打破语义边界：分布引导的语义探索用于创造性生成
Breaking Semantic Boundaries: Distribution-Guided Semantic Exploration for Creative Generation
提出分布条件生成范式，将类别分布作为条件输入扩散模型，并通过视觉语言模型对齐生成图像的语义分布，为可控创造性生成提供了可复用的技术思路。
原文依据 we propose Distribution-Conditional Generation, a paradigm that models novel concepts as image synthesis conditioned on class distributions
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论文本到图像（T2I）扩散模型能有效生成语义对齐的图像，但其对训练分布的依赖限制了合成真正新颖的分布外概念的能力。现有方法试图通过语义探索来增强创造性，例如融合已知概念对，但生成的图像仍可用语言描述且局限于熟悉的语义空间。受分类器在新颖或分布外输入上的软概率输出启发，我们提出分布条件生成，一种将新颖概念建模为以类别分布为条件的图像合成的范式，从而实现可控但语义无约束的创造性生成。在此基础上，我们提出DisTok，一个编码器-解码器框架，通过将潜变量表示——随机采样或从条件（如类别分布）映射而来——解码为表示新颖概念的token，统一了条件与无条件创造性生成。DisTok通过从动态池中迭代采样和融合概念对来训练，以建模逐渐复杂的分布，同时通过视觉语言模型强制语义一致性，使生成图像的类别分布与输入分布对齐。大量实验表明，DisTok能够高效灵活地进行语义探索以实现token级创造性合成，在文本-图像对齐和人类偏好方面达到最优。
英文原文摘要 1520 字符
Text-to-image (T2I) diffusion models effectively produce semantically aligned images, but their reliance on training distributions constrains their capacity for synthesizing truly novel, out-of-distribution concepts. Existing methods attempt to enhance creativity through semantic exploration, such as fusing known concept pairs, but the resulting images remain linguistically describable and confined to familiar semantic spaces. Inspired by the soft probabilistic outputs of classifiers on novel or out-of-distribution inputs, we propose Distribution-Conditional Generation, a paradigm that models novel concepts as image synthesis conditioned on class distributions, enabling controllable yet semantically unconstrained creative generation. Building on this, we propose DisTok, an encoder-decoder framework that unifies conditional and unconditional creative generation by decoding latent representations--either randomly sampled or mapped from conditions (e.g., class distributions)--into tokens representing novel concepts. DisTok is trained by iteratively sampling and fusing concept pairs from a dynamic pool to model progressively complex distributions, while enforcing semantic consistency through a vision-language model that aligns the class distributions of generated images with the input distributions. Extensive experiments demonstrate that DisTok enables efficient and flexible semantic exploration for token-level creative synthesis, achieving state-of-the-art text-image alignment and human preference.