海报排版 / PROMPT
建筑空间场景渲染
从公开案例收录的海报排版提示词,适合参考其画面结构、风格控制和细节描述。
适用模型GPT Image 2
已实测灵感来源于 awesome-gpt-image-2 案例 128;当前提示词与效果图由 Fastgoing 使用 GPT Image 2 实测重新制作。
完整提示词COPY / USE
{
"type": "anime-style animated movie poster",
"scene": "Magical glowing multi-story treehouse restaurant in a dark enchanted forest at night, illuminated by string lights and warm window glow.",
"subjects": {
"children": "2 children in center foreground facing the restaurant: a boy with a backpack and lantern, and a girl in a red coat and beret with a lantern.",
"animals": "4 anthropomorphic animals: a bear chef holding a MENU book (bottom left), an owl playing violin and a squirrel playing flute on a branch (top left), a badger playing cello (mid right), and a rabbit in a suit holding a sign (bottom right).",
"creatures": "3 small black soot-sprite-like creatures with glowing eyes (bottom right).",
"floating_food": "4 glowing food items floating in the air: soup, pancakes, omurice, and a fruit parfait."
},
"layout": {
"top_text": "{argument name=\"top catchphrase\" default=\"おいしい奇跡が、今夜はじまる。\"}",
"building_sign": "{argument name=\"restaurant name\" default=\"森のレストラン\"}",
"left_board": "本日のおすすめ\n・森のスープ\n・星のオムライス\n・ふわふわパンケーキ\n・しあわせのパフェ\n...and more!",
"right_board": "いらっしゃいませ!\nここは、だれでも\n笑顔になれる場所。",
"main_title": {
"text": "{argument name=\"movie title\" default=\"ふしぎな森のレストラン\"}",
"styling": "Large stylized typography with a chef hat, fork, and spoon motifs."
},
"rabbit_sign": "ごちそうさま!またきてね!",
"bottom_left_badge": "{argument name=\"genre badge\" default=\"家族みんなで楽しめる!心あたたまる冒険ファンタジー\"}",
"bottom_center_credits": "Fictional cast and staff names in Japanese.",
"bottom_release_date": "{argument name=\"release date\" default=\"2025年 夏休みロードショー!\"}",
"bottom_right": "QR code with text '最新情報はこちら!'"
}
}复制后,可参考下方“建议替换”项目改成你的主题;带【方括号】的内容请优先替换。
PROVENANCE / 来源与验证
它从哪里来,是否完成实测?
灵感来源于 awesome-gpt-image-2 案例 128;当前提示词与效果图由 Fastgoing 使用 GPT Image 2 实测重新制作。
GPT Image 2 · 2026-08-19 实测HOW TO USE
不用学提示词工程,也能改成自己的。
建议替换
01【海报主题】02【标题文案】03【主视觉】04【配色与比例】使用小贴士
先只替换变量跑一次,观察最不符合预期的部分;第二轮只补充那一项约束,通常比一次塞进很多要求更稳定。
