v110-v112 自定义模式完善 + 模板导出增强 + 多模态兼容优化

- 自定义模式：分析模型输出 delta 唯一改动指令，生图模板 custom_image_prompt.md（{delta} 占位符），不再使用负向提示词；generate_design 按 custom_mode 分支，Pinterest 模式保留原创化指令，两模式互不影响
- 多模态分析 response_format 三级回退（json_schema → json_object → none），兼容 DeepSeek
- 模板导出：details 扩展列（细节1/2/3）、target_audience 扩展列（适用人群1）、固定值风格1=休闲/风格2=运动
- 童装特征库更新 + 标题模板外部化 + 图源映射增强

graph/nodes/compose_node.py

@@ -125,7 +125,8 @@ def generate_design(ib, brief: Dict[str, Any], design_dir: Path, out_stem: str,
                     seed: Optional[int] = None,
                     on_400=None,
                     on_503=None,
-                    size: str = "1024x1024") -> Optional[str]:
+                    size: str = "1024x1024",
+                    custom_mode: bool = False) -> Optional[str]:
     """生成单张纯印花设计稿（图2）。返回设计稿路径；失败 / 全局 MD5 重复返回 None。
 
     out_stem: 输出文件名主干（不含扩展名），最终文件 = {out_stem}_design.png。
@@ -146,16 +147,24 @@ def generate_design(ib, brief: Dict[str, Any], design_dir: Path, out_stem: str,
         ref_images = [str(p) for p in (brief.get("ref_images") or []) if str(p)]
         if ref_images and hasattr(ib, "print"):
             try:
-                # 图生图：以爬取图为参考，按分析简报生成原创设计（不复制原图）
-                ref_prompt = img_prompt + (
-                    " Create an ORIGINAL, non-copying flat print design inspired ONLY by "
-                    "the reference image's style and mood. Do NOT reproduce the reference "
-                    "image, its characters, logos, or any text.")
-                out_path = ib.print(
-                    ref_prompt, ref_images[0], out_path,
-                    brief.get("composite_negative", ""),
-                    extra_images=ref_images[1:] or None,
-                    size=size, seed=seed)   # 设计稿尺寸按 config compose.design_size
+                if custom_mode:
+                    # 自定义模式：按 image_prompt 图生图（模板已内置防复制/防服装约束，不追加原创化指令）
+                    out_path = ib.print(
+                        img_prompt, ref_images[0], out_path,
+                        brief.get("composite_negative", ""),
+                        extra_images=ref_images[1:] or None,
+                        size=size, seed=seed)   # 设计稿尺寸按 config compose.design_size
+                else:
+                    # Pinterest 模式：追加原创化指令（防止复制原图，保持原有行为）
+                    ref_prompt = img_prompt + (
+                        " Create an ORIGINAL, non-copying flat print design inspired ONLY by "
+                        "the reference image's style and mood. Do NOT reproduce the reference "
+                        "image, its characters, logos, or any text.")
+                    out_path = ib.print(
+                        ref_prompt, ref_images[0], out_path,
+                        brief.get("composite_negative", ""),
+                        extra_images=ref_images[1:] or None,
+                        size=size, seed=seed)   # 设计稿尺寸按 config compose.design_size
             except Exception as e:  # noqa: BLE001
                 print(f"[compose] 图生图（参考图）失败，回退文生图 {brief.get('topic','')}: {e}")
                 _notify_400(on_400, e)
@@ -255,7 +264,8 @@ def compose_node(state: Dict[str, Any]) -> Dict[str, Any]:
             """单张设计稿生成（并发线程内调用，每设计一线程）。"""
             out_path = generate_design(ib, b, design_dir, f"{country}_{i:02d}",
                                        _safe_errors, seed=_seed,
-                                       size=compose_cfg.get("design_size", "1024x1024"))
+                                       size=compose_cfg.get("design_size", "1024x1024"),
+                                       custom_mode=bool(state.get("custom_mode")))
             if out_path is None:
                 return i, b, None, None
             return i, b, out_path, None

graph/nodes/oss_upload_node.py

@@ -63,9 +63,9 @@ def oss_upload_node(state: Dict[str, Any]) -> Dict[str, Any]:
                 print(f"[oss] 货号计数已达上限 999，停止上传后续图片（{src}）")
                 break
             try:
-                code = f"{prefix}{seq:03d}"           # 货号：前缀 + 3 位计数（000 起）
+                code = str(r.get("img_code") or "") or f"{prefix}{seq:03d}"   # 货号：优先用 product 生成的 img_code，缺省自增
                 compressed = compress_for_oss(src, str(Path(src).with_suffix(".oss.jpg")))
-                key = build_oss_key(country, ts, code, _gen_rand4())
+                key = build_oss_key(country, ts, code, _gen_rand4(), local=compressed)
                 url = upload_to_oss(oss_cfg, compressed, key)
                 if url:
                     r[f"{kind}_url"] = url
@@ -91,7 +91,7 @@ def oss_upload_node(state: Dict[str, Any]) -> Dict[str, Any]:
             try:
                 code = f"{prefix}{seq:03d}"
                 compressed = compress_for_oss(src, str(Path(src).with_suffix(".oss.jpg")))
-                key = build_oss_key(country, ts, code, _gen_rand4())
+                key = build_oss_key(country, ts, code, _gen_rand4(), local=compressed)
                 url = upload_to_oss(oss_cfg, compressed, key)
                 if url:
                     cc["url"] = url

graph/nodes/pinterest_analyze_node.py

@@ -75,6 +75,7 @@ def _enrich_briefs(raw_briefs: List[Dict[str, Any]], country: str,
             "design_category": classify(term),
             "concept": f"围绕「{term}」的原创印花设计",
             "image_prompt": str(b.get("image_prompt") or "").strip(),
+            "delta": str(b.get("delta") or "").strip(),
             "ref_images": [str(p) for p in (b.get("ref_images") or []) if str(p)],
             "source_md5": str(b.get("source_md5") or "").strip().lower(),
             "brief_id": str(b.get("brief_id") or "").strip(),

graph/nodes/product_node.py

@@ -195,7 +195,7 @@ def _process_spu(
     db_path, basemap_root, material_root, category, prod_dir, brief, ib,
     spu, sku_code, pcfg, errors, shared_design=None, title_backend=None, country="",
     img_code="", model_img=None, design_size="1024x1024", compose_size="1536x2048",
-    on_503=None, model_kind="model",
+    on_503=None, model_kind="model", category_path="",
 ) -> Optional[Dict[str, Any]]:
     """处理单个款号：选色 → 底图 → 设计稿 → (mark==1) 模特 → 合成 → 模板导出。
     shared_design: compose 节点生成的纯印花设计稿路径（图2）；为 None 时回退本节点 generate。
@@ -434,7 +434,7 @@ def _process_spu(
         title_img = (result.get("composite_path") or result.get("printed_path")
                      or result.get("design_path"))
         if title_img:
-            t = title_backend.generate_title(title_img, country=country)
+            t = title_backend.generate_title(title_img, country=country, category_path=category_path)
             if t.get("en_title") or t.get("cn_title") or t.get("ja_title") or t.get("es_title"):
                 result["en_title"] = t.get("en_title", "")
                 result["cn_title"] = t.get("cn_title", "")
@@ -482,6 +482,17 @@ def product_node(state: Dict[str, Any]) -> Dict[str, Any]:
     spu_tasks = pcfg.get("spu_tasks") or []
     spu_count = int(pcfg.get("spu_count") or 0)
 
+    # 标题生成用：模版「类目」完整路径（如 服装、鞋靴和珠宝饰品>女童时尚>…>女童T恤），
+    # 注入标题提示词 {category_path} 占位符；模板未配置/读取失败为空串
+    category_path = ""
+    try:
+        _tp = str(pcfg.get("template_path") or "").strip()
+        if _tp:
+            from graph.seed_shot import read_template_category
+            category_path = read_template_category(_tp)
+    except Exception as _e:  # noqa: BLE001
+        print(f"[product] 读取模版类目路径失败（标题 category_path 留空）: {_e}")
+
     # 1) 选简报（优先 safe）
     safe = [b for b in briefs if b.get("risk_level") == "safe"] or briefs
     if not safe:
@@ -634,6 +645,7 @@ def product_node(state: Dict[str, Any]) -> Dict[str, Any]:
                              country, img_code=img_code,
                              model_img=_src.get("img"),       # 按任务序号取独立随机图源
                              model_kind=_src.get("kind", "model"),
+                             category_path=category_path,
                              design_size=str((config.get("compose") or {}).get("design_size") or "1024x1024"),
                              compose_size=str((config.get("compose") or {}).get("size") or "1536x2048"))
             if r:

graph/nodes/prompt_node.py

@@ -39,9 +39,9 @@ REVIEW_REBRAND_HINT = (
     "a generic, non-infringing homage in the same mood, clearly distinct from the original."
 )
 
-# —— 自定义模式（custom）生图模板：固定前缀 + 分析模型 image_prompt + 固定负向 ——
-# 自定义模式分析模型产出的是「新设计描述」，生图时套用这套固定模板（含防复制/防服装约束），
-# 负向用固定文本（写入 composite_negative，compose 生图时作为负向参数传给图像后端）。
+# —— 自定义模式（custom）生图模板：固定前缀 + 分析模型 image_prompt + delta 改动指令 ——
+# 自定义模式分析模型产出的是「新设计描述 + 唯一改动指令 delta」，生图时套用这套固定模板
+# （含防复制/防服装约束），不再使用负向提示词——约束已全部内置进模板。
 CUSTOM_IMAGE_PROMPT_TEMPLATE = (
     "Use the attached bestseller product photo only as loose inspiration for "
     "overall mood, theme, era and style genre — do NOT reproduce, trace, "
@@ -53,11 +53,6 @@ CUSTOM_IMAGE_PROMPT_TEMPLATE = (
     "base the new design on THOSE observed traits. Ignore the model, background "
     "and photo quality. Then: {image_prompt}"
 )
-CUSTOM_NEGATIVE_PROMPT = (
-    "copy of reference artwork, lookalike of the bestseller print, characters, "
-    "mascots, likenesses, logos, trademarks, watermark, photorealistic shirt, "
-    "apparel, product mockup, model, garment, hanger, busy background"
-)
 
 # 图像生成策略敏感词 → 安全等效描述（生成设计稿前清洗 motif，
 # 避免 gpt-image 等内容策略频繁拦截导致"生图限制多"）
@@ -84,6 +79,18 @@ def _safe_motif(motif: str) -> str:
     return motif
 
 
+def _read_custom_prompt_md(filename: str) -> str:
+    """读取 prompts/<filename>（自定义模式生图模板），优先运行根 exe 旁、回退数据根；无/空返回空串。"""
+    from graph.paths import project_root, runtime_root
+    for base in (runtime_root(), project_root()):
+        p = base / "prompts" / filename
+        if p.exists():
+            t = p.read_text(encoding="utf-8").strip()
+            if t:
+                return t
+    return ""
+
+
 @with_fallback("prompt_build")
 def prompt_node(state: Dict[str, Any]) -> Dict[str, Any]:
     screened: List[Dict[str, Any]] = state.get("screened") or []
@@ -101,8 +108,11 @@ def prompt_node(state: Dict[str, Any]) -> Dict[str, Any]:
     print_suffix = (pp.get("print_suffix") or "").strip() or PINTEREST_PRINT_SUFFIX
     spelling_rule = (pp.get("spelling_rule") or "").strip() or SPELLING_RULE
     review_rebrand_hint = (pp.get("review_rebrand_hint") or "").strip() or REVIEW_REBRAND_HINT
-    custom_ip_tpl = (pp.get("image_prompt_template") or "").strip() or CUSTOM_IMAGE_PROMPT_TEMPLATE
-    custom_neg = (pp.get("negative_prompt") or "").strip() or CUSTOM_NEGATIVE_PROMPT
+    # 自定义模式生图模板优先读 prompts/custom_image_prompt.md（可编辑），
+    # 其次 config.custom.prompt_pieces，最后回退代码内置默认。
+    # 自定义模式不再使用负向提示词（约束已内置进模板），不再读取 custom_negative_prompt.md。
+    custom_ip_tpl = _read_custom_prompt_md("custom_image_prompt.md") \
+        or (pp.get("image_prompt_template") or "").strip() or CUSTOM_IMAGE_PROMPT_TEMPLATE
 
     briefs: List[Dict[str, Any]] = []
     for r in screened:
@@ -122,15 +132,20 @@ def prompt_node(state: Dict[str, Any]) -> Dict[str, Any]:
 
         prompts = assemble_prompts(motif, art_style, palette, composition, tpls, country)
         llm_ip = (r.get("image_prompt") or "").strip()
-        if r.get("source") == "pinterest" and llm_ip:
+        delta = (r.get("delta") or "").strip()
+        if r.get("source") == "pinterest":
             if bool(state.get("custom_mode")):
-                # —— 自定义模式：固定模板（前缀 + 分析 image_prompt），负向用固定文本 ——
-                # 模板含防复制/防服装约束，不再追加 print_suffix；负向写入 composite_negative，
-                # compose 生图时作为负向参数传给图像后端。
-                prompts["image_prompt"] = custom_ip_tpl.replace("{image_prompt}", llm_ip)
-                prompts["composite_negative"] = custom_neg
-                print(f"[prompt] 自定义模式按固定模板拼 image_prompt（含固定负向）: 「{r['topic']}」")
-            else:
+                # —— 自定义模式：固定模板（前缀 + 分析 image_prompt + delta 改动指令），负向留空 ——
+                # 新分析模板只产出 delta（无 image_prompt），生图模板也只引用 {delta}；
+                # 只要 delta 或 image_prompt 任一存在即可装配，不再使用负向提示词
+                # （约束已全部内置进模板），composite_negative 置空避免后端追加 Negative 段。
+                if llm_ip or delta:
+                    prompts["image_prompt"] = (
+                        custom_ip_tpl.replace("{delta}", delta).replace("{image_prompt}", llm_ip)
+                    )
+                    prompts["composite_negative"] = ""
+                    print(f"[prompt] 自定义模式按固定模板拼 image_prompt（含 delta 改动指令，无负向）: 「{r['topic']}」")
+            elif llm_ip:
                 # —— Pinterest 参考模式：跳过四要素模板，按 3 段结构拼 image_prompt ——
                 #   ① image_prompt（分析模型产出） + 固定输出形态后缀 PINTEREST_PRINT_SUFFIX
                 #   ② 拼写锁定句 SPELLING_RULE（仅当 LLM image_prompt 已含引号文字段时）

graph/nodes/seed_shot_node.py

@@ -164,7 +164,7 @@ def seed_shot_node(state: Dict[str, Any]) -> Dict[str, Any]:
                 try:
                     compressed = compress_for_oss(pth, str(Path(pth).with_suffix(".oss.jpg")))
                     url = upload_to_oss(oss_cfg, compressed,
-                                        build_oss_key(country, ts, code, _gen_rand4()))
+                                        build_oss_key(country, ts, code, _gen_rand4(), local=compressed))
                     if url:
                         urls.append(url)
                         r["seed_shot_urls"] = urls
@@ -175,8 +175,10 @@ def seed_shot_node(state: Dict[str, Any]) -> Dict[str, Any]:
         return {"spu_code": r.get("spu_code"), "sku_code": r.get("sku_code"),
                 "paths": paths, "urls": urls}
 
-    # 并发：每个产品一个独立线程（默认）；config.seed_shot.concurrency 可覆盖
-    seed_concurrency = int((config.get("seed_shot") or {}).get("concurrency") or 0) or len(products) or 1
+    # 并发：每个产品一个独立线程；默认上限 5（与 product 一致，避免多产品压垮图像网关），
+    # config.seed_shot.concurrency 可显式覆盖（含 0/留空→默认 5）
+    seed_concurrency = int((config.get("seed_shot") or {}).get("concurrency") or 0) or 5
+    seed_concurrency = min(seed_concurrency, len(products)) if products else 1
     if len(products) > 1:
         print(f"[seed_shot] 并发 {seed_concurrency} 生成种草图（{len(products)} 个产品）")
     with concurrent.futures.ThreadPoolExecutor(max_workers=seed_concurrency) as _ex:

graph/nodes/template_export_node.py

@@ -101,7 +101,8 @@ def template_export_node(state: Dict[str, Any]) -> Dict[str, Any]:
 
     from graph.template_export import (export_products, _resolve_component_map,
                                        _resolve_season_map, _resolve_pattern_map,
-                                       _resolve_target_audience_map, _resolve_kids_type_map)
+                                       _resolve_target_audience_map, _resolve_kids_type_map,
+                                       _resolve_kids_pattern_map)
     tdir = (pcfg.get("template_dir") or "").strip() or str(Path(tp).parent)
     prod_dir = output_dir / "product"
     prod_dir.mkdir(parents=True, exist_ok=True)
@@ -159,6 +160,7 @@ def template_export_node(state: Dict[str, Any]) -> Dict[str, Any]:
                 pattern_map=_resolve_pattern_map(config),
                 target_audience_map=_resolve_target_audience_map(config),
                 kids_type_map=_resolve_kids_type_map(config),
+                kids_pattern_map=_resolve_kids_pattern_map(config),
             )
             for r in products:
                 if (r.get("composite_path") or r.get("printed_path")) and (r.get("en_title") or "").strip():