让AI画画不再千篇一律：按语义浏览创意变体
现在的AI画图工具，你给一句提示词，它只给你一张最“标准”的图——换个种子可能只是光影或角度微调，而不是你真正想要的“换个风格”或“换个物体位置”。这篇论文让AI先理解画面里有哪些可变的“语义零件”（比如主体、背景、色调），然后像逛画廊一样，让你沿着这些有意义的轴去浏览变体：向左滑是“把猫换成狗”，向右滑是“从白天变黄昏”。它不靠随机噪声，而是让一个视觉语言模型在文本层面主动生成不同的描述，再画出来。你明天用不上，但它指明了方向：AI创作工具的下一个进化，是从“生成一张图”变成“生成一个可探索的创意空间”。
📄 原文摘要(英文)
Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence comes at the cost of diversity: generated samples tend to collapse into a single visual interpretation. Existing methods to improve diversity produce outputs driven by incidental variations rather than meaningful design choices. This motivates a new variant of the diversity task where structure is enforced on the generated samples. We introduce a method for controlled diversity that enables Semantic Browsing, where users can navigate structured image galleries and experience creative exploration through a systematic traversal of meaningful, interpretable axes of variation. Achieving this level of semantic control requires a deep understanding of the scene. We exploit the fact that recent text-to-image models are trained on elaborated captions, effectively decoupling semantic decision-making from pixel generation. This enables a paradigm shift: instead of relying on stochastic variation within the text-to-image model, we induce diversity directly at the text level. By leveraging rich textual representations, we allow a Vision Language Model (VLM) to operate on the full scene context. To overcome the generic outputs typical of standard VLMs, we employ an agentic workflow that explicitly enforces structured variation attuned to the original prompt. We demonstrate that our method produces diverse and navigable design spaces where every variation corresponds to a specific, user-understandable semantic decision.