UR Divine Fox Priestess Anime TCG Card — Sora 2 Prompt
Generate a stunning, ultra-rare fantasy collectible card featuring a shrine priestess and sacred fox spirit, ideal for gacha game art or high-end character design.

一张奢华的奇幻集换式卡牌插画,采用优质动漫抽卡风格,竖版构图,带有华丽的镀金边框和宝石镶嵌,设计如同超稀有角色卡。卡牌展示了一位名为 argcharacter name: カヤ / KAYA 的全身神社祭司,她站在画面中心偏右的位置,背景是充满樱花、发光灯笼、薄雾、小瀑布和神社建筑的梦幻粉紫色圣林。她是一位美丽优雅的年轻女性,拥有非常飘逸的 arghair color: 柔粉色 长发、狐狸耳朵,头上侧戴着一个白色狐狸面具,以仪式感造型半遮住上半脸。她身着精致的层叠和服与祭司袍,配色为粉色、玫瑰色、梅红色和金色,配有复杂的刺绣、肩甲、编织绳、纸符、装饰性流苏和发光的宝石饰品。她手持一根华丽的仪式法杖,顶端饰有圆环、符咒、丝带和多颗巨大的紫色水晶球。她左侧坐着一只神圣的白狐灵兽,拥有九条巨大的发光尾巴和精致的金色藤蔓状花纹。整幅画面充满了魔法闪光、柔和的泛光照明、闪烁的微粒和空灵的粉色光效。 卡牌布局必须包含精确可见的区域和标签。顶部中央是一块带有金边的深色牌匾,写着“神異 - Divine Anomaly -”。左上角有一个巨大的渐变稀有度标记“UR”,下方直接排列着 7 颗金星。左侧包含 4 个堆叠的黑金属性面板,带有图标和双语标签:1) 水滴图标,“属性”,“水・祈”,“WATER + PRAYER”;2) 狐头图标,“守護神”,“黄金の蔓の白狐”,“Golden Vine White Fox”;3) 合十礼图标,“役割”,“祈癒・支援”,“Heal Support”;4) 圆形印记图标,“技能”,“祈灯结界”,“Prayer Light Barrier”。底部中央是一块大型铭牌,写着“カヤ / KAYA”,下方副标题为“東の里の巫女 / Priestess of the Eastern Village”。沿底部边缘包含 5 个属性面板,按此精确顺序排列图标和数值:ATK 760、DEF 690、SPD 820、MAG 960、SUP 990。 采用高度精炼的收藏级卡牌美学,密集的装饰性花丝、雕刻金边、宝石边角、戏剧性的奇幻光影、超精细的服装设计、柔和的动漫绘画渲染、发光的魔法氛围,以及粉色、金色、象牙白和紫罗兰色的丰富色彩和谐。最终图像应呈现出高稀有度神圣狐仙神谕卡的感觉,宏伟、神秘、优雅且高端。
About this prompt
Generate a stunning, ultra-rare fantasy collectible card featuring a shrine priestess and sacred fox spirit, ideal for gacha game art or high-end character design. Use it as a Concept Art starting point for GPT Image 2: keep the visual structure and style constraints intact, then swap in your own subject, brand, or scene.
Start by replacing character name and hair color, then keep the camera, composition, and material cues in the same order. This makes the output easier to compare across variations.
How to use this prompt
- Copy the entire prompt text above.
- Paste it into your preferred AI image generation model (e.g., Sora 2, DALL-E, Midjourney).
- Optionally customize the
{character name}and{hair color}variables to personalize the character. - Submit the prompt and generate the image.
- Iterate by adjusting details or using different model settings for varied results.



