优化复刻视频脚本和下拉框样式

2026-08-31 10:59:54 +08:00
parent 284748eeff
commit f59e9d0418
20 changed files with 775 additions and 212 deletions
@@ -9,10 +9,12 @@
 """
 from __future__ import annotations

+import json
 import logging
 import re
 import uuid
 from decimal import Decimal
+from pathlib import Path

 from django.conf import settings
 from django.db import transaction
@@ -80,9 +82,8 @@ def get_inflight_video_replace(team):
    )

 PRODUCT_PROMPT = (
-    "按下面这份分镜稿生成视频。镜头数量、每镜时长、景别、机位、运镜、人物、场景、口播全部按稿执行，"
-    "不要增删镜头、不要改顺序。"
-    "把稿里的原商品换成@目标商品，外观、材质、包装以参考图为准。"
+    "按下面的商品语义重构分镜生成视频。@目标商品只用于锁定外观、材质与包装。"
+    "严格遵守每镜的新商品动作、场景与口播；不要恢复参考片原商品、原动作、原卖点或原台词。"
 )
 CHARACTER_PROMPT = (
    "按下面这份分镜稿生成视频。镜头数量、每镜时长、景别、机位、运镜、商品、场景、口播全部按稿执行，"
@@ -344,6 +345,99 @@ def _digest_for_seedance(digest_text: str) -> str:
    return (head if sep else body).strip()


+def _semantic_skill_dir() -> Path:
+    """语义重构 skill 必须在 backend/skills 中，确保镜像内也能加载。"""
+    base = Path(settings.BASE_DIR)
+    for candidate in (base / "skills", base.parent.parent / "skills"):
+        skill = candidate / "product-semantic-video-remix" / "SKILL.md"
+        if skill.is_file():
+            return skill
+    return base / "skills" / "product-semantic-video-remix" / "SKILL.md"
+
+
+def load_product_semantic_remix_skill() -> str:
+    path = _semantic_skill_dir()
+    try:
+        return path.read_text(encoding="utf-8")
+    except OSError as exc:
+        raise ValueError("商品语义重构规则未部署，请联系管理员") from exc
+
+
+def _product_semantic_facts(product: Product) -> dict:
+    """只取商品库中已填写的事实，绝不从商品图片猜用途或功效。"""
+    selling_points = [
+        {"标题": point.title.strip(), "说明": point.detail.strip()}
+        for point in product.selling_points.all()
+        if point.title.strip() or point.detail.strip()
+    ]
+    return {
+        "商品名称": product.title.strip(),
+        "品牌": product.brand.strip(),
+        "商品品类": product.category.strip(),
+        "商品描述": product.description.strip(),
+        "真实卖点": selling_points,
+        "目标用户": product.target_audience.strip(),
+        "规格": product.specs or {},
+        "资料状态": "完整" if (product.category.strip() and (product.description.strip() or selling_points)) else "不足",
+    }
+
+
+def build_product_semantic_remix_messages(*, digest_text: str, product_facts: dict, duration: int, aspect_ratio: str) -> list[dict]:
+    """参考片只作为结构输入；商品事实和参考片信息严格分栏，防止语义串台。"""
+    facts = json.dumps(product_facts, ensure_ascii=False, indent=2)
+    return [
+        {"role": "system", "content": load_product_semantic_remix_skill()},
+        {
+            "role": "user",
+            "content": (
+                "请执行商品语义重构，不要直接把参考分镜改几个名词。\n"
+                f"成片时长：{duration} 秒；画面比例：{aspect_ratio}。\n\n"
+                "【新商品的唯一事实来源】\n"
+                f"{facts}\n\n"
+                "【参考视频拆解稿，仅可继承角色关系、环境、镜头语言、剪辑节奏和叙事功能】\n"
+                f"{_digest_for_seedance(digest_text)}\n\n"
+                "输出给视频模型的中文分镜稿：保留【镜头 01】格式和每镜时间、景别、机位、运镜；"
+                "每镜的画面、人物动作、台词/旁白必须按新商品真实用途重写。"
+                "不要输出分析过程、分类标签或 Markdown。"
+            ),
+        },
+    ]
+
+
+def validate_product_semantic_remix(text: str) -> str:
+    """拒绝模型在事实不足时瞎编，或只给一段泛泛分析而没有可出片分镜。"""
+    cleaned = (text or "").strip()
+    if "MISSING_PRODUCT_FACTS" in cleaned:
+        raise ValueError("商品资料不足：请先在商品库补充品类，以及商品描述或至少一条真实卖点后再复刻")
+    if len(cleaned) < 120 or "【镜头" not in cleaned:
+        raise ValueError("商品语义重构结果不完整，请重试")
+    return cleaned
+
+
+def rewrite_product_semantic_remix(*, task, digest_text: str, product_facts: dict, duration: int, aspect_ratio: str) -> str:
+    """Gemini 负责重写商品语义，Seedance 只负责按这份新分镜出片。"""
+    from .video_digest import DIGEST_MAX_TOKENS, resolve_digest_model_config
+    from .services import _collect_extract_text, get_text_provider
+
+    model_config = resolve_digest_model_config()
+    if model_config is None:
+        raise ValueError("商品语义重构模型未配置，请联系管理员")
+    provider = get_text_provider(model_config)
+    text, _payload = _collect_extract_text(
+        provider,
+        model_config,
+        build_product_semantic_remix_messages(
+            digest_text=digest_text,
+            product_facts=product_facts,
+            duration=duration,
+            aspect_ratio=aspect_ratio,
+        ),
+        temperature=0.2,
+        extra_body={"max_tokens": DIGEST_MAX_TOKENS},
+    )
+    return validate_product_semantic_remix(text)
+
+
 def build_character_replace_prompt(
    digest_text: str,
    *,
@@ -497,11 +591,18 @@ def submit_video_replace(*, team, user, params: dict):

    if has_product:
        subject_name, image_refs, subject_source = _product_library_refs(team, product_id)
+        product_facts = _product_semantic_facts_for_id(team, product_id)
    elif has_model:
        subject_name, image_refs, subject_source = _character_library_refs(team, model_id)
+        product_facts = {}
    else:
        noun = "商品" if replace_mode == "product" else "角色"
        subject_name, image_refs, subject_source = _temporary_image_refs(team, image_ids, noun=noun)
+        product_facts = {
+            "商品名称": subject_name,
+            "资料状态": "不足",
+            "说明": "临时上传图片只提供外观，未提供品类、用途或真实卖点。",
+        }

    duration = _output_duration(params.get("duration"), video_seconds)
    extra = {
@@ -510,6 +611,7 @@ def submit_video_replace(*, team, user, params: dict):
        "subject_source": subject_source,
        "product_id": str(product_id) if product_id else "",
        "model_id": str(model_id) if model_id else "",
+        "product_facts": product_facts,
    }
    base_params = {
        "mode": "universal",
@@ -643,10 +745,15 @@ def run_replace_digest(task) -> None:
                digest, source_seconds=source_seconds, output_seconds=output_seconds,
            )
        else:
-            prompt = build_product_replace_prompt(
-                digest, subject, has_triview=has_triview,
-                source_seconds=source_seconds, output_seconds=output_seconds,
+            prompt = rewrite_product_semantic_remix(
+                task=task,
+                digest_text=digest,
+                product_facts=payload.get("product_facts") or {"商品名称": subject, "资料状态": "不足"},
+                duration=output_seconds,
+                aspect_ratio=str(payload.get("aspect_ratio") or "9:16"),
            )
+            if has_triview and TRIVIEW_LABEL not in prompt:
+                prompt = f"{prompt}\n商品各面与材质细节以@{TRIVIEW_LABEL}为准。"
    except (VideoDigestError, ValueError) as exc:
        _fail_reviewing_task(task, str(exc), error_code="processing_failed")
        return
@@ -663,6 +770,7 @@ def run_replace_digest(task) -> None:
        next_payload.update(meta)
        next_payload["digest_pending"] = False
        next_payload["digest_text"] = digest[:32000]
+        next_payload["semantic_remix"] = replace_mode == "product"
        next_payload["prompt"] = prompt
        next_payload["references"] = _image_refs(next_payload.get("references") or [])
        next_payload.pop("shot_plan", None)
@@ -1583,6 +1691,17 @@ def _product_triview_asset(team, product_id: uuid.UUID):
    )


+def _product_semantic_facts_for_id(team, product_id: uuid.UUID) -> dict:
+    product = (
+        Product.objects.filter(id=product_id, team=team, purged_at__isnull=True, status=Product.Status.ACTIVE)
+        .prefetch_related("selling_points")
+        .first()
+    )
+    if product is None:
+        raise ValueError("商品不存在或已被删除")
+    return _product_semantic_facts(product)
+
+
 def _product_library_refs(team, product_id: uuid.UUID) -> tuple[str, list, str]:
    product = (
        Product.objects.filter(id=product_id, team=team, purged_at__isnull=True, status=Product.Status.ACTIVE)