3218485270
5ab5cf6586
- 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
180 lines
10 KiB
Python
180 lines
10 KiB
Python
"""节点 5/6：提示词构造（prompt_build）。
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读取 prompts/<country>/ 的 extra 风格规则，用固定模板装配四种最终提示词
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（image_prompt / wearable_prompt / composite_prompt / composite_negative）。
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四要素缺失时用 derive_style_palette 动态兜底，保证每条提示词结构一致、有规则。
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"""
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from typing import Any, Dict, List
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import random
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from graph.style_rules import derive_style_palette, derive_composition
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from graph.templates import assemble_prompts
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from graph.validate import validate_brief, with_fallback
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# —— Pinterest 图生图生最终设计稿时统一追加的「小印花 + 纯白底」约束段 ——
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# (从热点搜集的文字生图模板里提炼：尺寸缩小、禁止自带背景/满幅)
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PINTEREST_PRINT_SUFFIX = (
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" standalone pure print design on a pure white background, "
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"the print artwork is SMALL and CENTERED with clearly larger white margins around it, "
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"print area between about 15x18 cm and 26x32 cm, "
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"do NOT fill the entire canvas, do NOT force full-bleed, "
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"do NOT add any gradient, texture or background color behind the artwork, "
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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 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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"correctly spelled."
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)
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# review（疑似商标/受保护主题）简报统一追加的「原创化魔改」引导段：
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# 只做风格参考，禁复刻品牌/商标/角色，换名换细节，生成通用非侵权致敬式设计。
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# 两个模式（热点采集 / Pinterest 参考）共用同一文本，避免不一致。
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REVIEW_REBRAND_HINT = (
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"IMPORTANT: this theme is ONLY a loose stylistic reference. "
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"Do NOT reproduce any brand logo, trademark, character, mascot, copyrighted artwork or real person. "
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"Create a fully ORIGINAL design with a different name and distinct visual details and colors — "
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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 + delta 改动指令 ——
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# 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
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# （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。
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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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# 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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"skull": "smiley mascot", "skeleton": "cute mascot", "blood": "red accents",
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"gore": "bold shapes", "gun": "star", "weapon": "tool", "bomb": "firework",
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"drug": "confetti", "demon": "cute monster", "devil": "mischievous imp",
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"occult": "mystic pattern", "satanic": "dark pattern", "nazi": "retro emblem",
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"hitler": "retro emblem", "zombie": "friendly ghoul", "horror": "spooky-cute",
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"vampire": "night owl", "politics": "abstract shapes", "political": "abstract",
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"president": "captain", "army": "team", "police": "officer",
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}
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def _safe_motif(motif: str) -> str:
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"""清洗 motif 中的图像策略敏感词（替换为安全等效描述），降低生图内容政策拦截率。"""
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low = motif.lower()
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for k, v in _IMG_RISKY_SWAP.items():
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if k in low:
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# 按词边界替换（避免误伤 "letterhead" 等）
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import re
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motif = re.sub(rf"\b{re.escape(k)}\b", v, motif, flags=re.IGNORECASE)
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low = motif.lower()
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return motif
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def _read_custom_prompt_md(filename: str) -> str:
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"""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。"""
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from graph.paths import project_root, runtime_root
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for base in (runtime_root(), project_root()):
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p = base / "prompts" / filename
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if p.exists():
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t = p.read_text(encoding="utf-8").strip()
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if t:
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return t
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return ""
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@with_fallback("prompt_build")
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def prompt_node(state: Dict[str, Any]) -> Dict[str, Any]:
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screened: List[Dict[str, Any]] = state.get("screened") or []
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config = state["config"]
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country = state["country"]
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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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# 自定义模式生图模板优先读 prompts/custom_image_prompt.md（可编辑），
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# 其次 config.custom.prompt_pieces，最后回退代码内置默认。
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# 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。
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custom_ip_tpl = _read_custom_prompt_md("custom_image_prompt.md") \
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or (pp.get("image_prompt_template") or "").strip() or CUSTOM_IMAGE_PROMPT_TEMPLATE
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briefs: List[Dict[str, Any]] = []
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for r in screened:
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r = validate_brief(r)
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art, pal = derive_style_palette(
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r["topic"], country, extra_rules=extra_rules, category=r.get("design_category")
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)
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motif = (r.get("motif") or "").strip() or r.get("topic", "")
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cleaned = _safe_motif(motif)
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if cleaned != motif:
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print(f"[prompt] motif 敏感词清洗: 「{motif}」→「{cleaned}」（降低生图内容政策拦截）")
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r["motif"] = cleaned
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motif = cleaned
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art_style = (r.get("art_style") or art).strip()
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palette = (r.get("color_palette") or pal).strip()
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composition = (r.get("composition") or derive_composition(r["topic"], r.get("design_category"))).strip()
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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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delta = (r.get("delta") or "").strip()
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if r.get("source") == "pinterest":
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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 + delta 改动指令），负向留空 ——
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# 新分析模板只产出 delta（无 image_prompt），生图模板也只引用 {delta}；
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# 只要 delta 或 image_prompt 任一存在即可装配，不再使用负向提示词
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# （约束已全部内置进模板），composite_negative 置空避免后端追加 Negative 段。
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if llm_ip or delta:
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prompts["image_prompt"] = (
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custom_ip_tpl.replace("{delta}", delta).replace("{image_prompt}", llm_ip)
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)
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prompts["composite_negative"] = ""
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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']}」")
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elif llm_ip:
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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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if slogan and random.random() < float(config.get("prompt_templates", {}).get("text_ratio", 0.3)):
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text_seg = (f', with the text "{slogan}" rendered as bold retro typography, '
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f'lettering clean and correctly spelled, high contrast, as the focal text of the print')
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prompts["image_prompt"] = prompts["image_prompt"] + text_seg
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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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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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r["art_style"] = art_style
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r["color_palette"] = palette
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r["composition"] = composition
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briefs.append(r)
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stats = dict(state.get("stats") or {})
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stats["prompt"] = {"briefs": len(briefs)}
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return {"briefs": briefs, "stats": stats}
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