feat: 修改模型提示词

server/services/prompts/__init__.py

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+"""套图提示词引擎：按模型家族分发，各家族独立封装。
+
+不同家族的生图语义差异极大，共用一套提示词会导致语义错配
+（gpt-image-2 按文字重造商品即由此而来），故按家族各自成册：
+
+  alibaba  通义 wan*/qwen*（DashScope）—— 主体参考语义
+  doubao   豆包 Seedream（火山方舟）—— 主体参考语义，与通义共用装配
+  gpt      gpt-image-2 / gpt-image-2-vip（RightAPI）—— /v1/images/edits 编辑语义
+  google   nano-banana 系列（RightAPI）—— 原生主体保持语义
+
+路由规则：provider 为主；rightapi 内再按模型名细分 gpt / google。
+"""
+from __future__ import annotations
+
+from . import alibaba, doubao, google, gpt
+from .common import build_context, type_name
+
+_MODULE_BY_FAMILY = {
+    "alibaba": alibaba,
+    "doubao": doubao,
+    "gpt": gpt,
+    "google": google,
+}
+
+
+def prompt_family(provider: str, model: str | None) -> str:
+    """(provider, model) → 提示词家族名。"""
+    if provider == "rightapi":
+        if (model or "").lower().startswith("nano-banana"):
+            return "google"
+        return "gpt"  # gpt-image-* 及未知中转模型默认按 edits 语义处理
+    if provider == "tongyi":
+        return "alibaba"
+    return "doubao"  # doubao 及默认 provider
+
+
+def build_prompt(provider: str, model: str | None, type_id: str, ctx: dict, style_set: int,
+                 lang: str, extra: dict | None = None, style_prompt: str | None = None,
+                 requirements: str | None = None) -> str:
+    """按模型家族构造指定图类型的完整生图 prompt。参数含义见各家族 build_prompt。"""
+    module = _MODULE_BY_FAMILY[prompt_family(provider, model)]
+    return module.build_prompt(
+        type_id, ctx, style_set, lang,
+        extra=extra, style_prompt=style_prompt, requirements=requirements,
+    )

server/services/prompts/alibaba.py

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+"""阿里通义（wan* 万相 / qwen* 千问）提示词：国产"主体参考"语义。
+
+生图 API 把参考图当商品锚（subject reference）、prompt 当场景描述，
+风格词/文字商品描述不会反噬商品本体，负面清单也可以安全写入 prompt。
+豆包（doubao.py）与此语义一致，直接复用本模块装配。
+"""
+from __future__ import annotations
+
+from .common import (
+    STYLE_SETS, TEXT_RENDER, requirements_block, resolve_style, selling_point_lines,
+)
+
+# ── 公共组件（主体参考语义专用）────────────────────────────────────────────
+
+QUALITY = (
+    "Shot on Sony A7R V with 85mm lens at f/2.0, ultra-detailed, photorealistic, "
+    "8K commercial image quality, professional retouching."
+)
+
+PRODUCT_REF_LOCK = (
+    "CRITICAL: The product must look EXACTLY the same as in the reference image — "
+    "identical silhouette, proportions, colors, print pattern, stitching and every design detail. "
+    "Only the background, camera angle, lighting and styling may change. "
+    "Do not redesign, add or remove any element of the product."
+)
+
+DEFAULT_NEGATIVE_INTENT = (
+    "no AI-generated look, no CGI quality, no plastic appearance, no watermark, "
+    "no distorted text, no deformed product, no extra limbs, no blurry areas"
+)
+
+# ── 各图类型 Prompt ───────────────────────────────────────────────────────
+
+def _prompt_white_bg(ctx: dict, style: dict, lang: str) -> str:
+    return (
+        f"E-commerce main product image on pure white background (RGB 255,255,255), "
+        f"product \"{ctx['title']}\" centered and filling about 85% of the frame, "
+        f"front view, even shadowless studio lighting with a faint natural contact shadow, "
+        f"{style['tone']}. No text, no props, no background elements. {QUALITY} {PRODUCT_REF_LOCK}"
+    )
+
+def _prompt_key_features(ctx: dict, style: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang) or ctx["title"]
+    return (
+        f"E-commerce key-features infographic for product \"{ctx['title']}\", square layout: "
+        f"product on the left two-thirds ({style['bg']}), right column lists 3 feature callouts "
+        f"with minimal line icons, thin leader lines pointing to product details. "
+        f"Feature callouts: {sp}. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
+    )
+
+def _prompt_selling_pt(ctx: dict, style: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang, 1) or ctx["title"]
+    return (
+        f"Single-selling-point e-commerce poster for product \"{ctx['title']}\": "
+        f"hero product close-up at dynamic angle ({style['bg']}), one large bold headline "
+        f"about \"{sp}\", generous negative space, one small magnified detail circle "
+        f"highlighting material or craft. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
+    )
+
+def _prompt_material(ctx: dict, style: dict, lang: str) -> str:
+    return (
+        f"Macro material close-up of product \"{ctx['title']}\": extreme detail shot revealing "
+        f"fabric weave / surface texture / stitching / finish, shallow depth of field, "
+        f"raking light across the surface, {style['tone']}. Small caption label in corner. "
+        f"{TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
+    )
+
+def _prompt_lifestyle(ctx: dict, style: dict, lang: str) -> str:
+    bg = f" ({style['bg']})" if style.get("bg") else ""
+    return (
+        f"Lifestyle in-context scene for product \"{ctx['title']}\": the product is naturally "
+        f"used / placed in a real environment{bg}, realistic human-scale surroundings, "
+        f"soft daylight, authentic candid mood, product remains the clear visual focus. "
+        f"{style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
+    )
+
+def _prompt_multi_scene(ctx: dict, style: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang)
+    return (
+        f"Triptych multi-scene e-commerce image for product \"{ctx['title']}\": three vertical panels "
+        f"separated by thin gutters, each panel shows the SAME product in a different usage scene "
+        f"(e.g. home interior / outdoor street / office desk), consistent color grading across panels. "
+        f"Panel captions: {sp}. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
+    )
+
+def _prompt_ecommerce_detail(ctx: dict, style: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang) or ctx["title"]
+    params = ctx["params_line"]
+    return (
+        f"E-commerce detail-page hero section for product \"{ctx['title']}\", square layout: "
+        f"top half is a hero banner with the product at a 3/4 angle ({style['bg']}); "
+        f"bottom half is a clean spec card listing 3 feature rows with line icons"
+        + (f" (specs: {params})" if params else "")
+        + f" and one highlighted row: {sp}. {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
+    )
+
+def _prompt_size_chart(ctx: dict, style: dict, lang: str) -> str:
+    dims = ctx["params_line"]
+    return (
+        f"Product size chart infographic for \"{ctx['title']}\": product shown in clean front and side views "
+        f"on light background, with thin measurement annotation lines (arrows) marking length, width and height, "
+        f"measurement values rendered next to each line"
+        + (f" (known specs: {dims})" if dims else "")
+        + f", small caption row, precise technical drawing aesthetic. {style['tone']}. "
+        f"{TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
+    )
+
+def _prompt_sku_collection(ctx: dict, style: dict, lang: str) -> str:
+    return (
+        f"Colorway collection image for product \"{ctx['title']}\": the SAME product in all its color/variant "
+        f"options arranged in a neat equal grid (2-4 items per row), each colorway with a small label chip below it, "
+        f"consistent lighting and scale across all items, clean e-commerce presentation. "
+        f"{style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
+    )
+
+def _prompt_custom(ctx: dict, style: dict, lang: str, extra: dict) -> str:
+    hint = (extra.get("prompt_hint") or "").strip()
+    purpose = extra.get("title") or ""
+    detail = extra.get("detail") or ""
+    bg = f" {style['bg']} as environment." if style.get("bg") else ""
+    composed = (
+        f"E-commerce marketing image for product \"{ctx['title']}\""
+        + (f" — {purpose}" if purpose else "")
+        + (f": {detail}" if detail else "")
+        + "."
+    )
+    if hint:
+        composed += f" Composition: {hint}."
+    return f"{composed}{bg} {style['tone']}. {TEXT_RENDER[lang]} {QUALITY} {PRODUCT_REF_LOCK}"
+
+_PROMPT_BUILDERS = {
+    "white_bg": _prompt_white_bg,
+    "key_features": _prompt_key_features,
+    "selling_pt": _prompt_selling_pt,
+    "material": _prompt_material,
+    "lifestyle": _prompt_lifestyle,
+    "multi_scene": _prompt_multi_scene,
+    "ecommerce_detail": _prompt_ecommerce_detail,
+    "size_chart": _prompt_size_chart,
+    "sku_collection": _prompt_sku_collection,
+}
+
+
+def build_prompt(type_id: str, ctx: dict, style_set: int, lang: str, extra: dict | None = None,
+                 style_prompt: str | None = None, requirements: str | None = None) -> str:
+    """构造指定图类型的完整生图 prompt（主体参考语义）。
+
+    extra: 方案项信息 {title, detail, prompt_hint}——custom 类型必需，
+    预设类型也会把 prompt_hint 作为构图补充注入。
+    style_prompt: 用户改写的风格提示词，覆盖 style_set 内置模板（tone/bg 整体替换）。
+    requirements: 生图要求（最高优先级，强制约束），置于 prompt 最前面，
+    声明覆盖一切冲突指令，用户可在此输入强制要求。
+    """
+    style = resolve_style(style_set, style_prompt)
+    extra = extra or {}
+    if type_id == "custom":
+        prompt = _prompt_custom(ctx, style, lang, extra)
+    else:
+        builder = _PROMPT_BUILDERS.get(type_id)
+        if builder is None:
+            raise ValueError(f"未知图类型: {type_id}")
+        prompt = builder(ctx, style, lang)
+        hint = (extra.get("prompt_hint") or "").strip()
+        if hint:
+            prompt = prompt.rstrip(".") + f". Additional composition guidance: {hint}."
+    req = requirements_block(requirements)
+    if req:
+        prompt = f"{req} {prompt}"
+    return prompt + ". " + DEFAULT_NEGATIVE_INTENT

server/services/prompts/common.py

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+"""提示词公共层：与模型家族无关的商品上下文、风格模板、图类型名与文案组件。
+
+各家族模块（alibaba / doubao / gpt / google）只负责"如何对模型说话"，
+商品信息提炼与风格体系统一在这里维护，避免多处漂移。
+"""
+from __future__ import annotations
+
+import re
+
+# ── 风格模板（与插件端 STYLE_SET_OPTIONS 对应；提示词可被用户在插件里改写覆盖）───
+
+STYLE_SETS: dict[int, dict] = {
+    1: {
+        "name": "北欧极简",
+        "tone": "北欧极简风：浅灰或米白背景，柔和漫射光，低饱和色调，画面留白充足，构图克制干净",
+        "bg": "",
+    },
+    2: {
+        "name": "清新明亮",
+        "tone": "清新明亮风：明亮的白色到浅蓝渐变背景，高调光线，色彩明快通透，整体轻盈干净",
+        "bg": "",
+    },
+    3: {
+        "name": "高级感深色",
+        "tone": "高级质感风：深灰或炭黑背景，戏剧性侧光打光，突出商品材质与光泽，沉稳高级",
+        "bg": "",
+    },
+    4: {
+        "name": "暖调生活",
+        "tone": "温暖生活风：暖米色背景，暖色灯光氛围，温馨的家居质感，亲和力强",
+        "bg": "",
+    },
+    5: {
+        "name": "纯净棚拍",
+        "tone": "标准电商棚拍：纯色浅背景，均匀的正面柔光，无杂物干扰，商品居中突出",
+        "bg": "",
+    },
+}
+
+# ── 图类型中文名（导出文件名用）───────────────────────────────────────────
+
+TYPE_NAMES_ZH: dict[str, str] = {
+    "white_bg": "白底主图",
+    "key_features": "核心卖点图",
+    "selling_pt": "卖点图",
+    "material": "材质图",
+    "lifestyle": "场景展示图",
+    "multi_scene": "多场景拼图",
+    "ecommerce_detail": "电商详情图",
+    "size_chart": "尺寸标注图",
+    "sku_collection": "SKU合集图",
+    "custom": "创意图",
+}
+
+# ── 图内营销文案渲染规范（各家族共用；语言由平台决定）──────────────────────
+
+TEXT_RENDER = {
+    "zh": (
+        "Render concise Chinese marketing text inside the image: main headline max 8 Chinese characters, "
+        "sub-lines max 12 characters each, font is modern clean sans-serif (Source Han Sans style), "
+        "high legibility, tasteful typography layout, colors harmonized with the composition. "
+        "No spelling errors, no garbled characters."
+    ),
+    "en": (
+        "Render concise English marketing text inside the image: headline max 5 words, "
+        "sub-lines max 8 words each, Helvetica Neue style sans-serif, high legibility, "
+        "tasteful typography layout, colors harmonized with the composition. No spelling errors."
+    ),
+    "ru": (
+        "Render concise Russian marketing text inside the image: headline max 4 words, "
+        "sub-lines max 6 words each, modern clean sans-serif (Inter / PT Sans style), "
+        "proper Cyrillic typography, high legibility, tasteful layout, colors harmonized with the composition. "
+        "No spelling errors, no mixed latin/cyrillic gibberish."
+    ),
+}
+
+
+def resolve_style(style_set: int, style_prompt: str | None = None) -> dict:
+    """用户改写的风格提示词整体覆盖内置模板（tone/bg 整体替换）。"""
+    if style_prompt and style_prompt.strip():
+        return {"name": "custom", "tone": style_prompt.strip(), "bg": ""}
+    return STYLE_SETS.get(style_set, STYLE_SETS[1])
+
+
+def requirements_block(requirements: str | None) -> str:
+    """用户强制要求块：最高优先级、置于提示词最前、覆盖冲突指令（原文保留不翻译）。"""
+    if requirements and requirements.strip():
+        return (
+            "STRICT REQUIREMENTS (highest priority, must be followed exactly, "
+            "override any conflicting instruction): "
+            + requirements.strip().rstrip(".")
+            + "."
+        )
+    return ""
+
+
+# ── 商品上下文提炼 ────────────────────────────────────────────────────────
+
+def _shorten(text: str, n: int) -> str:
+    text = re.sub(r"\s+", " ", (text or "")).strip()
+    return text[:n]
+
+def _clean_title(title: str) -> str:
+    """去掉常见堆砌词，让标题更可读。"""
+    t = _shorten(title, 60)
+    return re.sub(r"[【【】】\\[\\]|/]", " ", t).strip()
+
+def build_context(raw: dict, fallback_name: str = "", fallback_desc: str = "") -> dict:
+    """从采集数据提炼生图上下文：标题、描述行、卖点列表、参数行。
+
+    raw: {title, desc, price, params: [{key, value}], sellingPoints}
+    """
+    title = _clean_title(raw.get("title") or fallback_name or "product")
+    desc = _shorten(raw.get("desc") or fallback_desc or "", 200)
+
+    # 卖点：优先显式卖点文本；否则从参数表里挑短而有信息量的键值对
+    selling_points: list[dict] = []
+    sp_text = raw.get("sellingPoints") or ""
+    if sp_text:
+        for chunk in re.split(r"[；;\n·]+|(?<!\d)\.(?!\d)", sp_text):
+            c = _shorten(chunk, 20)
+            if c and len(selling_points) < 5:
+                selling_points.append({"zh": c, "en": c})
+    if not selling_points:
+        for p in (raw.get("params") or [])[:12]:
+            k, v = _shorten(p.get("key", ""), 10), _shorten(str(p.get("value", "")), 16)
+            if k and v and k.lower() not in {"货号", "sku", "isbn", "上架时间"}:
+                selling_points.append({"zh": f"{k} {v}", "en": f"{k} {v}"})
+            if len(selling_points) >= 5:
+                break
+
+    params_line = "; ".join(
+        f"{p.get('key')}: {p.get('value')}" for p in (raw.get("params") or [])[:8]
+    )
+    return {
+        "title": title,
+        "title_en": title,  # 采集源多为中文标题，英文场景直接用原词避免乱翻译
+        "desc": desc,
+        "selling_points": selling_points[:3],
+        "params_line": params_line,
+        "price": raw.get("price") or "",
+    }
+
+def selling_point_lines(ctx: dict, lang: str, max_n: int = 3) -> str:
+    """卖点列表 → 单行文案（图内 callout/标题用），无卖点返回空串。"""
+    sps = ctx["selling_points"][:max_n]
+    if not sps:
+        return ""
+    key = "zh" if lang == "zh" else "en"
+    return "; ".join(s[key] for s in sps if s.get(key))
+
+
+def type_name(type_id: str) -> str:
+    return TYPE_NAMES_ZH.get(type_id, type_id)

server/services/prompts/doubao.py

@@ -0,0 +1,9 @@
+"""豆包（火山方舟 Seedream）提示词。
+
+豆包与通义同为国产"主体参考"生图模型：参考图即商品锚、prompt 为场景描述，
+提示词语义一致，直接复用阿里系装配；差异（去 AI 味后缀）在 generator 层追加。
+独立成文件便于后续按豆包特性分化。
+"""
+from __future__ import annotations
+
+from .alibaba import build_prompt as build_prompt  # noqa: F401  主体参考语义与通义共用

server/services/prompts/google.py

@@ -0,0 +1,163 @@
+"""Google 图像模型（nano-banana / nano-banana-2 / nano-banana-2-lite / nano-banana-pro）提示词。
+
+语义：Gemini 图像编辑 —— 原生主体保持能力强，输入图即"主体 + 底图"，
+对自然语言指令遵循好。不套用 GPT 的编辑契约（冗长的拒绝条款反而稀释指令），
+也不用负面清单（无 negative_prompt 参数）。要点：
+  - 开头一句话钉死"主体 = 第一张图里的商品，逐像素保持"；
+  - 指令自然语言描述目标画面（场景/排版/文案），不重述商品外观；
+  - 标题/参数仅作识别背景并声明以图为准。
+"""
+from __future__ import annotations
+
+from .common import TEXT_RENDER, requirements_block, resolve_style, selling_point_lines
+
+_SUBJECT_LOCK = (
+    "SUBJECT LOCK (highest priority): the product in the first image is the subject. "
+    "Keep it exactly as photographed — same shape, proportions, colors, print/pattern, "
+    "logo, label and every detail — and place that very product into the result. "
+    "A second image, when present, is another view of the same product for reference only."
+)
+
+_QUALITY = (
+    "OUTPUT: photorealistic commercial e-commerce photography, ultra-detailed, "
+    "natural light and shadow, professional retouching."
+)
+
+_REMINDER = (
+    "Reminder: keep the product exactly as in the first image; change only its surroundings, "
+    "composition, lighting and overlay graphics."
+)
+
+
+def _anchor(ctx: dict) -> str:
+    """商品文字锚定：仅供识别，明确以图为准（同 gpt 模块，避免文字反噬商品）。"""
+    line = f"Context (identification only): the product is \"{ctx['title']}\""
+    if ctx.get("params_line"):
+        line += f" ({ctx['params_line']})"
+    return line + ". The image, not this text, defines the product's appearance."
+
+
+# ── 各图类型指令（自然语言编辑口吻）────────────────────────────────────────
+
+def _task_white_bg(ctx: dict, lang: str) -> str:
+    return (
+        "Replace the background of this product photo with seamless pure white (RGB 255,255,255): "
+        "product centered in front view filling about 85% of the frame, even studio lighting with only "
+        "a faint natural contact shadow. No props, no added text, no background elements."
+    )
+
+def _task_key_features(ctx: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang) or ctx["title"]
+    return (
+        "Create a square key-features infographic: the unchanged product on the left two-thirds, "
+        "a clean right-hand panel with 3 feature callouts using minimal line icons and thin leader "
+        f"lines pointing at the product. Callout copy: {sp}."
+    )
+
+def _task_selling_pt(ctx: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang, 1) or ctx["title"]
+    return (
+        "Turn the photo into a single-selling-point poster: hero close-up of the unchanged product at "
+        f"a dynamic angle, one large bold headline about \"{sp}\", generous negative space, and a small "
+        "magnified circle zooming into an existing detail of the product."
+    )
+
+def _task_material(ctx: dict, lang: str) -> str:
+    return (
+        "Create an extreme macro close-up of an existing area of the product's surface, showing its "
+        "true fabric weave / texture / stitching exactly as in the photo; shallow depth of field, "
+        "raking light, small caption in a corner."
+    )
+
+def _task_lifestyle(ctx: dict, lang: str) -> str:
+    return (
+        "Place the unchanged product into a realistic everyday scene where it would naturally be used: "
+        "human-scale surroundings, soft daylight, authentic candid mood, the product as the clear visual focus."
+    )
+
+def _task_multi_scene(ctx: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang)
+    task = (
+        "Build a triptych of three vertical panels separated by thin gutters: each panel shows an "
+        "identical copy of the product in a different usage scene (home interior / outdoor street / "
+        "office desk), with consistent color grading across panels."
+    )
+    if sp:
+        task += f" Panel captions: {sp}."
+    return task
+
+def _task_ecommerce_detail(ctx: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang) or ctx["title"]
+    params = ctx["params_line"]
+    return (
+        "Compose a square detail-page hero section: top half a hero banner with the unchanged product "
+        "at a 3/4 angle; bottom half a clean spec card with 3 feature rows and line icons"
+        + (f" (specs: {params})" if params else "")
+        + f", one highlighted row: {sp}."
+    )
+
+def _task_size_chart(ctx: dict, lang: str) -> str:
+    dims = ctx["params_line"]
+    return (
+        "Create a size chart: the unchanged product in clean front and side views on a light background, "
+        "thin measurement annotation lines (arrows) marking length, width and height with values beside "
+        "each line"
+        + (f" (known specs: {dims})" if dims else "")
+        + ", small caption row, precise technical-drawing aesthetic."
+    )
+
+def _task_sku_collection(ctx: dict, lang: str) -> str:
+    # 不展开"全部配色"：会凭空造出新商品；只排列同一件的多个副本
+    return (
+        "Arrange several identical copies of the product in a neat equal grid (2-4 per row) with a small "
+        "label chip below each copy; identical lighting and scale across copies. Every copy shows this "
+        "exact product — do not invent other colorways or variants."
+    )
+
+_TASK_BUILDERS = {
+    "white_bg": (_task_white_bg, False),
+    "key_features": (_task_key_features, True),
+    "selling_pt": (_task_selling_pt, True),
+    "material": (_task_material, True),
+    "lifestyle": (_task_lifestyle, True),
+    "multi_scene": (_task_multi_scene, True),
+    "ecommerce_detail": (_task_ecommerce_detail, True),
+    "size_chart": (_task_size_chart, True),
+    "sku_collection": (_task_sku_collection, True),
+}
+
+
+def build_prompt(type_id: str, ctx: dict, style_set: int, lang: str, extra: dict | None = None,
+                 style_prompt: str | None = None, requirements: str | None = None) -> str:
+    """构造指定图类型的 prompt：要求块 → 指令 → 主体锁 → 锚定 → 风格 → 文案 → 画质 → 提醒。"""
+    style = resolve_style(style_set, style_prompt)
+    extra = extra or {}
+    hint = (extra.get("prompt_hint") or "").strip()
+
+    if type_id == "custom":
+        purpose = extra.get("title") or ""
+        detail = extra.get("detail") or ""
+        task = "Create an e-commerce marketing image featuring the product from the first image"
+        task += f" — {purpose}" if purpose else ""
+        task += f": {detail}" if detail else ""
+        task += "."
+        wants_text = True
+    else:
+        entry = _TASK_BUILDERS.get(type_id)
+        if entry is None:
+            raise ValueError(f"未知图类型: {type_id}")
+        builder, wants_text = entry
+        task = builder(ctx, lang)
+    if hint:
+        task += f" Composition guidance: {hint}."
+
+    parts = [p for p in (requirements_block(requirements),) if p]
+    parts.append(task)
+    parts.append(_SUBJECT_LOCK)
+    parts.append(_anchor(ctx))
+    parts.append(f"Scene style (scene and background only, never the product): {style['tone']}.")
+    if wants_text:
+        parts.append(f"Text overlay (a graphic layer, never printed on the product): {TEXT_RENDER[lang]}")
+    parts.append(_QUALITY)
+    parts.append(_REMINDER)
+    return "\n\n".join(parts)

server/services/prompts/gpt.py

@@ -0,0 +1,195 @@
+"""GPT 图像模型（gpt-image-2 / gpt-image-2-vip，RightAPI 中转）提示词。
+
+语义：/v1/images/edits —— 输入图是"被编辑的照片"，prompt 是编辑指令；
+与通义/豆包的"主体参考"语义完全不同：参考图不是商品锚，模型会按文字指令
+重新渲染整张图。此前与国产模型共用场景提示词，再用文字锚定商品并要求输出
+"匹配商品描述"，导致模型把商品改造成营销关键词描述的样子（必现商品被改）。
+
+本模块写法原则：
+  1. 商品只由 Image 1 定义；标题/参数仅作识别背景并声明"以图为准"，
+     绝不要求输出匹配文字描述（那等于授权模型改商品）；
+  2. 指令只说"改什么"（背景/场景/排版/文案），不描述商品外观；
+  3. 分节精简、首尾重申保真；不用负面清单（gpt 无 negative_prompt 参数，
+     罗列畸形反而往上下文植入概念）；
+  4. sku 合集 / 多拼图明确"复制同一件商品，禁止发明新配色或变体"。
+"""
+from __future__ import annotations
+
+from .common import TEXT_RENDER, requirements_block, resolve_style, selling_point_lines
+
+# 保真锁：商品由 Image 1 唯一定义，其余指令一律不得触碰商品本体
+_PRESERVE = (
+    "PRESERVE (absolute, overrides every other instruction below): the product shown in Image 1. "
+    "Reuse the photographed product exactly as it is — identical shape, silhouette, proportions, "
+    "colors, print/pattern, logo and label text, materials, stitching and surface details. "
+    "Do not redesign, restyle, recolor, re-pattern, tidy up or substitute the product, "
+    "and do not let any style or text instruction below alter it. Image 2 is a secondary "
+    "view of the same product for reference only."
+)
+
+_STYLE = (
+    "SCENE STYLE (applies to background, scene, props and lighting only — never to the product): "
+)
+
+_QUALITY = (
+    "OUTPUT: photorealistic commercial e-commerce photography, ultra-detailed, "
+    "natural light and shadow, professional retouching."
+)
+
+_REMINDER = (
+    "FINAL CHECK: the product itself must remain exactly as photographed in Image 1 — "
+    "only its surroundings, composition, lighting and overlay graphics may differ."
+)
+
+# 图内文案：明确是"排版图层"，不落在商品本体上
+_TEXT_SCOPE = (
+    "TEXT OVERLAY (a graphic layer on the composition, never printed on the product): "
+)
+
+
+def _anchor(ctx: dict) -> str:
+    """商品文字锚定：仅供识别，明确声明以图为准。
+
+    只放标题 + 参数、不放营销描述——描述里的卖点词（"卡通""加固""防水"等）
+    在 edits 语义下会被执行到商品上；官逆通道（-vip）参考图被弱化时，
+    文字锚定用于帮模型认出"是哪件商品"，而不是"长什么样"。
+    """
+    line = f"CONTEXT (identification only): the product is \"{ctx['title']}\""
+    if ctx.get("params_line"):
+        line += f" ({ctx['params_line']})"
+    return (
+        line
+        + ". Image 1 — not this text — defines the product's appearance; "
+        "if they ever conflict, follow Image 1."
+    )
+
+
+# ── 各图类型的编辑指令（只描述改动，不描述商品）────────────────────────────
+
+def _task_white_bg(ctx: dict, lang: str) -> str:
+    return (
+        "TASK: Clean up this product photo for a marketplace listing. Replace the entire "
+        "background with seamless pure white (RGB 255,255,255); recompose with the product "
+        "centered in front view filling about 85% of the frame; keep only a faint natural "
+        "contact shadow. No props, no text, no background elements."
+    )
+
+def _task_key_features(ctx: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang) or ctx["title"]
+    return (
+        "TASK: Feature infographic on a square canvas. Keep the product unchanged on the left "
+        "two-thirds; build the right third as a clean info panel listing 3 feature callouts with "
+        f"minimal line icons and thin leader lines pointing at parts of the product. Callout copy: {sp}."
+    )
+
+def _task_selling_pt(ctx: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang, 1) or ctx["title"]
+    return (
+        "TASK: Single-selling-point poster. Hero close-up of the unchanged product at a dynamic "
+        f"angle, generous negative space, one large bold headline about \"{sp}\", plus one small "
+        "magnified circle zooming into an existing detail of the product (zoom only — do not "
+        "invent details that are not in the photo)."
+    )
+
+def _task_material(ctx: dict, lang: str) -> str:
+    return (
+        "TASK: Material close-up. Zoom tightly into an existing area of the product's surface and "
+        "show its true texture — fabric weave, surface finish, stitching — exactly as it appears in "
+        "Image 1; shallow depth of field, raking light across the surface; small caption label in a corner."
+    )
+
+def _task_lifestyle(ctx: dict, lang: str) -> str:
+    return (
+        "TASK: Lifestyle scene. Place the unchanged product into a realistic everyday environment "
+        "where it would naturally be used: human-scale surroundings, soft daylight, authentic candid "
+        "mood, matched shadows and color temperature, the product remaining the clear visual focus."
+    )
+
+def _task_multi_scene(ctx: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang)
+    task = (
+        "TASK: Triptych showcase. Build three vertical panels separated by thin gutters; every panel "
+        "contains an IDENTICAL copy of the product from Image 1 (do not re-render it differently per "
+        "panel) placed in a different usage scene (e.g. home interior / outdoor street / office desk), "
+        "with consistent color grading across panels."
+    )
+    if sp:
+        task += f" Panel captions: {sp}."
+    return task
+
+def _task_ecommerce_detail(ctx: dict, lang: str) -> str:
+    sp = selling_point_lines(ctx, lang) or ctx["title"]
+    params = ctx["params_line"]
+    return (
+        "TASK: Detail-page hero section on a square canvas. Top half: hero banner with the unchanged "
+        "product at a 3/4 angle. Bottom half: clean spec card with 3 feature rows and line icons"
+        + (f" (specs: {params})" if params else "")
+        + f", one highlighted row: {sp}."
+    )
+
+def _task_size_chart(ctx: dict, lang: str) -> str:
+    dims = ctx["params_line"]
+    return (
+        "TASK: Measurement chart. Show the unchanged product in clean front and side views on a light "
+        "background; overlay thin technical annotation lines (arrows) marking length, width and height "
+        "with measurement values rendered beside each line"
+        + (f" (known specs: {dims})" if dims else "")
+        + "; precise technical-drawing aesthetic, small caption row."
+    )
+
+def _task_sku_collection(ctx: dict, lang: str) -> str:
+    # 关键差异：不允许像国产模型那样展开"全部配色"——edits 语义下那会凭空造出新商品
+    return (
+        "TASK: Product lineup. Arrange several IDENTICAL copies of the product from Image 1 in a neat "
+        "equal grid (2-4 per row) with a small label chip below each copy; identical lighting and scale "
+        "across copies. Every copy must show this exact product — do NOT invent other colorways, "
+        "variants or versions."
+    )
+
+_TASK_BUILDERS = {
+    "white_bg": (_task_white_bg, False),
+    "key_features": (_task_key_features, True),
+    "selling_pt": (_task_selling_pt, True),
+    "material": (_task_material, True),
+    "lifestyle": (_task_lifestyle, True),
+    "multi_scene": (_task_multi_scene, True),
+    "ecommerce_detail": (_task_ecommerce_detail, True),
+    "size_chart": (_task_size_chart, True),
+    "sku_collection": (_task_sku_collection, True),
+}
+
+
+def build_prompt(type_id: str, ctx: dict, style_set: int, lang: str, extra: dict | None = None,
+                 style_prompt: str | None = None, requirements: str | None = None) -> str:
+    """构造指定图类型的 edits 语义 prompt：要求块 → 编辑指令 → 保真锁 → 锚定 → 风格 → 文案 → 画质 → 终检。"""
+    style = resolve_style(style_set, style_prompt)
+    extra = extra or {}
+    hint = (extra.get("prompt_hint") or "").strip()
+
+    if type_id == "custom":
+        purpose = extra.get("title") or ""
+        detail = extra.get("detail") or ""
+        task = "TASK: Create an e-commerce marketing image featuring the product from Image 1"
+        task += f" — {purpose}" if purpose else ""
+        task += f": {detail}" if detail else ""
+        task += "."
+        wants_text = True
+    else:
+        entry = _TASK_BUILDERS.get(type_id)
+        if entry is None:
+            raise ValueError(f"未知图类型: {type_id}")
+        builder, wants_text = entry
+        task = builder(ctx, lang)
+    if hint:
+        task += f" Composition guidance: {hint}."
+
+    parts = [p for p in (requirements_block(requirements),) if p]
+    parts.append(task)
+    parts.append(_PRESERVE)
+    parts.append(_anchor(ctx))
+    parts.append(f"{_STYLE}{style['tone']}.")
+    if wants_text:
+        parts.append(f"{_TEXT_SCOPE}{TEXT_RENDER[lang]}")
+    parts.append(_QUALITY)
+    parts.append(_REMINDER)
+    return "\n\n".join(parts)