arXiv:2411.03595v1 Announce Type: new
Abstract: 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.
Conceptual Blending in Text-to-Image Diffusion Models
In recent years, text-to-image diffusion models have shown promising results in generating images from textual descriptions. These models have the ability to capture the semantics and visual appearance of the text, producing images that are intuitively imaginable from the given descriptions. However, one interesting use case that has not been extensively explored is nonword-to-image generation, where the goal is to generate images based on non-existing words or concepts.
In a recent study, researchers focused on associating nonwords with similar-sounding words in order to generate images that depict the blended concepts. This approach allows for the generation of images that are not directly linked to any existing words or concepts, opening up creative possibilities. However, the effectiveness of this approach has not been quantitatively evaluated in either diffusion models or the nonword-to-image generation paradigm.
In this paper, the authors analyze the conceptual blending in a pretrained diffusion model called Stable Diffusion. By inputting an embedding interpolating between the text embeddings of two text prompts referring to different concepts, they found a high percentage of generated images depicting blended concepts. This suggests that the diffusion model is able to capture and represent the blended concepts effectively.
Multi-disciplinary Nature
The concepts discussed in this paper have a multi-disciplinary nature, encompassing areas such as computational psycholinguistics, artificial intelligence, and computer vision. The use of text-to-image diffusion models bridges the gap between natural language processing and computer vision, allowing for the generation of visually coherent and semantically meaningful images.
Furthermore, the exploration of nonword-to-image generation expands the possibilities of creativity and imagination. By generating images based on non-existing words, the potential for artistic expression and novel ideas is increased. This intersects with the field of multimedia information systems, where the combination of different media types, such as text and images, is a central focus.
Relation to Multimedia Information Systems, Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The research presented in this paper is closely related to the wider field of multimedia information systems and its various applications, including animations, artificial reality, augmented reality, and virtual realities.
Text-to-image diffusion models have been used in the creation of animations, where textual descriptions are converted into visual sequences. By incorporating the concept of conceptual blending, these models can generate animations that seamlessly transition between different concepts, creating a visually engaging and dynamic experience.
In terms of artificial reality, such as virtual realities and augmented reality, the ability to generate images based on non-existing words can greatly enhance the immersive experience. For example, in virtual reality environments, users can interact with objects or environments that are not constrained by real-world limitations. By generating images that blend different concepts, the virtual reality experience can be enriched, providing a more diverse and imaginative environment.
Overall, the research presented in this paper contributes to the advancement of text-to-image diffusion models and their applications in the broader field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By understanding and quantitatively evaluating the effects of conceptual blending, further advancements can be made to improve the quality and creativity of generated images.