Story-to-Images Translation: Leveraging Diffusion Models and Large Language Models for Sequence Image Generation
Abstract
Diffusion models are catalyzing breakthroughs in creative fields, with a notable impact on text-to-image generation. This study centers on the transformation of textual narratives into coherent sequences of images - a process currently hampered by issues of consistency and contextual fidelity. To address these challenges, we propose a method utilizing a large language model, with an emphasis on context and character information. Empirical evaluations, carried out using Hollywood movie scripts, clearly indicate that our approach improves both the consistency and contextual fidelity of the resulting image sequences.
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