认选择（{picked.size}）
+            </button>
+          </div>
+        )}
+      </div>
+    </div>
+  )
+}

apps/web/src/pages/viral-video/components/PresetVoicePickerModal.tsx

@@ -0,0 +1,355 @@
+/**
+ * 内置音色选择弹窗（浅色紫调版）
+ * - 标题「选择音色」+ 搜索框 + 分类筛选 + 3列卡片网格 + 试听 + 选中 + 完成选择
+ */
+import React, { useEffect, useMemo, useRef, useState } from "react"
+import {
+  CloseOutlined,
+  SearchOutlined,
+  PlayCircleOutlined,
+  PauseCircleOutlined,
+  UserOutlined,
+} from "@ant-design/icons"
+import { Select, Input } from "antd"
+
+export interface PresetVoice {
+  id: string
+  name: string
+  gender?: "female" | "male" | "child" | "other"
+  gender_label?: string
+  category?: string
+  avatar_url?: string
+  sample_audio_url?: string
+  desc?: string
+}
+
+interface Props {
+  open: boolean
+  voices?: PresetVoice[]
+  loading?: boolean
+  selectedId?: string
+  onClose: () => void
+  onConfirm: (voice: PresetVoice) => void
+}
+
+/** 兜底 mock 音色（后端 /api/v1/tts/presets 返回字段不够时使用） */
+const MOCK_VOICES: PresetVoice[] = [
+  // ⚠️ 兜底 mock，仅在 /voices/presets 接口不可达时使用；ID 必须与后端
+  // packages/domain/preset_voices.py PRESET_VOICES 的 voice_id 对齐（v3后缀）
+  {
+    id: "longxiaochun_v3",
+    name: "龙小淳",
+    gender: "female",
+    category: "女声",
+    desc: "知性积极女声，适合语音助手",
+  },
+  {
+    id: "longxiaoxia_v3",
+    name: "龙小夏",
+    gender: "female",
+    category: "女声",
+    desc: "沉稳权威女声，适合新闻播报",
+  },
+  {
+    id: "longsanshu_v3",
+    name: "龙三叔",
+    gender: "male",
+    category: "男声",
+    desc: "沉稳质感男声，适合有声书",
+  },
+  {
+    id: "longyue_v3",
+    name: "龙悦",
+    gender: "female",
+    category: "女声",
+    desc: "温暖磁性女声，适合广告配音",
+  },
+  {
+    id: "longshu_v3",
+    name: "龙书",
+    gender: "male",
+    category: "男声",
+    desc: "沉稳青年男声，适合教育讲解",
+  },
+  {
+    id: "longyingjing_v3",
+    name: "龙应静",
+    gender: "female",
+    category: "女声",
+    desc: "低调冷静女声，适合纪录片解说",
+  },
+  {
+    id: "longshuo_v3",
+    name: "龙硕",
+    gender: "male",
+    category: "男声",
+    desc: "博才干练男声，适合科技类内容",
+  },
+  {
+    id: "longtian_v3",
+    name: "龙甜",
+    gender: "female",
+    category: "女声",
+    desc: "活泼女声，适合短视频配音",
+  },
+]
+
+const CATEGORY_LABELS: Record<string, string> = {
+  all: "全部分类",
+  female: "女声",
+  male: "男声",
+  child: "童声",
+  dialect: "方言",
+  emotion: "情绪",
+}
+
+const GENDER_LABEL = (v: PresetVoice) => {
+  if (v.gender_label) return v.gender_label
+  const g = v.gender
+  if (g === "female") return "女声·女声"
+  if (g === "male") return "男声·男声"
+  if (g === "child") return "童声·童声"
+  return "性别未标注·其他"
+}
+
+const AVATAR_BG = (gender?: string) => {
+  if (gender === "female") return "#fce7f3"
+  if (gender === "male") return "#dbeafe"
+  if (gender === "child") return "#fef3c7"
+  return "#f3f0ff"
+}
+const AVATAR_COLOR = (gender?: string) => {
+  if (gender === "female") return "#be185d"
+  if (gender === "male") return "#1d4ed8"
+  if (gender === "child") return "#b45309"
+  return "#7c3aed"
+}
+
+const PresetVoicePickerModal: React.FC<Props> = ({
+  open,
+  voices,
+  loading,
+  selectedId,
+  onClose,
+  onConfirm,
+}) => {
+  const [keyword, setKeyword] = useState("")
+  const [category, setCategory] = useState<string>("all")
+  const [pickedId, setPickedId] = useState<string | undefined>(selectedId)
+  const [playingId, setPlayingId] = useState<string | null>(null)
+  const audioRef = useRef<HTMLAudioElement | null>(null)
+
+  useEffect(() => {
+    if (open) {
+      setKeyword("")
+      setCategory("all")
+      setPickedId(selectedId)
+      setPlayingId(null)
+    }
+  }, [open, selectedId])
+
+  // 停止播放
+  useEffect(() => {
+    return () => {
+      audioRef.current?.pause()
+      audioRef.current = null
+    }
+  }, [])
+
+  // 合并真实数据和 mock：如果真实数据 gender/category 缺失，用 mock 兜底
+  const allVoices: PresetVoice[] = useMemo(() => {
+    // 真实 API 返回的 voice_id 以 API 为准（如 longxiaochun_v3），前端不做硬编码覆盖
+    const realList: PresetVoice[] = (voices || []).map((v) => {
+      // 按 id 精确匹配 mock 获取补充元信息（id 即 voice_id，唯一稳定键）
+      const mockMatch = MOCK_VOICES.find((m) => m.id === v.id)
+      return {
+        ...v,
+        gender: v.gender || mockMatch?.gender,
+        category:
+          v.category ||
+          mockMatch?.category ||
+          (v.gender === "female" ? "女声" : v.gender === "male" ? "男声" : "其他"),
+        desc: v.desc || mockMatch?.desc,
+        sample_audio_url: v.sample_audio_url,
+      }
+    })
+    // 如果没有真实数据，使用兜底 mock（接口失败时）
+    return realList.length > 0 ? realList : MOCK_VOICES
+  }, [voices])
+
+  const categories = useMemo(() => {
+    const set = new Set<string>()
+    allVoices.forEach((v) => {
+      if (v.category) set.add(v.category)
+    })
+    return Array.from(set)
+  }, [allVoices])
+
+  const filtered = useMemo(() => {
+    const kw = keyword.trim().toLowerCase()
+    return allVoices.filter((v) => {
+      if (category !== "all") {
+        if (v.category !== category && category !== CATEGORY_LABELS[v.gender || ""]) {
+          // gender 兜底匹配
+          if (
+            !(category === "女声" && v.gender === "female") &&
+            !(category === "男声" && v.gender === "male") &&
+            !(category === "童声" && v.gender === "child") &&
+            !(category === "方言" && v.category === "方言") &&
+            !(category === "情绪" && v.category === "情绪")
+          ) {
+            return false
+          }
+        }
+      }
+      if (!kw) return true
+      return (
+        v.name?.toLowerCase().includes(kw) ||
+        v.desc?.toLowerCase().includes(kw) ||
+        v.category?.toLowerCase().includes(kw)
+      )
+    })
+  }, [allVoices, keyword, category])
+
+  const handlePreview = (v: PresetVoice) => {
+    if (!v.sample_audio_url) {
+      // 无示例音频
+      return
+    }
+    if (playingId === v.id) {
+      audioRef.current?.pause()
+      setPlayingId(null)
+      return
+    }
+    audioRef.current?.pause()
+    const a = new Audio(v.sample_audio_url)
+    a.onended = () => setPlayingId(null)
+    a.onerror = () => setPlayingId(null)
+    a.play().catch(() => {})
+    audioRef.current = a
+    setPlayingId(v.id)
+  }
+
+  const handleConfirm = () => {
+    const picked = allVoices.find((v) => v.id === pickedId)
+    if (!picked) return
+    onConfirm(picked)
+  }
+
+  if (!open) return null
+
+  return (
+    <div className="vv-modal-mask" onClick={onClose}>
+      <div className="vv-modal vv-modal-lg" onClick={(e) => e.stopPropagation()}>
+        <div className="vv-modal-head">
+          <div className="vv-modal-title">选择音色</div>
+          <button className="vv-modal-close" onClick={onClose}>
+            <CloseOutlined />
+          </button>
+        </div>
+        <div className="vv-modal-body">
+          {/* 搜索 */}
+          <Input
+            className="vv-voice-search"
+            placeholder="搜索音色名称或风格"
+            prefix={<SearchOutlined style={{ color: "#9ca3af" }} />}
+            value={keyword}
+            onChange={(e) => setKeyword(e.target.value)}
+            allowClear
+            size="large"
+          />
+          {/* 分类筛选 */}
+          <div className="vv-voice-cat-row">
+            <span className="vv-voice-cat-label">音色分类</span>
+            <Select
+              value={category}
+              onChange={setCategory}
+              style={{ width: 180 }}
+              options={[
+                { value: "all", label: "全部分类" },
+                ...[
+                  "女声",
+                  "男声",
+                  "童声",
+                  "方言",
+                  "情绪",
+                  ...categories.filter(
+                    (c) => !["女声", "男声", "童声", "方言", "情绪"].includes(c),
+                  ),
+                ].map((c) => ({ value: c, label: c })),
+              ]}
+            />
+          </div>
+          {/* 卡片网格 */}
+          <div className="vv-voice-grid">
+            {loading && filtered.length === 0 ? (
+              <div className="vv-modal-empty">加载中…</div>
+            ) : filtered.length === 0 ? (
+              <div className="vv-modal-empty">没有匹配的音色</div>
+            ) : (
+              filtered.map((v) => {
+                const isPicked = pickedId === v.id
+                const isPlaying = playingId === v.id
+                return (
+                  <div
+                    key={v.id}
+                    className={`vv-voice-card ${isPicked ? "selected" : ""}`}
+                    onClick={() => setPickedId(v.id)}
+                  >
+                    <div
+                      className="vv-voice-card-avatar"
+                      style={{ background: AVATAR_BG(v.gender), color: AVATAR_COLOR(v.gender) }}
+                    >
+                      {v.avatar_url ? (
+                        <img src={v.avatar_url} alt={v.name} />
+                      ) : (
+                        <UserOutlined style={{ fontSize: 22 }} />
+                      )}
+                    </div>
+                    <div className="vv-voice-card-name" title={v.name}>
+                      {v.name}
+                    </div>
+                    <div className="vv-voice-card-gender">{GENDER_LABEL(v)}</div>
+                    {v.desc && <div className="vv-voice-card-desc">{v.desc}</div>}
+                    <div className="vv-voice-card-actions">
+                      <button
+                        className={`vv-voice-card-btn ${isPicked ? "picked" : ""}`}
+                        onClick={(e) => {
+                          e.stopPropagation()
+                          setPickedId(v.id)
+                        }}
+                      >
+                        {isPicked ? "✓ 已选择" : "选择"}
+                      </button>
+                      <button
+                        className={`vv-voice-card-btn vv-voice-card-btn-preview ${isPlaying ? "playing" : ""} ${!v.sample_audio_url ? "disabled" : ""}`}
+                        onClick={(e) => {
+                          e.stopPropagation()
+                          handlePreview(v)
+                        }}
+                        disabled={!v.sample_audio_url}
+                      >
+                        {isPlaying ? <PauseCircleOutlined /> : <PlayCircleOutlined />}
+                        {isPlaying ? "停止" : "试听"}
+                      </button>
+                    </div>
+                  </div>
+                )
+              })
+            )}
+          </div>
+        </div>
+        <div className="vv-modal-foot">
+          <button className="vv-btn vv-btn-ghost" onClick={onClose}>
+            取消
+          </button>
+          <button className="vv-btn vv-btn-primary" onClick={handleConfirm} disabled={!pickedId}>
+            完成选择
+          </button>
+        </div>
+      </div>
+    </div>
+  )
+}
+
+export default PresetVoicePickerModal

apps/web/src/pages/viral-video/hooks/useViralVideoPolling.ts

@@ -0,0 +1,74 @@
+import { useCallback, useEffect, useRef } from "react"
+import { getViralVideoJob } from "@/api/viral-video"
+import { isAnalysisStage, type ViralVideoJob, type ViralVideoStatus } from "@/api/viral-video/types"
+
+const TERMINAL: ViralVideoStatus[] = ["completed", "failed", "cancelled"]
+
+export interface UseViralVideoPollingOptions {
+  /** 轮询间隔（毫秒），默认 1500 */
+  intervalMs?: number
+}
+
+/**
+ * 爆款视频任务 HTTP 轮询 hook。
+ * 负责持续拉取任务状态并回调给上层；上层负责根据状态/阶段切换 UI 文案。
+ * 任务进入终态（completed/failed/cancelled）后自动停止。
+ */
+export function useViralVideoPolling(
+  jobId: string | null | undefined,
+  onUpdate: (job: ViralVideoJob) => void,
+  options: UseViralVideoPollingOptions = {},
+) {
+  const { intervalMs = 1500 } = options
+  const timerRef = useRef<ReturnType<typeof setTimeout> | null>(null)
+  const stoppedRef = useRef(false)
+  const failCountRef = useRef(0)
+
+  const stop = useCallback(() => {
+    stoppedRef.current = true
+    if (timerRef.current) {
+      clearTimeout(timerRef.current)
+      timerRef.current = null
+    }
+  }, [])
+
+  const pollOnce = useCallback(
+    async (id: string) => {
+      try {
+        const job = await getViralVideoJob(id)
+        failCountRef.current = 0
+        onUpdate(job)
+        if (TERMINAL.includes(job.status)) {
+          stop()
+          return
+        }
+        if (stoppedRef.current) return
+        // 视频渲染阶段（Seedance 多段视频生成较慢）拉长轮询间隔
+        const inRender = job.progress_stage === "rendering"
+        // 分析阶段走默认间隔即可
+        const isAnalyzing = isAnalysisStage(job.progress_stage)
+        const nextDelay = inRender ? 3000 : isAnalyzing ? 2000 : intervalMs
+        timerRef.current = setTimeout(() => pollOnce(id), nextDelay)
+      } catch (_err) {
+        failCountRef.current += 1
+        if (stoppedRef.current) return
+        const delay = Math.min(intervalMs * 2 ** Math.min(failCountRef.current, 3), 10000)
+        timerRef.current = setTimeout(() => pollOnce(id), delay)
+      }
+    },
+    [intervalMs, onUpdate, stop],
+  )
+
+  useEffect(() => {
+    stoppedRef.current = false
+    failCountRef.current = 0
+    if (!jobId) {
+      stop()
+      return
+    }
+    pollOnce(jobId)
+    return stop
+  }, [jobId, pollOnce, stop])
+
+  return { stop }
+}

apps/web/src/pages/voice-materials/hooks/useVoiceMaterials/actions/useVoiceUpload.ts

@@ -1,25 +1,26 @@
 import { useState, useCallback } from "react"
 import { useMutation, useQueryClient } from "@tanstack/react-query"
 import { message } from "antd"
-import {
-  uploadAssetDirect,
-  getAssetLibraries,
-  getIngestJob,
-  type AssetLibraryItem,
-} from "@/api/assets"
+import { uploadAssetDirect, getIngestJob, type AssetLibraryItem } from "@/api/assets"
 import { tagAsset } from "@/api/tags"
 import { type VoiceGender, type VoiceMaterial } from "../../../types"
 
 interface UseVoiceUploadOptions {
   voiceLibrary?: { id: string; kind: string }
-  createLibMutation: { mutateAsync: () => Promise<AssetLibraryItem>; isPending: boolean }
+  createLibMutation?: {
+    mutateAsync: () => Promise<AssetLibraryItem>
+    isPending: boolean
+  }
 }
 
 /**
  * 配音素材上传 Hook
  * 封装上传流程：获取库 → 上传文件 → 获取时长 → 创建记录 → 打标签
  */
-export function useVoiceUpload({ voiceLibrary, createLibMutation }: UseVoiceUploadOptions) {
+export function useVoiceUpload({
+  voiceLibrary,
+  createLibMutation: _createLibMutation,
+}: UseVoiceUploadOptions) {
   const queryClient = useQueryClient()
   const [uploadProgress, setUploadProgress] = useState<number | null>(null)
 
@@ -33,24 +34,12 @@ export function useVoiceUpload({ voiceLibrary, createLibMutation }: UseVoiceUplo
     }) => {
       setUploadProgress(0)
       try {
-        // 1. 获取或等待 voice library
-        let lib = voiceLibrary
-        if (!lib) {
-          if (createLibMutation.isPending) {
-            await createLibMutation.mutateAsync()
-          }
-          const libs = await queryClient.fetchQuery({
-            queryKey: ["asset-libraries"],
-            queryFn: () => getAssetLibraries(),
-          })
-          lib = libs.find((l: AssetLibraryItem) => l.kind === "voice")
-          if (!lib) throw new Error("无法创建配音库")
-        }
-
-        // 2. 上传文件（带进度，后端自动创建 ingest job）
+        // 1. 上传文件：后端自动在默认项目下确保配音库存在（P0 404 修复）
+        //    兼容 voiceLibrary 参数：若调用方已传入正确的库 ID 则直接复用，否则内部自动解析
         const complete = await uploadAssetDirect({
           file: data.file,
-          library_id: lib.id,
+          library_id: voiceLibrary?.id,
+          kind: "voice",
           onProgress: (p) => setUploadProgress(p),
         })
 

apps/web/src/pages/voices/hooks/useVoiceUpload.ts

@@ -1,6 +1,6 @@
 import { useState, useCallback } from "react"
 import { useMutation, useQueryClient } from "@tanstack/react-query"
-import { uploadAssetDirect, getAssetLibraries, getIngestJob } from "@/api/assets"
+import { uploadAssetDirect, getIngestJob } from "@/api/assets"
 
 /**
  * 配音上传 Hook
@@ -23,18 +23,10 @@ export function useVoiceUpload({ showToast }: UseVoiceUploadProps) {
     mutationFn: async (data: { file: File; name: string; description: string }) => {
       setUploadProgress(0)
       try {
-        /* 获取或创建默认配音库 */
-        const libs = await queryClient.fetchQuery({
-          queryKey: ["asset-libraries"],
-          queryFn: () => getAssetLibraries(),
-        })
-        const lib = libs.find((l) => l.kind === "voice")
-        if (!lib) throw new Error("配音库不存在，请先在配音库页面创建")
-
-        /* 直传文件（后端会自动创建 ingest job） */
+        /* 直传文件（后端会自动在默认项目下确保配音库存在，P0 404 修复） */
         const complete = await uploadAssetDirect({
           file: data.file,
-          library_id: lib.id,
+          kind: "voice",
           onProgress: (p) => setUploadProgress(p),
         })
 

apps/web/src/router/appRoutes.tsx

@@ -52,6 +52,10 @@ const appChildren: RouteObject[] = [
     path: "ai-avatar",
     lazy: lazyRoute(() => import("@/pages/ai-avatar/AiAvatarPage")),
   },
+  {
+    path: "viral-video",
+    lazy: lazyRoute(() => import("@/pages/viral-video/ViralVideoPage")),
+  },
   {
     path: "voice-clone",
     lazy: lazyRoute(() => import("@/pages/voice-clone/VoiceClone")),

apps/web/src/test/api/errors.test.ts

@@ -0,0 +1,45 @@
+import { describe, it, expect } from "vitest"
+import { getErrorMessage, isErrorMsgShown } from "@/api/errors"
+
+describe("api/errors", () => {
+  it("returns string error directly", () => {
+    expect(getErrorMessage("plain")).toBe("plain")
+  })
+  it("uses Error.message", () => {
+    expect(getErrorMessage(new Error("boom"))).toBe("boom")
+  })
+  it("returns fallback for empty/unknown", () => {
+    expect(getErrorMessage(null)).toBe("操作失败，请稍后重试")
+    expect(getErrorMessage(undefined, "f")).toBe("f")
+  })
+  it("reads axios-like response.data.detail", () => {
+    const err = { response: { data: { detail: "后端报错" } }, isAxiosError: true }
+    expect(getErrorMessage(err)).toContain("后端报错")
+  })
+  it("reads axios-like response.data.message", () => {
+    const err = { response: { data: { message: "消息字段" } }, isAxiosError: true }
+    expect(getErrorMessage(err)).toContain("消息字段")
+  })
+  it("HTTP 404 fallback", () => {
+    const err = { response: { status: 404, data: null }, isAxiosError: true }
+    expect(getErrorMessage(err)).toContain("404")
+  })
+  it("HTTP 401 fallback", () => {
+    const err = { response: { status: 401, data: null }, isAxiosError: true }
+    expect(getErrorMessage(err)).toContain("登录")
+  })
+  it("network error", () => {
+    const err = { request: {}, isAxiosError: true }
+    expect(getErrorMessage(err)).toContain("网络")
+  })
+  it("isErrorMsgShown returns false for auth/abort", () => {
+    const authErr = { response: { status: 401 } }
+    const abortErr = { code: "ECONNABORTED" }
+    expect(isErrorMsgShown(authErr)).toBe(false)
+    expect(isErrorMsgShown(abortErr)).toBe(false)
+    const e: any = new Error("x")
+    e.__msgShown = true
+    expect(isErrorMsgShown(e)).toBe(true)
+    expect(isErrorMsgShown(new Error("x"))).toBe(false)
+  })
+})

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

@@ -0,0 +1,226 @@
+import { describe, expect, it, vi, beforeEach, afterEach } from "vitest"
+import {
+  generateViralVideo,
+  getViralVideoJob,
+  confirmViralVideoIntent,
+  retryViralVideo,
+  getViralVideoHistory,
+  getViralStyleTemplates,
+  analyzeViralStyle,
+  mockImageAnalysis,
+  mockGenerateCopy,
+  analyzeViralImages,
+  generateViralCopy,
+  confirmViralCopy,
+} from "@/api/viral-video"
+import {
+  VALID_DURATIONS,
+  VALID_RATIOS,
+  isVideoStage,
+  isImageAnalysisStage,
+  isCopyStage,
+  isAnalysisStage,
+} from "@/api/viral-video/types"
+
+const mockGet = vi.fn()
+const mockPost = vi.fn()
+
+vi.mock("@/api/client", () => ({
+  default: {
+    get: (...args: unknown[]) => mockGet(...args),
+    post: (...args: unknown[]) => mockPost(...args),
+  },
+}))
+
+vi.mock("antd", () => ({ message: { error: vi.fn(), success: vi.fn() } }))
+
+// 让 setTimeout 同步执行，避免测试等待 1.8s/2.2s
+beforeEach(() => {
+  vi.useFakeTimers()
+  vi.clearAllMocks()
+  mockGet.mockResolvedValue({ data: {} })
+  mockPost.mockResolvedValue({ data: {} })
+})
+afterEach(() => {
+  vi.useRealTimers()
+})
+
+describe("viral-video constants & stage helpers", () => {
+  afterEach(() => {
+    vi.useRealTimers()
+  })
+  beforeEach(() => {
+    vi.useFakeTimers()
+    vi.clearAllMocks()
+  })
+
+  it("VALID_DURATIONS/VALID_RATIOS", () => {
+    expect(VALID_DURATIONS).toEqual([5, 10, 15, 20, 25, 30])
+    expect(VALID_RATIOS).toEqual(expect.arrayContaining(["9:16", "16:9", "1:1"]))
+  })
+
+  it("isVideoStage", () => {
+    expect(isVideoStage("tts")).toBe(true)
+    expect(isVideoStage("rendering")).toBe(true)
+    expect(isVideoStage("uploading")).toBe(true)
+    expect(isVideoStage("script_generation")).toBe(false)
+    expect(isVideoStage("completed")).toBe(false)
+    expect(isVideoStage(undefined)).toBe(false)
+  })
+
+  it("isImageAnalysisStage", () => {
+    expect(isImageAnalysisStage("image_analysis")).toBe(true)
+    expect(isImageAnalysisStage("video_analysis")).toBe(true)
+    expect(isImageAnalysisStage("script_generation")).toBe(false)
+    expect(isImageAnalysisStage(undefined)).toBe(false)
+  })
+
+  it("isCopyStage", () => {
+    expect(isCopyStage("intent_parsing")).toBe(true)
+    expect(isCopyStage("script_generation")).toBe(true)
+    expect(isCopyStage("review")).toBe(true)
+    expect(isCopyStage("tts")).toBe(false)
+  })
+
+  it("isAnalysisStage is union", () => {
+    expect(isAnalysisStage("image_analysis")).toBe(true)
+    expect(isAnalysisStage("script_generation")).toBe(true)
+    expect(isAnalysisStage("tts")).toBe(false)
+    expect(isAnalysisStage(undefined)).toBe(false)
+  })
+})
+
+describe("viral-video API wrappers", () => {
+  afterEach(() => {
+    vi.useRealTimers()
+  })
+  beforeEach(() => {
+    vi.useFakeTimers()
+    vi.clearAllMocks()
+    mockGet.mockResolvedValue({ data: {} })
+    mockPost.mockResolvedValue({ data: {} })
+  })
+
+  it("generateViralVideo", async () => {
+    mockPost.mockResolvedValue({ data: { id: "j1" } })
+    const r = generateViralVideo({ images: ["img1"] } as never)
+    vi.runAllTimersAsync()
+    expect(await r).toEqual({ id: "j1" })
+    expect(mockPost).toHaveBeenCalledWith("/viral-video/generate", { images: ["img1"] })
+  })
+
+  it("getViralVideoJob", async () => {
+    mockGet.mockResolvedValue({ data: { id: "j2" } })
+    const r = getViralVideoJob("j2")
+    vi.runAllTimersAsync()
+    expect(await r).toEqual({ id: "j2" })
+    expect(mockGet).toHaveBeenCalledWith("/viral-video/j2")
+  })
+
+  it("confirmViralVideoIntent", async () => {
+    mockPost.mockResolvedValue({ data: { id: "j3" } })
+    const r = confirmViralVideoIntent("j3", { confirmed_copy: "hi" })
+    vi.runAllTimersAsync()
+    await r
+    expect(mockPost).toHaveBeenCalledWith("/viral-video/j3/confirm-intent", {
+      confirmed_copy: "hi",
+    })
+  })
+
+  it("retryViralVideo", async () => {
+    mockPost.mockResolvedValue({ data: { id: "j4" } })
+    await retryViralVideo("j4")
+    expect(mockPost).toHaveBeenCalledWith("/viral-video/j4/retry")
+  })
+
+  it("getViralVideoHistory", async () => {
+    mockGet.mockResolvedValue({ data: { items: [], total: 0 } })
+    await getViralVideoHistory({ page: 1, page_size: 20 })
+    expect(mockGet).toHaveBeenCalledWith("/viral-video/history", {
+      params: { page: 1, page_size: 20 },
+    })
+  })
+
+  it("getViralStyleTemplates", async () => {
+    mockGet.mockResolvedValue({ data: [] })
+    await getViralStyleTemplates()
+    expect(mockGet).toHaveBeenCalledWith("/viral-video/style-templates")
+  })
+
+  it("analyzeViralStyle", async () => {
+    mockPost.mockResolvedValue({ data: { id: "j5" } })
+    await analyzeViralStyle("j5")
+    expect(mockPost).toHaveBeenCalledWith("/viral-video/j5/analyze-style")
+  })
+
+  it("analyzeViralImages", async () => {
+    mockPost.mockResolvedValue({ data: { id: "j6" } })
+    await analyzeViralImages({ images: ["a.png"] } as never)
+    expect(mockPost).toHaveBeenCalledWith("/viral-video/analyze-images", { images: ["a.png"] })
+  })
+
+  it("generateViralCopy", async () => {
+    mockPost.mockResolvedValue({ data: { id: "j7" } })
+    await generateViralCopy("j7", { duration: 15 } as never)
+    expect(mockPost).toHaveBeenCalledWith("/viral-video/j7/generate-copy", { duration: 15 })
+  })
+
+  it("confirmViralCopy", async () => {
+    mockPost.mockResolvedValue({ data: { id: "j8" } })
+    await confirmViralCopy("j8", { edited_copy: "xxx" })
+    expect(mockPost).toHaveBeenCalledWith("/viral-video/j8/confirm-copy", { edited_copy: "xxx" })
+    mockPost.mockClear()
+    await confirmViralCopy("j8")
+    expect(mockPost).toHaveBeenCalledWith("/viral-video/j8/confirm-copy", {})
+  })
+})
+
+describe("viral-video client mocks", () => {
+  beforeEach(() => {
+    vi.useFakeTimers()
+    vi.clearAllMocks()
+  })
+  afterEach(() => {
+    vi.useRealTimers()
+  })
+
+  it("mockImageAnalysis returns product list", async () => {
+    const p = mockImageAnalysis([
+      { name: "a.png" },
+      { name: "b.jpg" },
+      { name: "c.webp" },
+      { name: "d.png" },
+    ])
+    vi.advanceTimersByTime(2000)
+    const r = await p
+    expect(r.products).toHaveLength(3)
+    expect(r.products[0].image_index).toBe(0)
+    expect(r.products[0].brand).toBe("示例品牌")
+    expect(r.products[1].spec).toBe("300g/盒")
+  })
+
+  it("mockImageAnalysis handles empty array", async () => {
+    const p = mockImageAnalysis([])
+    vi.advanceTimersByTime(2000)
+    const r = await p
+    expect(r.products).toHaveLength(0)
+  })
+
+  it("mockGenerateCopy returns copy_result shape", async () => {
+    const p = mockGenerateCopy({ product: "矿泉水", industry: "饮料", marketingPurpose: "种草" })
+    vi.advanceTimersByTime(3000)
+    const r = await p
+    expect(r.title).toContain("种草")
+    expect(r.title).toContain("矿泉水")
+    expect(r.final_copy.length).toBeGreaterThan(50)
+    expect(r.suggested_copy).toBeTruthy()
+  })
+
+  it("mockGenerateCopy uses defaults when params missing", async () => {
+    const p = mockGenerateCopy({} as never)
+    vi.advanceTimersByTime(3000)
+    const r = await p
+    expect(r.title).toContain("品牌种草")
+    expect(r.final_copy).toContain("这款产品")
+  })
+})

apps/web/src/test/pages/generate/phase.test.ts

@@ -0,0 +1,21 @@
+import { describe, it, expect } from "vitest"
+import { getGenerationPhase } from "@/pages/generate/hooks/generate-video/phase"
+
+describe("getGenerationPhase", () => {
+  it("returns 分析素材与配置 for p<20", () => {
+    expect(getGenerationPhase(0)).toEqual({ label: "分析素材与配置", icon: "🔍" })
+    expect(getGenerationPhase(19).label).toBe("分析素材与配置")
+  })
+  it("returns 智能剪辑合成 for 20<=p<50", () => {
+    expect(getGenerationPhase(20).label).toBe("智能剪辑合成")
+    expect(getGenerationPhase(49).label).toBe("智能剪辑合成")
+  })
+  it("returns 渲染视频中 for 50<=p<80", () => {
+    expect(getGenerationPhase(50).label).toBe("渲染视频中")
+    expect(getGenerationPhase(79).label).toBe("渲染视频中")
+  })
+  it("returns 即将完成 for p>=80", () => {
+    expect(getGenerationPhase(80)).toEqual({ label: "即将完成", icon: "✨" })
+    expect(getGenerationPhase(100).label).toBe("即将完成")
+  })
+})

apps/web/src/test/pages/products/detailUtils.test.ts

@@ -0,0 +1,26 @@
+import { describe, it, expect, vi, afterEach } from "vitest"
+import { formatDuration, formatFileSize, formatDate } from "@/pages/products/detailUtils"
+
+describe("products/detailUtils", () => {
+  afterEach(() => {
+    vi.useRealTimers()
+  })
+  it("formatDuration", () => {
+    expect(formatDuration(0)).toBe("00:00")
+    expect(formatDuration(-1)).toBe("00:00")
+    expect(formatDuration(5)).toBe("00:05")
+    expect(formatDuration(65)).toBe("01:05")
+    expect(formatDuration(3600)).toBe("60:00")
+  })
+  it("formatFileSize MB/GB", () => {
+    expect(formatFileSize(0)).toBe("-")
+    expect(formatFileSize(-1)).toBe("-")
+    expect(formatFileSize(5.3)).toBe("5.3 MB")
+    expect(formatFileSize(2048)).toBe("2.00 GB")
+  })
+  it("formatDate returns zh-CN format", () => {
+    vi.setSystemTime(new Date("2026-01-15T10:30:00"))
+    expect(formatDate("2026-01-15T10:30:00Z")).toMatch(/2026/)
+    expect(formatDate("")).toBe("-")
+  })
+})

apps/web/src/test/pages/viral-video/useViralVideoPolling.test.ts

@@ -0,0 +1,54 @@
+import { describe, it, expect, beforeEach, vi, afterEach } from "vitest"
+import { renderHook, act } from "@testing-library/react"
+import { useViralVideoPolling } from "@/pages/viral-video/hooks/useViralVideoPolling"
+
+const getViralVideoJobMock = vi.fn()
+vi.mock("@/api/viral-video", () => ({
+  getViralVideoJob: (...args: unknown[]) => getViralVideoJobMock(...args),
+}))
+
+describe("useViralVideoPolling", () => {
+  beforeEach(() => {
+    vi.clearAllMocks()
+    vi.useFakeTimers()
+  })
+  afterEach(() => {
+    vi.useRealTimers()
+  })
+
+  it("不传入 jobId 时不发起请求", () => {
+    renderHook(() => useViralVideoPolling(null, vi.fn()))
+    expect(getViralVideoJobMock).not.toHaveBeenCalled()
+  })
+
+  it("传入 jobId 后立即调用 getViralVideoJob", () => {
+    getViralVideoJobMock.mockResolvedValue({
+      id: "j1",
+      status: "completed",
+      progress_stage: "completed",
+    })
+    renderHook(() => useViralVideoPolling("j1", vi.fn()))
+    expect(getViralVideoJobMock).toHaveBeenCalledWith("j1")
+  })
+
+  it("stop() 会停止后续轮询（终态也会 stop）", async () => {
+    getViralVideoJobMock.mockResolvedValue({
+      id: "j2",
+      status: "completed",
+      progress_stage: "completed",
+    })
+    const { result } = renderHook(() => useViralVideoPolling("j2", vi.fn(), { intervalMs: 50 }))
+    // 等第一次 promise 完成
+    await act(async () => {
+      await Promise.resolve()
+      await Promise.resolve()
+    })
+    // 终态后不会再调度新请求
+    const calls = getViralVideoJobMock.mock.calls.length
+    act(() => {
+      vi.advanceTimersByTime(2000)
+    })
+    expect(getViralVideoJobMock).toHaveBeenCalledTimes(calls)
+    expect(result.current.stop).toBeTypeOf("function")
+  })
+})

apps/web/src/test/pages/voices/utils.test.ts

@@ -0,0 +1,35 @@
+import { describe, it, expect } from "vitest"
+import {
+  genderLabel,
+  languageLabel,
+  genderClass,
+  formatTime,
+  formatFileSize,
+} from "@/pages/voices/utils/format"
+
+describe("voices utils/format", () => {
+  it("genderLabel returns label or falls back to value", () => {
+    expect(genderLabel("female")).toContain("女")
+    expect(genderLabel("male")).toContain("男")
+    expect(genderLabel("unknown" as never)).toBe("unknown")
+  })
+  it("languageLabel returns label or falls back", () => {
+    expect(languageLabel("zh-CN" as never)).toBeTruthy()
+    expect(languageLabel("xx-XX" as never)).toBe("xx-XX")
+  })
+  it("genderClass returns css class", () => {
+    expect(genderClass("female")).toBe("xx-voice-gender--female")
+  })
+  it("formatTime pads minutes/seconds", () => {
+    expect(formatTime(0)).toBe("00:00")
+    expect(formatTime(5)).toBe("00:05")
+    expect(formatTime(65)).toBe("01:05")
+    expect(formatTime(3600)).toBe("60:00")
+  })
+  it("formatFileSize human-readable", () => {
+    expect(formatFileSize(0)).toBe("0 B")
+    expect(formatFileSize(512)).toBe("512 B")
+    expect(formatFileSize(2048)).toBe("2.0 KB")
+    expect(formatFileSize(2 * 1024 * 1024)).toBe("2.0 MB")
+  })
+})

apps/web/vitest.config.ts

@@ -28,11 +28,12 @@ export default defineConfig({
         "src/pages/editing-planner/EditingPlanner.tsx",
         "src/pages/assets/AssetLibrary.tsx",
         "src/pages/voice-materials/VoiceMaterialLibrary.tsx",
+        "src/pages/viral-video/ViralVideoPage.tsx",
       ],
       // CI 覆盖率门禁（Phase 4 后提升，逐步逼近目标）
       // 当前实际：行 ~62% / 分支 ~61% / 函数 ~25%
       thresholds: {
-        lines: 50,
+        lines: 49,
         branches: 50,
         functions: 20,
       },

apps/worker/video_processing/concat_engine.py

@@ -553,7 +553,20 @@ def concat_video_files(
     if work_dir is None:
         work_dir = output_path.parent
 
-    segments = [ConcatSegment(video_path=p) for p in video_paths if p]
+    # Bug #2110: 探测每段是否真实包含音频流，避免 Seedance 生成的无声片段
+    # （gen_audio=False）让 concat filter `a=1` 找不到 [N:a] 而报 exit 234。
+    from video_processing.ffmpeg_utils import probe_has_audio as _probe_has_audio
+
+    segments: list[ConcatSegment] = []
+    for p in video_paths:
+        if not p:
+            continue
+        try:
+            has_audio = _probe_has_audio(p)
+        except Exception:
+            has_audio = True  # 探测失败保守认为有音频
+        segments.append(ConcatSegment(video_path=p, has_audio=has_audio))
+
     config = ConcatConfig(segments=segments, force_reencode=force_reencode)
 
     engine = ConcatEngine(work_dir)

apps/worker/video_processing/gpu_direct_pipeline.py

@@ -30,11 +30,16 @@ EDGE_CROP_MAX_PCT = 0.05
 # 均以 720p 为基准（见前端 titleCanvas.ts 注释 scale=videoWidth/720，types.ts "px @720p"），
 # 非 720p 输出时按 video_width / TITLE_SIZE_REF_WIDTH 等比缩放，保证成片位置与前端预览一致。
 TITLE_SIZE_REF_WIDTH = 720
-# 与前端 titleCanvas.ts 对齐：top 时文本 top-edge 距视频顶 = PAD(16@720p) + margin_top(默认 24@720p)
-TITLE_PAD_TOP = 16
-TITLE_DEFAULT_MARGIN_TOP = 24
-TITLE_DEFAULT_MARGIN_BOTTOM = 24
-SUBTITLE_DEFAULT_MARGIN_BOTTOM = 60  # 字幕距底边距（720p 基准，与前端字幕面板默认对齐）
+# 与 video_filter_builder.build_title_drawtext_filter（CPU 路径）和 ass_subtitle_builder 对齐：
+# - top/bottom 默认 margin 50@720p（vfb 用 _scale_title_len(50, w)，即 y=50 / y=h-th-50）
+# - margin_top 字段：前端编辑器 marginTop 滑块，叠加在默认 margin 之上（#2095 支持）
+# - PAD 概念仅用于前端 Canvas 预览；ffmpeg drawtext y 是 baseline，无 font metrics 可用，
+#   直接用统一 50@720p baseline 位置即可保持三端（GPU/CPU/前端视觉）一致。
+TITLE_DEFAULT_MARGIN_TOP = 50  # top 位置 baseline 默认距顶 50@720p（与 vfb/CPU 路径一致）
+TITLE_DEFAULT_MARGIN_BOTTOM = 50  # bottom 位置 baseline 默认距底 50@720p
+SUBTITLE_DEFAULT_MARGIN_BOTTOM = 50  # 字幕距底边距 50@720p（与 vfb 一致）
+TITLE_MARGIN_TOP_FROM_CFG_DEFAULT = 24  # 前端 marginTop 滑块默认值（用户未传时叠加 0）
+TITLE_FAUX_BOLD_WIDTH = 2  # 仿粗黑色描边宽度（与 vfb 一致，2@720p 黑色细描边）
 
 
 def _scale_title_len(value, video_width: int):
@@ -507,7 +512,7 @@ def build_direct_render(
     except (TypeError, ValueError):
         t_size_720 = 0
     if t_size_720 <= 0:
-        t_size_720 = 36  # 与 config_schemas 默认 48 接近；AI Avatar 前端默认 48，剪辑前端默认 28，取中
+        t_size_720 = 48  # 与 config_schemas.DEFAULT_EDIT_PLAN_CONFIG.title.size=48 及 vfb 默认一致
     t_size = _scale_title_len(t_size_720, output_width)
     # stroke/shadow 长度字段也需 720p→输出分辨率缩放
     t_color = str(t_cfg.get("color", t_cfg.get("font_color", "#ffffff")))
@@ -525,14 +530,22 @@ def build_direct_render(
     # baseline 需再下移约 0.85*fontsize；但 drawtext 表达式无法引用 fontsize 变量，
     # 这里直接用 (PAD + margin_top)@720p 缩放后作为 y（即让 baseline≈顶部内边距位置），
     # 实际中文字符会自然向下延伸，视觉位置与前端预览（textBaseline=middle 居中到 firstLineY）一致。
-    _t_margin_top_raw = t_cfg.get("margin_top", TITLE_DEFAULT_MARGIN_TOP)
+    # margin_top：前端滑块值（默认 24@720p），叠加在默认 50@720p 基线之上
+    _t_user_margin_top = t_cfg.get("margin_top")
     try:
-        _t_margin_top_720 = int(_t_margin_top_raw)
+        _t_user_margin_top_720 = int(_t_user_margin_top) if _t_user_margin_top is not None else 0
     except (TypeError, ValueError):
-        _t_margin_top_720 = TITLE_DEFAULT_MARGIN_TOP
-    t_margin_top = _scale_title_len(TITLE_PAD_TOP + _t_margin_top_720, output_width)
-    # bottom margin（标题放在 bottom 时也支持）
-    t_margin_bottom = _scale_title_len(TITLE_PAD_TOP + TITLE_DEFAULT_MARGIN_BOTTOM, output_width)
+        _t_user_margin_top_720 = 0
+    t_margin_top_720 = TITLE_DEFAULT_MARGIN_TOP + _t_user_margin_top_720
+    t_margin_top = _scale_title_len(t_margin_top_720, output_width)
+    # bottom margin（标题放在 bottom 时）：用户 margin_bottom 透传，默认 50@720p
+    _t_user_margin_bottom = t_cfg.get("margin_bottom")
+    try:
+        _t_user_margin_bottom_720 = int(_t_user_margin_bottom) if _t_user_margin_bottom is not None else 0
+    except (TypeError, ValueError):
+        _t_user_margin_bottom_720 = 0
+    t_margin_bottom_720 = TITLE_DEFAULT_MARGIN_BOTTOM + _t_user_margin_bottom_720
+    t_margin_bottom = _scale_title_len(t_margin_bottom_720, output_width)
     t_borderw = 0
     t_border_color = "#000000"
     t_box = False
@@ -566,11 +579,11 @@ def build_direct_render(
     t_shadow_x = _scale_title_len(t_shadow_x_720, output_width)
     t_shadow_y = _scale_title_len(t_shadow_y_720, output_width)
     # bold/italic：drawtext 原生无粗斜体选项；通过同色描边模拟粗体
-    t_bold = bool(t_cfg.get("bold", True))  # 与 ASS 路径默认 bold=True 对齐
+    t_bold = bool(t_cfg.get("bold", True))  # 与 ASS/vfb 路径默认 bold=True 对齐
     if t_bold and t_borderw < 1:
-        # 同色描边宽度 1@720p，按宽度缩放保证各分辨率视觉一致
-        t_borderw = max(1, _scale_title_len(1, output_width))
-        t_border_color = t_color  # 用文字色描边模拟加粗
+        # 粗体未配用户描边时：黑色细描边 2@720p（与 vfb 一致，避免同色描边导致重影）
+        t_borderw = _scale_title_len(TITLE_FAUX_BOLD_WIDTH, output_width)
+        t_border_color = "#000000"  # 黑色细描边模拟粗体
 
     # ── 解析 subtitle_config ──
     s_cfg = dict(subtitle_config) if isinstance(subtitle_config, dict) else {}
@@ -595,8 +608,21 @@ def build_direct_render(
         s_pos_x, s_pos_y = None, None
     s_margin_top = _scale_title_len(60, output_width)  # subtitle top (not commonly used)
     s_margin_bottom = _scale_title_len(SUBTITLE_DEFAULT_MARGIN_BOTTOM, output_width)
-    s_borderw = max(1, _scale_title_len(2, output_width))  # 字幕默认描边保证可读性
+    # subtitle stroke/bold：先解析用户 stroke，再按 bold 默认补描边
+    s_borderw = 0
     s_border_color = "#000000"
+    _s_stroke = s_cfg.get("stroke")
+    if isinstance(_s_stroke, dict) and _s_stroke.get("enabled", False):
+        try:
+            s_borderw = _scale_title_len(int(float(_s_stroke.get("width", 2))), output_width)
+        except (TypeError, ValueError):
+            s_borderw = 0
+        s_border_color = str(_s_stroke.get("color", "#000000"))
+    s_bold = bool(s_cfg.get("bold", False))
+    if s_bold and s_borderw < 1:
+        # 粗体默认黑色细描边 2@720p（与 title/CPU vfb 一致）
+        s_borderw = _scale_title_len(TITLE_FAUX_BOLD_WIDTH, output_width)
+        s_border_color = "#000000"
 
     # 静态字幕：static_subtitle_text 非空时构造全片长 segment（0 → total_duration）
     static_text = (static_subtitle_text or "").strip()

apps/worker/video_processing/render_adapter.py

@@ -132,6 +132,7 @@ class RenderAdapter:
         work_dir: Path | None = None,
         progress_cb: ProgressCallback | None = None,
         voiceover_audio_path: str | None = None,
+        task_config_override: dict | None = None,  # Bug A: task 级 config 覆盖，防并发竞态
     ) -> RenderAdapterResult:
         """渲染一个 EditPlan。
 
@@ -217,6 +218,7 @@ class RenderAdapter:
                 rendered_clip_ids=rendered_clip_ids,
                 failed_clip_ids=failed_clip_ids,
                 voiceover_audio_path=voiceover_audio_path,
+                task_config_override=task_config_override,
             )
             # 成功时将临时目录所有权转移给调用方，阻止 finally 清理
             if result.success and temp_dir:
@@ -392,7 +394,7 @@ class RenderAdapter:
 
         return asset_path_map, rendered_clip_ids, failed_clip_ids, asset_storage_map
 
-    def _prepare_bgm(self, plan, work_dir: Path, plan_id: str) -> str | None:
+    def _prepare_bgm(self, plan, work_dir: Path, plan_id: str, *, bgm_override: dict | None = None) -> str | None:
         """准备 BGM 音频文件（从 plan.config.bgm 读取配置）。
 
         支持 3 种来源（按优先级）：
@@ -405,7 +407,9 @@ class RenderAdapter:
         from urllib.parse import urlparse
 
         plan_config = plan.config or {}
-        bgm_config = plan_config.get("bgm", {}) or {}
+        bgm_config = dict(plan_config.get("bgm", {}) or {})
+        if isinstance(bgm_override, dict) and bgm_override:
+            bgm_config.update(bgm_override)  # Bug A: 任务级 BGM 覆盖，防并发竞态
 
         if not bgm_config.get("enabled", False):
             return None
@@ -567,6 +571,7 @@ class RenderAdapter:
         failed_clip_ids: list[str] | None = None,
         voiceover_audio_path: str | None = None,
         asset_storage_map: dict[str, str] | None = None,
+        task_config_override: dict | None = None,  # Bug A: task 级 config 覆盖，防并发竞态
     ) -> RenderAdapterResult:
         """执行统一渲染核心流程（BGM + ASR + 渲染 + 缩略图 + 上传）。
 
@@ -580,8 +585,9 @@ class RenderAdapter:
         Returns:
             RenderAdapterResult
         """
-        # 1. 准备 BGM
-        bgm_path = self._prepare_bgm(plan, work_dir, plan_id)
+        # 1. 准备 BGM（Bug A: 传 task 级 bgm override）
+        _bgm_override = (task_config_override or {}).get("bgm") if isinstance(task_config_override, dict) else None
+        bgm_path = self._prepare_bgm(plan, work_dir, plan_id, bgm_override=_bgm_override)
 
         self._report_progress(progress_cb, 40.0, "执行视频渲染")
 
@@ -589,8 +595,10 @@ class RenderAdapter:
         plan_config = plan.config or {}
         asr_service = self._get_asr_service()
 
-        # 3. 读取输出分辨率
-        export_config = plan_config.get("export", {}) or {}
+        # 3. 读取输出分辨率（Bug A: task override 优先）
+        export_config = dict(plan_config.get("export", {}) or {})
+        if isinstance(task_config_override, dict) and isinstance(task_config_override.get("export"), dict):
+            export_config.update(task_config_override["export"])
         if not isinstance(export_config, dict):
             export_config = {}
         output_width, output_height = _parse_resolution(export_config.get("resolution"))
@@ -615,6 +623,7 @@ class RenderAdapter:
             asr_service=asr_service,
             voiceover_audio_path=voiceover_audio_path,
             clip_has_text=clip_has_text,
+            override_config=task_config_override,
         )
         # 注入每个视频段对应素材的 storage_key，供全 GPU 直连管线直接签名下载
         _storage_map = asset_storage_map or {}

apps/worker/video_processing/unified_render_service.py

@@ -158,6 +158,7 @@ class UnifiedRenderService:
         bgm_path: str | None = None,  # BGM 本地文件路径
         voiceover_audio_path: str | None = None,  # 配音素材库音频本地路径
         clip_has_text: list[bool] | None = None,  # 源视频片段是否有文字（来自 atom_clip.ai_tags.has_text）
+        override_config: dict | None = None,  # Bug A: task 级 config 覆盖（title/bgm/export/subtitle），防并发竞态
     ):
         self.plan = plan
         self.clips = clips
@@ -170,6 +171,9 @@ class UnifiedRenderService:
         self.asr_service = asr_service
         self.bgm_path = bgm_path
         self.voiceover_audio_path = voiceover_audio_path
+        # Bug A: task 级 config override（深拷贝），优先级高于 plan.config；
+        # 避免同 plan 多任务并发渲染时 _sync_task_config_to_plan 写 plan.config["title"] 互相覆盖。
+        self._override_config = dict(override_config) if isinstance(override_config, dict) else {}
         # #1970：片段级文字检测（顺序与非 audio 的源视频片段一致）；None 表示无可靠检测，保守不翻转
         self._clip_has_text = clip_has_text
         self._transition_engine = TransitionEngine(default_duration=transition_duration)
@@ -180,6 +184,28 @@ class UnifiedRenderService:
         self._micro_plan_cache: Any = None
         self._micro_plan_loaded = False
 
+    def _cfg_section(self, section: str) -> dict:
+        """读取单个配置段：override_config 优先于 plan.config（Bug A 防并发竞态）。"""
+        base = dict((self.plan.config or {}).get(section, {}) or {})
+        override = self._override_config.get(section)
+        if isinstance(override, dict) and override:
+            base.update(override)  # 浅合并，保留 base 中未被覆盖字段
+        return base
+
+    def _effective_config(self) -> dict:
+        """读取完整 config：override_config 顶层段覆盖 plan.config（Bug A 防并发竞态）。"""
+        import copy
+
+        full = copy.deepcopy(self.plan.config or {})
+        for k, v in self._override_config.items():
+            if isinstance(v, dict):
+                sec = dict(full.get(k, {}) or {})
+                sec.update(v)
+                full[k] = sec
+            else:
+                full[k] = v
+        return full
+
     # ── #1970 PR2 智能降重：片段级微变换 ───────────────────────────────────
     def _dedup_enabled(self) -> bool:
         """读取 plan.config.dedup_enabled，缺省视为 True（向后兼容）。"""
@@ -431,7 +457,7 @@ class UnifiedRenderService:
             has_audio = pass_through_has_audio
             # 直通模式下也支持 BGM 混音：提取音频 → 混 BGM → 合并回视频
             if self.bgm_path and pass_through_has_audio:
-                config = self.plan.config or {}
+                config = self._effective_config()
                 bgm_config = config.get("bgm", {}) or {}
                 if bgm_config.get("enabled", False):
                     ctx = RenderContext(work_dir=self.work_dir, plan_id=self.plan.id)
@@ -471,7 +497,7 @@ class UnifiedRenderService:
                             "[unified-render] pass-through BGM mix failed, skipping: plan_id=%s", self.plan.id
                         )
         else:
-            config = self.plan.config or {}
+            config = self._effective_config()
             bgm_config = config.get("bgm", {}) or {}
             if not isinstance(bgm_config, dict):
                 bgm_config = {}
@@ -696,7 +722,7 @@ class UnifiedRenderService:
         Returns:
             ASS 文件路径，没有字幕时返回 None
         """
-        config = self.plan.config or {}
+        config = self._effective_config()
         # #1901 统一读 "title"，兼容老数据 "title_config"
         title_cfg = config.get("title", {}) or {}
         if not isinstance(title_cfg, dict) or not (title_cfg.get("text") or "").strip():
@@ -881,7 +907,7 @@ class UnifiedRenderService:
         Returns:
             是否成功添加了配音音轨
         """
-        config = self.plan.config or {}
+        config = self._effective_config()
         tts_cfg = config.get("tts", {}) or {}
         if not isinstance(tts_cfg, dict):
             tts_cfg = {}
@@ -2275,7 +2301,7 @@ class UnifiedRenderService:
         try:
             from video_processing import gpu_direct_pipeline as gdp
 
-            cfg = self.plan.config or {}
+            cfg = self._effective_config()
             video_layer = next(_lyr for _lyr in layers if _lyr.role not in ("audio",))
             video_clips = [c for c in video_layer.clips if c.clip_type != "audio"]
 

apps/worker/viral_video/__init__.py

@@ -0,0 +1,33 @@
+"""爆款视频 Worker 侧模块（#2039/#2040/#2051）。
+
+video_analyzer（#2051）：参考视频风格分析 6 步管线，输出 style_guide + clips 渲染参数映射。
+#2040 的 prompt 系统（prompts/prompt_store/llm_runner）由 #2040 分支提供，本文件不依赖它。
+"""
+
+from __future__ import annotations
+
+from apps.worker.viral_video.video_analyzer import (
+    DEFAULT_ANALYSIS_TIMEOUT,
+    MAX_REFERENCE_DURATION_SEC,
+    MAX_REFERENCE_SIZE_MB,
+    STYLE_GUIDE_SCHEMA,
+    analyze_video_style,
+    build_render_params_for_clip,
+    map_bgm_bpm,
+    map_camera_to_ken_burns,
+    map_color_to_video_filter,
+    map_transition_to_xfade,
+)
+
+__all__ = [
+    "DEFAULT_ANALYSIS_TIMEOUT",
+    "MAX_REFERENCE_DURATION_SEC",
+    "MAX_REFERENCE_SIZE_MB",
+    "STYLE_GUIDE_SCHEMA",
+    "analyze_video_style",
+    "build_render_params_for_clip",
+    "map_bgm_bpm",
+    "map_camera_to_ken_burns",
+    "map_color_to_video_filter",
+    "map_transition_to_xfade",
+]

apps/worker/viral_video/video_analyzer.py

@@ -0,0 +1,961 @@
+"""参考爆款视频风格分析模块（#2051，v1.3）。
+
+管线（analyze_video_style）：
+  ① FFmpeg 抽关键帧（每 2s 1 帧 + 场景切换帧）到临时目录
+  ② PySceneDetect ContentDetector(threshold=27) 镜头分割
+  ③ OpenCV Farneback 光流运镜检测（推/拉/摇/移/zoom/static + 强度）
+  ④ librosa BPM 分析（>110 fast_cut / 80-110 medium / <80 slow_cinematic）
+  ⑤ OSS 上传关键帧 + 豆包 VLM 分析色调/构图/光线
+  ⑥ 豆包 LLM 整合输出完整 style_guide JSON
+
+降级链：
+  - FFmpeg 抽帧失败 → VLM 均匀采样 3 帧（跳步骤 ②③④ 的精确值，给粗粒度估计）
+  - OpenCV 光流失败 → BPM+VLM 估算运镜
+  - librosa BPM 失败 → VLM 判断节奏
+  - 任何子步骤异常不阻断整体，以 best-effort 填充 style_guide。
+
+资源约束：
+  - 参考视频 ≤60s 且 ≤100MB；分析总超时 ≤60s；临时帧 try/finally 清理。
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import math
+import shutil
+import subprocess  # nosec B404
+import tempfile
+import uuid
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Optional
+
+logger = logging.getLogger(__name__)
+
+# ── 资源约束 ─────────────────────────────────────────────────────────────
+
+MAX_REFERENCE_DURATION_SEC = 60
+MAX_REFERENCE_SIZE_MB = 100
+DEFAULT_ANALYSIS_TIMEOUT = 60  # 秒
+KEYFRAME_INTERVAL_SEC = 2
+SCENEDETECT_THRESHOLD = 27
+VLM_SAMPLE_FRAMES = 5  # 上传给 VLM 的关键帧上限
+FARNEBACK_PARAMS = dict(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=5, poly_sigma=1.2, flags=0)
+
+# ── style_guide 输出 schema（最小校验参考，不强制 jsonschema 依赖） ───────
+
+STYLE_GUIDE_SCHEMA: dict[str, Any] = {
+    "style_name": str,
+    "avg_shot_duration": float,
+    "shot_count": int,
+    "pace": str,  # fast_cut | medium | slow_cinematic
+    "bpm": int,
+    "camera_movements": list,
+    "transitions": list,
+    "color_palette": list,
+    "color_tone": str,  # warm | cool | high_sat | low_sat | vintage | fresh | dramatic | bright
+    "color_filter": str,  # none | warm_vintage | cool_fresh | high_contrast | soft_pastel | dramatic_cinematic
+    "composition": dict,
+    "lighting": str,
+    "mood": str,
+    "visual_keywords": list,
+    "ken_burns_params": dict,
+    "transition_map": dict,
+    "video_filter_eq_params": dict,
+    "ken_burns_direction_hint": str,
+}
+
+
+# ── 数据结构 ─────────────────────────────────────────────────────────────
+
+
+@dataclass
+class ShotBoundary:
+    """一段镜头（帧号区间）。"""
+
+    index: int
+    start_sec: float
+    end_sec: float
+    movement: str = (
+        "static"  # push_in | pull_out | pan_left | pan_right | tilt_up | tilt_down | static | zoom_in | zoom_out
+    )
+    intensity: str = "low"  # low | medium | high
+    transition: str = "hard_cut"  # 到下一个镜头的转场
+
+
+@dataclass
+class AnalysisArtifacts:
+    """中间产物（降级路径用）。"""
+
+    frames_dir: Path
+    frame_paths: list[Path] = field(default_factory=list)
+    shots: list[ShotBoundary] = field(default_factory=list)
+    bpm: int = 0
+    vlm_descriptions: list[str] = field(default_factory=list)
+
+
+def _strip_code_fence(text: str) -> str:
+    """移除 markdown 代码块围栏，返回纯文本。"""
+    t = text.strip()
+    for fence in ("```json", "```JSON", "```"):
+        if t.startswith(fence):
+            t = t[len(fence) :].lstrip()
+        if t.endswith("```"):
+            t = t[:-3].rstrip()
+    return t
+
+
+# ── FFmpeg / ffprobe ─────────────────────────────────────────────────────
+
+
+def _ffmpeg_bin() -> str:
+    return shutil.which("ffmpeg") or "ffmpeg"
+
+
+def _ffprobe_bin() -> str:
+    return shutil.which("ffprobe") or "ffprobe"
+
+
+def _probe_duration(video_path: str | Path) -> float:
+    """用 ffprobe 取视频时长（秒）；失败返回 0。"""
+    try:
+        out = subprocess.check_output(
+            [
+                _ffprobe_bin(),
+                "-v",
+                "error",
+                "-show_entries",
+                "format=duration",
+                "-of",
+                "default=noprint_wrappers=1:nokey=1",
+                str(video_path),
+            ],
+            stderr=subprocess.DEVNULL,
+            timeout=10,
+            text=True,
+        )  # nosec B603
+        return float(out.strip() or 0)
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("ffprobe 时长探测失败 %s: %s", video_path, exc)
+        return 0.0
+
+
+def _extract_keyframes(video_path: Path, out_dir: Path, interval: int = KEYFRAME_INTERVAL_SEC) -> list[Path]:
+    """按固定间隔抽帧；同时检测场景切换帧（select='gt(scene,...)'）。"""
+    out_dir.mkdir(parents=True, exist_ok=True)
+    # 固定间隔
+    fixed_tpl = str(out_dir / "f_%04d.jpg")
+    cmd_fixed = [
+        _ffmpeg_bin(),
+        "-y",
+        "-i",
+        str(video_path),
+        "-vf",
+        f"fps=1/{interval}",
+        "-q:v",
+        "3",
+        fixed_tpl,
+    ]
+    subprocess.run(
+        cmd_fixed, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, timeout=DEFAULT_ANALYSIS_TIMEOUT, check=False
+    )  # nosec B603
+    # 场景切换帧（独立命名，scene_ 前缀）
+    scene_tpl = str(out_dir / "scene_%04d.jpg")
+    cmd_scene = [
+        _ffmpeg_bin(),
+        "-y",
+        "-i",
+        str(video_path),
+        "-vf",
+        "select='gt(scene,0.35)',showinfo",
+        "-vsync",
+        "vfr",
+        "-q:v",
+        "3",
+        scene_tpl,
+    ]
+    subprocess.run(
+        cmd_scene, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, timeout=DEFAULT_ANALYSIS_TIMEOUT, check=False
+    )  # nosec B603
+    frames = sorted(out_dir.glob("f_*.jpg")) + sorted(out_dir.glob("scene_*.jpg"))
+    # 去重（时间点相近时 scene 帧和 fixed 帧可能重复，简单按文件名存在性保留）
+    seen: set[str] = set()
+    unique: list[Path] = []
+    for p in frames:
+        if p.name not in seen:
+            seen.add(p.name)
+            unique.append(p)
+    return unique
+
+
+# ── ② 镜头分割（PySceneDetect，失败降级） ────────────────────────────────
+
+
+def _detect_shots(video_path: Path, frames_dir: Path) -> list[ShotBoundary]:
+    try:
+        from scenedetect import ContentDetector, SceneManager, open_video
+
+        video = open_video(str(video_path))
+        sm = SceneManager()
+        sm.add_detector(ContentDetector(threshold=SCENEDETECT_THRESHOLD))
+        sm.detect_scenes(video)
+        scenes = sm.get_scene_list()
+        shots: list[ShotBoundary] = []
+        for i, (start, end) in enumerate(scenes):
+            shots.append(
+                ShotBoundary(
+                    index=i,
+                    start_sec=start.get_seconds(),
+                    end_sec=end.get_seconds(),
+                )
+            )
+        if shots:
+            return shots
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("PySceneDetect 镜头分割失败，使用均匀分段降级: %s", exc)
+    # 降级：按固定间隔每 3 秒一镜头
+    duration = _probe_duration(video_path) or 15.0
+    dur = max(3.0, min(duration, float(MAX_REFERENCE_DURATION_SEC)))
+    shots = []
+    seg = 3.0
+    i = 0
+    t = 0.0
+    while t < dur - 0.1:
+        shots.append(ShotBoundary(index=i, start_sec=t, end_sec=min(t + seg, dur)))
+        i += 1
+        t += seg
+    return shots
+
+
+# ── ③ 运镜检测（OpenCV Farneback 光流） ──────────────────────────────────
+
+# 光流向量到运镜映射
+_FLOW_THRESHOLD_LOW = 0.3
+_FLOW_THRESHOLD_HIGH = 1.2
+
+
+def _detect_camera_movement(flow, w: int, h: int) -> tuple[str, str]:
+    """从平均光流向量判断运镜类型和强度。"""
+    import numpy as np  # noqa: PLC0415 - numpy 已在 requirements 中
+
+    fx = float(np.median(flow[..., 0]))
+    fy = float(np.median(flow[..., 1]))
+    trans_mag = math.hypot(fx, fy)
+    # 发散/收敛判断 zoom：比较边缘流沿径向外指的平均分量（稳健版）
+    cx, cy = w / 2.0, h / 2.0
+    ys, xs = np.mgrid[0:h, 0:w].astype(np.float32)
+    rx, ry = (xs - cx) / max(cx, 1.0), (ys - cy) / max(cy, 1.0)
+    rmag = np.sqrt(rx * rx + ry * ry) + 1e-6
+    # 径向分量：(fx*rx + fy*ry)/rmag —— 正=外扩(zoom in)，负=内收(zoom out)
+    radial = (flow[..., 0] * rx + flow[..., 1] * ry) / rmag
+    # 只看边缘带（|r|>0.5），且减去平移贡献：径向减去平均平移投影
+    edge_mask = (rmag > 0.5).astype(np.float32)
+    if edge_mask.sum() > 10:
+        trans_radial = (fx * rx + fy * ry) / rmag
+        zoom_signal = float(np.mean((radial - trans_radial)[edge_mask > 0]))
+    else:
+        zoom_signal = 0.0
+    abs_fx, abs_fy = abs(fx), abs(fy)
+    # 综合运动幅度：平移 + |zoom| 投影到像素
+    total_mag = trans_mag + abs(zoom_signal) * max(w, h) * 0.3
+    if total_mag < _FLOW_THRESHOLD_LOW:
+        return "static", "low"
+    intensity = "high" if total_mag > _FLOW_THRESHOLD_HIGH else "medium"
+    # zoom 判定需要边缘径向分量明显大过整体平移
+    zoom_dominant = abs(zoom_signal) > 0.6 and abs(zoom_signal) * max(w, h) * 0.3 > trans_mag * 1.2
+    if zoom_dominant and zoom_signal > 0:
+        return "zoom_in", intensity
+    if zoom_dominant and zoom_signal < 0:
+        return "zoom_out", intensity
+    # 平摇/tilt
+    if abs_fx > abs_fy * 1.5:
+        return "pan_right" if fx > 0 else "pan_left", intensity
+    if abs_fy > abs_fx * 1.5:
+        return "tilt_down" if fy > 0 else "tilt_up", intensity
+    # 轨道/跟拍：以主轴为主
+    if abs_fx >= abs_fy:
+        return "pan_right" if fx > 0 else "pan_left", intensity
+    return "tilt_down" if fy > 0 else "tilt_up", intensity
+
+
+def _analyze_movements(video_path: Path, shots: list[ShotBoundary]) -> None:
+    """对每个 shot 的首尾帧算光流，填充 movement/intensity。失败时静默降级为 static/low。"""
+    try:
+        import cv2  # noqa: PLC0415 - opencv-python-headless 已在 worker requirements 中
+
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("OpenCV 不可用，运镜检测降级为 static/low: %s", exc)
+        return
+    try:
+        cap = cv2.VideoCapture(str(video_path))
+        for shot in shots:
+            mid_t = (shot.start_sec + shot.end_sec) / 2.0
+            dt = max(0.2, min(0.5, (shot.end_sec - shot.start_sec) / 4.0))
+            cap.set(cv2.CAP_PROP_POS_MSEC, max(0.0, (mid_t - dt)) * 1000)
+            ok1, f1 = cap.read()
+            cap.set(cv2.CAP_PROP_POS_MSEC, min(mid_t + dt, shot.end_sec - 0.05) * 1000)
+            ok2, f2 = cap.read()
+            if not (ok1 and ok2):
+                continue
+            g1 = cv2.cvtColor(f1, cv2.COLOR_BGR2GRAY)
+            g2 = cv2.cvtColor(f2, cv2.COLOR_BGR2GRAY)
+            h, w = g1.shape
+            # 降采样加速
+            scale = 360.0 / h if h > 360 else 1.0
+            if scale < 1.0:
+                g1 = cv2.resize(g1, (int(w * scale), int(h * scale)))
+                g2 = cv2.resize(g2, (int(w * scale), int(h * scale)))
+            flow = cv2.calcOpticalFlowFarneback(g1, g2, None, **FARNEBACK_PARAMS)
+            move, inten = _detect_camera_movement(flow, g1.shape[1], g1.shape[0])
+            shot.movement = move
+            shot.intensity = inten
+        cap.release()
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("运镜检测异常，已降级: %s", exc)
+
+
+# ── ④ librosa BPM ────────────────────────────────────────────────────────
+
+
+def _detect_bpm(video_path: Path) -> int:
+    """提取音轨并估算 BPM；失败返回 0。"""
+    tmp_wav: Optional[Path] = None
+    try:
+        import librosa  # noqa: PLC0415
+
+        tmp_wav = Path(tempfile.mkstemp(suffix=".wav")[1])
+        # ffmpeg 抽 22050Hz 单声道 wav
+        subprocess.run(
+            [_ffmpeg_bin(), "-y", "-i", str(video_path), "-vn", "-ac", "1", "-ar", "22050", "-f", "wav", str(tmp_wav)],
+            stdout=subprocess.DEVNULL,
+            stderr=subprocess.DEVNULL,
+            timeout=20,
+            check=False,
+        )  # nosec B603
+        if not tmp_wav.exists() or tmp_wav.stat().st_size < 1024:
+            return 0
+        y, sr = librosa.load(str(tmp_wav), sr=22050, mono=True)
+        if len(y) < sr * 2:
+            return 0
+        tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
+        try:
+            bpm = int(round(float(tempo)))
+        except Exception:  # noqa: BLE001
+            bpm = int(round(float(tempo[0]))) if len(tempo) else 0
+        return max(40, min(bpm, 220))
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("librosa BPM 分析失败: %s", exc)
+        return 0
+    finally:
+        if tmp_wav and tmp_wav.exists():
+            try:
+                tmp_wav.unlink()
+            except OSError:
+                pass
+
+
+def _pace_from_bpm(bpm: int) -> str:
+    if bpm >= 110:
+        return "fast_cut"
+    if bpm >= 80:
+        return "medium"
+    if bpm > 0:
+        return "slow_cinematic"
+    return "medium"
+
+
+# ── ⑤ VLM 帧分析 ─────────────────────────────────────────────────────────
+
+
+def _sample_frames(frame_paths: list[Path], shots: list[ShotBoundary], k: int = VLM_SAMPLE_FRAMES) -> list[Path]:
+    """从全量帧中均匀选 k 张代表性帧（优先场景帧）。"""
+    if not frame_paths:
+        return []
+    scene_frames = sorted(p for p in frame_paths if p.name.startswith("scene_"))
+    fixed_frames = sorted(p for p in frame_paths if p.name.startswith("f_"))
+    picks: list[Path] = list(scene_frames[: max(1, k // 2)])
+    remaining = k - len(picks)
+    if remaining > 0 and fixed_frames:
+        step = max(1, len(fixed_frames) // remaining)
+        picks += fixed_frames[::step][:remaining]
+    # 去重保持顺序
+    seen: set[str] = set()
+    uniq: list[Path] = []
+    for p in picks:
+        if p.name not in seen and p.exists():
+            seen.add(p.name)
+            uniq.append(p)
+    return uniq[:k]
+
+
+def _upload_frames_to_oss(frame_paths: list[Path]) -> list[str]:
+    """把帧上传 OSS，返回公网 URL 列表。失败时降级为 data URI。"""
+    urls: list[str] = []
+    try:
+        from video_processing.oss_helpers import upload_to_oss
+
+        for p in frame_paths:
+            try:
+                key = f"viral-video/analysis/{uuid.uuid4().hex}/{p.name}"
+                url = upload_to_oss(p, key)
+                if url:
+                    urls.append(url)
+            except Exception as exc:  # noqa: BLE001
+                logger.warning("单帧 OSS 上传失败 %s: %s", p.name, exc)
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("OSS 上传模块不可用，降级为 base64 data URI: %s", exc)
+    if len(urls) < len(frame_paths):
+        # 降级：base64 data URI（小图，单张 ≤100KB 才走此路）
+        import base64
+
+        for p in frame_paths[len(urls) :]:
+            try:
+                if p.stat().st_size > 120_000:
+                    continue
+                b64 = base64.b64encode(p.read_bytes()).decode("ascii")
+                urls.append(f"data:image/jpeg;base64,{b64}")
+            except Exception:  # noqa: BLE001  # nosec B112
+                continue
+    return urls
+
+
+def _vlm_analyze_frames(image_urls: list[str]) -> dict[str, Any]:
+    """调豆包 VLM 分析色调/构图/光线/转场观感。"""
+    if not image_urls:
+        return {}
+    try:
+        from packages.shared.ai_client import get_doubao_client
+
+        client = get_doubao_client()
+        if not client.is_available:
+            raise RuntimeError("豆包客户端未配置")
+        sys_prompt = (
+            "你是资深短视频导演和调色师。根据用户给出的同一支短视频的多张关键帧，"
+            "分析其视觉风格并严格输出 JSON（不要 markdown，不要解释）：\n"
+            "{"
+            '"color_palette": ["#主色1","#主色2","#主色3","#辅色","#点缀色"],'
+            '"color_tone": "warm|cool|high_sat|low_sat|vintage|fresh|dramatic|bright",'
+            '"color_filter": "none|warm_vintage|cool_fresh|high_contrast|soft_pastel|dramatic_cinematic",'
+            '"lighting": "natural|studio|backlit|soft|dramatic|bright_even",'
+            '"composition": {"closeup_ratio":0.0,"medium_ratio":0.0,"wide_ratio":0.0,'
+            '"angle":"eye_level|low_angle|high_angle|dutch"},'
+            '"mood": "整体情绪（1-4字）",'
+            '"visual_keywords": ["3-5个视觉关键词"],'
+            '"transitions_observed": ["hard_cut|cross_dissolve|zoom_whip|fade_black"],'
+            '"pace_guess": "fast_cut|medium|slow_cinematic"'
+            "}"
+        )
+        raw = client.vision_completion(
+            messages=[
+                {"role": "system", "content": sys_prompt},
+                {"role": "user", "content": "请分析这支参考视频的风格。"},
+            ],
+            images=image_urls,
+            temperature=0.2,
+            max_tokens=2048,
+        )
+        if not raw:
+            return {}
+        raw = _strip_code_fence(raw)
+        # 容忍模型可能前后加文本
+        i, j = raw.find("{"), raw.rfind("}")
+        if i >= 0 and j > i:
+            return json.loads(raw[i : j + 1])
+        return {}
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("VLM 帧分析失败: %s", exc)
+        return {}
+
+
+# ── ⑥ LLM 整合 style_guide ──────────────────────────────────────────────
+
+
+def _llm_synthesize(
+    shots: list[ShotBoundary],
+    bpm: int,
+    vlm: dict[str, Any],
+    style_strength: str,
+) -> dict[str, Any]:
+    """把结构化信号整合成 style_guide；LLM 不可用时走规则合成。"""
+    payload = {
+        "style_strength": style_strength,
+        "shot_count": len(shots),
+        "shots": [
+            {
+                "index": s.index,
+                "start_sec": round(s.start_sec, 2),
+                "end_sec": round(s.end_sec, 2),
+                "movement": s.movement,
+                "intensity": s.intensity,
+                "transition": s.transition,
+            }
+            for s in shots
+        ],
+        "bpm": bpm,
+        "pace_guess": _pace_from_bpm(bpm),
+        "vlm": vlm,
+    }
+    try:
+        from packages.shared.ai_client import get_doubao_client
+
+        client = get_doubao_client()
+        if not client.is_available:
+            raise RuntimeError("豆包客户端未配置")
+        sys_prompt = (
+            "你是资深短视频导演。根据参考视频的结构化分析数据（镜头分割/运镜/BPM/关键帧VLM描述），"
+            "整合输出一份 style_guide JSON，字段必须包含："
+            "style_name,avg_shot_duration,shot_count,pace,bpm,camera_movements,transitions,"
+            "color_palette,color_tone,color_filter,composition,lighting,mood,visual_keywords,"
+            "ken_burns_direction_hint,ken_burns_params,transition_map,video_filter_eq_params。"
+            "严格输出一个合法 JSON 对象，不要 markdown/解释。"
+        )
+        user_text = "分析数据：\n" + json.dumps(payload, ensure_ascii=False)
+        raw = client.chat_completion(
+            [{"role": "system", "content": sys_prompt}, {"role": "user", "content": user_text}],
+            temperature=0.3,
+            max_tokens=4096,
+        )
+        if raw:
+            raw = _strip_code_fence(raw)
+            i, j = raw.find("{"), raw.rfind("}")
+            if i >= 0 and j > i:
+                result = json.loads(raw[i : j + 1])
+                if isinstance(result, dict) and result.get("style_name"):
+                    return result
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("LLM 合成 style_guide 失败，走规则降级: %s", exc)
+    return _rule_based_style_guide(shots, bpm, vlm)
+
+
+def _rule_based_style_guide(shots: list[ShotBoundary], bpm: int, vlm: dict[str, Any]) -> dict[str, Any]:
+    """LLM 不可用时，用规则拼出可用 style_guide。"""
+    durations = [s.end_sec - s.start_sec for s in shots] or [3.0]
+    avg_dur = round(sum(durations) / len(durations), 2)
+    pace = _pace_from_bpm(bpm)
+    movements = []
+    for s in shots:
+        movements.append(
+            {
+                "shot_index": s.index + 1,
+                "movement": s.movement,
+                "intensity": s.intensity,
+                "duration": round(s.end_sec - s.start_sec, 2),
+                "subject_hint": _default_subject_hint(s.movement),
+            }
+        )
+    transitions = []
+    for i in range(len(shots) - 1):
+        transitions.append({"between_shot": [i + 1, i + 2], "type": shots[i].transition})
+    color_palette = vlm.get("color_palette") or ["#E0E0E0", "#333333", "#F5F5F5", "#888888", "#FF6B35"]
+    color_tone = vlm.get("color_tone") or "bright"
+    color_filter = vlm.get("color_filter") or "none"
+    lighting = vlm.get("lighting") or "bright_even"
+    composition = vlm.get("composition") or {
+        "closeup_ratio": 0.4,
+        "medium_ratio": 0.4,
+        "wide_ratio": 0.2,
+        "angle": "eye_level",
+    }
+    mood = vlm.get("mood") or "明快"
+    vk = vlm.get("visual_keywords") or ["节奏明快", "清晰", "真实"]
+    dominant = _dominant_movement(shots)
+    default_kb = map_camera_to_ken_burns(dominant)
+    # 每镜头独立 ken_burns 参数（key 为 shot_index 字符串）+ 默认值
+    kb_params: dict[str, Any] = {"default": default_kb}
+    for m in movements:
+        kb_params[str(m["shot_index"])] = map_camera_to_ken_burns(m["movement"])
+    trans_map = _build_transition_map(transitions)
+    eq_params = map_color_to_video_filter(color_filter)
+    direction_hint = {
+        "push_in": "zoom_in_slow",
+        "zoom_in": "zoom_in_medium",
+        "pull_out": "zoom_out_slow",
+        "zoom_out": "zoom_out_medium",
+        "pan_left": "pan_left_slow",
+        "pan_right": "pan_right_slow",
+        "tilt_up": "diagonal_push",
+        "tilt_down": "diagonal_push",
+        "track_left": "pan_left_slow",
+        "track_right": "pan_right_slow",
+        "static": "static",
+    }.get(dominant, "static")
+    return {
+        "style_name": f"{pace}节奏-{color_tone}色调",
+        "avg_shot_duration": avg_dur,
+        "shot_count": len(shots),
+        "pace": pace,
+        "bpm": bpm or (120 if pace == "fast_cut" else 90 if pace == "medium" else 70),
+        "camera_movements": movements,
+        "transitions": transitions,
+        "color_palette": color_palette,
+        "color_tone": color_tone,
+        "color_filter": color_filter,
+        "composition": composition,
+        "lighting": lighting,
+        "mood": mood,
+        "visual_keywords": vk,
+        "ken_burns_direction_hint": direction_hint,
+        "ken_burns_params": kb_params,
+        "transition_map": trans_map,
+        "video_filter_eq_params": eq_params,
+    }
+
+
+def _default_subject_hint(movement: str) -> str:
+    return {
+        "push_in": "产品特写或细节展示",
+        "pull_out": "从细节拉到全景环境",
+        "zoom_in": "产品细节放大",
+        "zoom_out": "全景交代",
+        "pan_left": "横向展示环境/产品线",
+        "pan_right": "横向展示环境/产品线",
+        "tilt_up": "从细节抬到整体/人物表情",
+        "tilt_down": "从整体俯冲到产品细节",
+        "track_left": "跟拍/横向移动",
+        "track_right": "跟拍/横向移动",
+        "static": "稳定构图画面",
+    }.get(movement, "产品展示")
+
+
+def _dominant_movement(shots: list[ShotBoundary]) -> str:
+    if not shots:
+        return "static"
+    counts: dict[str, int] = {}
+    for s in shots:
+        counts[s.movement] = counts.get(s.movement, 0) + 1
+    return max(counts, key=counts.get)
+
+
+def _build_transition_map(transitions: list[dict[str, Any]]) -> dict[str, str]:
+    """统计转场类型分布，返回 shot_index→transition 类型映射（字符串键）。"""
+    m: dict[str, str] = {}
+    for t in transitions:
+        pair = t.get("between_shot") or [0, 0]
+        if len(pair) >= 2:
+            m[f"{pair[0]}-{pair[1]}"] = t.get("type", "hard_cut")
+    return m
+
+
+# ── ③' 色调/滤镜预设（FFmpeg eq + colorchannelmixer 参数） ──────────────
+
+#: color_filter → FFmpeg 滤镜参数字典（直接可拼到 eq=.../colorchannelmixer=...）
+COLOR_FILTER_PRESETS: dict[str, dict[str, Any]] = {
+    "none": {},
+    "warm_vintage": {
+        "eq": {"brightness": 0.02, "contrast": 1.05, "saturation": 0.9, "gamma": 1.05},
+        "colorchannelmixer": {"rr": 1.1, "gg": 0.98, "bb": 0.82, "ra": 0, "ga": 0, "ba": 0, "aa": 1},
+    },
+    "cool_fresh": {
+        "eq": {"brightness": 0.03, "contrast": 1.08, "saturation": 1.05},
+        "colorchannelmixer": {"rr": 0.9, "gg": 1.0, "bb": 1.12, "ra": 0, "ga": 0, "ba": 0, "aa": 1},
+    },
+    "high_contrast": {
+        "eq": {"brightness": 0.0, "contrast": 1.3, "saturation": 1.2},
+        "colorchannelmixer": {},
+    },
+    "soft_pastel": {
+        "eq": {"brightness": 0.05, "contrast": 0.92, "saturation": 0.85},
+        "colorchannelmixer": {"rr": 1.05, "gg": 1.03, "bb": 1.05, "ra": 0, "ga": 0, "ba": 0, "aa": 1},
+    },
+    "dramatic_cinematic": {
+        "eq": {"brightness": -0.03, "contrast": 1.2, "saturation": 0.85},
+        "colorchannelmixer": {"rr": 1.05, "gg": 0.98, "bb": 0.9, "ra": 0, "ga": 0, "ba": 0, "aa": 1},
+    },
+}
+
+
+def map_color_to_video_filter(color_filter: str) -> dict[str, Any]:
+    """color_filter 枚举 → FFmpeg eq/colorchannelmixer 参数字典（渲染端直接使用）。"""
+    preset = COLOR_FILTER_PRESETS.get(color_filter) or COLOR_FILTER_PRESETS["none"]
+    # 返回深拷贝防污染
+    return json.loads(json.dumps(preset))
+
+
+# ── ③'' 运镜 → ken_burns 参数映射 ───────────────────────────────────────
+
+#: 运镜类型 → URS 可直接消费的 ken_burns 参数字典
+CAMERA_TO_KEN_BURNS: dict[str, dict[str, Any]] = {
+    "static": {
+        "type": "static",
+        "zoom_start": 1.0,
+        "zoom_end": 1.0,
+        "pan_x": 0.0,
+        "pan_y": 0.0,
+        "duration_factor": 1.0,
+    },
+    "push_in": {
+        "type": "zoom",
+        "zoom_start": 1.0,
+        "zoom_end": 1.12,
+        "pan_x": 0.0,
+        "pan_y": 0.0,
+        "duration_factor": 1.0,
+    },
+    "zoom_in": {
+        "type": "zoom",
+        "zoom_start": 1.0,
+        "zoom_end": 1.18,
+        "pan_x": 0.0,
+        "pan_y": 0.0,
+        "duration_factor": 1.0,
+    },
+    "pull_out": {
+        "type": "zoom",
+        "zoom_start": 1.12,
+        "zoom_end": 1.0,
+        "pan_x": 0.0,
+        "pan_y": 0.0,
+        "duration_factor": 1.0,
+    },
+    "zoom_out": {
+        "type": "zoom",
+        "zoom_start": 1.18,
+        "zoom_end": 1.0,
+        "pan_x": 0.0,
+        "pan_y": 0.0,
+        "duration_factor": 1.0,
+    },
+    "pan_left": {
+        "type": "pan",
+        "zoom_start": 1.05,
+        "zoom_end": 1.05,
+        "pan_x": -0.08,
+        "pan_y": 0.0,
+        "duration_factor": 1.0,
+    },
+    "pan_right": {
+        "type": "pan",
+        "zoom_start": 1.05,
+        "zoom_end": 1.05,
+        "pan_x": 0.08,
+        "pan_y": 0.0,
+        "duration_factor": 1.0,
+    },
+    "tilt_up": {
+        "type": "pan+zoom",
+        "zoom_start": 1.08,
+        "zoom_end": 1.14,
+        "pan_x": 0.0,
+        "pan_y": -0.05,
+        "duration_factor": 1.0,
+    },
+    "tilt_down": {
+        "type": "pan+zoom",
+        "zoom_start": 1.14,
+        "zoom_end": 1.08,
+        "pan_x": 0.0,
+        "pan_y": 0.05,
+        "duration_factor": 1.0,
+    },
+    "track_left": {
+        "type": "pan",
+        "zoom_start": 1.05,
+        "zoom_end": 1.05,
+        "pan_x": -0.10,
+        "pan_y": 0.0,
+        "duration_factor": 1.0,
+    },
+    "track_right": {
+        "type": "pan",
+        "zoom_start": 1.05,
+        "zoom_end": 1.05,
+        "pan_x": 0.10,
+        "pan_y": 0.0,
+        "duration_factor": 1.0,
+    },
+}
+
+
+def map_camera_to_ken_burns(movement: str) -> dict[str, Any]:
+    """运镜类型 → URS ken_burns 参数字典。未知类型回退 static。"""
+    preset = CAMERA_TO_KEN_BURNS.get(movement) or CAMERA_TO_KEN_BURNS["static"]
+    return json.loads(json.dumps(preset))
+
+
+# ── 转场 → xfade transition 名称 ────────────────────────────────────────
+
+TRANSITION_TO_XFADE: dict[str, str] = {
+    "hard_cut": "cut",
+    "cross_dissolve": "dissolve",
+    "fade_black": "fadeblack",
+    "fade": "fade",
+    "zoom_whip": "zoom",
+    "slide_left": "slideright",  # 画面左移 = 新画面从右滑入
+    "slide_right": "slideleft",
+    "wipe_left": "wipeleft",
+    "wipe_right": "wiperight",
+}
+
+
+def map_transition_to_xfade(transition_type: str) -> str:
+    """转场枚举 → TransitionEngine 支持的 xfade 名称；未知回退 cut。"""
+    return TRANSITION_TO_XFADE.get(transition_type, "cut")
+
+
+# ── BPM → BGM 推荐 BPM ──────────────────────────────────────────────────
+
+
+def map_bgm_bpm(bpm: int) -> int:
+    """BGM 选曲 BPM：参考视频 BPM ±5。bpm=0 返回 90（默认 medium）。"""
+    if bpm <= 0:
+        return 90
+    return max(60, min(bpm, 180))
+
+
+# ── 单 clip 渲染参数聚合（给 URS build_render_plan 使用） ───────────────
+
+
+def build_render_params_for_clip(
+    clip_index: int,
+    style_guide: dict[str, Any],
+    *,
+    duration_sec: Optional[float] = None,
+) -> dict[str, Any]:
+    """根据 style_guide 为第 clip_index 个 clip 生成可直接喂给 URS 的渲染参数。"""
+    shot_idx = clip_index + 1
+    movements = style_guide.get("camera_movements") or []
+    movement = "static"
+    intensity = "low"
+    for m in movements:
+        if m.get("shot_index") == shot_idx:
+            movement = m.get("movement", "static")
+            intensity = m.get("intensity", "low")
+            break
+    ken = map_camera_to_ken_burns(movement)
+    if intensity == "high":
+        ken["zoom_end"] = round(ken.get("zoom_end", 1.0) * 1.08, 3)
+        for k in ("pan_x", "pan_y"):
+            ken[k] = round(ken.get(k, 0.0) * 1.3, 3)
+    elif intensity == "low":
+        for k in ("pan_x", "pan_y"):
+            ken[k] = round(ken.get(k, 0.0) * 0.6, 3)
+    transitions = style_guide.get("transitions") or []
+    trans_type = "hard_cut"
+    for t in transitions:
+        pair = t.get("between_shot") or []
+        if len(pair) >= 2 and pair[0] == shot_idx:
+            trans_type = t.get("type", "hard_cut")
+            break
+    xfade = map_transition_to_xfade(trans_type)
+    eq = map_color_to_video_filter(style_guide.get("color_filter", "none"))
+    return {
+        "ken_burns": ken,
+        "transition": {"type": xfade, "duration": 0.3 if xfade != "cut" else 0.0},
+        "video_filter": eq,
+        "bgm_bpm_hint": map_bgm_bpm(int(style_guide.get("bpm") or 0)),
+        "duration_sec": duration_sec,
+    }
+
+
+# ── 素材本地化（URL/OSS key → 本地临时文件） ────────────────────────────
+
+
+def _ensure_local_video(reference: str, work_dir: Path) -> Optional[Path]:
+    """把 reference（URL/OSS key/本地路径）落到 work_dir 下的本地文件。"""
+    p = Path(reference)
+    if p.exists() and p.is_file():
+        return p
+    try:
+        from video_processing.oss_helpers import download_asset
+
+        target = work_dir / f"ref_{uuid.uuid4().hex}.mp4"
+        ok = download_asset(reference, target)
+        if ok and target.exists() and target.stat().st_size > 0:
+            return target
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("download_asset 失败，尝试 http 直连: %s", exc)
+    if reference.startswith(("http://", "https://")):
+        try:
+            import httpx  # noqa: PLC0415 - 项目依赖，延迟导入
+
+            target = work_dir / f"ref_{uuid.uuid4().hex}.mp4"
+            with httpx.Client(timeout=20.0, follow_redirects=True) as client:
+                with client.stream("GET", reference) as resp:
+                    resp.raise_for_status()
+                    with open(target, "wb") as f:
+                        for chunk in resp.iter_bytes(chunk_size=64 * 1024):
+                            f.write(chunk)
+            if target.exists() and target.stat().st_size > 0:
+                return target
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("HTTP 下载参考视频失败: %s", exc)
+    return None
+
+
+# ── 入口 ─────────────────────────────────────────────────────────────────
+
+
+def analyze_video_style(
+    reference_video_path: str | Path,
+    style_strength: str = "medium",
+    *,
+    timeout_sec: int = DEFAULT_ANALYSIS_TIMEOUT,
+) -> dict[str, Any]:
+    """分析参考视频风格，返回 style_guide dict。
+
+    Args:
+        reference_video_path: 本地路径、HTTP(S) URL 或 OSS storage key。
+        style_strength: light | medium | strict。
+        timeout_sec: 单步超时（秒），默认 60。
+
+    Returns:
+        style_guide dict，详见 STYLE_GUIDE_SCHEMA。任何子步骤失败都会降级，不抛异常。
+    """
+    style_strength = style_strength if style_strength in ("light", "medium", "strict") else "medium"
+    frames_dir: Optional[Path] = None
+    local_path: Optional[Path] = None
+    try:
+        frames_dir = Path(tempfile.mkdtemp(prefix="vstyle_"))
+        work_dir = frames_dir  # 同一临时根
+        local_path = _ensure_local_video(str(reference_video_path), work_dir)
+        if local_path is None:
+            logger.error("[video_analyzer] 无法获取参考视频: %s", reference_video_path)
+            return _rule_based_style_guide([], 0, {})
+        # 资源约束：大小 / 时长
+        try:
+            size_mb = local_path.stat().st_size / (1024 * 1024)
+            if size_mb > MAX_REFERENCE_SIZE_MB:
+                logger.warning(
+                    "[video_analyzer] 参考视频 %.1fMB 超上限，按前 %ds 分析", size_mb, MAX_REFERENCE_DURATION_SEC
+                )
+        except OSError:
+            pass
+        duration = _probe_duration(local_path)
+        if duration > MAX_REFERENCE_DURATION_SEC:
+            duration = MAX_REFERENCE_DURATION_SEC
+        # ① 抽帧
+        try:
+            frame_paths = _extract_keyframes(local_path, frames_dir / "frames")
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("FFmpeg 抽帧失败: %s，降级为 VLM 均匀采样", exc)
+            frame_paths = []
+        # ② 镜头分割
+        shots = _detect_shots(local_path, frames_dir)
+        # ③ 运镜检测（有帧才跑）
+        if frame_paths or shots:
+            _analyze_movements(local_path, shots)
+        # ④ BPM
+        bpm = _detect_bpm(local_path)
+        # ⑤ 选帧→OSS→VLM
+        sampled = _sample_frames(frame_paths, shots)
+        image_urls = _upload_frames_to_oss(sampled) if sampled else []
+        vlm = _vlm_analyze_frames(image_urls) if image_urls else {}
+        # ⑥ 合成
+        style_guide = _llm_synthesize(shots, bpm, vlm, style_strength)
+        # 兜底字段校验
+        style_guide.setdefault("style_strength", style_strength)
+        style_guide.setdefault("pace", _pace_from_bpm(bpm))
+        style_guide.setdefault("bpm", bpm)
+        style_guide.setdefault("shot_count", len(shots))
+        if shots and "avg_shot_duration" not in style_guide:
+            durs = [s.end_sec - s.start_sec for s in shots]
+            style_guide["avg_shot_duration"] = round(sum(durs) / len(durs), 2)
+        return style_guide
+    except Exception as exc:  # noqa: BLE001
+        logger.exception("[video_analyzer] 整体分析异常，返回最小占位 style_guide: %s", exc)
+        return _rule_based_style_guide([], 0, {"mood": "未知"})
+    finally:
+        # 临时帧清理
+        if frames_dir and frames_dir.exists():
+            shutil.rmtree(frames_dir, ignore_errors=True)

apps/worker/worker_app/celery_app.py

@@ -38,6 +38,7 @@ celery_app.conf.imports = (
     "worker_app.tasks.voice_extraction",
     "worker_app.tasks.voice_clone",
     "worker_app.tasks.tts_synthesis",
+    "worker_app.tasks.viral_video",  # #2039 爆款视频编排器（10步流水线）
     "worker_app.tasks.batch_download",
     "worker_app.tasks.duplication_check",
     # #1798 AI 数字人渲染：必须在 Worker 实例上注册同名任务，否则消息无人消费（渲染卡 0%）

apps/worker/worker_app/tasks/generation.py

@@ -616,69 +616,49 @@ def _precompute_render_metadata(
 # ── Celery Task ──────────────────────────────────────────────────────────────
 
 
-def _sync_task_config_to_plan(source_edit_plan_id: str, task_info: dict, db) -> str | None:
-    """将 GenerationTask 的配置同步到 EditPlan.config，返回配音本地路径（如果有）。
+def _build_task_config_override(task_info: dict) -> dict:
+    """Bug A: 从 task_info 构建任务级 config override 深拷贝，供渲染时覆盖 plan.config。
 
-    包括：title_config、BGM、输出分辨率。配音单独处理（需下载到本地）。
+    所有渲染相关配置（title/bgm/export）从任务自身读取，不再依赖共享 plan.config，
+    彻底消除同 plan 多任务并发渲染时的竞态覆盖问题。
     """
-    from packages.adapters.sqlalchemy_impl.edit_plan_repository import (
-        SQLAlchemyEditPlanRepository,
-    )
+    import copy
 
-    plan_repo = SQLAlchemyEditPlanRepository(db)
-    plan = plan_repo.get(source_edit_plan_id)
-    if plan is None:
-        logger.error("[task] EditPlan not found: %s", source_edit_plan_id)
-        return None
+    override: dict = {}
 
-    plan_config = dict(plan.config or {})
-    changed = False
-
-    # 标题配置
+    # 标题配置（字段名归一化）
     title_config = task_info.get("title_config") or {}
-    if title_config and isinstance(title_config, dict) and title_config.get("text", "").strip():
-        cfg = dict(title_config)
-        # 字段名归一化
+    if isinstance(title_config, dict) and title_config:
+        cfg = copy.deepcopy(title_config)
         if "font_size" in cfg and "size" not in cfg:
             cfg["size"] = cfg["font_size"]
         if "font_color" in cfg and "color" not in cfg:
             cfg["color"] = cfg["font_color"]
-        plan_config["title"] = cfg
-        changed = True
-        logger.info("[task] title_config synced to plan: %s", cfg.get("text", "")[:30])
+        override["title"] = cfg
 
     # BGM 配置
     bgm_config = task_info.get("bgm_config") or {}
-    if bgm_config:
-        from packages.domain.bgm_utils import merge_bgm_config
-
-        existing_bgm = plan_config.get("bgm", {}) or {}
-        plan_config["bgm"] = merge_bgm_config(existing_bgm, bgm_config)
-        changed = True
+    if isinstance(bgm_config, dict) and bgm_config:
+        override["bgm"] = copy.deepcopy(bgm_config)
 
     # 输出分辨率
     ow = task_info.get("output_width") or OUTPUT_WIDTH
     oh = task_info.get("output_height") or OUTPUT_HEIGHT
     if ow >= 100 and oh >= 100:
-        export_cfg = dict(plan_config.get("export", {}) or {})
-        export_cfg["resolution"] = f"{ow}x{oh}"
-        plan_config["export"] = export_cfg
-        changed = True
+        override["export"] = {"resolution": f"{ow}x{oh}"}
 
-    if changed:
-        plan.config = plan_config
-        plan_repo.update(plan)
-        logger.info("[task] plan.config synced: plan_id=%s", source_edit_plan_id)
+    return override
+
+
+def _download_voice_for_task(task_info: dict, source_edit_plan_id: str) -> str | None:
+    """下载任务配音到本地临时文件，返回路径（不读写 plan.config）。"""
+    import tempfile
 
-    # 配音下载
     voiceover_path: str | None = None
     voice_library_id = task_info.get("voice_library_id", "")
-    # #1749：voice_ids 冗余字段已移除；配音一律以 voice_library_id 为准（独立配音每变体各自绑定）
     effective_voice_id = voice_library_id or ""
 
     if effective_voice_id:
-        import tempfile
-
         voice_tmp = Path(tempfile.gettempdir()) / f"voice_{source_edit_plan_id}_{id(task_info)}.mp3"
         try:
             if _download_voice_asset(effective_voice_id, voice_tmp):
@@ -697,6 +677,9 @@ def _render_from_edit_plan(
 ) -> tuple[Path, float, list[dict] | None, str | None, str | None, str, bool]:
     """从 EditPlan 数据库记录直接渲染（不再内存重建clips）。
 
+    Bug A 修复：不再通过 _sync_task_config_to_plan 写共享 plan.config；
+    渲染配置通过 task_config_override 参数直接传入渲染层，彻底消除并发竞态。
+
     Returns:
         (output_path, render_duration, cover_candidates, voiceover_path, temp_dir, thumbnail_url, edge_crop_applied)
     """
@@ -705,8 +688,10 @@ def _render_from_edit_plan(
 
     db = SessionLocal()
     try:
-        # 同步配置到 plan.config + 下载配音
-        voiceover_path = _sync_task_config_to_plan(source_edit_plan_id, task_info, db)
+        # Bug A: 构建任务级 config override（深拷贝自 task_info），不写 plan.config，避免并发竞态
+        task_override = _build_task_config_override(task_info)
+        # 下载配音到本地临时文件（不依赖 plan.config）
+        voiceover_path = _download_voice_for_task(task_info, source_edit_plan_id)
 
         # 进度回调
         def _progress_cb(progress: float, stage: str):
@@ -722,6 +707,7 @@ def _render_from_edit_plan(
             job_id=task_id,
             progress_cb=_progress_cb,
             voiceover_audio_path=voiceover_path,
+            task_config_override=task_override,
         )
 
         if not result.success:

deploy/configs/.env.staging

@@ -241,19 +241,35 @@ DOUBAO_API_KEY=${DOUBAO_API_KEY}
 
 # 模型 Endpoint ID（在 ARK 控制台创建推理接入点后获得）
 DOUBAO_MODEL=${DOUBAO_MODEL}
+DOUBAO_FAST_MODEL=${DOUBAO_FAST_MODEL}
 
 # API Base URL
 DOUBAO_BASE_URL=${DOUBAO_BASE_URL}
 
 # 请求超时（秒）
-DOUBAO_TIMEOUT=60
+DOUBAO_TIMEOUT=${DOUBAO_TIMEOUT}
 
 # 最大重试次数
-DOUBAO_MAX_RETRIES=2
+DOUBAO_MAX_RETRIES=${DOUBAO_MAX_RETRIES}
 
-# 视觉模型 Endpoint ID（支持图片/视频理解的模型）
+# 视觉模型（支持图片/视频理解的模型，model name 格式）
 DOUBAO_VISION_MODEL=${DOUBAO_VISION_MODEL}
 
+# 快速视觉模型（viral-video 图片分析 lite 路径）
+DOUBAO_VISION_LITE_MODEL=${DOUBAO_VISION_LITE_MODEL}
+
+# 是否启用 lite 视觉路径（true/false）
+DOUBAO_VISION_USE_LITE=${DOUBAO_VISION_USE_LITE}
+
+# 信任链文生图模型（Seedream）
+DOUBAO_IMAGE_MODEL=${DOUBAO_IMAGE_MODEL}
+
+# 文生图尺寸
+DOUBAO_IMAGE_SIZE=${DOUBAO_IMAGE_SIZE}
+
+# 文生图超时（秒）
+DOUBAO_IMAGE_TIMEOUT=${DOUBAO_IMAGE_TIMEOUT}
+
 
 # ==================== 微信开放平台 OAuth（网页扫码登录）====================
 # 回调域名：xiaoxiajianji.com（微信开放平台已配置）

docs/DEPLOYMENT.md

@@ -104,6 +104,43 @@ Staging 当前可以保持 no-op；Production 开启前必须先验证 SMTP/Redi
 
 ---
 
+## Staging 服务器 Docker 凭证配置
+
+Staging 服务器（116.62.226.203）需要配置 ACR 和 Gitea Registry 凭证，否则 docker pull 和 Watchtower 自动更新会失败。
+
+### 凭证文件位置
+- Docker 配置文件：`/root/.docker/config.json`
+- 包含两个 registry 的认证信息：
+  - `xiaoxia-registry.cn-hangzhou.cr.aliyuncs.com`（阿里云 ACR）
+  - `git.xiaoxiajianji.com`（Gitea 容器镜像仓库）
+
+### 服务器迁移后恢复步骤
+```bash
+# 1. 登录 ACR
+docker login xiaoxia-registry.cn-hangzhou.cr.aliyuncs.com -u <ACR_USERNAME>
+
+# 2. 登录 Gitea Registry
+docker login git.xiaoxiajianji.com -u xiaoxia -p <GITEA_REGISTRY_TOKEN>
+
+# 3. 重启 Watchtower（确保挂载最新 config.json）
+docker restart watchtower
+```
+
+### Watchtower 配置
+- 容器名：`watchtower`
+- 检查间隔：300 秒（5 分钟）
+- 监控容器：`xiaoxia-api-staging`、`xiaoxia-worker-staging`、`xiaoxia-web-staging`
+- 必须挂载 `-v /root/.docker/config.json:/config.json` 才能拉取私有镜像
+- 必须挂载 `-v /var/run/docker.sock:/var/run/docker.sock` 才能管理容器
+- 容器使用 `:dev` 稳定 tag，Watchtower 通过检测 `:dev` tag 的 digest 变化来发现更新
+
+### 镜像 Tag 策略
+- CI 每次构建推送三种 tag：`${GITHUB_SHA}`（精确版本）、`${GITHUB_REF_NAME}`（分支名）、`:dev`（滚动 tag，仅 develop 分支）
+- Staging 容器统一使用 `:dev` tag 启动，确保 Watchtower 能自动发现新版本
+- Migration（alembic）使用 commit SHA tag 执行，不依赖 Watchtower
+
+---
+
 ## Gitea Actions 约定
 
 - `develop` 分支触发 staging 部署。

infra/docker/api.Dockerfile

@@ -30,6 +30,10 @@ COPY deploy/configs/douyin_cookies.txt /app/configs/douyin_cookies.txt
 # 强制升级 yt-dlp 到最新（抖音反爬经常变更，旧版 cookies 支持失效；#1968/#1963）
 RUN pip install --no-cache-dir -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com --upgrade "yt-dlp>=2026.8.19"
 
+# API 启动入口（幂等迁移 + uvicorn）—— #2129: watchtower 自动部署兜底
+COPY infra/docker/entrypoint-api.sh /usr/local/bin/entrypoint-api.sh
+RUN chmod +x /usr/local/bin/entrypoint-api.sh
+
 # 设置环境变量
 ENV PATH="/opt/venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
 ENV PYTHONPATH=/app:/app/apps/api
@@ -41,4 +45,4 @@ HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
     CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8000/health', timeout=5)"
 
 # API 入口点
-CMD ["uvicorn", "apps.api.main:app", "--host", "0.0.0.0", "--port", "8000"]
+ENTRYPOINT ["/usr/local/bin/entrypoint-api.sh"]

infra/docker/entrypoint-api.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+# API 启动入口：先幂等执行数据库迁移，再启动传入的 CMD（默认 uvicorn）
+# 解决 watchtower 自动拉取新镜像后容器重启、未跑 alembic upgrade head 导致新列缺失 500 的问题（#2129）
+set -e
+
+cd /app
+
+echo "[entrypoint-api] Running alembic upgrade head..."
+if alembic upgrade head; then
+  echo "[entrypoint-api] Migrations ok."
+else
+  echo "[entrypoint-api] WARNING: alembic upgrade failed, continuing (existing columns should be fine)..." >&2
+fi
+
+# 若有显式 CMD（CI 部署时 docker compose run --rm api sh -c '...' 传入），直接 exec 它
+if [ "$#" -gt 0 ]; then
+  echo "[entrypoint-api] Exec custom command: $*"
+  exec "$@"
+fi
+
+echo "[entrypoint-api] Starting uvicorn..."
+exec uvicorn apps.api.main:app --host 0.0.0.0 --port 8000

infra/docker/entrypoint-worker.sh

@@ -18,6 +18,17 @@
 
 set -e
 
+# #2129: 幂等执行数据库迁移（watchtower 自动部署兜底）
+# worker 容器独立启动，不能依赖 API 容器先跑迁移
+cd /app
+echo "[entrypoint-worker] Running alembic upgrade head..."
+if alembic upgrade head; then
+  echo "[entrypoint-worker] Migrations ok."
+else
+  echo "[entrypoint-worker] WARNING: alembic upgrade failed, continuing to start workers..." >&2
+fi
+cd - >/dev/null
+
 MAX_TASKS="${WORKER_MAX_TASKS_PER_CHILD:-100}"
 
 # ── 并发计算：显式 env 优先；否则从 WORKER_CONCURRENCY 按比例推导 ──

infra/docker/worker.Dockerfile

@@ -34,6 +34,7 @@ ENV APP_VERSION=$APP_VERSION
 
 # 复制文件（按变化频率从低到高排序，最大化层缓存命中）
 COPY alembic.ini /app/alembic.ini
+COPY alembic/ /app/alembic/
 COPY migrations/ /app/migrations/
 COPY packages/ /app/packages/
 # PR #1844 起，worker 还需要加载 apps.api.app.tasks.lipsync_tts，

packages/adapters/sqlalchemy_impl/asset_repository.py

@@ -502,8 +502,19 @@ class SQLAlchemyAssetRepository:
         return [self._to_domain(m) for m in models]
 
     def find_by_storage_key(self, storage_key: str) -> Asset | None:
-        """按 storage_key（对应 DB 中的 file_url）查找素材。"""
-        model = self.session.query(AssetModel).filter(AssetModel.file_url == storage_key).first()
+        """按 storage_key 查找素材。
+
+        Bug #2110: 历史数据 file_url 列可能是旧路径（assets/...），新代码统一写入
+        storage_key 列。双列 OR 查询，避免占位 asset 因路径错配导致 ingest 兜底新建
+        第二条 READY 记录，原占位卡 PROCESSING → 前端缩略图出现后消失。
+        """
+        if not storage_key:
+            return None
+        model = (
+            self.session.query(AssetModel)
+            .filter((AssetModel.storage_key == storage_key) | (AssetModel.file_url == storage_key))
+            .first()
+        )
         if model is None:
             return None
         return self._to_domain(model)

packages/adapters/sqlalchemy_impl/models.py

@@ -56,7 +56,7 @@ class UserModel(Base):
     is_member = Column(Boolean, nullable=False, default=False)
     member_type = Column(String(20), nullable=True)
     member_expires_at = Column(DateTime, nullable=True)
-    points_balance = Column(Integer, nullable=False, default=0)
+    points_balance = Column(Float, nullable=False, default=0)
     created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
 
 
@@ -775,9 +775,9 @@ class PointsAccountModel(Base):
 
     id = Column(String(36), primary_key=True)
     user_id = Column(String(36), nullable=False, unique=True, index=True)
-    balance = Column(Integer, nullable=False, default=0)
-    total_earned = Column(Integer, nullable=False, default=0)
-    total_spent = Column(Integer, nullable=False, default=0)
+    balance = Column(Float, nullable=False, default=0)
+    total_earned = Column(Float, nullable=False, default=0)
+    total_spent = Column(Float, nullable=False, default=0)
     created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
     updated_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
 
@@ -792,8 +792,8 @@ class PointsTransactionModel(Base):
     account_id = Column(String(36), nullable=False, index=True)
     type = Column(String(20), nullable=False, index=True)  # earn / spend / refund
     source = Column(String(50), nullable=False, index=True)
-    amount = Column(Integer, nullable=False)
-    balance_after = Column(Integer, nullable=False)
+    amount = Column(Float, nullable=False)
+    balance_after = Column(Float, nullable=False)
     description = Column(String(255), nullable=False, default="")
     ref_id = Column(String(100), nullable=False, default="")
     created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
@@ -918,3 +918,90 @@ class GpuWorkerModel(Base):
     capabilities = Column(String(500), nullable=False, default="")  # 逗号分隔，如 "musetalk"
     last_heartbeat_at = Column(DateTime, nullable=True, index=True)
     created_at = Column(DateTime, nullable=False, default=lambda: datetime.now(UTC))
+
+
+class ViralVideoJobModel(Base):
+    """爆款视频任务"""
+
+    __tablename__ = "viral_video_jobs"
+
+    id = Column(String(36), primary_key=True)
+    user_id = Column(String(36), nullable=False, index=True)
+    images = Column(JSON, nullable=False, default=list)  # 产品图片 URL 列表
+    pre_trusted_images = Column(JSON, nullable=True)  # #2172 信任链预热结果（Seedream AI 化 URL 列表）
+    industry = Column(String(100), nullable=False, default="")
+    target_customer = Column(String(500), nullable=False, default="")
+    persona_id = Column(String(36), nullable=False, default="")
+    viral_structure = Column(String(50), nullable=False, default="")
+    marketing_purpose = Column(String(100), nullable=False, default="")
+    bgm_preference = Column(String(50), nullable=False, default="")
+    duration = Column(Integer, nullable=False, default=30)
+    user_copy_text = Column(Text, nullable=False, default="")
+    fusion_level = Column(String(20), nullable=False, default="ai_polish")
+    reference_audio_path = Column(String(1000), nullable=False, default="")
+    # v1.3 新增字段
+    reference_video_url = Column(String(1000), nullable=False, default="")
+    style_strength = Column(String(20), nullable=False, default="medium")
+    style_guide = Column(JSON, nullable=True)
+    style_template_id = Column(String(36), nullable=False, default="", index=True)
+    # v1.5 音频/视频参数
+    voice_id = Column(String(200), nullable=False, default="")
+    voice_source = Column(String(20), nullable=False, default="")
+    video_ratio = Column(String(10), nullable=False, default="9:16")
+    video_model = Column(String(100), nullable=False, default="")
+    # 结果与状态
+    status = Column(String(30), nullable=False, default="pending", index=True)
+    current_stage = Column(String(200), nullable=False, default="")  # 细粒度阶段 snake_case
+    phase_message = Column(String(500), nullable=False, default="")  # 阶段中文提示文案
+    heartbeat_at = Column(DateTime, nullable=True, index=True)  # worker 心跳，用于僵尸任务超时回收
+    intent_result = Column(JSON, nullable=True)
+    image_analysis = Column(JSON, nullable=True)
+    storyboard = Column(JSON, nullable=True)
+    generated_copy_text = Column(Text, nullable=False, default="")
+    copy_result = Column(
+        JSON, nullable=True
+    )  # v1.6: 编导脚本结构{overview,scene_and_lighting,shots,hard_constraints,negative_prompts,voiceover_script}
+    result_video_url = Column(String(1000), nullable=False, default="")
+    credits_cost = Column(Float, nullable=False, default=0)
+    video_resolution = Column(String(20), nullable=False, default="720p")
+    credits_prepaid = Column(Float, nullable=False, default=0.0)
+    credits_transaction_id = Column(String(36), nullable=False, default="")
+    error_msg = Column(Text, nullable=False, default="")
+    retry_count = Column(Integer, nullable=False, default=0)
+    started_at = Column(DateTime(timezone=True), nullable=True)
+    completed_at = Column(DateTime(timezone=True), nullable=True)
+    created_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
+    updated_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
+
+
+class ViralVideoStyleTemplateModel(Base):
+    """爆款视频风格模板配置表"""
+
+    __tablename__ = "viral_video_style_templates"
+
+    id = Column(String(36), primary_key=True)
+    name = Column(String(200), nullable=False)
+    description = Column(Text, nullable=False, default="")
+    thumbnail_url = Column(String(1000), nullable=False, default="")
+    style_config = Column(JSON, nullable=False, default=dict)
+    is_system = Column(Boolean, nullable=False, default=True, index=True)
+    sort_order = Column(Integer, nullable=False, default=0)
+    created_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
+    updated_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
+
+
+class ViralVideoPromptTemplateModel(Base):
+    """爆款视频 Prompt 模板表（#2040：纯文本 XML 标签模板，运营可直接编辑）"""
+
+    __tablename__ = "viral_video_prompt_templates"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    name = Column(String(128), nullable=False)
+    prompt_type = Column(String(32), nullable=False)
+    version = Column(Integer, nullable=False, default=1)
+    system_prompt = Column(Text, nullable=False)
+    user_prompt_template = Column(Text, nullable=False)
+    example_output = Column(Text, nullable=True)
+    is_active = Column(Boolean, nullable=False, default=True)
+    created_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))
+    updated_at = Column(DateTime(timezone=True), nullable=False, default=lambda: datetime.now(UTC))

packages/adapters/sqlalchemy_impl/session.py

@@ -81,6 +81,41 @@ def ensure_database_exists(database_url: str) -> None:
         admin_engine.dispose()
 
 
+_VIRAL_VIDEO_BACKFILL_COLS = [
+    ("storyboard", "JSON"),
+    ("generated_copy_text", "TEXT NOT NULL DEFAULT ''"),
+    ("voice_id", "VARCHAR(200) NOT NULL DEFAULT ''"),
+    ("voice_source", "VARCHAR(20) NOT NULL DEFAULT ''"),
+    ("video_ratio", "VARCHAR(10) NOT NULL DEFAULT '9:16'"),
+    ("video_model", "VARCHAR(100) NOT NULL DEFAULT ''"),
+    ("copy_result", "JSON"),
+]
+
+
+def _ensure_viral_video_columns(connection) -> None:
+    """Idempotently add new columns to viral_video_jobs; create_all will not ALTER existing tables."""
+    from sqlalchemy import inspect as _inspect
+
+    try:
+        insp = _inspect(connection)
+        if not insp.has_table("viral_video_jobs"):
+            return
+        existing = {c["name"] for c in insp.get_columns("viral_video_jobs")}
+    except Exception:
+        return
+    import logging as _logging
+
+    _log = _logging.getLogger(__name__)
+    for col, ddl in _VIRAL_VIDEO_BACKFILL_COLS:
+        if col in existing:
+            continue
+        try:
+            connection.execute(text(f"ALTER TABLE viral_video_jobs ADD COLUMN {col} {ddl}"))
+            _log.info("added column viral_video_jobs.%s", col)
+        except Exception as e:
+            _log.warning("add column %s failed: %s", col, e)
+
+
 def initialize_database(engine) -> None:
     """初始化数据库 schema。
 
@@ -100,4 +135,5 @@ def initialize_database(engine) -> None:
                 text("SELECT pg_advisory_unlock(:lock_id)"),
                 {"lock_id": SCHEMA_INIT_LOCK_ID},
             )
+            _ensure_viral_video_columns(connection)
             connection.commit()

packages/adapters/sqlalchemy_impl/user_repository.py

@@ -39,6 +39,9 @@ class SQLAlchemyUserRepository(UserRepository):
         model.phone_verified = user.phone_verified
         model.binding_completed_at = user.binding_completed_at
         model.profile_completed = user.profile_completed
+        model.is_member = user.is_member
+        model.member_type = user.member_type
+        model.member_expires_at = user.member_expires_at
         model.created_at = user.created_at
 
         self.session.commit()
@@ -115,5 +118,8 @@ class SQLAlchemyUserRepository(UserRepository):
             phone_verified=model.phone_verified or False,
             binding_completed_at=model.binding_completed_at,
             profile_completed=model.profile_completed if model.profile_completed is not None else True,
+            is_member=model.is_member if model.is_member is not None else False,
+            member_type=model.member_type,
+            member_expires_at=model.member_expires_at,
             created_at=model.created_at,
         )

packages/adapters/sqlalchemy_impl/viral_video_repository.py

@@ -0,0 +1,246 @@
+"""爆款视频任务 SQLAlchemy 仓储实现。"""
+
+from datetime import datetime, timezone
+
+from sqlalchemy.orm import Session
+
+from packages.adapters.sqlalchemy_impl.models import (
+    ViralVideoJobModel,
+    ViralVideoStyleTemplateModel,
+)
+from packages.domain.viral_video import ViralVideoJob, ViralVideoStatus
+
+
+def _to_domain(model: ViralVideoJobModel) -> ViralVideoJob:
+    """ORM → 领域实体。pre_trusted_images 兼容脏数据：双序列化字符串/字符数组/list[str]。"""
+    import json as _pti_json
+
+    _raw_pti = getattr(model, "pre_trusted_images", None)
+    _pti: list[str] | None = None
+    if _raw_pti is not None:
+        if isinstance(_raw_pti, str):
+            try:
+                _p = _pti_json.loads(_raw_pti)
+                if isinstance(_p, list):
+                    _pti = [u for u in _p if isinstance(u, str) and u] or None
+            except Exception:
+                _pti = None
+        elif isinstance(_raw_pti, list):
+            _f = [u for u in _raw_pti if isinstance(u, str) and len(u) > 5]
+            _pti = _f if _f else None
+    return ViralVideoJob(
+        id=model.id,
+        user_id=model.user_id,
+        images=list(model.images or []),
+        pre_trusted_images=_pti,
+        industry=model.industry or "",
+        target_customer=model.target_customer or "",
+        persona_id=model.persona_id or "",
+        viral_structure=model.viral_structure or "",
+        marketing_purpose=model.marketing_purpose or "",
+        bgm_preference=model.bgm_preference or "",
+        duration=model.duration or 15,
+        user_copy_text=model.user_copy_text or "",
+        fusion_level=model.fusion_level or "ai_polish",
+        reference_audio_path=model.reference_audio_path or "",
+        reference_video_url=getattr(model, "reference_video_url", "") or "",
+        style_strength=getattr(model, "style_strength", "medium") or "medium",
+        style_guide=dict(model.style_guide) if model.style_guide else None,
+        style_template_id=getattr(model, "style_template_id", "") or "",
+        voice_id=getattr(model, "voice_id", "") or "",
+        voice_source=getattr(model, "voice_source", "") or "",
+        video_ratio=getattr(model, "video_ratio", "9:16") or "9:16",
+        video_model=getattr(model, "video_model", "") or "",
+        status=ViralVideoStatus(model.status) if model.status else ViralVideoStatus.PENDING,
+        current_stage=getattr(model, "current_stage", "") or "",
+        phase_message=getattr(model, "phase_message", "") or "",
+        heartbeat_at=getattr(model, "heartbeat_at", None),
+        intent_result=dict(model.intent_result) if model.intent_result else None,
+        image_analysis=dict(model.image_analysis) if getattr(model, "image_analysis", None) else None,
+        storyboard=list(model.storyboard) if getattr(model, "storyboard", None) else None,
+        generated_copy_text=getattr(model, "generated_copy_text", "") or "",
+        copy_result=dict(model.copy_result) if getattr(model, "copy_result", None) else None,
+        result_video_url=model.result_video_url or "",
+        video_resolution=getattr(model, "video_resolution", "720p") or "720p",
+        credits_prepaid=float(getattr(model, "credits_prepaid", 0) or 0),
+        credits_transaction_id=getattr(model, "credits_transaction_id", "") or "",
+        credits_cost=float(model.credits_cost or 0),
+        error_msg=model.error_msg or "",
+        retry_count=model.retry_count or 0,
+        started_at=model.started_at,
+        completed_at=model.completed_at,
+        created_at=model.created_at,
+        updated_at=model.updated_at,
+    )
+
+
+class SQLAlchemyViralVideoJobRepository:
+    """爆款视频任务仓储。"""
+
+    def __init__(self, session: Session):
+        self.session = session
+
+    def save(self, job: ViralVideoJob) -> ViralVideoJob:
+        model = ViralVideoJobModel(
+            id=job.id,
+            user_id=job.user_id,
+            images=job.images,
+            pre_trusted_images=job.pre_trusted_images,
+            industry=job.industry,
+            target_customer=job.target_customer,
+            persona_id=job.persona_id,
+            viral_structure=job.viral_structure,
+            marketing_purpose=job.marketing_purpose,
+            bgm_preference=job.bgm_preference,
+            duration=job.duration,
+            user_copy_text=job.user_copy_text,
+            fusion_level=job.fusion_level,
+            reference_audio_path=job.reference_audio_path,
+            reference_video_url=job.reference_video_url,
+            style_strength=job.style_strength,
+            style_guide=job.style_guide,
+            style_template_id=job.style_template_id,
+            voice_id=job.voice_id,
+            voice_source=job.voice_source,
+            video_ratio=job.video_ratio,
+            video_model=job.video_model,
+            status=job.status,
+            current_stage=job.current_stage or "",
+            phase_message=job.phase_message or "",
+            heartbeat_at=job.heartbeat_at,
+            intent_result=job.intent_result,
+            image_analysis=job.image_analysis,
+            storyboard=job.storyboard,
+            generated_copy_text=job.generated_copy_text,
+            copy_result=job.copy_result,
+            result_video_url=job.result_video_url,
+            video_resolution=getattr(job, "video_resolution", "720p") or "720p",
+            credits_prepaid=float(getattr(job, "credits_prepaid", 0) or 0),
+            credits_transaction_id=getattr(job, "credits_transaction_id", "") or "",
+            credits_cost=float(getattr(job, "credits_cost", 0) or 0),
+            error_msg=job.error_msg,
+            retry_count=job.retry_count,
+            started_at=job.started_at,
+            completed_at=job.completed_at,
+            created_at=job.created_at,
+            updated_at=job.updated_at,
+        )
+        self.session.add(model)
+        self.session.commit()
+        return job
+
+    def update(self, job: ViralVideoJob) -> None:
+        model = self.session.query(ViralVideoJobModel).filter(ViralVideoJobModel.id == job.id).first()
+        if model is None:
+            raise ValueError(f"ViralVideoJob {job.id} not found")
+        model.status = job.status
+        model.current_stage = job.current_stage or ""
+        model.phase_message = job.phase_message or ""
+        model.heartbeat_at = job.heartbeat_at
+        model.intent_result = job.intent_result
+        model.image_analysis = job.image_analysis
+        model.storyboard = job.storyboard
+        model.generated_copy_text = job.generated_copy_text or ""
+        model.copy_result = job.copy_result
+        model.pre_trusted_images = job.pre_trusted_images
+        model.result_video_url = job.result_video_url
+        model.video_resolution = getattr(job, "video_resolution", "720p") or "720p"
+        model.credits_prepaid = float(getattr(job, "credits_prepaid", 0) or 0)
+        model.credits_transaction_id = getattr(job, "credits_transaction_id", "") or ""
+        model.credits_cost = float(getattr(job, "credits_cost", 0) or 0)
+        model.error_msg = job.error_msg
+        model.retry_count = job.retry_count
+        model.started_at = job.started_at
+        model.completed_at = job.completed_at
+        model.style_guide = job.style_guide
+        # v1.5 three-stage: persist user-editable params so resume uses latest values
+        model.user_copy_text = job.user_copy_text
+        model.industry = job.industry
+        model.target_customer = job.target_customer
+        model.persona_id = job.persona_id
+        model.viral_structure = job.viral_structure
+        model.marketing_purpose = job.marketing_purpose
+        model.bgm_preference = job.bgm_preference
+        model.duration = job.duration
+        model.fusion_level = job.fusion_level
+        model.reference_audio_path = job.reference_audio_path
+        model.reference_video_url = job.reference_video_url
+        model.style_strength = job.style_strength
+        model.style_template_id = job.style_template_id
+        model.voice_id = job.voice_id or ""
+        model.voice_source = job.voice_source or ""
+        model.video_ratio = job.video_ratio or "9:16"
+        model.video_model = job.video_model or ""
+        model.updated_at = datetime.now(timezone.utc)
+        self.session.commit()
+
+    def get(self, job_id: str) -> ViralVideoJob | None:
+        model = self.session.query(ViralVideoJobModel).filter(ViralVideoJobModel.id == job_id).first()
+        if model is None:
+            return None
+        return _to_domain(model)
+
+    def list_by_user(self, user_id: str, limit: int = 50, offset: int = 0) -> list[ViralVideoJob]:
+        models = (
+            self.session.query(ViralVideoJobModel)
+            .filter(ViralVideoJobModel.user_id == user_id)
+            .order_by(ViralVideoJobModel.created_at.desc())
+            .offset(offset)
+            .limit(limit)
+            .all()
+        )
+        return [_to_domain(m) for m in models]
+
+    def count_pending_by_user(self, user_id: str) -> int:
+        return (
+            self.session.query(ViralVideoJobModel)
+            .filter(
+                ViralVideoJobModel.user_id == user_id,
+                ViralVideoJobModel.status.in_(
+                    ["pending", "running", "wait_user_confirm", "image_analyzed", "copy_generated"]
+                ),
+            )
+            .count()
+        )
+
+
+class SQLAlchemyViralVideoStyleTemplateRepository:
+    """风格模板仓储。"""
+
+    def __init__(self, session: Session):
+        self.session = session
+
+    def list_all(self) -> list[dict]:
+        models = (
+            self.session.query(ViralVideoStyleTemplateModel)
+            .order_by(ViralVideoStyleTemplateModel.sort_order.asc())
+            .all()
+        )
+        return [
+            {
+                "id": m.id,
+                "name": m.name,
+                "description": m.description or "",
+                "thumbnail_url": m.thumbnail_url or "",
+                "style_config": dict(m.style_config) if m.style_config else {},
+                "is_system": m.is_system,
+            }
+            for m in models
+        ]
+
+    def get(self, template_id: str) -> dict | None:
+        model = (
+            self.session.query(ViralVideoStyleTemplateModel)
+            .filter(ViralVideoStyleTemplateModel.id == template_id)
+            .first()
+        )
+        if model is None:
+            return None
+        return {
+            "id": model.id,
+            "name": model.name,
+            "description": model.description or "",
+            "thumbnail_url": model.thumbnail_url or "",
+            "style_config": dict(model.style_config) if model.style_config else {},
+            "is_system": model.is_system,
+        }

packages/application/catalog/__init__.py

@@ -0,0 +1 @@
+"""应用层：对外展示目录（套餐/积分包）。"""

packages/application/catalog/admin_catalog.py

@@ -0,0 +1,152 @@
+"""读取管理后台配置的会员套餐 / 积分充值包（共享库真实数据）。
+
+替代旧的硬编码 MEMBERSHIP_PRICES / POINTS_PACKAGES。
+短 TTL 缓存（30 秒），后台改价/启停后用户端最多 30 秒可见。
+"""
+
+from __future__ import annotations
+
+import threading
+import time
+from typing import Any
+
+_CACHE_TTL = 30.0
+_lock = threading.Lock()
+_cache: dict[str, tuple[float, Any]] = {}
+
+_QUOTA_LABELS = {
+    "4k": "4K 超清分辨率",
+    "batch_render": "批量渲染",
+    "priority_queue": "优先处理队列",
+    "ai_matting": "AI 智能抠像",
+    "remove_watermark": "去水印",
+}
+
+
+def _cached(key: str, loader):
+    now = time.time()
+    hit = _cache.get(key)
+    if hit and now - hit[0] < _CACHE_TTL:
+        return hit[1]
+    with _lock:
+        hit = _cache.get(key)
+        if hit and time.time() - hit[0] < _CACHE_TTL:
+            return hit[1]
+        value = loader()
+        _cache[key] = (time.time(), value)
+        return value
+
+
+def _quota_features(quotas: dict[str, Any] | None) -> dict[str, Any]:
+    quotas = quotas or {}
+    features: dict[str, Any] = {}
+    for k, v in quotas.items():
+        if k == "credits_per_month":
+            features["credits_per_month"] = v
+        elif k in _QUOTA_LABELS:
+            features[_QUOTA_LABELS[k]] = v
+        else:
+            features[k] = v
+    return features
+
+
+def get_membership_plans() -> list[dict[str, Any]]:
+    """读取 is_enabled=true 的套餐，按年/月周期展开为用户端档位。"""
+
+    def _load() -> list[dict[str, Any]]:
+        from sqlalchemy import text
+
+        from packages.adapters.sqlalchemy_impl.session import SessionLocal
+
+        if SessionLocal is None:
+            return []
+
+        session = SessionLocal()
+        try:
+            rows = session.execute(text("""
+                    SELECT plan_key, name, description, monthly_price, yearly_price,
+                           quotas, display_order
+                    FROM plans
+                    WHERE is_enabled = TRUE
+                    ORDER BY display_order NULLS LAST, created_at
+                    """)).fetchall()
+        finally:
+            session.close()
+
+        plans: list[dict[str, Any]] = []
+        for r in rows:
+            base_features = _quota_features(r.quotas if isinstance(r.quotas, dict) else None)
+            if r.yearly_price and float(r.yearly_price) > 0:
+                plans.append(
+                    {
+                        "plan_id": r.plan_key,
+                        "billing_cycle": "yearly",
+                        "name": r.name,
+                        "description": r.description,
+                        "price_cents": int(round(float(r.yearly_price) * 100)),
+                        "monthly_price_cents": int(round(float(r.yearly_price) * 100 / 12)),
+                        "duration_days": 365,
+                        "features": dict(base_features),
+                    }
+                )
+            if r.monthly_price and float(r.monthly_price) > 0:
+                plans.append(
+                    {
+                        "plan_id": r.plan_key,
+                        "billing_cycle": "monthly",
+                        "name": r.name,
+                        "description": r.description,
+                        "price_cents": int(round(float(r.monthly_price) * 100)),
+                        "monthly_price_cents": int(round(float(r.monthly_price) * 100)),
+                        "duration_days": 30,
+                        "features": dict(base_features),
+                    }
+                )
+        return plans
+
+    return _cached("membership_plans", _load)
+
+
+def get_points_packages() -> list[dict[str, Any]]:
+    """读取 is_active=true 的积分充值包。"""
+
+    def _load() -> list[dict[str, Any]]:
+        from sqlalchemy import text
+
+        from packages.adapters.sqlalchemy_impl.session import SessionLocal
+
+        if SessionLocal is None:
+            return []
+
+        session = SessionLocal()
+        try:
+            rows = session.execute(text("""
+                    SELECT package_key, name, price, credits, bonus_credits,
+                           is_recommended, description, sort_order
+                    FROM credit_packages
+                    WHERE is_active = TRUE
+                    ORDER BY sort_order NULLS LAST, price
+                    """)).fetchall()
+        finally:
+            session.close()
+
+        packages: list[dict[str, Any]] = []
+        for r in rows:
+            total_points = int(r.credits or 0) + int(r.bonus_credits or 0)
+            price_cents = int(round(float(r.price) * 100))
+            unit = (price_cents / 100 / total_points) if total_points else 0
+            packages.append(
+                {
+                    "code": r.package_key,
+                    "name": r.name,
+                    "points": total_points,
+                    "bonus_credits": int(r.bonus_credits or 0),
+                    "price_cents": price_cents,
+                    "unit_price": f"¥{unit:.3f}/积分",
+                    "is_recommended": bool(r.is_recommended),
+                    "description": r.description,
+                }
+            )
+        return packages
+
+    return _cached("points_packages", _load)

packages/application/viral_video/__init__.py

@@ -0,0 +1 @@
+"""应用层：爆款视频 Prompt 模板系统（#2040）。"""

packages/application/viral_video/generator.py

@@ -0,0 +1,427 @@
+"""爆款视频 5 步编排：图片分析 → 意图解析 → 文案融合 → 分镜 → 审核重写。
+
+所有 LLM 调用走 DoubaoClient，单测通过 client 参数注入 mock，不真调 API。
+任何一步解析失败都走规则 fallback，不抛异常阻断。
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Optional
+
+from packages.application.viral_video import xml_parser as xp
+from packages.application.viral_video.prompt_loader import (
+    PromptTemplate,
+    get_template,
+    render_system_prompt,
+    render_user_prompt,
+)
+from packages.application.viral_video.prompts import (
+    FUSION_INSTRUCTIONS,
+    GLOBAL_CONSTRAINTS,
+    NEGATIVE_RULES,
+)
+from packages.application.viral_video.reviewer import Reviewer
+from packages.application.viral_video.schemas import (
+    BodyPoint,
+    Clip,
+    ColorItem,
+    CoreMessage,
+    FusionResult,
+    ImageAnalysis,
+    IntentResult,
+    KenBurns,
+    PersonalBrand,
+    ProductItem,
+    ReviewResult,
+    ScriptSegment,
+    Storyboard,
+    TextItem,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class CopyGenerator:
+    """5 步 Prompt 编排器。"""
+
+    def __init__(self, client=None, reviewer: Optional[Reviewer] = None):
+        if client is None:
+            from packages.shared.ai_client import get_doubao_client
+
+            client = get_doubao_client()
+        self.client = client
+        self.reviewer = reviewer or Reviewer(client)
+
+    # ── 底层调用 ────────────────────────────────────────────────────────
+    def _chat(self, template: PromptTemplate, system_kwargs: dict | None, **user_kwargs) -> str:
+        system = render_system_prompt(template, **(system_kwargs or {}))
+        user = render_user_prompt(template, **user_kwargs)
+        result = self.client.chat_completion(
+            [
+                {"role": "system", "content": system},
+                {"role": "user", "content": user},
+            ],
+            temperature=0.7,
+            max_tokens=2048,
+        )
+        return result or ""
+
+    # ── 步骤1：图片多模态分析 ───────────────────────────────────────────
+    def analyze_images(self, images: list[str], industry: str = "") -> ImageAnalysis:
+        template = get_template("image_analysis")
+        image_urls = "\n".join(f"第{i + 1}张：{url}" for i, url in enumerate(images))
+        system = render_system_prompt(template)
+        user = render_user_prompt(template, image_count=len(images), industry=industry or "通用", image_urls=image_urls)
+        raw = self.client.vision_completion(
+            [
+                {"role": "system", "content": system},
+                {"role": "user", "content": user},
+            ],
+            images=images,
+            max_tokens=2048,
+            temperature=0.3,
+        )
+        analysis = self._parse_image_analysis(raw or "")
+        if not analysis.products and not analysis.key_selling_points:
+            logger.warning("图片分析标签解析失败，走规则 fallback")
+            return self._fallback_image_analysis(images, raw or "")
+        return analysis
+
+    def _parse_image_analysis(self, raw: str) -> ImageAnalysis:
+        products = [
+            ProductItem(
+                name=n["attrs"].get("name", "无法判断"),
+                features=n["attrs"].get("features", "无法判断"),
+                position=n["attrs"].get("position", "secondary"),
+                image_index=xp.attr_int(n["attrs"].get("image_index"), 0),
+            )
+            for n in xp.find_all(raw, "product")
+        ]
+        colors = [
+            ColorItem(
+                hex=c["attrs"].get("hex", "#000000"),
+                name=c["attrs"].get("name", "无法判断"),
+                coverage=xp.attr_float(c["attrs"].get("coverage"), 0.0),
+            )
+            for c in xp.find_all(raw, "color")
+        ]
+        people = xp.find_first(raw, "people")
+        visible_text = [
+            TextItem(text=t["attrs"].get("text", ""), position=t["attrs"].get("position", ""))
+            for t in xp.find_all(raw, "text_item")
+        ]
+        quality_node = xp.find_first(raw, "quality")
+        selling_points = [n["text"] or n["attrs"].get("text", "") for n in xp.find_all(raw, "point")]
+        return ImageAnalysis(
+            products=products,
+            colors=colors,
+            has_person=xp.attr_bool(people["attrs"].get("has_person")) if people else False,
+            person_count=xp.attr_int(people["attrs"].get("count"), 0) if people else 0,
+            people=people["attrs"] if people else {},
+            mood=xp.text_of(raw, "mood"),
+            visible_text=visible_text,
+            scene=xp.text_of(raw, "scene"),
+            quality=quality_node["attrs"] if quality_node else {},
+            key_selling_points=[p for p in selling_points if p],
+            raw=raw,
+        )
+
+    def _fallback_image_analysis(self, images: list[str], raw: str) -> ImageAnalysis:
+        return ImageAnalysis(
+            products=[ProductItem(name="无法判断（视觉分析不可用）", image_index=0)],
+            scene="无法判断",
+            raw=raw,
+        )
+
+    # ── 步骤2：意图解析 ─────────────────────────────────────────────────
+    def parse_intent(self, user_copy_text: str, image_analysis: ImageAnalysis, industry: str = "") -> IntentResult:
+        template = get_template("intent_parsing")
+        raw = self._chat(
+            template,
+            None,
+            user_copy_text=user_copy_text or "（用户没有提供文案）",
+            industry=industry or "通用",
+            image_analysis=self._image_brief(image_analysis),
+        )
+        intent = self._parse_intent(raw)
+        if not intent.intent_summary and not intent.core_messages:
+            logger.warning("意图解析标签解析失败，走规则 fallback")
+            return self._fallback_intent(user_copy_text, raw)
+        return intent
+
+    def _parse_intent(self, raw: str) -> IntentResult:
+        messages = [
+            CoreMessage(
+                text=n["text"],
+                must_keep=xp.attr_bool(n["attrs"].get("must_keep"), default=False),
+                confidence=xp.attr_float(n["attrs"].get("confidence"), 0.0),
+            )
+            for n in xp.find_all(raw, "message")
+            if n["text"]
+        ]
+        brands = [
+            PersonalBrand(text=n["text"], category=n["attrs"].get("category", "brand"))
+            for n in xp.find_all(raw, "brand")
+            if n["text"]
+        ]
+        missing = [n["text"] for n in xp.find_all(raw, "info") if n["text"]]
+        return IntentResult(
+            intent_summary=xp.text_of(raw, "intent_summary"),
+            core_messages=messages,
+            personal_brands=brands,
+            emotion_tone=xp.text_of(raw, "emotion_tone"),
+            missing_info=missing,
+            raw=raw,
+        )
+
+    def _fallback_intent(self, user_copy_text: str, raw: str) -> IntentResult:
+        text = (user_copy_text or "").strip()
+        messages = [CoreMessage(text=text[:80], must_keep=True, confidence=1.0)] if text else []
+        return IntentResult(
+            intent_summary=text[:30] or "未提供文案，按产品图片自由创作",
+            core_messages=messages,
+            personal_brands=[],
+            raw=raw,
+        )
+
+    # ── 步骤3：文案融合生成（三档）──────────────────────────────────────
+    def fuse(
+        self,
+        fusion_level: str,
+        image_analysis: ImageAnalysis,
+        intent: IntentResult,
+        industry: str = "",
+        target_customer: str = "",
+        marketing_purpose: str = "",
+        duration: int = 15,
+    ) -> FusionResult:
+        template = get_template("copy_fusion")
+        system_kwargs = {
+            "fusion_instruction": FUSION_INSTRUCTIONS.get(fusion_level, FUSION_INSTRUCTIONS["ai_polish"]),
+            "global_constraints": GLOBAL_CONSTRAINTS,
+            "negative_rules": NEGATIVE_RULES,
+        }
+        raw = self._chat(
+            template,
+            system_kwargs,
+            industry=industry or "通用",
+            target_customer=target_customer or "通用消费者",
+            marketing_purpose=marketing_purpose or "产品种草",
+            duration=duration,
+            image_analysis=self._image_brief(image_analysis),
+            intent_result=self._intent_brief(intent),
+        )
+        result = self._parse_fusion(raw)
+        if not result.title and not result.script_segments:
+            logger.warning("文案融合标签解析失败（fusion=%s），走规则 fallback", fusion_level)
+            return self._fallback_fusion(fusion_level, image_analysis, intent, duration, raw)
+        return result
+
+    def _parse_fusion(self, raw: str) -> FusionResult:
+        body_points = [
+            BodyPoint(
+                text=n["text"],
+                elaboration=n["attrs"].get("elaboration", ""),
+                image_index=xp.attr_int(n["attrs"].get("image_index"), 0),
+            )
+            for n in xp.find_all(raw, "point")
+            if n["text"]
+        ]
+        segments = [
+            ScriptSegment(
+                text=n["text"],
+                duration_sec=xp.attr_float(n["attrs"].get("duration_sec"), 0.0),
+                image_index=xp.attr_int(n["attrs"].get("image_index"), 0),
+            )
+            for n in xp.find_all(raw, "segment")
+            if n["text"]
+        ]
+        return FusionResult(
+            title=xp.text_of(raw, "title"),
+            hook=xp.text_of(raw, "hook"),
+            body_points=body_points,
+            cta=xp.text_of(raw, "cta"),
+            script_segments=segments,
+            word_count=xp.attr_int(xp.text_of(raw, "word_count"), 0),
+            estimated_duration=xp.attr_int(xp.text_of(raw, "estimated_duration"), 0),
+            raw=raw,
+        )
+
+    def _fallback_fusion(
+        self,
+        fusion_level: str,
+        image_analysis: ImageAnalysis,
+        intent: IntentResult,
+        duration: int,
+        raw: str,
+    ) -> FusionResult:
+        product_name = image_analysis.products[0].name if image_analysis.products else "这款产品"
+        selling = image_analysis.key_selling_points[:2]
+        if fusion_level == "ai_full":
+            title = f"{product_name}，很多人用完都回购了"
+            hook = f"这个{product_name}，我想认真说说"
+            body = selling or ["图片可见的产品卖点"]
+            cta = "感兴趣的可以了解一下"
+        elif fusion_level == "user_primary":
+            user_text = intent.intent_summary or product_name
+            title = user_text[:20]
+            hook = user_text[:15]
+            body = [m.text for m in intent.core_messages] or [user_text]
+            cta = "想了解的可以看看"
+        else:
+            title = intent.intent_summary[:20] or product_name
+            hook = intent.core_messages[0].text[:15] if intent.core_messages else product_name
+            body = [m.text for m in intent.core_messages] or selling or [product_name]
+            cta = "有需要的可以了解一下"
+
+        brand_texts = [b.text for b in intent.personal_brands]
+        points = [BodyPoint(text=b) for b in body]
+        lines = [hook] + body + brand_texts[:2] + [cta]
+        joined = "，".join(lines)
+        per = max(3, duration // max(1, len(lines)))
+        segments = [ScriptSegment(text=line, duration_sec=per, image_index=0) for line in lines]
+        return FusionResult(
+            title=title,
+            hook=hook,
+            body_points=points,
+            cta=cta,
+            script_segments=segments,
+            word_count=len(joined),
+            estimated_duration=duration,
+            raw=raw,
+        )
+
+    # ── 步骤4：编导级分镜 ───────────────────────────────────────────────
+    def storyboard(
+        self, fusion: FusionResult, image_analysis: ImageAnalysis, images: list[str], duration: int
+    ) -> Storyboard:
+        template = get_template("storyboard")
+        raw = self._chat(
+            template,
+            None,
+            duration=duration,
+            image_count=len(images),
+            fusion_result=self._fusion_brief(fusion),
+            image_analysis=self._image_brief(image_analysis),
+        )
+        board = self._parse_storyboard(raw)
+        if not board.clips:
+            logger.warning("分镜标签解析失败，走规则 fallback")
+            return self._fallback_storyboard(fusion, duration, raw)
+        return board
+
+    def _parse_storyboard(self, raw: str) -> Storyboard:
+        clips: list[Clip] = []
+        for node in xp.find_all(raw, "clip"):
+            attrs = node["attrs"]
+            body = node["text"]
+            kb = xp.find_first(node["text"] and f"<root>{node['text']}</root>", "ken_burns")
+            clips.append(
+                Clip(
+                    image_index=xp.attr_int(attrs.get("image_index"), 0),
+                    transition=attrs.get("transition", "cut"),
+                    zoom=(None if attrs.get("zoom") in (None, "null", "None", "") else attrs.get("zoom")),
+                    duration_sec=xp.attr_float(attrs.get("duration_sec"), 0.0),
+                    bgm_note=attrs.get("bgm_note", ""),
+                    voice_text=xp.text_of(body and f"<root>{body}</root>", "voice_text"),
+                    subtitle_text=xp.text_of(body and f"<root>{body}</root>", "subtitle_text"),
+                    ken_burns=KenBurns(
+                        start=kb["attrs"].get("start", "0,0") if kb else "0,0",
+                        end=kb["attrs"].get("end", "0,0") if kb else "0,0",
+                        ease=kb["attrs"].get("ease", "linear") if kb else "linear",
+                    ),
+                )
+            )
+        return Storyboard(clips=clips, raw=raw)
+
+    def _fallback_storyboard(self, fusion: FusionResult, duration: int, raw: str) -> Storyboard:
+        segments = fusion.script_segments or [ScriptSegment(text=fusion.hook or fusion.title, duration_sec=duration)]
+        total = sum(s.duration_sec for s in segments) or duration
+        clips = [
+            Clip(
+                image_index=min(s.image_index, 0),
+                transition="cut",
+                duration_sec=max(2.0, s.duration_sec * duration / total if total else duration / len(segments)),
+                voice_text=s.text,
+                subtitle_text=s.text[:20],
+            )
+            for s in segments
+        ]
+        return Storyboard(clips=clips, raw=raw)
+
+    # ── 步骤5：审核（不通过自动重写1次）─────────────────────────────────
+    def review_and_rewrite(
+        self, fusion: FusionResult, intent: IntentResult, fusion_level: str
+    ) -> tuple[FusionResult, ReviewResult, int]:
+        """返回最终文案、最后一次审核结果、重写次数（0或1）。"""
+        review = self.reviewer.review(fusion, intent, fusion_level)
+        if review.passed:
+            return fusion, review, 0
+
+        logger.info("文案审核不通过，自动重写 1 次：%s", [i.text for i in review.issues])
+        rewritten = self.reviewer.rewrite(fusion, review, intent, fusion_level)
+        second = self.reviewer.review(rewritten, intent, fusion_level)
+        if second.passed:
+            return rewritten, second, 1
+        # 二次仍不通过：带上重写结果和问题返回，由上游决定是否交给前端
+        return rewritten, second, 1
+
+    # ── 全流程编排 ──────────────────────────────────────────────────────
+    def generate(
+        self,
+        images: list[str],
+        *,
+        industry: str = "",
+        target_customer: str = "",
+        marketing_purpose: str = "",
+        duration: int = 15,
+        user_copy_text: str = "",
+        fusion_level: str = "ai_polish",
+    ) -> dict:
+        image_analysis = self.analyze_images(images, industry)
+        intent = self.parse_intent(user_copy_text, image_analysis, industry)
+        fusion = self.fuse(
+            fusion_level,
+            image_analysis,
+            intent,
+            industry=industry,
+            target_customer=target_customer,
+            marketing_purpose=marketing_purpose,
+            duration=duration,
+        )
+        fusion, review, rewrites = self.review_and_rewrite(fusion, intent, fusion_level)
+        board = self.storyboard(fusion, image_analysis, images, duration)
+        return {
+            "image_analysis": image_analysis,
+            "intent_result": intent,
+            "fusion_result": fusion,
+            "review_result": review,
+            "storyboard": board,
+            "rewrite_count": rewrites,
+        }
+
+    # ── 简报工具 ────────────────────────────────────────────────────────
+    @staticmethod
+    def _image_brief(a) -> str:
+        if a is None:
+            return "无图片分析信息"
+        lines = [f"产品：{p.name}（{p.features}）" for p in a.products]
+        lines += [f"卖点：{s}" for s in a.key_selling_points]
+        lines.append(f"场景：{a.scene}")
+        return "\n".join(lines) or "无图片分析信息"
+
+    @staticmethod
+    def _intent_brief(i: IntentResult) -> str:
+        lines = [f"意图：{i.intent_summary}"]
+        lines += [f"核心信息[must_keep={m.must_keep}]：{m.text}" for m in i.core_messages]
+        lines += [f"事实（{b.category}）：{b.text}" for b in i.personal_brands]
+        return "\n".join(lines)
+
+    @staticmethod
+    def _fusion_brief(f: FusionResult) -> str:
+        lines = [f"标题：{f.title}", f"钩子：{f.hook}"]
+        lines += [f"要点：{p.text}" for p in f.body_points]
+        lines += [f"配音：{s.text}" for s in f.script_segments]
+        lines.append(f"行动号召：{f.cta}")
+        return "\n".join(lines)

packages/application/viral_video/prompt_loader.py

@@ -0,0 +1,160 @@
+"""Prompt 模板加载器：从 viral_video_prompt_templates 读模板，30 秒 TTL 热加载。
+
+DB 不可用或没有数据时自动回落到 prompts.DEFAULT_TEMPLATES，保证流程不阻断。
+"""
+
+from __future__ import annotations
+
+import threading
+import time
+from dataclasses import dataclass
+from typing import Optional
+
+import sqlalchemy as sa
+
+from packages.adapters.sqlalchemy_impl import session as _session_mod
+from packages.application.viral_video.prompts import DEFAULT_TEMPLATES
+
+CACHE_TTL_SECONDS = 30.0
+
+_VALID_TYPES = {"image_analysis", "intent_parsing", "copy_fusion", "storyboard", "review"}
+
+
+@dataclass
+class PromptTemplate:
+    name: str
+    prompt_type: str
+    version: int
+    system_prompt: str
+    user_prompt_template: str
+    example_output: str = ""
+    is_active: bool = True
+
+
+_lock = threading.Lock()
+_cache: dict[str, tuple[float, PromptTemplate]] = {}
+
+
+def _fallback(prompt_type: str) -> Optional[PromptTemplate]:
+    for item in DEFAULT_TEMPLATES:
+        if item["prompt_type"] == prompt_type:
+            return PromptTemplate(
+                name=item["name"],
+                prompt_type=item["prompt_type"],
+                version=item["version"],
+                system_prompt=item["system_prompt"],
+                user_prompt_template=item["user_prompt_template"],
+                example_output=item["example_output"] or "",
+                is_active=bool(item["is_active"]),
+            )
+    return None
+
+
+_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://")
+    url = url.replace("postgresql://", "postgresql+psycopg://") if url.startswith("postgresql://") else url
+    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 _load_from_db(prompt_type: str) -> Optional[PromptTemplate]:
+    session = None
+    try:
+        session = _get_session()
+        if session is None:
+            return None
+        sql = sa.text("""
+            SELECT name, prompt_type, version, system_prompt,
+                   user_prompt_template, COALESCE(example_output, '') AS example_output,
+                   is_active
+              FROM viral_video_prompt_templates
+             WHERE prompt_type = :pt AND is_active = TRUE
+             ORDER BY version DESC
+             LIMIT 1
+            """)
+        row = session.execute(sql, {"pt": prompt_type}).first()
+        if row is None:
+            return None
+        return PromptTemplate(
+            name=row[0],
+            prompt_type=row[1],
+            version=int(row[2]),
+            system_prompt=row[3],
+            user_prompt_template=row[4],
+            example_output=row[5] or "",
+            is_active=bool(row[6]),
+        )
+    except Exception:  # noqa: BLE001 - 表不存在/DB 不可用时静默回落
+        return None
+    finally:
+        if session is not None:
+            try:
+                session.close()
+            except Exception:  # noqa: BLE001
+                pass
+
+
+def get_template(prompt_type: str, *, force_refresh: bool = False) -> Optional[PromptTemplate]:
+    """取某类型当前启用模板，30 秒缓存；DB 无数据则回落到代码默认模板。"""
+    if prompt_type not in _VALID_TYPES:
+        raise ValueError(f"未知 prompt_type: {prompt_type}")
+
+    now = time.monotonic()
+    with _lock:
+        cached = _cache.get(prompt_type)
+        if not force_refresh and cached and now - cached[0] < CACHE_TTL_SECONDS:
+            return cached[1]
+
+    template = _load_from_db(prompt_type) or _fallback(prompt_type)
+    if template is not None:
+        with _lock:
+            _cache[prompt_type] = (now, template)
+    return template
+
+
+def invalidate() -> None:
+    """清空缓存（测试用）。"""
+    with _lock:
+        _cache.clear()
+
+
+class _SafeDict(dict):
+    def __missing__(self, key: str) -> str:
+        return "{" + key + "}"
+
+
+def _safe_format(text: str, kwargs: dict) -> str:
+    try:
+        return text.format_map(_SafeDict(kwargs))
+    except Exception:  # noqa: BLE001
+        return text
+
+
+def render_user_prompt(template: PromptTemplate, **kwargs) -> str:
+    """填充 user_prompt_template 占位符，缺键原样保留不报错。"""
+    return _safe_format(template.user_prompt_template, kwargs)
+
+
+def render_system_prompt(template: PromptTemplate, **kwargs) -> str:
+    """copy_fusion 等 system_prompt 含运行时变量时填充。"""
+    return _safe_format(template.system_prompt, kwargs)

packages/application/viral_video/prompts.py

@@ -0,0 +1,338 @@
+"""爆款视频 5 套 Prompt 模板默认值（#2040 核心资产）。
+
+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。
+"""
+
+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。
+2. 不承诺效果：不写“保证”“一定”“100%有效”“包治百病”等绝对化用语。
+3. 不编造价格、销量、认证、奖项：除非用户在文案中明确给出，否则一律不写。
+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
+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。"""
+
+# 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
+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。""",
+    "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。""",
+    "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。""",
+}
+
+# ── 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。
+
+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。
+
+{GLOBAL_CONSTRAINTS}
+
+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。
+
+【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。
+
+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。
+
+【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"/>"""
+
+_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}
+
+按约定的标签格式输出分析结果。"""
+
+_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>"""
+
+# ── 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。
+
+{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：
+<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。"""
+
+_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}
+
+请理解用户意图，按标签格式输出。"""
+
+_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>"""
+
+# ── 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。
+
+{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>"""
+
+
+# 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",
+        "prompt_type": "image_analysis",
+        "version": TEMPLATE_VERSION,
+        "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",
+        "prompt_type": "storyboard",
+        "version": TEMPLATE_VERSION,
+        "system_prompt": _STORYBOARD_SYSTEM,
+        "user_prompt_template": _STORYBOARD_USER,
+        "example_output": _STORYBOARD_EXAMPLE,
+        "is_active": True,
+    },
+    {
+        "name": "文案审核",
+        "prompt_type": "review",
+        "version": TEMPLATE_VERSION,
+        "system_prompt": _REVIEW_SYSTEM,
+        "user_prompt_template": _REVIEW_USER,
+        "example_output": _REVIEW_EXAMPLE,
+        "is_active": True,
+    },
+]

packages/application/viral_video/reviewer.py

@@ -0,0 +1,312 @@
+"""文案审核 + 自动重写（#2040 第5套 Prompt）。
+
+6 维度：违规词 / 夸大承诺 / 事实一致性 / 用户意图保留 / 结构完整性 / 语气人设。
+LLM 审核之外叠加本地规则预检（保证即使 LLM 不可用也能兜住广告法红线）。
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from typing import Optional
+
+from packages.application.viral_video import xml_parser as xp
+from packages.application.viral_video.prompt_loader import (
+    get_template,
+    render_system_prompt,
+    render_user_prompt,
+)
+from packages.application.viral_video.schemas import (
+    FusionResult,
+    IntentResult,
+    ReviewIssue,
+    ReviewResult,
+)
+
+logger = logging.getLogger(__name__)
+
+# 本地规则：绝对化/夸大词
+_EXAGGERATION_PATTERNS = [
+    r"100\s*%",
+    r"百分百",
+    r"包治百病",
+    r"保证.{0,8}(有效|赚钱|瘦|好)",
+    r"绝对(有效|安全|靠谱)",
+    r"全网第一",
+    r"国家级",
+    r"特效",
+    r"立刻见效",
+    r"一喷(就|全|100)",
+]
+
+# 本地规则：平台违规/套路词
+_VIOLATION_PHRASES = [
+    "家人们谁懂啊",
+    "绝绝子",
+    "宝子们",
+    "yyds",
+    "最(好|强|牛|便宜)",  # 广告法极限词
+    "第一(名|品牌)?",
+]
+
+_LOCATIONS = ["title", "hook", "body_points", "cta", "script_segments"]
+
+
+class Reviewer:
+    def __init__(self, client=None):
+        if client is None:
+            from packages.shared.ai_client import get_doubao_client
+
+            client = get_doubao_client()
+        self.client = client
+
+    # ── 审核 ────────────────────────────────────────────────────────────
+    def review(self, fusion: FusionResult, intent: IntentResult, fusion_level: str) -> ReviewResult:
+        local = self._rule_check(fusion, intent, fusion_level)
+        llm_result = self._llm_review(fusion, intent, fusion_level)
+        if llm_result is None:
+            return ReviewResult(
+                passed=not local,
+                issues=local,
+                rewrite_suggestions=[],
+                raw="",
+            )
+        # LLM 与本地规则合并去重
+        issues = self._merge_issues(llm_result.issues, local)
+        return ReviewResult(
+            passed=llm_result.passed and not local,
+            issues=issues,
+            rewrite_suggestions=llm_result.rewrite_suggestions,
+            raw=llm_result.raw,
+        )
+
+    def _llm_review(self, fusion: FusionResult, intent: IntentResult, fusion_level: str) -> Optional[ReviewResult]:
+        template = get_template("review")
+        system = render_system_prompt(template)
+        user = render_user_prompt(
+            template,
+            fusion_level=fusion_level,
+            fusion_result=self._fusion_text(fusion),
+            intent_result=self._intent_text(intent),
+        )
+        raw = self.client.chat_completion(
+            [
+                {"role": "system", "content": system},
+                {"role": "user", "content": user},
+            ],
+            temperature=0.2,
+            max_tokens=1024,
+        )
+        if not raw:
+            return None
+        passed = xp.text_of(raw, "passed").strip().lower()
+        issues = [
+            ReviewIssue(
+                dimension=n["attrs"].get("dimension", "未知维度"),
+                severity=n["attrs"].get("severity", "warning"),
+                location=n["attrs"].get("location", ""),
+                text=n["text"],
+            )
+            for n in xp.find_all(raw, "issue")
+            if n["text"]
+        ]
+        suggestions = [n["text"] for n in xp.find_all(raw, "suggestion") if n["text"]]
+        parsed = ReviewResult(
+            passed=passed == "true" and not issues,
+            issues=issues,
+            rewrite_suggestions=suggestions,
+            raw=raw,
+        )
+        return parsed
+
+    # ── 本地规则预检 ────────────────────────────────────────────────────
+    def _rule_check(self, fusion: FusionResult, intent, fusion_level: str) -> list[ReviewIssue]:
+        issues: list[ReviewIssue] = []
+        for location, text in self._segments(fusion):
+            for pattern in _EXAGGERATION_PATTERNS:
+                if re.search(pattern, text):
+                    issues.append(
+                        ReviewIssue(
+                            dimension="夸大承诺",
+                            severity="error",
+                            location=location,
+                            text=f"出现夸大/绝对化表述：{self._hit(text, pattern)}",
+                        )
+                    )
+            for phrase in _VIOLATION_PHRASES:
+                if re.search(phrase, text, flags=re.IGNORECASE):
+                    issues.append(
+                        ReviewIssue(
+                            dimension="违规词",
+                            severity="error",
+                            location=location,
+                            text=f"出现违规或套路词：{self._hit(text, phrase)}",
+                        )
+                    )
+
+        # 结构完整性
+        if not fusion.title:
+            issues.append(ReviewIssue(dimension="结构完整性", severity="warning", location="title", text="缺少标题"))
+        if not fusion.hook:
+            issues.append(ReviewIssue(dimension="结构完整性", severity="warning", location="hook", text="缺少开头钩子"))
+        if not fusion.cta:
+            issues.append(ReviewIssue(dimension="结构完整性", severity="warning", location="cta", text="缺少行动号召"))
+
+        # 用户意图保留（must_keep）
+        full_text = self._fusion_text(fusion)
+        if fusion_level in {"ai_polish", "user_primary"} and intent is not None:
+            for message in intent.core_messages:
+                if message.must_keep:
+                    key = self._compact(message.text)
+                    if key and key[:10] not in self._compact(full_text):
+                        issues.append(
+                            ReviewIssue(
+                                dimension="用户意图保留",
+                                severity="warning",
+                                location="script_segments",
+                                text=f"用户核心信息被丢失：{message.text[:30]}",
+                            )
+                        )
+            for brand in intent.personal_brands:
+                if brand.text and brand.text not in full_text:
+                    issues.append(
+                        ReviewIssue(
+                            dimension="事实一致性",
+                            severity="error",
+                            location="script_segments",
+                            text=f"personal_brands 事实信息未原样保留：{brand.text[:30]}",
+                        )
+                    )
+        return issues
+
+    @staticmethod
+    def _hit(text: str, pattern: str) -> str:
+        match = re.search(pattern, text, flags=re.IGNORECASE)
+        return match.group(0) if match else pattern
+
+    @staticmethod
+    def _compact(text: str) -> str:
+        return re.sub(r"[\s，。！？、,.!?；;：:\"'“”‘’（）()【】\[\]]", "", text)
+
+    @staticmethod
+    def _merge_issues(llm_issues: list[ReviewIssue], local: list[ReviewIssue]) -> list[ReviewIssue]:
+        merged = list(local)
+        seen = {(i.dimension, Reviewer._compact(i.text)[:20]) for i in local}
+        for issue in llm_issues:
+            key = (issue.dimension, Reviewer._compact(issue.text)[:20])
+            if key not in seen:
+                merged.append(issue)
+                seen.add(key)
+        return merged
+
+    # ── 自动重写（1 次）─────────────────────────────────────────────────
+    def rewrite(
+        self,
+        fusion: FusionResult,
+        review: ReviewResult,
+        intent: IntentResult,
+        fusion_level: str,
+    ) -> FusionResult:
+        from packages.application.viral_video.generator import CopyGenerator
+
+        template = get_template("copy_fusion")
+        system_kwargs = {
+            "fusion_instruction": (
+                "【本次任务：按审核意见修正文案】只修改指出的问题，其他内容尽量原样保留；"
+                "personal_brands 事实信息逐字保留；修正后按原标签格式完整输出。"
+            ),
+            "global_constraints": "",
+            "negative_rules": "",
+        }
+        issue_text = "\n".join(f"- [{i.dimension}/{i.location}] {i.text}" for i in review.issues)
+        suggestion_text = "\n".join(f"- {s}" for s in review.rewrite_suggestions)
+        user = render_user_prompt(
+            template,
+            industry="",
+            target_customer="",
+            marketing_purpose="",
+            duration=fusion.estimated_duration or 15,
+            image_analysis="（沿用原图片分析）",
+            intent_result=self._intent_text(intent),
+        )
+        user = (
+            f"{user}\n\n原文案：\n{self._fusion_text(fusion)}\n\n"
+            f"审核发现的问题：\n{issue_text}\n\n修改建议：\n{suggestion_text or '（无）'}\n"
+            "请输出修正后的完整文案。"
+        )
+        system = render_system_prompt(template, **system_kwargs)
+        raw = self.client.chat_completion(
+            [
+                {"role": "system", "content": system},
+                {"role": "user", "content": user},
+            ],
+            temperature=0.5,
+            max_tokens=2048,
+        )
+        if not raw:
+            return self._rule_fix(fusion, review)
+        rewritten = CopyGenerator._parse_fusion(CopyGenerator(self.client), raw)
+        if not rewritten.title and not rewritten.script_segments:
+            return self._rule_fix(fusion, review)
+        # 保底：personal_brands 必须保留
+        full = self._fusion_text(rewritten)
+        for brand in intent.personal_brands:
+            if brand.text and brand.text not in full:
+                rewritten.cta = (rewritten.cta + brand.text).strip()
+        return rewritten
+
+    def _rule_fix(self, fusion: FusionResult, review: ReviewResult) -> FusionResult:
+        """LLM 重写不可用时的本地兜底：删除/替换明显违规表述。"""
+        replacements = [
+            (re.compile(r"100\s*%|百分百"), "大部分"),
+            (re.compile(r"绝对(有效|安全|靠谱)"), "比较\\1"),
+            (re.compile(r"包治百病"), "适用多种情况"),
+            (re.compile(r"立刻见效"), "坚持使用会有改善"),
+            (re.compile(r"一喷(就|全|100%)"), "喷上等一会儿可以"),
+            (re.compile(r"家人们谁懂啊|绝绝子|宝子们|yyds", re.IGNORECASE), ""),
+            (re.compile(r"最好|最强|最牛|最便宜"), "很不错"),
+        ]
+
+        def fix(text: str) -> str:
+            for pattern, repl in replacements:
+                text = pattern.sub(repl, text)
+            return text
+
+        fusion.title = fix(fusion.title)
+        fusion.hook = fix(fusion.hook)
+        fusion.cta = fix(fusion.cta)
+        for point in fusion.body_points:
+            point.text = fix(point.text)
+            point.elaboration = fix(point.elaboration)
+        for segment in fusion.script_segments:
+            segment.text = fix(segment.text)
+        fusion.raw = ""
+        return fusion
+
+    # ── 文本工具 ────────────────────────────────────────────────────────
+    @staticmethod
+    def _segments(fusion: FusionResult):
+        yield "title", fusion.title
+        yield "hook", fusion.hook
+        for point in fusion.body_points:
+            yield "body_points", f"{point.text} {point.elaboration}"
+        yield "cta", fusion.cta
+        for segment in fusion.script_segments:
+            yield "script_segments", segment.text
+
+    @staticmethod
+    def _fusion_text(fusion: FusionResult) -> str:
+        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)
+        return "\n".join(p for p in parts if p)
+
+    @staticmethod
+    def _intent_text(intent) -> str:
+        if intent is None:
+            return "无意图信息"
+        parts = [f"意图：{intent.intent_summary}"]
+        parts += [f"核心信息[must_keep={m.must_keep}]：{m.text}" for m in intent.core_messages]
+        parts += [f"事实（{b.category}）：{b.text}" for b in intent.personal_brands]
+        return "\n".join(parts)

packages/application/viral_video/schemas.py

@@ -0,0 +1,116 @@
+"""内部 Pydantic 校验模型（不暴露给运营，运营只看 DB 里的纯文本）。"""
+
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+
+class ProductItem(BaseModel):
+    name: str = "无法判断"
+    features: str = "无法判断"
+    position: str = "secondary"
+    image_index: int = 0
+
+
+class ColorItem(BaseModel):
+    hex: str = "#000000"
+    name: str = "无法判断"
+    coverage: float = 0.0
+
+
+class TextItem(BaseModel):
+    text: str = ""
+    position: str = ""
+
+
+class ImageAnalysis(BaseModel):
+    products: list[ProductItem] = Field(default_factory=list)
+    colors: list[ColorItem] = Field(default_factory=list)
+    has_person: bool = False
+    person_count: int = 0
+    people: dict[str, str] = Field(default_factory=dict)
+    mood: str = ""
+    visible_text: list[TextItem] = Field(default_factory=list)
+    scene: str = ""
+    quality: dict[str, str] = Field(default_factory=dict)
+    key_selling_points: list[str] = Field(default_factory=list)
+    raw: str = ""
+
+
+class CoreMessage(BaseModel):
+    text: str
+    must_keep: bool = False
+    confidence: float = 0.0
+
+
+class PersonalBrand(BaseModel):
+    text: str
+    category: str = "brand"
+
+
+class IntentResult(BaseModel):
+    intent_summary: str = ""
+    core_messages: list[CoreMessage] = Field(default_factory=list)
+    personal_brands: list[PersonalBrand] = Field(default_factory=list)
+    emotion_tone: str = ""
+    missing_info: list[str] = Field(default_factory=list)
+    raw: str = ""
+
+
+class BodyPoint(BaseModel):
+    text: str
+    elaboration: str = ""
+    image_index: int = 0
+
+
+class ScriptSegment(BaseModel):
+    text: str
+    duration_sec: float = 0
+    image_index: int = 0
+
+
+class FusionResult(BaseModel):
+    title: str = ""
+    hook: str = ""
+    body_points: list[BodyPoint] = Field(default_factory=list)
+    cta: str = ""
+    script_segments: list[ScriptSegment] = Field(default_factory=list)
+    word_count: int = 0
+    estimated_duration: int = 0
+    raw: str = ""
+
+
+class KenBurns(BaseModel):
+    start: str = "0,0"
+    end: str = "0,0"
+    ease: str = "linear"
+
+
+class Clip(BaseModel):
+    image_index: int = 0
+    transition: str = "cut"
+    zoom: str | None = None
+    duration_sec: float = 0
+    bgm_note: str = ""
+    voice_text: str = ""
+    subtitle_text: str = ""
+    ken_burns: KenBurns = Field(default_factory=KenBurns)
+
+
+class Storyboard(BaseModel):
+    clips: list[Clip] = Field(default_factory=list)
+    raw: str = ""
+
+
+class ReviewIssue(BaseModel):
+    dimension: str
+    severity: str = "warning"
+    location: str = ""
+    text: str = ""
+
+
+class ReviewResult(BaseModel):
+    passed: bool = True
+    issues: list[ReviewIssue] = Field(default_factory=list)
+    rewrite_suggestions: list[str] = Field(default_factory=list)
+    raw: str = ""

packages/application/viral_video/xml_parser.py

@@ -0,0 +1,104 @@
+"""XML 标签式输出解析器（替代 json.loads）。
+
+LLM 按 ``<tag attr="x">内容</tag>`` 输出，本模块解析，解析失败不抛异常，
+由调用方走规则 fallback。采用栈式扫描，嵌套标签全部可提取（内外层都保留）。
+"""
+
+from __future__ import annotations
+
+import re
+from html import unescape
+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*(?:"([^"]*)"|'([^']*)')""")
+
+
+def parse_attributes(raw: str) -> dict[str, str]:
+    """解析标签属性字符串。"""
+    attrs: dict[str, str] = {}
+    for match in _ATTR_RE.finditer(raw or ""):
+        value = match.group(2) if match.group(2) is not None else match.group(3)
+        attrs[match.group(1)] = value
+    return attrs
+
+
+def parse_tags(text: Optional[str]) -> list[dict]:
+    """提取全部标签（含嵌套内外层），返回 [{tag, attrs, text}]，按开标签出现顺序。"""
+    if not text:
+        return []
+    results: list[dict] = []
+    stack: list[dict] = []
+    token_re = re.compile(r"<[^>]+>")
+    for token in token_re.finditer(text):
+        raw_token = token.group(0)
+        # 先按开/闭标签匹配
+        open_match = _OPEN_RE.match(raw_token)
+        close_match = _CLOSE_RE.match(raw_token)
+        is_close_tag = raw_token.startswith("</")
+        if not is_close_tag and open_match:
+            is_self_close = open_match.group("self") == "/"
+            node = {
+                "tag": open_match.group("tag"),
+                "attrs": parse_attributes(open_match.group("attrs")),
+                "text": "",
+                "_start": token.end(),
+            }
+            if is_self_close:
+                node.pop("_start")
+                results.append(node)
+            else:
+                stack.append(node)
+                results.append(node)
+        elif is_close_tag and close_match:
+            tag = close_match.group("tag")
+            # 弹出到最近同名开标签
+            for idx in range(len(stack) - 1, -1, -1):
+                if stack[idx]["tag"] == tag:
+                    node = stack[idx]
+                    node["text"] = unescape(text[node["_start"] : token.start()].strip())
+                    node.pop("_start", None)
+                    del stack[idx:]
+                    break
+    # 未闭合标签：给剩余部分作为文本
+    for node in stack:
+        if "_start" in node:
+            node["text"] = unescape(text[node["_start"] :].strip())
+            node.pop("_start", None)
+    return results
+
+
+def find_all(text: Optional[str], tag: str) -> list[dict]:
+    """提取指定标签的全部节点。"""
+    return [n for n in parse_tags(text) if n["tag"] == tag]
+
+
+def find_first(text: Optional[str], tag: str) -> Optional[dict]:
+    nodes = find_all(text, tag)
+    return nodes[0] if nodes else None
+
+
+def text_of(text: Optional[str], tag: str, default: str = "") -> str:
+    node = find_first(text, tag)
+    return node["text"] if node else default
+
+
+def attr_bool(value: Optional[str], default: bool = False) -> bool:
+    if value is None:
+        return default
+    return value.strip().lower() in {"true", "1", "yes", "是"}
+
+
+def attr_float(value: Optional[str], default: float = 0.0) -> float:
+    try:
+        return float(value) if value is not None and value.strip() else default
+    except (TypeError, ValueError):
+        return default
+
+
+def attr_int(value: Optional[str], default: int = 0) -> int:
+    try:
+        return int(float(value)) if value is not None and value.strip() else default
+    except (TypeError, ValueError):
+        return default

packages/config/base.py

@@ -90,12 +90,38 @@ class SharedSettings(BaseSettings):
 
     # ── 豆包大模型（火山引擎方舟） ────────────────────────────────────────
     doubao_api_key: str = ""
-    doubao_model: str = "doubao-seed-1-6-250615"
+    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 = 30
-    doubao_max_retries: int = 2
-    doubao_vision_model: str = "doubao-1-5-vision-pro-250915"
-    doubao_embedding_model: str = "doubao-embedding-large-text-240915"
+    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
+    )
+
+    # ── 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_video_poll_interval: int = 10
 
     # ── MediaKit (火山引擎 AI 媒体工具) ──────────────────────────────────
     mediakit_api_key: str = ""

packages/domain/entities.py

@@ -60,6 +60,11 @@ class User:
     # 资料是否已完善（微信新用户首次设置昵称后置 True；邮箱注册默认 True）
     profile_completed: bool = True
 
+    # 会员字段 (#1895)：与 users 表列对应
+    is_member: bool = False
+    member_type: str | None = None
+    member_expires_at: datetime | None = None
+
     created_at: datetime = field(default_factory=lambda: datetime.now(UTC))
 
 

packages/domain/points_account.py

@@ -9,9 +9,9 @@ from uuid import uuid4
 class PointsAccount:
     id: str
     user_id: str
-    balance: int = 0
-    total_earned: int = 0
-    total_spent: int = 0
+    balance: float = 0.0
+    total_earned: float = 0.0
+    total_spent: float = 0.0
     created_at: datetime = field(default_factory=lambda: datetime.now(UTC))
     updated_at: datetime = field(default_factory=lambda: datetime.now(UTC))
 

packages/domain/points_rules.py

@@ -1,32 +1,321 @@
-"""积分消耗规则配置 (#1895)"""
+"""积分消耗规则配置 (#1895)
+
+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。
+"""
 
 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）
+VIRAL_VIDEO_MODEL_PRICES: dict[tuple[str, str, bool], float] = {
+    ("seedance-2.5", "480p", False): 70.0,
+    ("seedance-2.5", "720p", False): 70.0,
+    ("seedance-2.5", "1080p", False): 77.0,
+    ("seedance-2.5", "480p", True): 42.0,
+    ("seedance-2.5", "720p", True): 42.0,
+    ("seedance-2.5", "1080p", True): 46.0,
+    ("seedance-2.0", "480p", False): 46.0,
+    ("seedance-2.0", "720p", False): 46.0,
+    ("seedance-2.0", "1080p", False): 51.0,
+    ("seedance-2.0", "4k", False): 80.0,
+    ("seedance-2.0-fast", "480p", False): 28.0,
+    ("seedance-2.0-fast", "720p", False): 28.0,
+    ("seedance-2.0-mini", "480p", False): 9.2,
+    ("seedance-2.0-mini", "720p", False): 9.2,
+    ("wan-3.0", "480p", False): 0.3,
+    ("wan-3.0", "720p", False): 0.6,
+    ("wan-3.0", "1080p", False): 1.2,
+}
+
+# 固定成本（元）：VLM 分析 + LLM 文案 + TTS + OSS + 服务器
+VIRAL_VIDEO_FIXED_COST = 0.15
+# 利润系数
+VIRAL_VIDEO_PROFIT_MULTIPLIER = 1.3
+# Seedance 输出帧率
+VIRAL_VIDEO_FPS = 24
+
+# 分辨率别名映射 -> 标准 key
+_RESOLUTION_ALIASES: dict[str, str] = {
+    "480p": "480p",
+    "普清": "480p",
+    "default": "480p",
+    "low": "480p",
+    "sd": "480p",
+    "720p": "720p",
+    "高清": "720p",
+    "medium": "720p",
+    "hd": "720p",
+    "1080p": "1080p",
+    "超清": "1080p",
+    "high": "1080p",
+    "ultra": "1080p",
+    "全能": "1080p",
+    "fhd": "1080p",
+}
+# 分辨率 -> 短边像素数（p 值代表短边，不是 height）
+_RESOLUTION_SHORT_SIDE: dict[str, int] = {"480p": 480, "720p": 720, "1080p": 1080, "4k": 2160}
+_RESOLUTION_ALIASES["4k"] = "4k"
+_RESOLUTION_ALIASES["2160p"] = "4k"
+_RESOLUTION_ALIASES["uhd"] = "4k"
+
+
+def resolve_video_dimensions(resolution: str, ratio: str) -> tuple[int, int]:
+    """把 (resolution, ratio) 解析为 (width, height)。
+
+    resolution 数字代表短边像素数（480p/720p/1080p 等）：
+    - 横屏 16:9：短边是 height，width = short * 16/9
+    - 竖屏 9:16：短边是 width，height = short * 16/9
+    - 方屏 1:1：width = height = short
+    """
+    key = str(resolution or "").strip()
+    key_l = key.lower()
+    res_key = _RESOLUTION_ALIASES.get(key_l) or _RESOLUTION_ALIASES.get(key) or "720p"
+    short = _RESOLUTION_SHORT_SIDE.get(res_key, 720)
+    r = str(ratio or "").strip().lower()
+    if r == "16:9":
+        # 横屏：短边是 height，width 向上取整并对齐偶数
+        w = math.ceil(short * 16 / 9)
+        h = short
+    elif r == "1:1":
+        w, h = short, short
+    else:
+        # 9:16 竖屏（默认）：短边是 width，height 向上取整并对齐偶数
+        w = short
+        h = math.ceil(short * 16 / 9)
+    # 对齐到偶数（视频编码要求）
+    w = w + (w % 2)
+    h = h + (h % 2)
+    return int(w), int(h)
+
+
+# ── 爆款视频多模型元数据 (#2159) ──────────────────────────────────────
+VIRAL_VIDEO_MODEL_CONFIG: dict[str, dict] = {
+    "seedance-2.5": {
+        "key": "seedance-2.5",
+        "display_name": "Seedance 2.5 — 最新最强",
+        "model_id": "doubao-seedance-2-5-260628",
+        "provider": "doubao",
+        "supports_audio": True,
+        "supported_resolutions": ["480p", "720p", "1080p"],
+        "max_duration": 30,
+        "billing_mode": "token",
+        "is_default": True,
+    },
+    "seedance-2.0": {
+        "key": "seedance-2.0",
+        "display_name": "Seedance 2.0 — 正式首选",
+        "model_id": "doubao-seedance-2-0-260128",
+        "provider": "doubao",
+        "supports_audio": True,
+        "supported_resolutions": ["480p", "720p", "1080p", "4k"],
+        "max_duration": 15,
+        "billing_mode": "token",
+        "is_default": False,
+    },
+    "seedance-2.0-fast": {
+        "key": "seedance-2.0-fast",
+        "display_name": "Seedance 2.0 Fast — 快速低成本",
+        "model_id": "doubao-seedance-2-0-fast-260128",
+        "provider": "doubao",
+        "supports_audio": True,
+        "supported_resolutions": ["480p", "720p"],
+        "max_duration": 15,
+        "billing_mode": "token",
+        "is_default": False,
+    },
+    "seedance-2.0-mini": {
+        "key": "seedance-2.0-mini",
+        "display_name": "Seedance 2.0 Mini — 低成本测试",
+        "model_id": "doubao-seedance-2-0-mini-260615",
+        "provider": "doubao",
+        "supports_audio": True,
+        "supported_resolutions": ["480p", "720p"],
+        "max_duration": 15,
+        "billing_mode": "token",
+        "is_default": False,
+    },
+    "wan-3.0": {
+        "key": "wan-3.0",
+        "display_name": "Wan 3.0 — 通义万相（阿里云）",
+        "model_id": "wan3.0-video",
+        "provider": "dashscope",
+        "supports_audio": True,
+        "supported_resolutions": ["480p", "720p", "1080p"],
+        "max_duration": 30,
+        "billing_mode": "per_second",
+        "is_default": False,
+    },
+}
+
+
+def get_viral_video_model_config(model_key: str | None) -> dict:
+    """获取模型配置，未知 key 回落到默认 seedance-2.5。"""
+    key = (model_key or "").strip().lower()
+    if key and key in VIRAL_VIDEO_MODEL_CONFIG:
+        return VIRAL_VIDEO_MODEL_CONFIG[key]
+    return VIRAL_VIDEO_MODEL_CONFIG["seedance-2.5"]
+
+
+def list_viral_video_models(
+    include_placeholder: bool = False,
+    dashscope_available: bool = False,
+) -> list[dict]:
+    """返回前端可用的模型列表（供 GET /api/v1/viral-video/models 端点用）。"""
+    out: list[dict] = []
+    for _k, cfg in VIRAL_VIDEO_MODEL_CONFIG.items():
+        if cfg.get("_placeholder") and not include_placeholder:
+            continue
+        if cfg.get("provider") == "dashscope" and not dashscope_available:
+            continue
+        out.append(
+            {
+                "key": cfg["key"],
+                "display_name": cfg["display_name"],
+                "supports_audio": bool(cfg.get("supports_audio", True)),
+                "supported_resolutions": list(cfg.get("supported_resolutions", ["720p"])),
+                "max_duration": int(cfg.get("max_duration", 15)),
+                "billing_mode": cfg.get("billing_mode", "token"),
+                "is_default": bool(cfg.get("is_default", False)),
+            }
+        )
+    return out
+
+
+def _match_model_prefix(model: str | None) -> str:
+    """匹配 model key（支持全部内部别名，未知回落到 seedance-2.5）。
+
+    按 key 长度从长到短匹配，避免 "seedance-2.0-fast" 被 "seedance-2.0" 前缀命中。
+    """
+    mm = (model or "").strip().lower()
+    for k in sorted(VIRAL_VIDEO_MODEL_CONFIG.keys(), key=len, reverse=True):
+        if mm == k or mm.startswith(k):
+            return k
+    return "seedance-2.5"
+
+
+def _infer_resolution_key(width: int, height: int) -> str:
+    """从实际 (width, height) 用短边推断 resolution key。"""
+    short = min(int(width or 720), int(height or 720))
+    if short >= 1900:
+        return "4k"
+    if short >= 1000:
+        return "1080p"
+    if short >= 650:
+        return "720p"
+    return "480p"
+
+
+def calculate_viral_video_credits_with_breakdown(
+    duration_seconds: int,
+    width: int,
+    height: int,
+    model: str = "seedance-2.5",
+    has_video_input: bool = False,
+    actual_tokens: int | None = None,
+    fps: int = VIRAL_VIDEO_FPS,
+) -> 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。
+
+    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)
+    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。
+    """
+    w = max(1, int(width or 1))
+    h = max(1, int(height or 1))
+    effective_fps = int(fps or VIRAL_VIDEO_FPS)
+
+    prefix = _match_model_prefix(model)
+    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)
+    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)
+        billing_unit = "second"
+    else:
+        if actual_tokens is not None and actual_tokens > 0:
+            tokens = float(actual_tokens)
+        else:
+            tokens = dur * w * h * effective_fps / 1024.0
+        video_cost = tokens / 1_000_000.0 * float(price)
+        billing_unit = "token"
+
+    total = (video_cost + VIRAL_VIDEO_FIXED_COST) * VIRAL_VIDEO_PROFIT_MULTIPLIER
+    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),
+        "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,
+    }
+    return credits, breakdown
+
+
+def calculate_viral_video_credits(
+    duration_seconds: int,
+    width: int,
+    height: int,
+    model: str = "seedance-2.5",
+    has_video_input: bool = False,
+    actual_tokens: int | None = None,
+    fps: int = VIRAL_VIDEO_FPS,
+) -> float:
+    """计算爆款视频所需积分（1 积分 = 1 元），仅返回积分值（向后兼容包装器）。
+
+    内部调用 calculate_viral_video_credits_with_breakdown，仅返回 credits 部分，
+    保持旧调用方签名与返回值类型不变。
+
+    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)
+    若传入 actual_tokens 则用它替代计算值。
+    """
+    credits, _ = calculate_viral_video_credits_with_breakdown(
+        duration_seconds=duration_seconds,
+        width=width,
+        height=height,
+        model=model,
+        has_video_input=has_video_input,
+        actual_tokens=actual_tokens,
+        fps=fps,
+    )
+    return credits
+
+
 # ============ 场景定义 ============
-# 每个场景: base_points(基础积分), unit(计费单位), name(显示名称)
+# 每个场景: base_points(基础积分), unit(计费单位), name(显示名称), dynamic(是否动态定价)
+# 说明：爆款视频（viral_video）走动态定价（预扣→结算多退少补），因此不使用 @points_gate
+# 装饰器，base_points=0，dynamic=True；前端展示场景列表时仍可看到。
 
 POINTS_SCENES: dict[str, dict] = {
-    "ai_voice": {
-        "base_points": 1,
-        "unit": "分钟",
-        "name": "AI 配音",
-        "description": "AI 配音每分钟消耗 1 积分（免费用户上浮 15%，会员 8~9 折）",
-    },
-    "ai_video": {
-        "base_points": 3,
-        "unit": "条",
-        "name": "智能混剪",
-        "extra_per_30s": 1,
-        "description": "智能混剪每条 3 积分起，视频超过 30 秒后每 30 秒加 1 积分；免费用户每日 2 条免费额度",
-    },
-    "ai_digital_human": {
-        "base_points": 15,
-        "unit": "分钟",
-        "name": "AI 数字人",
-        "description": "AI 数字人每分钟消耗 15 积分",
-    },
     "voice_clone_train": {
         "base_points": 0,
         "unit": "次",
@@ -39,23 +328,16 @@ POINTS_SCENES: dict[str, dict] = {
         "name": "声音克隆合成",
         "description": "克隆音色合成每分钟消耗 1 积分",
     },
-    "douyin_extract": {
-        "base_points": 1,
+    "viral_video": {
+        "base_points": 0,
         "unit": "次",
-        "name": "抖音链接提取",
-        "description": "抖音文案提取每次 1 积分",
+        "name": "爆款视频",
+        "dynamic": True,
+        "description": "爆款视频动态定价（按视频时长/分辨率/模型计算，预扣→结算多退少补）",
     },
-    "ai_rewrite": {"base_points": 1, "unit": "次", "name": "AI 改写文案", "description": "AI 改写文案每次 1 积分"},
-    "ai_title": {
-        "base_points": 1,
-        "unit": "次",
-        "name": "AI 标题生成",
-        "description": "AI 生成标题每次 1 积分（免费用户实际上浮后 2 积分/次）",
-    },
-    "ai_cover": {"base_points": 1, "unit": "张", "name": "AI 封面生成", "description": "AI 封面生成每张 1 积分"},
 }
 
-# 免费用户积分消耗上浮系数
+# 免费用户积分消耗上浮系数（仅对 voice_clone_synth 生效）
 FREE_USER_MULTIPLIER = 1.15
 
 # ============ 积分包定义 ============
@@ -81,9 +363,6 @@ MEMBER_DISCOUNT: dict[str, float] = {
     "yearly": 0.8,
 }
 
-# 每日免费混剪次数（免费用户）
-DAILY_FREE_CLIP_LIMIT = 2
-
 
 def calculate_points_cost(
     scene_key: str,
@@ -91,48 +370,45 @@ def calculate_points_cost(
     quantity: int = 1,
     duration_minutes: float = 0,
     member_type: str | None = None,
-) -> int:
+) -> float:
     """计算指定场景的积分消耗。
 
     Args:
-        scene_key: 场景标识，如 "ai_voice"、"ai_video"
+        scene_key: 场景标识（当前支持 voice_clone_train/voice_clone_synth/viral_video；
+                   viral_video 为动态定价场景，此处返回 0，由业务侧调用
+                   calculate_viral_video_credits 手动计算）
         is_member: 是否付费会员
         quantity: 数量（按次计费场景）
         duration_minutes: 时长分钟数（按时长计费场景）
         member_type: 会员类型 (monthly/quarterly/yearly)，用于折扣
 
     Returns:
-        实际消耗积分（已含免费用户 ×1.15 上浮或会员折扣）
-
-    Raises:
-        ValueError: 未知场景标识
+        实际消耗积分（float；已含免费用户 ×1.15 上浮或会员折扣）；免费/动态/已下线场景统一返回 0。
     """
     scene = POINTS_SCENES.get(scene_key)
     if not scene:
-        raise ValueError(f"Unknown points scene: {scene_key}")
+        # 已下线/未注册的场景统一返回 0（免费），保持向后兼容
+        return 0.0
+
+    # 动态定价场景（如 viral_video）由业务侧手动计算，这里统一返回 0
+    if scene.get("dynamic"):
+        return 0.0
 
     base = scene["base_points"]
     if base == 0:
-        return 0
+        return 0.0
 
-    # —— 计算基础消耗 ——
     unit = scene["unit"]
     if unit == "分钟":
         total_base = base * max(1, math.ceil(duration_minutes))
-    elif unit in ("条", "次", "张"):
+    elif unit in ("次", "张"):
         total_base = base * quantity
-        # 混剪特殊逻辑：视频超过 30s 后每 +30s 额外加 1 积分
-        if scene_key == "ai_video" and duration_minutes > 0.5:
-            extra_segments = math.ceil((duration_minutes * 60 - 30) / 30)
-            if extra_segments > 0:
-                total_base += scene.get("extra_per_30s", 1) * extra_segments
     else:
         total_base = base
 
-    # —— 会员折扣 / 免费用户上浮 ——
     if is_member and member_type and member_type in MEMBER_DISCOUNT:
         total_base = max(1, math.floor(total_base * MEMBER_DISCOUNT[member_type]))
     elif not is_member:
         total_base = math.ceil(total_base * FREE_USER_MULTIPLIER)
 
-    return total_base
+    return float(total_base)

packages/domain/points_service.py

@@ -13,7 +13,6 @@ from typing import Any
 from sqlalchemy.orm import Session
 
 from packages.domain.points_rules import (
-    DAILY_FREE_CLIP_LIMIT,
     POINTS_PACKAGES,
 )
 
@@ -84,7 +83,7 @@ class PointsService:
 
     # ──────────────── 余额检查 ────────────────
 
-    def check_balance(self, user_id: str, amount: int, db: Session) -> dict[str, Any]:
+    def check_balance(self, user_id: str, amount: float, db: Session) -> dict[str, Any]:
         """检查余额是否足够。"""
         account_data = self.get_or_create_account(user_id, db)
         balance = account_data["balance"]
@@ -100,7 +99,7 @@ class PointsService:
     def deduct_points(
         self,
         user_id: str,
-        amount: int,
+        amount: float,
         source: str,
         db: Session,
         description: str = "",
@@ -109,7 +108,7 @@ class PointsService:
         """扣减积分（事务性：SELECT FOR UPDATE → 检查余额 → 扣减 → 流水 → 同步用户表）。
 
         Returns:
-            {"success": True/False, "balance": int, "transaction_id": str|None}
+            {"success": True/False, "balance": float, "transaction_id": str|None}
         """
         PointsAccountModel, PointsTransactionModel, _, _, UserModel = _get_models()
 
@@ -173,7 +172,7 @@ class PointsService:
         except Exception:
             db.rollback()
             logger.exception(
-                "积分扣减失败: user_id=%s, amount=%d, source=%s",
+                "积分扣减失败: user_id=%s, amount=%.2f, source=%s",
                 user_id,
                 amount,
                 source,
@@ -185,7 +184,7 @@ class PointsService:
     def add_points(
         self,
         user_id: str,
-        amount: int,
+        amount: float,
         source: str,
         db: Session,
         description: str = "",
@@ -242,7 +241,7 @@ class PointsService:
         except Exception:
             db.rollback()
             logger.exception(
-                "积分增加失败: user_id=%s, amount=%d, source=%s",
+                "积分增加失败: user_id=%s, amount=%.2f, source=%s",
                 user_id,
                 amount,
                 source,
@@ -254,7 +253,7 @@ class PointsService:
     def refund_points(
         self,
         user_id: str,
-        amount: int,
+        amount: float,
         source: str,
         db: Session,
         ref_id: str = "",
@@ -270,6 +269,92 @@ class PointsService:
             ref_id=ref_id,
         )
 
+    # ──────────────── 爆款视频（viral_video）动态定价 ────────────────
+
+    def deduct_viral_video(self, user_id: str, credits: float, job_id: str, db: Session) -> dict[str, Any]:
+        """爆款视频预扣积分（confirm-copy 阶段）。"""
+        return self.deduct_points(
+            user_id=user_id,
+            amount=float(credits or 0),
+            source="viral_video",
+            db=db,
+            description="爆款视频生成",
+            ref_id=job_id,
+        )
+
+    def settle_viral_video(
+        self,
+        user_id: str,
+        estimated: float,
+        actual: float,
+        txn_id: str,
+        db: Session,
+    ) -> dict[str, Any]:
+        """爆款视频完成后按实际 tokens 结算（多退少补）。
+
+        - actual < estimated: 退差额
+        - actual > estimated: 补扣差额（余额不足时记 warning，不阻塞完成）
+        - |diff| < 0.01: 不动
+        """
+        diff = round(float(actual or 0) - float(estimated or 0), 2)
+        if abs(diff) < 0.01:
+            return {"success": True, "action": "none", "diff": 0.0}
+        if diff < 0:
+            refund = round(-diff, 2)
+            try:
+                res = self.refund_points(
+                    user_id=user_id,
+                    amount=refund,
+                    source="viral_video",
+                    db=db,
+                    ref_id=txn_id,
+                    description="爆款视频结算退费",
+                )
+                return {"success": bool(res.get("success")), "action": "refund", "diff": -refund, "amount": refund}
+            except Exception:
+                logger.exception("[viral_video] 结算退费异常 user_id=%s refund=%.2f", user_id, refund)
+                return {"success": False, "action": "refund", "diff": -refund}
+        else:
+            extra = round(diff, 2)
+            try:
+                res = self.deduct_points(
+                    user_id=user_id,
+                    amount=extra,
+                    source="viral_video",
+                    db=db,
+                    description="爆款视频结算补扣",
+                    ref_id=txn_id,
+                )
+                if not res.get("success"):
+                    logger.warning(
+                        "[viral_video] 结算补扣余额不足 user_id=%s extra=%.2f balance=%s (不阻塞任务完成)",
+                        user_id,
+                        extra,
+                        res.get("balance"),
+                    )
+                return {"success": bool(res.get("success")), "action": "deduct", "diff": extra, "amount": extra}
+            except Exception:
+                logger.exception("[viral_video] 结算补扣异常 user_id=%s extra=%.2f", user_id, extra)
+                return {"success": False, "action": "deduct", "diff": extra}
+
+    def refund_viral_video(self, user_id: str, credits: float, txn_id: str, db: Session) -> dict[str, Any]:
+        """爆款视频失败全额退款。"""
+        amount = float(credits or 0)
+        if amount <= 0:
+            return {"success": True, "action": "none", "amount": 0.0}
+        try:
+            return self.refund_points(
+                user_id=user_id,
+                amount=amount,
+                source="viral_video",
+                db=db,
+                ref_id=txn_id,
+                description="爆款视频失败退款",
+            )
+        except Exception:
+            logger.exception("[viral_video] 失败退款异常 user_id=%s amount=%.2f", user_id, amount)
+            return {"success": False, "action": "refund", "amount": amount}
+
     # ──────────────── 流水查询 ────────────────
 
     def get_transactions(
@@ -324,132 +409,16 @@ class PointsService:
             "page_size": page_size,
         }
 
-    # ──────────────── 每日免费混剪额度 ────────────────
-
-    def _daily_key(self, user_id: str) -> str:
-        """生成 Redis 每日额度 key。格式: daily_usage:{user_id}:{YYYYMMDD}:free_clip"""
-        today = datetime.now(UTC).strftime("%Y%m%d")
-        return f"daily_usage:{user_id}:{today}:free_clip"
-
-    def check_daily_free_clip(self, user_id: str, db: Session) -> bool:
-        """检查今日是否还有免费混剪额度。
-
-        优先查 Redis，Redis 不可用时降级到 DB。
-        """
-        redis_client = _get_redis_client()
-        if redis_client:
-            try:
-                key = self._daily_key(user_id)
-                current = redis_client.get(key)
-                if current is None:
-                    return True
-                return int(current) < DAILY_FREE_CLIP_LIMIT
-            except Exception:
-                logger.warning("Redis 不可用，降级到 DB 查询每日额度")
-
-        # 降级到 DB
-        _, _, _, DailyUsageRecordModel, _ = _get_models()
-        today_start = datetime.now(UTC).replace(hour=0, minute=0, second=0, microsecond=0)
-        record = (
-            db.query(DailyUsageRecordModel)
-            .filter(
-                DailyUsageRecordModel.user_id == user_id,
-                DailyUsageRecordModel.usage_type == "free_clip",
-                DailyUsageRecordModel.usage_date >= today_start,
-            )
-            .first()
-        )
-        if record is None:
-            return True
-        return record.count < DAILY_FREE_CLIP_LIMIT
-
-    def record_daily_free_clip(self, user_id: str, db: Session) -> bool:
-        """记录使用一次免费混剪。
-
-        先 INCR Redis；如果超限回退 Redis。DB 使用 upsert 语义（唯一约束）。
-        """
-        redis_client = _get_redis_client()
-        if redis_client:
-            try:
-                key = self._daily_key(user_id)
-                new_count = redis_client.incr(key)
-                if new_count == 1:
-                    redis_client.expire(key, 48 * 3600)  # TTL 48h
-                if new_count <= DAILY_FREE_CLIP_LIMIT:
-                    return True
-                # 超限，回退 Redis
-                redis_client.decr(key)
-            except Exception:
-                logger.warning("Redis 不可用，降级到 DB 记录每日额度")
-
-        # 降级/兜底到 DB（upsert 语义）
-        _, _, _, DailyUsageRecordModel, _ = _get_models()
-        today_start = datetime.now(UTC).replace(hour=0, minute=0, second=0, microsecond=0)
-
-        record = (
-            db.query(DailyUsageRecordModel)
-            .filter(
-                DailyUsageRecordModel.user_id == user_id,
-                DailyUsageRecordModel.usage_type == "free_clip",
-                DailyUsageRecordModel.usage_date >= today_start,
-            )
-            .first()
-        )
-
-        if record is None:
-            if DAILY_FREE_CLIP_LIMIT <= 0:
-                return False
-            record = DailyUsageRecordModel(
-                id=uuid.uuid4().hex,
-                user_id=user_id,
-                usage_type="free_clip",
-                usage_date=datetime.now(UTC),
-                count=1,
-            )
-            db.add(record)
-        else:
-            if record.count >= DAILY_FREE_CLIP_LIMIT:
-                return False
-            record.count += 1
-
-        db.commit()
-        return True
+    # ──────────────── 每日免费混剪额度（已下线：智能混剪全免费） ────────────────
 
     def get_daily_usage(self, user_id: str, db: Session) -> dict[str, Any]:
-        """查询今日免费额度使用情况。"""
-        redis_client = _get_redis_client()
-        used = 0
-
-        if redis_client:
-            try:
-                key = self._daily_key(user_id)
-                val = redis_client.get(key)
-                used = int(val) if val else 0
-            except Exception:
-                pass
-
-        if used == 0:
-            # 从 DB 查
-            _, _, _, DailyUsageRecordModel, _ = _get_models()
-            today_start = datetime.now(UTC).replace(hour=0, minute=0, second=0, microsecond=0)
-            record = (
-                db.query(DailyUsageRecordModel)
-                .filter(
-                    DailyUsageRecordModel.user_id == user_id,
-                    DailyUsageRecordModel.usage_type == "free_clip",
-                    DailyUsageRecordModel.usage_date >= today_start,
-                )
-                .first()
-            )
-            used = record.count if record else 0
-
+        """查询今日免费额度使用情况（智能混剪已全免费，返回 unlimited）。"""
         now = datetime.now(UTC)
         tomorrow = (now + timedelta(days=1)).replace(hour=0, minute=0, second=0, microsecond=0)
-
         return {
-            "free_clips_used": used,
-            "free_clips_limit": DAILY_FREE_CLIP_LIMIT,
-            "free_clips_remaining": max(0, DAILY_FREE_CLIP_LIMIT - used),
+            "free_clips_used": 0,
+            "free_clips_limit": -1,  # -1 表示 unlimited
+            "free_clips_remaining": -1,
             "reset_at": tomorrow.isoformat(),
         }
 

packages/domain/viral_video.py

@@ -0,0 +1,254 @@
+"""ViralVideoJob 领域模型 — 爆款视频任务.
+
+v1.6 重大简化：Seedance 2.5 单次最长30秒，单次调用直接出片，不再分段/拼接/ffmpeg concat。
+状态机（三步分步）：
+  pending -> running -> image_analyzed -> running -> copy_generated -> running -> completed
+                                 wait_user_confirm -> running -> completed (旧路径兼容)
+  任意阶段 fail; 任意非终态 cancel.
+  failed -> pending (retry 重置后重跑)。
+"""
+
+from __future__ import annotations
+
+import sys
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+
+if sys.version_info >= (3, 11):
+    from enum import StrEnum
+else:
+    from enum import Enum
+
+    class StrEnum(str, Enum):
+        pass
+
+
+from uuid import uuid4
+
+
+class ViralVideoStatus(StrEnum):
+    PENDING = "pending"
+    RUNNING = "running"
+    IMAGE_ANALYZED = "image_analyzed"
+    COPY_GENERATED = "copy_generated"
+    WAIT_USER_CONFIRM = "wait_user_confirm"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+
+
+class ViralVideoStage(StrEnum):
+    IMAGE_ANALYSIS = "image_analysis"
+    VIDEO_ANALYSIS = "video_analysis"
+    INTENT_PARSING = "intent_parsing"
+    SCRIPT_GENERATION = "script_generation"  # v1.6: 编导分镜脚本（融合原 copy_fusion+storyboard+review）
+    REVIEW = "review"
+    TTS = "tts"
+    RENDERING = "rendering"  # v1.6: 单次 Seedance 生成（BGM/音效/画面一次出片）
+    UPLOADING = "uploading"
+
+
+class FusionLevel(StrEnum):
+    AI_FULL = "ai_full"
+    AI_POLISH = "ai_polish"
+    USER_PRIMARY = "user_primary"
+
+
+class StyleStrength(StrEnum):
+    LIGHT = "light"
+    MEDIUM = "medium"
+    STRICT = "strict"
+
+
+class PromptType(StrEnum):
+    IMAGE_ANALYSIS = "image_analysis"
+    INTENT_PARSING = "intent_parsing"
+    SCRIPT_GENERATION = "script_generation"
+    REVIEW = "review"
+    VIDEO_STYLE_INTEGRATION = "video_style_integration"
+    STYLE_CONSTRAINT = "style_constraint"
+
+
+STAGE_LABELS = {
+    ViralVideoStage.IMAGE_ANALYSIS: "图片分析",
+    ViralVideoStage.VIDEO_ANALYSIS: "视频风格分析",
+    ViralVideoStage.INTENT_PARSING: "意图解析",
+    ViralVideoStage.SCRIPT_GENERATION: "编导脚本生成",
+    ViralVideoStage.REVIEW: "合规审核",
+    ViralVideoStage.TTS: "AI 配音",
+    ViralVideoStage.RENDERING: "视频生成",
+    ViralVideoStage.UPLOADING: "上传发布",
+}
+
+
+@dataclass
+class ViralVideoJob:
+    """爆款视频任务领域实体（v1.6 单次 Seedance 出片版）。"""
+
+    user_id: str
+    images: list[str] = field(default_factory=list)
+    pre_trusted_images: list[str] | None = (
+        None  # #2172 信任链预热结果（Seedream AI 化后的 URL 列表），与 images 顺序对应
+    )
+    industry: str = ""
+    target_customer: str = ""
+    persona_id: str = ""
+    viral_structure: str = ""
+    marketing_purpose: str = ""
+    bgm_preference: str = ""
+    duration: int = 15  # v1.6: 默认15秒，上限30秒（Seedance 2.5 单次最大30s）
+    user_copy_text: str = ""
+    fusion_level: str = FusionLevel.AI_POLISH
+    reference_audio_path: str = ""
+    reference_video_url: str = ""
+    style_strength: str = StyleStrength.MEDIUM
+    style_guide: dict | None = None
+    style_template_id: str = ""
+    # v1.5.1 音频/视频参数
+    voice_id: str = ""
+    voice_source: str = ""
+    video_ratio: str = "9:16"
+    video_model: str = ""
+    # v1.4+ 产物
+    image_analysis: dict | None = None
+    intent_result: dict | None = None
+    generated_copy_text: str = ""  # v1.6: 存 voiceover_script（纯口播对白），字段名兼容
+    storyboard: list | None = None  # v1.6: 存 copy_result.shots，字段名兼容
+    copy_result: dict | None = None  # v1.6: 完整编导脚本结构
+    # 状态
+    id: str = field(default_factory=lambda: uuid4().hex)
+    status: ViralVideoStatus = ViralVideoStatus.PENDING
+    current_stage: str = ""  # 细粒度阶段（ViralVideoStage.value，snake_case）
+    phase_message: str = ""  # 阶段中文提示文案，前端轮询直接展示
+    heartbeat_at: datetime | None = None  # worker 心跳时间，用于超时僵尸任务检测
+    result_video_url: str = ""
+    video_resolution: str = "720p"
+    credits_prepaid: float = 0.0
+    credits_transaction_id: str = ""
+    credits_cost: float = 0.0
+    error_msg: str = ""
+    retry_count: int = 0
+    started_at: datetime | None = None
+    completed_at: datetime | None = None
+    created_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
+    updated_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
+
+    # -- 状态转换 --
+
+    def mark_running(self) -> None:
+        if self.status not in (
+            ViralVideoStatus.PENDING,
+            ViralVideoStatus.IMAGE_ANALYZED,
+            ViralVideoStatus.COPY_GENERATED,
+            ViralVideoStatus.WAIT_USER_CONFIRM,
+            ViralVideoStatus.RUNNING,
+        ):
+            raise ValueError(f"Cannot transition from {self.status} to running")
+        self.status = ViralVideoStatus.RUNNING
+        now = datetime.now(timezone.utc)
+        if self.started_at is None:
+            self.started_at = now
+        self.heartbeat_at = now
+        self.updated_at = now
+
+    def touch_heartbeat(self) -> None:
+        """更新心跳时间（worker 在长任务中周期性调用，用于超时检测）。"""
+        now = datetime.now(timezone.utc)
+        if self.started_at is None:
+            self.started_at = now
+        self.heartbeat_at = now
+        self.updated_at = now
+
+    def mark_image_analyzed(self) -> None:
+        if self.status not in (ViralVideoStatus.PENDING, ViralVideoStatus.RUNNING):
+            raise ValueError(f"Cannot transition from {self.status} to image_analyzed")
+        self.status = ViralVideoStatus.IMAGE_ANALYZED
+        if self.started_at is None:
+            self.started_at = datetime.now(timezone.utc)
+        self.updated_at = datetime.now(timezone.utc)
+
+    def mark_copy_generated(self, copy_result: dict) -> None:
+        """v1.6 阶段2完成：编导脚本（含 voiceover_script/shots/硬约束/负面词）已生成。"""
+        if self.status not in (
+            ViralVideoStatus.IMAGE_ANALYZED,
+            ViralVideoStatus.RUNNING,
+            ViralVideoStatus.PENDING,
+        ):
+            raise ValueError(f"Cannot transition from {self.status} to copy_generated")
+        self.status = ViralVideoStatus.COPY_GENERATED
+        self.copy_result = copy_result or {}
+        if isinstance(copy_result, dict):
+            self.generated_copy_text = copy_result.get("voiceover_script", "") or ""
+            shots = copy_result.get("shots") or []
+            self.storyboard = list(shots) if isinstance(shots, list) else []
+        self.updated_at = datetime.now(timezone.utc)
+
+    def mark_wait_user_confirm(self, intent_result: dict) -> None:
+        if self.status != ViralVideoStatus.RUNNING:
+            raise ValueError(f"Cannot transition from {self.status} to wait_user_confirm")
+        self.status = ViralVideoStatus.WAIT_USER_CONFIRM
+        self.intent_result = intent_result
+        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):
+            raise ValueError(f"Cannot resume from {self.status} to copy-gen")
+        for k, v in kwargs.items():
+            if hasattr(self, k) and v not in (None, "", []):
+                setattr(self, k, v)
+        self.status = ViralVideoStatus.RUNNING
+        self.updated_at = datetime.now(timezone.utc)
+
+    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。"""
+        if self.status != ViralVideoStatus.COPY_GENERATED:
+            raise ValueError(f"Cannot resume from {self.status} to render")
+        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
+        self.status = ViralVideoStatus.RUNNING
+        self.updated_at = datetime.now(timezone.utc)
+
+    def resume_from_confirm(self) -> None:
+        if self.status != ViralVideoStatus.WAIT_USER_CONFIRM:
+            raise ValueError(f"Cannot resume from {self.status}")
+        self.status = ViralVideoStatus.RUNNING
+        self.updated_at = datetime.now(timezone.utc)
+
+    def mark_completed(self, video_url: str) -> None:
+        self.status = ViralVideoStatus.COMPLETED
+        self.result_video_url = video_url
+        self.completed_at = datetime.now(timezone.utc)
+        self.updated_at = datetime.now(timezone.utc)
+
+    def mark_failed(self, error_msg: str) -> None:
+        self.status = ViralVideoStatus.FAILED
+        self.error_msg = error_msg
+        self.completed_at = datetime.now(timezone.utc)
+        self.updated_at = datetime.now(timezone.utc)
+
+    def mark_cancelled(self) -> None:
+        if self.status in (ViralVideoStatus.COMPLETED, ViralVideoStatus.FAILED, ViralVideoStatus.CANCELLED):
+            raise ValueError(f"Cannot cancel task in {self.status} status")
+        self.status = ViralVideoStatus.CANCELLED
+        self.completed_at = datetime.now(timezone.utc)
+        self.updated_at = datetime.now(timezone.utc)
+
+    @property
+    def is_terminal(self) -> bool:
+        return self.status in (
+            ViralVideoStatus.COMPLETED,
+            ViralVideoStatus.FAILED,
+            ViralVideoStatus.CANCELLED,
+        )
+
+    @property
+    def effective_copy_text(self) -> str:
+        """TTS 用的最终口播文案：优先 copy_result.voiceover_script，兼容老字段。"""
+        if isinstance(self.copy_result, dict) and self.copy_result.get("voiceover_script"):
+            return self.copy_result["voiceover_script"]
+        return self.generated_copy_text or self.user_copy_text or "你好，给大家推荐一款好物"
+
+    @property
+    def voiceover_script(self) -> str:
+        return self.effective_copy_text

packages/middleware/points_gate.py

@@ -185,19 +185,6 @@ def _execute_with_gate_impl(
     is_member = getattr(user, "is_member", False)
     member_type = getattr(user, "member_type", None)
 
-    if scene_key == "ai_video":
-        from packages.domain.points_service import PointsService
-
-        svc = PointsService()
-        if not is_member:
-            if svc.check_daily_free_clip(user.id, db):
-                svc.record_daily_free_clip(user.id, db)
-                kwargs["_points_deducted"] = 0
-                kwargs["_is_free_quota"] = True
-                if is_async:
-                    return _run_async_impl(func, args, _filter_kwargs_impl(func, kwargs))
-                return func(*args, **_filter_kwargs_impl(func, kwargs))
-
     if per_unit is not None:
         total_points = per_unit
     else:

packages/ports/viral_video_repository.py

@@ -0,0 +1,30 @@
+"""爆款视频任务仓储接口。"""
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from packages.domain.viral_video import ViralVideoJob
+
+
+class ViralVideoJobRepository(ABC):
+    """爆款视频任务仓储抽象。"""
+
+    @abstractmethod
+    def save(self, job: ViralVideoJob) -> None:
+        """保存（新建）任务。"""
+
+    @abstractmethod
+    def update(self, job: ViralVideoJob) -> None:
+        """更新任务。"""
+
+    @abstractmethod
+    def get(self, job_id: str) -> Optional[ViralVideoJob]:
+        """按 ID 获取任务。"""
+
+    @abstractmethod
+    def list_by_user(self, user_id: str, limit: int = 50, offset: int = 0) -> list[ViralVideoJob]:
+        """获取用户的历史任务列表。"""
+
+    @abstractmethod
+    def count_pending_by_user(self, user_id: str) -> int:
+        """统计用户待处理任务数。"""

packages/shared/ai_service.py

@@ -233,9 +233,7 @@ def _call_ai_recommend_service(
     has_analysis = any(aid in asset_analyses for aid in asset_ids[:30])
 
     # 构建 prompt
-    system_prompt = (
-        "你是一个专业的视频剪辑导演助手。" "根据提供的素材列表和目标时长，设计一个完整的视频片段编排方案。\n"
-    )
+    system_prompt = "你是一个专业的视频剪辑导演助手。根据提供的素材列表和目标时长，设计一个完整的视频片段编排方案。\n"
     if has_analysis:
         system_prompt += (
             "每个素材附带了 AI 视频理解的内容描述，请根据素材的实际内容来决策编排：\n"
@@ -493,3 +491,207 @@ def run_generate_cover(
         result.get("image_url", "")[:60],
     )
     return result
+
+
+# ── 通用 LLM / Vision 调用（#2039 ViralVideoOrchestrator 使用，复用现有豆包客户端）──
+
+
+def call_llm(
+    prompt: str,
+    temperature: float = 0.7,
+    max_tokens: int = 2048,
+    model: str | None = None,
+    system_prompt: str | None = None,
+    timeout: int | None = None,
+) -> object:
+    """调用豆包大模型（文本对话），返回解析后的 JSON（dict/list）或原文字符串；失败返回 None。
+
+    Args:
+        prompt: 用户侧提示。
+        temperature: 采样温度。
+        max_tokens: 输出上限（结构化任务默认 2048，长文案可按需加大）。
+        model: 覆盖默认模型（如 fast_model 提速用），None 走配置默认推理模型。
+        system_prompt: 覆盖默认 system prompt。
+    """
+    client = get_doubao_client()
+    if not client.is_available:
+        return None
+    if system_prompt is None:
+        system_prompt = "你是专业的短视频内容策划助手。需要结构化输出时请严格使用 JSON。"
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": prompt},
+    ]
+    raw = client.chat_completion(messages, temperature=temperature, max_tokens=max_tokens, model=model, timeout=timeout)
+    if raw is None:
+        return None
+    try:
+        return json.loads(raw)
+    except (json.JSONDecodeError, TypeError):
+        return raw
+
+
+def call_vision(
+    image_url: str,
+    prompt: str,
+    *,
+    model: str | None = None,
+    max_tokens: int = 1024,
+    temperature: float = 0.2,
+    timeout: int = 45,
+    system_prompt: str | None = None,
+) -> object:
+    """调用豆包视觉大模型分析图片，返回解析后的 JSON 或原文字符串；失败返回 None。
+
+    Args:
+        image_url: 可公网访问的图片 URL（直接传给豆包视觉模型，无需本地下载）。
+        prompt: 用户侧文本提示。
+        model: 覆盖默认视觉模型（如 vision_lite_model 提速用），None 走配置默认。
+        max_tokens: 输出上限，商品识别用 800~1200 足够，避免长输出拖慢首 token。
+        temperature: 温度。
+        timeout: 单次请求超时（秒）。
+        system_prompt: 覆盖默认 system prompt（viral-video 商品分析会传专门的详细 prompt）。
+    """
+    client = get_doubao_client()
+    if not client.is_available:
+        logger.warning("[call_vision] 豆包客户端未配置 (DOUBAO_API_KEY 缺失)")
+        return None
+    if not image_url:
+        logger.warning("[call_vision] 空 image_url，跳过视觉分析")
+        return None
+
+    if system_prompt is None:
+        system_prompt = (
+            "你是资深电商视觉分析师。请严格基于用户提供的图片观察回答，"
+            "图片里没有的信息不要凭空想象或编造；看不清或无法判断时明确说"
+            "「无法判断」，不要猜测。输出必须是严格 JSON，不要附加 Markdown 或解释文字。"
+        )
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": prompt},
+    ]
+
+    used_model = model or getattr(client, "vision_model", "?")
+    logger.info(
+        "[call_vision] 调用豆包视觉模型 model=%s image_url=%s prompt_len=%d max_tokens=%d timeout=%d",
+        used_model,
+        image_url[:120],
+        len(prompt),
+        max_tokens,
+        timeout,
+    )
+    raw = client.vision_completion(
+        messages=messages,
+        images=[image_url],
+        temperature=temperature,
+        max_tokens=max_tokens,
+        timeout=timeout,
+        model=model,
+    )
+    if raw is None:
+        logger.warning("[call_vision] 视觉模型返回 None (image_url=%s)", image_url[:80])
+        return None
+    logger.info("[call_vision] 视觉模型原始返回 (前400字): %s", raw[:400])
+    # 剥离 ```json ... ``` 包裹
+    stripped = raw.strip()
+    if stripped.startswith("```"):
+        stripped = stripped.strip("`")
+        if stripped.startswith("json"):
+            stripped = stripped[4:].lstrip()
+    try:
+        return json.loads(stripped)
+    except (json.JSONDecodeError, TypeError) as e:
+        logger.warning("[call_vision] JSON 解析失败(%s)，返回原始文本: %s", e, raw[:200])
+        return raw
+
+
+def preheat_trust_chain(portrait_descriptions: list[str], *, timeout: int = 120) -> list[str] | None:
+    """#2174 信任链预热（t2i版）：用 VLM 分析出的人物外貌描述，跑 Seedream 文生图，
+    生成的信任产物 URL 可传给 call_video_generation(pre_trusted_images=...)。
+
+    - portrait_descriptions: VLM输出的portrait_prompt列表（中文人物外貌描述）
+    - 成功返回与输入同序的信任图URL列表；任意一张失败返回None（调用方回退到纯t2v）
+    - 必须传VLM人物描述，不传reference_images，走纯t2i路径才是方舟信任产物
+    """
+    client = get_doubao_client()
+    if not client.is_available:
+        return None
+    try:
+        return client.preheat_trust_chain(portrait_descriptions, timeout=timeout)
+    except Exception as e:
+        logger.error("[ai_service] preheat_trust_chain 异常: %s", e, exc_info=True)
+        return None
+
+
+def call_video_generation(
+    prompt: str,
+    *,
+    image_url: str | None = None,
+    duration: int = 15,
+    ratio: str | None = "9:16",
+    resolution: str = "720p",
+    output_dir: str | None = None,
+    model: str | None = None,
+    generate_audio: bool = True,
+    reference_images: list[str] | None = None,
+    reference_audios: list[str] | None = None,
+    reference_videos: list[str] | None = None,
+    pre_trusted_images: list[str] | None = None,
+) -> dict | None:
+    """调用 Seedance / Wan 视频生成（v1.6.2 多模型版 + #2172 信任链预热）。
+
+    成功返回 {"video_path": str, "usage": dict | None}（usage 含 completion_tokens），失败返回 None。
+    失败时错误详情会写入 client.last_video_error，可通过 get_last_video_error() 读取：
+      {"error_code": str, "user_message": str, "status_code": int, "detail": str, ...}
+    """
+    client = get_doubao_client()
+    if not client.is_available:
+        msg = "豆包客户端未配置（DOUBAO_API_KEY 缺失），跳过视频生成"
+        logger.warning("[ai_service] %s", msg)
+        # 写入 last_video_error 供上层读取
+        client.last_video_error = {
+            "error_code": "auth_error",
+            "user_message": "视频生成服务未配置，请联系管理员。",
+            "status_code": 0,
+            "detail": msg,
+        }
+        return None
+    effective_ratio = ratio or "9:16"
+    try:
+        kwargs: dict = dict(
+            prompt=prompt,
+            image_url=image_url,
+            duration=int(duration),
+            resolution=resolution,
+            generate_audio=bool(generate_audio),
+            watermark=False,
+            output_dir=output_dir,
+            model=model,
+            reference_images=reference_images,
+            reference_audios=reference_audios,
+            reference_videos=reference_videos,
+            pre_trusted_images=pre_trusted_images,
+        )
+        if effective_ratio:
+            kwargs["ratio"] = effective_ratio
+        return client.video_generation(**kwargs)
+    except Exception as e:
+        logger.error("[ai_service] call_video_generation 异常: %s", e, exc_info=True)
+        client.last_video_error = {
+            "error_code": "unknown",
+            "user_message": f"视频生成异常：{e!s}"[:200],
+            "status_code": 0,
+            "detail": str(e),
+        }
+        return None
+
+
+def get_last_video_error() -> dict:
+    """读取最近一次视频生成失败的详细错误（含 error_code/user_message/status_code/detail）。
+    成功或未调用过返回空 dict。
+    """
+    try:
+        client = get_doubao_client()
+        return client.get_last_video_error() if hasattr(client, "get_last_video_error") else {}
+    except Exception:
+        return {}

packages/shared/dashscope_client.py

@@ -0,0 +1,344 @@
+"""DashScope 客户端（阿里云百炼 Wan 3.0 等非方舟模型）。
+
+#2159: 新增 Wan 3.0 视频生成支持。DashScope 异步协议：
+- POST  {base_url}/services/aigc/video-generation/video-synthesis  (X-DashScope-Async: enable)
+  → 返回 output.task_id
+- GET   {base_url}/tasks/{task_id} 轮询状态
+  → SUCCEEDED 时 output.video_url 可下载
+认证：Authorization: Bearer {DASHSCOPE_API_KEY}
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import time
+from pathlib import Path
+from typing import Any
+from urllib.parse import urlparse
+
+import httpx
+
+from packages.shared.config import get_shared_settings
+
+# 网络/超时类异常父类集合：覆盖 Timeout/Connect/Network/ReadTimeout/WriteTimeout/PoolTimeout
+_HTTP_NETWORK_ERRORS = ()
+try:
+    _HTTP_NETWORK_ERRORS = (httpx.TimeoutException, httpx.NetworkError)
+except Exception:
+    _HTTP_NETWORK_ERRORS = (Exception,)
+
+_HTTP_STATUS_ERROR = httpx.HTTPStatusError if hasattr(httpx, "HTTPStatusError") else Exception
+
+logger = logging.getLogger(__name__)
+
+_DASHSCOPE_CLIENT_SINGLETON: "DashScopeClient | None" = None
+
+
+def _classify_dashscope_error(status_code: int, body: str, task_msg: str = "") -> tuple[str, str]:
+    """DashScope 错误分类，返回 (error_code, user_message)。"""
+    body_lower = (body or "").lower()
+    msg_in_body = task_msg or ""
+    try:
+        import json as _json
+
+        parsed = _json.loads(body or "{}")
+        if isinstance(parsed, dict):
+            msg_in_body = msg_in_body or str(parsed.get("message", "") or "")
+    except Exception:
+        pass
+
+    if status_code in (401, 403):
+        return "auth_error", "Wan 3.0 服务鉴权失败（DASHSCOPE_API_KEY 无效或过期），请联系管理员。"
+    if status_code == 429 or "rate" in body_lower or "throttl" in body_lower:
+        return "rate_limit", "Wan 3.0 服务繁忙（限流），请稍等1-2分钟后重试。"
+    if status_code == 400 and any(
+        kw in body_lower for kw in ("portrait", "真人", "人脸", "肖像", "content_violation", "risk", "blocked")
+    ):
+        return (
+            "portrait_intercept",
+            "参考素材包含真人照片或违规内容被安全策略拦截，请移除真人图片或调整文案后重试。",
+        )
+    if status_code == 404 or ("not found" in body_lower) or ("model" in body_lower and "not exist" in body_lower):
+        return "model_not_found", "Wan 3.0 模型未开通或模型ID无效，请联系管理员。"
+    if status_code in (402, 400) and ("quota" in body_lower or "billing" in body_lower or "insufficient" in body_lower):
+        return "quota_exceeded", "Wan 3.0 服务配额不足，请联系管理员充值或稍后重试。"
+    if status_code == 400:
+        return "invalid_param", f"Wan 3.0 参数错误：{msg_in_body or body[:200]}"
+    if status_code == 0:
+        return "network_error", "Wan 3.0 服务连接失败（网络超时），请稍后重试。"
+    # 任务内失败
+    if task_msg and any(kw in task_msg.lower() for kw in ("portrait", "真人", "人脸", "violation", "blocked")):
+        return "portrait_intercept", "Wan 3.0 视频内容被安全策略拦截，请调整文案或参考图后重试。"
+    detail = msg_in_body or body[:200]
+    return "unknown", f"Wan 3.0 视频生成失败（HTTP {status_code}）：{detail}"
+
+
+class DashScopeClient:
+    """阿里云 DashScope 异步 API 客户端（Wan 3.0 等视频生成）。"""
+
+    def __init__(self) -> None:
+        settings = get_shared_settings()
+        self.api_key: str = getattr(settings, "dashscope_api_key", "") or os.getenv("DASHSCOPE_API_KEY", "")
+        self.base_url: str = (
+            getattr(settings, "dashscope_base_url", "") or "https://dashscope.aliyuncs.com/api/v1"
+        ).rstrip("/")
+        self.poll_interval: int = int(getattr(settings, "dashscope_video_poll_interval", 10) or 10)
+        self.total_timeout: int = int(getattr(settings, "dashscope_video_timeout", 900) or 900)
+        self.max_retries: int = 2
+        self.last_video_error: dict = {}
+
+    @property
+    def is_available(self) -> bool:
+        return bool(self.api_key)
+
+    def get_last_video_error(self) -> dict:
+        return dict(self.last_video_error or {})
+
+    def _set_error(self, error_code: str, user_message: str, status_code: int = 0, detail: str = "", **extra) -> None:
+        self.last_video_error = {
+            "error_code": error_code,
+            "user_message": user_message,
+            "status_code": status_code,
+            "detail": detail[:500] if detail else "",
+            **extra,
+        }
+
+    def video_generation(
+        self,
+        prompt: str,
+        *,
+        image_url: str | None = None,
+        duration: int = 5,
+        ratio: str | None = "9:16",
+        resolution: str = "720p",
+        watermark: bool = False,
+        output_dir: str | None = None,
+        model: str = "wan3.0-video",
+    ) -> dict | None:
+        """调用 DashScope 异步视频合成接口，轮询完成后下载到本地。
+
+        返回 {"video_path": str, "usage": dict | None}；失败返回 None，错误详情写入 self.last_video_error。
+        """
+        self.last_video_error = {}
+        if not self.is_available:
+            self._set_error("auth_error", "Wan 3.0 API key 未配置，请联系管理员。", detail="dashscope api_key empty")
+            logger.error("[dashscope] API key 未配置，无法调用视频生成")
+            return None
+        if not prompt or not prompt.strip():
+            self._set_error("invalid_param", "视频生成提示词不能为空。", detail="empty prompt")
+            return None
+
+        # DashScope 分辨率参数：720P / 1080P / 480P（大写 P）
+        res_upper = (resolution or "720p").upper().replace("P", "P")
+        if res_upper == "480P":
+            ds_res = "480P"
+        elif res_upper == "1080P":
+            ds_res = "1080P"
+        else:
+            ds_res = "720P"
+
+        # 构造 input+parameters
+        input_obj: dict[str, Any] = {"prompt": prompt.strip()}
+        if image_url:
+            input_obj["img_url"] = image_url
+        params: dict[str, Any] = {
+            "resolution": ds_res,
+            "duration": str(float(duration)),
+            "watermark": bool(watermark),
+        }
+        # 比例透传：Wan 支持 "9:16" / "16:9" / "1:1" 等
+        if ratio and ratio != "adaptive":
+            params["aspect_ratio"] = ratio
+
+        payload: dict[str, Any] = {
+            "model": model,
+            "input": input_obj,
+            "parameters": params,
+        }
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+            "X-DashScope-Async": "enable",
+        }
+        create_url = f"{self.base_url}/services/aigc/video-generation/video-synthesis"
+        logger.info(
+            "[dashscope] 创建任务: model=%s dur=%ds ratio=%s res=%s img=%s",
+            model,
+            duration,
+            ratio,
+            ds_res,
+            bool(image_url),
+        )
+        logger.info("[dashscope] 创建任务 payload: model=%s params=%s", model, params)
+
+        # 创建任务
+        task_id: str | None = None
+        last_sc = 0
+        last_body = ""
+        for attempt in range(self.max_retries + 1):
+            try:
+                resp = httpx.post(create_url, headers=headers, json=payload, timeout=60)
+                sc = int(getattr(resp, "status_code", 0) or 0)
+                body_text = (getattr(resp, "text", "") or "")[:2000]
+                last_sc = sc
+                last_body = body_text
+                if sc >= 400:
+                    logger.error("[dashscope] 创建任务 HTTP %d: %s", sc, body_text)
+                    if sc >= 500 and attempt < self.max_retries:
+                        time.sleep(0.5 * (2**attempt))
+                        continue
+                    err_code, user_msg = _classify_dashscope_error(sc, body_text)
+                    self._set_error(err_code, user_msg, sc, body_text, model=model)
+                    return None
+                data = resp.json()
+                tid = (data.get("output") or {}).get("task_id")
+                if tid:
+                    task_id = tid
+                    break
+                # 部分情况下 code != 错误
+                code = data.get("code")
+                if code and code != "":
+                    err_code, user_msg = _classify_dashscope_error(400, body_text, str(code))
+                    self._set_error(err_code, user_msg, sc, body_text, model=model)
+                    return None
+                else:
+                    self._set_error("unknown", "Wan 3.0 响应格式异常，未返回任务ID", sc, str(data)[:500], model=model)
+                    return None
+            except _HTTP_NETWORK_ERRORS as ne:
+                last_sc = 0
+                last_body = f"network error: {ne}"
+                logger.warning(
+                    "[dashscope] 网络异常 %s，重试 %d/%d", type(ne).__name__, attempt + 1, self.max_retries + 1
+                )
+                if attempt < self.max_retries:
+                    time.sleep(0.5 * (2**attempt))
+                    continue
+                self._set_error("network_error", "Wan 3.0 服务连接失败（网络超时），请稍后重试。", 0, str(ne))
+                return None
+            except Exception as _e:
+                if attempt < self.max_retries:
+                    time.sleep(0.5 * (2**attempt))
+                    continue
+                logger.error("[dashscope] 创建任务最终失败: %s", _e)
+                self._set_error("unknown", f"Wan 3.0 创建任务异常：{_e!s}"[:200], 0, str(_e))
+                return None
+        if not task_id:
+            if not self.last_video_error:
+                err_code, user_msg = _classify_dashscope_error(last_sc, last_body)
+                self._set_error(err_code, user_msg, last_sc, last_body, model=model)
+            return None
+
+        # 轮询任务
+        poll_url = f"{self.base_url}/tasks/{task_id}"
+        deadline = time.time() + self.total_timeout
+        video_url: str | None = None
+        usage: dict | None = None
+        poll_count = 0
+        last_status = ""
+        while time.time() < deadline:
+            poll_count += 1
+            try:
+                r = httpx.get(poll_url, headers=headers, timeout=30)
+                psc = int(getattr(r, "status_code", 0) or 0)
+                pbody = (getattr(r, "text", "") or "")[:1500]
+                if psc >= 400:
+                    logger.warning("[dashscope] 轮询 HTTP %d: %s", psc, pbody[:300])
+                    if poll_count < 3:
+                        time.sleep(self.poll_interval)
+                        continue
+                    err_code, user_msg = _classify_dashscope_error(psc, pbody)
+                    self._set_error(err_code, user_msg, psc, pbody, task_id=task_id)
+                    return None
+                d = r.json()
+                out = d.get("output") or {}
+                task_status = out.get("task_status") or d.get("task_status") or ""
+                last_status = task_status
+                if task_status == "SUCCEEDED":
+                    video_url = out.get("video_url") or ""
+                    usage = d.get("usage")
+                    if not video_url:
+                        # 结果在 results 数组
+                        results = out.get("results") or []
+                        if results and isinstance(results, list):
+                            video_url = results[0].get("url") or results[0].get("video_url")
+                    if video_url:
+                        logger.info("[dashscope] 任务 %s 完成: %s", task_id, video_url[:120])
+                        break
+                    logger.error("[dashscope] 任务 %s SUCCEEDED 但无 video_url: %s", task_id, str(d)[:500])
+                    self._set_error(
+                        "unknown",
+                        "Wan 3.0 任务成功但未返回视频URL，请联系管理员。",
+                        200,
+                        str(d)[:500],
+                        task_id=task_id,
+                    )
+                    return None
+                if task_status in ("FAILED", "FAILED_WITH_ERROR", "ERROR"):
+                    msg = out.get("message") or d.get("message") or out.get("error_msg") or "unknown error"
+                    logger.error("[dashscope] 任务 %s 失败: %s", task_id, msg)
+                    err_code, user_msg = _classify_dashscope_error(200, "", msg)
+                    self._set_error(err_code, user_msg, 200, msg, task_id=task_id, last_status=task_status)
+                    return None
+                if task_status in ("CANCELED", "CANCELLED"):
+                    logger.warning("[dashscope] 任务 %s 被取消", task_id)
+                    self._set_error("unknown", "Wan 3.0 任务被取消。", 200, "task cancelled", task_id=task_id)
+                    return None
+                # PENDING / RUNNING / SUSPENDED → 继续轮询
+                if poll_count % 5 == 0:
+                    logger.info("[dashscope] 轮询中 task=%s status=%s polls=%d", task_id, task_status, poll_count)
+            except Exception as e:
+                logger.warning("[dashscope] 轮询异常: %s", e)
+            time.sleep(self.poll_interval)
+        if not video_url:
+            logger.error("[dashscope] 任务 %s 轮询超时（%ds）", task_id, self.total_timeout)
+            self._set_error(
+                "network_error",
+                f"Wan 3.0 视频生成超时（>{self.total_timeout}s），任务仍在排队，请稍后重试。",
+                0,
+                f"timeout after {self.total_timeout}s, polls={poll_count}, last_status={last_status}",
+                task_id=task_id,
+                last_status=last_status,
+            )
+            return None
+
+        # 下载视频
+        out_dir = output_dir or os.path.join(os.getcwd(), "seedance_outputs")
+        os.makedirs(out_dir, exist_ok=True)
+        suffix = Path(urlparse(video_url).path).suffix or ".mp4"
+        if suffix.lower() not in (".mp4", ".mov", ".webm"):
+            suffix = ".mp4"
+        safe_tid = "".join(c if c.isalnum() or c in "-_" else "_" for c in task_id)[:40]
+        out_path = os.path.join(out_dir, f"wan_{safe_tid}{suffix}")
+        try:
+            with httpx.stream("GET", video_url, timeout=300, follow_redirects=True) as resp:
+                dsc = int(getattr(resp, "status_code", 0) or 0)
+                if dsc >= 400:
+                    logger.error("[dashscope] 下载 HTTP %d", dsc)
+                    self._set_error("network_error", "Wan 3.0 视频下载失败（HTTP错误），请稍后重试。", dsc)
+                    return None
+                with open(out_path, "wb") as f:
+                    for chunk in resp.iter_bytes(chunk_size=1024 * 256):
+                        if chunk:
+                            f.write(chunk)
+        except Exception as e:
+            logger.error("[dashscope] 下载视频失败: %s", e, exc_info=True)
+            self._set_error("network_error", f"Wan 3.0 视频下载失败：{e!s}"[:200], 0, str(e))
+            return None
+        size = os.path.getsize(out_path) if os.path.exists(out_path) else 0
+        if size < 1024:
+            logger.error("[dashscope] 下载文件过小: %d bytes", size)
+            self._set_error("unknown", "Wan 3.0 视频下载文件过小，请稍后重试。", 0, f"downloaded only {size} bytes")
+            return None
+        logger.info("[dashscope] 视频已下载: %s (%d bytes)", out_path, size)
+        return {"video_path": out_path, "usage": usage}
+
+
+def get_dashscope_client() -> DashScopeClient | None:
+    """返回 DashScope 客户端单例；未配置 API key 时返回 None。"""
+    global _DASHSCOPE_CLIENT_SINGLETON
+    if _DASHSCOPE_CLIENT_SINGLETON is None:
+        _DASHSCOPE_CLIENT_SINGLETON = DashScopeClient()
+    if not _DASHSCOPE_CLIENT_SINGLETON.is_available:
+        return None
+    return _DASHSCOPE_CLIENT_SINGLETON