baseline: Clover 独立仓库首次基线提交

将 Clover 从上层产品包旧仓库中独立出来,建立专属版本控制。
当前状态=纵切片端到端已打通(登录→选品→出文出图→审核→下载包),
M1文案质量去套路化已验收。此提交作为后续按核销清单逐条修复的基线。

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

test/e2e_image_check.py

@@ -0,0 +1,94 @@
+"""
+端到端生图验证（补齐后质量检查）
+- 真实 DB 解密 key → 真实 AIClients → 真实 plan_image_set → 真实调 apiports 出 6 图
+- 只验证生图链路；文案侧用占位 note，不走文案生成
+用法（容器内）：python3 /app/test/e2e_image_check.py
+"""
+import asyncio
+import os
+import sys
+
+sys.path.insert(0, "/app")
+
+OUT_DIR = "/app/test/output_e2e"
+REF_IMG = "/app/test/ref_product.png"   # 预先拷入的真实参考图
+IMAGE_COUNT = 6
+
+
+def load_clients_and_product():
+    """用真实 DB 解密 key 构建 clients，并取产品 dict。"""
+    from app.core.database import SessionLocal
+    from app.models.product import Product
+    from app.workers.pipeline_steps import (
+        decrypt_user_key, build_clients_and_clear_key, build_product_dict,
+    )
+    db = SessionLocal()
+    try:
+        product = db.query(Product).order_by(Product.id.desc()).first()
+        if not product:
+            raise SystemExit("库里没有产品记录")
+        product_dict = build_product_dict(product)
+        # 用产品所属 workspace 的 key（user_id 取 product.workspace_id 同号那条）
+        plain_key = decrypt_user_key(db, product.workspace_id, product.workspace_id)
+        clients = build_clients_and_clear_key(plain_key)
+        plain_key = None
+        return clients, product_dict
+    finally:
+        db.close()
+
+
+def fake_note(product_dict: dict) -> dict:
+    """占位文案 note（不走文案生成，只为生图提供 hook/卖点上下文）。"""
+    pts = product_dict.get("selling_points") or ["温和不刺激", "成分干净", "上脸快吸收"]
+    return {
+        "title": f"{product_dict['name']}用一周的真实变化",
+        "content": "姐妹们这个我真的要安利，敏感肌也能用，质地清爽不黏腻，"
+                   "坚持用上脸状态肉眼可见地稳定了，分享给同样在找平替的你。",
+        "tags": ["敏感肌", "护肤平替", "成分党", "种草"],
+        "selling_points": pts,
+    }
+
+
+async def main():
+    os.makedirs(OUT_DIR, exist_ok=True)
+    if not os.path.isfile(REF_IMG):
+        raise SystemExit(f"参考图不存在：{REF_IMG}（请先拷入）")
+    with open(REF_IMG, "rb") as f:
+        ref_bytes = f.read()
+    print(f"参考图已加载：{len(ref_bytes)} bytes")
+
+    clients, product_dict = load_clients_and_product()
+    print(f"产品：{product_dict['name']} | 类目：{product_dict['category']} "
+          f"| style_tone：{product_dict['style_tone']}")
+    print(f"卖点：{product_dict['selling_points']}")
+
+    note = fake_note(product_dict)
+
+    from app.services.ai_engine.image_gen import generate_storyboard_images
+    results = await generate_storyboard_images(
+        client=clients,
+        note=note,
+        product=product_dict,
+        image_count=IMAGE_COUNT,
+        reference_images=[ref_bytes],
+    )
+
+    ok = 0
+    for i, r in enumerate(results, 1):
+        role = r.get("role", f"img{i}")
+        if r.get("error"):
+            print(f"  [{i}] {role}  ❌ {r['error']}")
+            continue
+        b = r.get("image_bytes") or b""
+        path = os.path.join(OUT_DIR, f"{i:02d}_{role}.png")
+        with open(path, "wb") as f:
+            f.write(b)
+        ok += 1
+        print(f"  [{i}] {role}  ✅ {len(b)} bytes → {path}")
+
+    print(f"\n完成：{ok}/{len(results)} 张成功，输出目录 {OUT_DIR}")
+    await clients.aclose()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())