v92-v105 多国适配 + 模板导出增强 + 生图可靠性优化
- 新增韩国（KR）适配：countries/pinterest 种子词、K-pop 提示词、UI 国家列表 - 爬虫跨运行持久化已采集 URL（.collected_urls.json），同关键词重复搜索采新图 - 模板导出增强：SPU 商品属性列名前缀剥离匹配、尺码下拉框 INDIRECT 动态引用、 SPU 字段映射（袖长/门襟/胸垫等）、季节/印花图案女装映射、建议售价统一必填、 SKU 分类/数量/单位直接填默认值 - 自定义模式：多模态提示词独立（custom_analyze_*）、生图提示词模板可配置 - Pinterest：空选品守卫、连续空分析保护（max_empty_analyze）、flat_prompt 配置覆盖 - 生图可靠性：开始/完成进度日志、异步任务轮询超时 60s→300s
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@@ -74,7 +74,6 @@ def _enrich_briefs(raw_briefs: List[Dict[str, Any]], country: str,
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"suitable_for_print": True,
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"design_category": classify(term),
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"concept": f"围绕「{term}」的原创印花设计",
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"negative_prompt": str(b.get("negative_prompt") or "").strip(),
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"image_prompt": str(b.get("image_prompt") or "").strip(),
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"ref_images": [str(p) for p in (b.get("ref_images") or []) if str(p)],
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"source_md5": str(b.get("source_md5") or "").strip().lower(),
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@@ -114,11 +113,11 @@ def pinterest_analyze_node(state: Dict[str, Any]) -> Dict[str, Any]:
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if batch_size <= 0:
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batch_size = max_designs # 自动：一次最多分析 max_designs 张（每张图→1条简报）
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need = batch_size
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# 自定义模式：本地图池是唯一且有限的图源，最多分析「选品清单总数」张即可，
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# 避免多余分析（超出的图源在简报达标后由自定义路由结束，不浪费配额）。
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if bool(state.get("custom_mode")):
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_t = int(state.get("pinterest_target") or 1) or 1
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need = max(0, min(need, _t))
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# 按目标简报数收敛：最多分析到满足选品清单所需的简报数即可（target=spu_tasks，每款一个设计），
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# 普通与自定义模式一致——避免单一产品任务也把整池未消费图片全部分析成冗余简报、浪费配额；
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# 分析出的简报若不适合可丢弃，缺口不足时再由路由补分析/search。
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_t = int(state.get("pinterest_target") or 1) or 1
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need = max(1, min(need, _t))
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# 1) 图池取未消费图片（md5 不在 used_images）；无 → 返回空，路由触发搜索
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pool = load_image_pool(output_dir, country)
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@@ -204,7 +203,8 @@ def pinterest_analyze_node(state: Dict[str, Any]) -> Dict[str, Any]:
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if pipe is not None and hasattr(pipe, "record_400"):
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if pipe.record_400():
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pipe._abort_current_term()
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res = llm.analyze_pinterest_images(paths, term, country, on_400=_on_400) or []
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res = llm.analyze_pinterest_images(paths, term, country, on_400=_on_400,
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custom_mode=bool(state.get("custom_mode"))) or []
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# 把每条简报的来源图路径回填为原始图（压缩图仅用于分析，参考图用原图）
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return _assign_refs(res, chunk)
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except Exception as e: # noqa: BLE001
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@@ -269,6 +269,16 @@ def pinterest_analyze_node(state: Dict[str, Any]) -> Dict[str, Any]:
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accumulated = list(state.get("briefs") or [])
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accumulated.extend(new_briefs)
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# 连续无产出计数器：本次无新增简报 → 递增（防"逐一取下一张参考图"空转）；
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# 有新增简报 → 重置为 0
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empty_rounds = int(state.get("pinterest_empty_rounds") or 0)
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if new_briefs:
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empty_rounds = 0
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else:
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empty_rounds += 1
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print(f"[pinterest_analyze] 本轮无新增简报（连续 {empty_rounds} 轮无产出），"
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f"超过上限后将自动结束")
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# 推送实际新增且保留的简报到并发生成流水线（简报池）：边分析边生成设计/三合一/种草图
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pushed = accumulated[old_count:]
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pipe = state.get("pinterest_pipeline")
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@@ -293,4 +303,5 @@ def pinterest_analyze_node(state: Dict[str, Any]) -> Dict[str, Any]:
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}
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print(f"[pinterest_analyze] 本轮分析 {len(batch)} 张图 → 简报 {len(new_briefs)} 条，"
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f"累计 {len(accumulated)} 条（{country}）")
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return {"pinterest_briefs": kept, "briefs": accumulated, "stats": stats, "errors": errors}
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return {"pinterest_briefs": kept, "briefs": accumulated, "stats": stats, "errors": errors,
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"pinterest_empty_rounds": empty_rounds}
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+50
-19
@@ -22,11 +22,7 @@ PINTEREST_PRINT_SUFFIX = (
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"no garment, no shirt, no model, no mannequin, no watermark"
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)
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# Pinterest 生图提示词 4 段结构中第 3 段的引导前缀：把 LLM 产出的 negative_prompt
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# 转成一条正向「Strictly avoid: ...」条款拼进 image_prompt，让防复制/防商标约束落到生成指令
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NEG_LEAD = "Strictly avoid: "
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# Pinterest 生图提示词 4 段结构中第 4 段（仅当设计含文字时追加）：
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# Pinterest 生图提示词 3 段结构中第 2 段（仅当设计含文字时追加）：
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# 要求模型把引号内的文字按原文逐字正确拼写，避免乱码/拼错
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SPELLING_RULE = (
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"Render every phrase shown in quotes exactly as written, "
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@@ -43,6 +39,26 @@ REVIEW_REBRAND_HINT = (
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"a generic, non-infringing homage in the same mood, clearly distinct from the original."
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)
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# —— 自定义模式（custom）生图模板：固定前缀 + 分析模型 image_prompt + 固定负向 ——
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# 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），
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# 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）。
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CUSTOM_IMAGE_PROMPT_TEMPLATE = (
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"Use the attached bestseller product photo only as loose inspiration for "
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"overall mood, theme, era and style genre — do NOT reproduce, trace, "
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"rearrange, recolor or closely imitate its printed artwork, characters, "
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"layout or text, and do NOT render a shirt, garment, model, hanger, photo "
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"scene, product mockup or background of any kind. First, carefully examine "
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"the printed artwork on the garment in the attached photo: zoom in mentally "
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"on the print area, identify its subject, technique, palette and layout, and "
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"base the new design on THOSE observed traits. Ignore the model, background "
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"and photo quality. Then: {image_prompt}"
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)
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CUSTOM_NEGATIVE_PROMPT = (
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"copy of reference artwork, lookalike of the bestseller print, characters, "
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"mascots, likenesses, logos, trademarks, watermark, photorealistic shirt, "
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"apparel, product mockup, model, garment, hanger, busy background"
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)
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# 图像生成策略敏感词 → 安全等效描述（生成设计稿前清洗 motif，
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# 避免 gpt-image 等内容策略频繁拦截导致"生图限制多"）
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_IMG_RISKY_SWAP = {
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@@ -76,6 +92,17 @@ def prompt_node(state: Dict[str, Any]) -> Dict[str, Any]:
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cc = state["country_config"]
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extra_rules = cc.get("extra_style_rules") or []
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tpls = config.get("prompt_templates") or {}
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# Pinterest 生图提示词固定段：可配置（config.pinterest.prompt_pieces），留空/缺失回退内置常量。
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# 自定义模式（custom_mode）用独立的一套固定段（config.custom.prompt_pieces），默认与 Pinterest 相同、可单独编辑。
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if bool(state.get("custom_mode")):
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pp = (config.get("custom") or {}).get("prompt_pieces") or {}
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else:
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pp = (config.get("pinterest") or {}).get("prompt_pieces") or {}
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print_suffix = (pp.get("print_suffix") or "").strip() or PINTEREST_PRINT_SUFFIX
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spelling_rule = (pp.get("spelling_rule") or "").strip() or SPELLING_RULE
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review_rebrand_hint = (pp.get("review_rebrand_hint") or "").strip() or REVIEW_REBRAND_HINT
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custom_ip_tpl = (pp.get("image_prompt_template") or "").strip() or CUSTOM_IMAGE_PROMPT_TEMPLATE
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custom_neg = (pp.get("negative_prompt") or "").strip() or CUSTOM_NEGATIVE_PROMPT
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briefs: List[Dict[str, Any]] = []
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for r in screened:
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@@ -95,20 +122,24 @@ def prompt_node(state: Dict[str, Any]) -> Dict[str, Any]:
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prompts = assemble_prompts(motif, art_style, palette, composition, tpls, country)
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llm_ip = (r.get("image_prompt") or "").strip()
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llm_neg = (r.get("negative_prompt") or "").strip()
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if r.get("source") == "pinterest" and llm_ip:
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# —— Pinterest 参考模式：跳过四要素模板，按 4 段结构拼 image_prompt ——
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# ① image_prompt（分析模型产出） + 固定输出形态后缀 PINTEREST_PRINT_SUFFIX
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# ② 负向条款（由 LLM negative_prompt 经 NEG_LEAD 引导，转化进正向指令）
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# ③ 拼写锁定句 SPELLING_RULE（仅当 LLM image_prompt 已含引号文字段时）
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# 是否含文字、拼写与否均由分析模型产出决定，本模式不注入 slogan。
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seg: List[str] = [llm_ip, PINTEREST_PRINT_SUFFIX.strip()]
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if llm_neg:
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seg.append(NEG_LEAD + llm_neg)
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if '"' in llm_ip:
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seg.append(SPELLING_RULE)
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prompts["image_prompt"] = ", ".join(seg)
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print(f"[prompt] Pinterest 简报按 4 段结构拼 image_prompt（跳过四要素模板）: 「{r['topic']}」")
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if bool(state.get("custom_mode")):
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# —— 自定义模式：固定模板（前缀 + 分析 image_prompt），负向用固定文本 ——
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# 模板含防复制/防服装约束，不再追加 print_suffix；负向写入 composite_negative，
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# compose 生图时作为负向参数传给图像后端。
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prompts["image_prompt"] = custom_ip_tpl.replace("{image_prompt}", llm_ip)
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prompts["composite_negative"] = custom_neg
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print(f"[prompt] 自定义模式按固定模板拼 image_prompt（含固定负向）: 「{r['topic']}」")
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else:
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# —— Pinterest 参考模式：跳过四要素模板，按 3 段结构拼 image_prompt ——
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# ① image_prompt（分析模型产出） + 固定输出形态后缀 PINTEREST_PRINT_SUFFIX
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# ② 拼写锁定句 SPELLING_RULE（仅当 LLM image_prompt 已含引号文字段时）
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# 是否含文字、拼写与否均由分析模型产出决定，本模式不注入 slogan。
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seg: List[str] = [llm_ip, print_suffix]
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if '"' in llm_ip:
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seg.append(spelling_rule)
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prompts["image_prompt"] = ", ".join(seg)
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print(f"[prompt] Pinterest 简报按 3 段结构拼 image_prompt（跳过四要素模板）: 「{r['topic']}」")
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else:
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# —— 热点采集模式：四要素模板装配 + 文字印花（约 30% 概率注入 slogan）——
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slogan = (r.get("slogan") or "").strip()
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@@ -119,7 +150,7 @@ def prompt_node(state: Dict[str, Any]) -> Dict[str, Any]:
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r["used_slogan"] = slogan
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# review（疑似商标/受保护主题）→ 追加「原创化魔改」引导（两个模式通用）
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if str(r.get("risk_level", "")).strip().lower() == "review":
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prompts["image_prompt"] = prompts["image_prompt"] + " " + REVIEW_REBRAND_HINT
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prompts["image_prompt"] = prompts["image_prompt"] + " " + review_rebrand_hint
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print(f"[prompt] review 简报注入原创化魔改引导: 「{r['topic']}」")
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r.update(prompts)
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r["motif"] = motif
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@@ -67,7 +67,8 @@ def template_export_node(state: Dict[str, Any]) -> Dict[str, Any]:
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db_path = root / db_path
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break
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from graph.template_export import export_products
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from graph.template_export import (export_products, _resolve_component_map,
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_resolve_season_map, _resolve_pattern_map)
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tdir = (pcfg.get("template_dir") or "").strip() or str(Path(tp).parent)
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prod_dir = output_dir / "product"
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prod_dir.mkdir(parents=True, exist_ok=True)
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@@ -120,6 +121,9 @@ def template_export_node(state: Dict[str, Any]) -> Dict[str, Any]:
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db_path, batch, tdir, tp, str(out),
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markup_percent=float(pcfg.get("markup_percent") or 0),
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suggested_price_ratio=float(pcfg.get("suggested_price_ratio") or 0),
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component_map=_resolve_component_map(config),
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season_map=_resolve_season_map(config),
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pattern_map=_resolve_pattern_map(config),
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)
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for r in products:
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if (r.get("composite_path") or r.get("printed_path")) and (r.get("en_title") or "").strip():
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