Text-to-image diffusion models sometimes depict blended concepts in the generated images. One promising use case of this effect would be the nonword-to-image generation task which attempts to generate images intuitively imaginable from a non-existing word (nonword). To realize nonword-to-image generation, an existing study focused on associating nonwords with similar-sounding words. Since each nonword can have multiple similar-sounding words, generating images containing their blended concepts would increase intuitiveness, facilitating creative activities and promoting computational psycholinguistics. Nevertheless, no existing study has quantitatively evaluated this effect in either diffusion models or the nonword-to-image generation paradigm. Therefore, this paper first analyzes the conceptual blending in a pretrained diffusion model, Stable Diffusion. The analysis reveals that a high percentage of generated images depict blended concepts when inputting an embedding interpolating between the text embeddings of two text prompts referring to different concepts. Next, this paper explores the best text embedding space conversion method of an existing nonword-to-image generation framework to ensure both the occurrence of conceptual blending and image generation quality. We compare the conventional direct prediction approach with the proposed method that combines $k$-nearest neighbor search and linear regression. Evaluation reveals that the enhanced accuracy of the embedding space conversion by the proposed method improves the image generation quality, while the emergence of conceptual blending could be attributed mainly to the specific dimensions of the high-dimensional text embedding space.

翻译：文本到图像扩散模型有时会在生成的图像中呈现融合概念。这一效应的一种潜在应用场景是非词到图像生成任务，该任务旨在从不存在词汇（非词）中直观地生成可想象的图像。为实现非词到图像生成，现有研究侧重于将非词与发音相似的词汇进行关联。由于每个非词可能对应多个发音相似的词汇，生成包含其融合概念的图像将增强直观性，从而促进创意活动并推动计算心理语言学发展。然而，现有研究尚未在扩散模型或非词到图像生成范式中对此效应进行定量评估。因此，本文首先分析了预训练扩散模型Stable Diffusion中的概念融合现象。分析表明，当输入介于两个指向不同概念的文本提示词嵌入之间的插值嵌入时，生成图像中呈现融合概念的比例较高。随后，本文探索了现有非词到图像生成框架中最佳的文本嵌入空间转换方法，以确保概念融合的发生与图像生成质量。我们比较了传统的直接预测方法与结合$k$近邻搜索和线性回归的改进方法。评估结果显示，改进方法通过提升嵌入空间转换的准确性改善了图像生成质量，而概念融合的出现主要可归因于高维文本嵌入空间中特定维度的作用。