Describe scenes narratively, not as keyword lists. Gemini's language model parses prose with full semantic understanding — narrative prompts encode spatial relationships, mood, and intent that comma-separated tags cannot express. Tag-style prompts lose compositional meaning and produce generic results.
Bad: "cat, wizard hat, magical, fantasy, 4k, detailed"
Good: "A fluffy orange tabby sits regally on a velvet cushion, wearing an ornate
purple wizard hat embroidered with silver stars. Soft candlelight illuminates
the scene from the left. The mood is whimsical yet dignified."
Describe positively, never via negation. Every concept named in a prompt biases the output toward that concept — even when preceded by "not", "no", or "do not". Diffusion models condition on tokens regardless of polarity. To exclude X, either (a) name a positive alternative that fills the same role, or (b) scope the prompt so X has no place to land.
Bad: "A clean studio backdrop. No warm tones, no cream, no beige, no tan."
Good: "A clean cool-neutral gray studio backdrop with subtle blue undertones."
Bad: "A headshot with no harsh shadows on the face, no distracting background."
Good: "A headshot on a clean neutral gray backdrop, even soft frontal fill light
that flatters the face."
This rule applies everywhere in the skill — t2i prompts, i2i directives, reference role descriptions, and framing instructions.
Name sources explicitly — leave no ambiguity in references. Every element in the prompt should trace to a specific source: "the man from Image 2" not "this man"; "the shirt from Image 3" not "the shirt". Ambiguous references bind to whichever source the model weights most, which is never reliably the right one. This isn't about over-describing — don't re-describe what the reference already shows. It's about making each reference point to exactly one source.
A useful formula:
[Subject] doing [Action] in [Context]. [Camera/Composition]. [Lighting]. [Style]. [Constraint].
Not every prompt needs every element — match detail to intent. If the user has a specific vision, be prescriptive (exact descriptions); if exploring, be open (general direction, let the model decide details). Ask if unclear.
用叙事性语言描述场景，而非关键词列表。 Gemini语言模型能解析具有完整语义的 prose——叙事性提示词能编码空间关系、氛围和意图，而逗号分隔的标签无法表达这些信息。标签式提示词会丢失构图意义，导致结果泛化。
错误示例： "cat, wizard hat, magical, fantasy, 4k, detailed"
正确示例： "一只毛茸茸的橙色虎斑猫威严地坐在天鹅绒垫子上，戴着一顶绣有银色星星的华丽紫色巫师帽。柔和的烛光从左侧照亮场景，氛围既奇趣又庄重。"
用肯定式描述，绝不使用否定式。 提示词中提及的任何概念都会引导输出向该概念倾斜——即使前面有"not"、"no"或"do not"。扩散模型会对所有标记词进行条件处理，无论正负。要排除X，要么（a）指定一个能替代X的正面选项，要么（b）限定场景范围，让X没有存在的空间。
错误示例： "干净的工作室背景。不要暖色调，不要米黄色，不要浅褐色，不要棕褐色。"
正确示例： "干净的冷中性灰色工作室背景，带有淡淡的蓝色基调。"
错误示例： "头像照片，面部没有刺眼阴影，背景无干扰。"
正确示例： "在干净的中性灰色背景下拍摄的头像，使用均匀柔和的正面补光，使面部轮廓更美观。"
此规则适用于技能的所有场景——t2i提示词、i2i指令、参考角色描述和取景说明。
明确指定来源——参考内容无歧义。 提示词中的每个元素都应指向特定来源：使用"图像2中的男子"而非"这个男子"；使用"图像3中的衬衫"而非"这件衬衫"。模糊的参考会绑定到模型权重最高的来源，而这通常不是正确的来源。这不是过度描述——无需重复描述参考图像已有的内容，而是让每个参考都精确指向一个来源。
一个实用的公式：
[主体] 在 [场景] 中做 [动作]。[镜头/构图]。[光线]。[风格]。[约束]。
并非每个提示词都需要所有元素——根据意图匹配细节程度。如果用户有明确愿景，要具体（精确描述）；如果是探索性需求，可开放（大致方向，让模型决定细节）。如有疑问请询问用户。