2x2 网格,为此为 4 位量子物理学家绘制,16:9 :class Visual_Pun_Logo: def __init__(self, …, 图片 1
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2x2 网格,为此为 4 位量子物理学家绘制,16:9 :class Visual_Pun_Logo: def __init__(self, …

公开 AI 图像创作记录,包含原图、提示词、使用模型、作者、标签与来源。

使用模型
None
创作者
@Gdgtify
发布时间
Tue Sep 01 03:51:00 +0000 2026
图片数量
1 张

完整提示词

源提示词按原始换行与标点完整保留
2x2 grid, do this for 4 quantum physicists, 16:9 : class Visual_Pun_Logo:      def __init__(self, subject="[$SUBJECT]", word="[$WORD]"):          self. style = "Minimalist flat vector illustration, corporate logo design, pure white background."          self.palette = "Limited Pantone spot colors (e.g., navy, red, yellow, flat white)."        def apply_gestalt_integration(self):          # AI AUTO-INFERENCE: Find the structural edge of [$SUBJECT]          structural_boundary = infer_upper_boundary(self.subject) # e.g., The rim of a bowl, the roof of a house                    # The Morphing Rule          integration = f"The letters of the word '{self.word}' physically curve to form the {structural_boundary}. The text IS the object."          return integration        def semantic_letter_transformation(self):          # AI AUTO-INFERENCE: Select ONE letter to become a prop          target_letter = select_center_letter(self.word)          prop = infer_associated_prop(self.subject) # e.g., noodles, steam, a straw                    transformation = f"The legs of the letter '{target_letter}' extend downward, seamlessly morphing into {prop} inside the {self.subject}."          return transformation    render_vector_logo(Visual_Pun_Logo())

提示词译文

译文与源提示词分开显示,不替代原文
2x2 网格,为此为 4 位量子物理学家绘制,16:9 :class Visual_Pun_Logo:      def __init__(self, subject="[$SUBJECT]", word="[$WORD]"):          self. style = "Minimalist flat vector illustration, corporate logo design, pure white background."          self.palette = "Limited Pantone spot colors (e.g., navy, red, yellow, flat white)."        def apply_gestalt_integration(self):          # AI AUTO-INFERENCE: Find the structural edge of [$SUBJECT]          structural_boundary = infer_upper_boundary(self.subject) # e.g., The rim of a bowl, the roof of a house                    # The Morphing Rule          integration = f"The letters of the word '{self.word}' physically curve to form the {structural_boundary}. The text IS the object."          return integration        def semantic_letter_transformation(self):          # AI AUTO-INFERENCE: Select ONE letter to become a prop          target_letter = select_center_letter(self.word)          prop = infer_associated_prop(self.subject) # e.g., noodles, steam, a straw                    transformation = f"The legs of the letter '{target_letter}' extend downward, seamlessly morphing into {prop} inside the {self.subject}."          return transformation    render_vector_logo(Visual_Pun_Logo())
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