Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models

• URL: http://arxiv.org/abs/2302.03900v1
• Date: Wed, 8 Feb 2023 06:24:06 GMT
• Title: Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models
• Authors: Hyeonho Jeong, Gihyun Kwon, Jong Chul Ye
• Abstract summary: We present a novel neural pipeline for generating a coherent storybook from the plain text of a story. We leverage a combination of a pre-trained Large Language Model and a text-guided Latent Diffusion Model to generate coherent images.
• Score: 43.32978092618245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in large scale text-to-image models have opened new possibilities for guiding the creation of images through human-devised natural language. However, while prior literature has primarily focused on the generation of individual images, it is essential to consider the capability of these models to ensure coherency within a sequence of images to fulfill the demands of real-world applications such as storytelling. To address this, here we present a novel neural pipeline for generating a coherent storybook from the plain text of a story. Specifically, we leverage a combination of a pre-trained Large Language Model and a text-guided Latent Diffusion Model to generate coherent images. While previous story synthesis frameworks typically require a large-scale text-to-image model trained on expensive image-caption pairs to maintain the coherency, we employ simple textual inversion techniques along with detector-based semantic image editing which allows zero-shot generation of the coherent storybook. Experimental results show that our proposed method outperforms state-of-the-art image editing baselines.

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