A Cloud-Based Distributed System for Story Visualization Using Stable Diffusion

Abstract

With the rapid development of generative artificial intelligence (GenAI), the task of story visualization, which transforms natural language narratives into coherent and consistent image sequences, has attracted growing research attention. However, existing methods still face limitations in balancing multi-frame character consistency and generation efficiency, which restricts their feasibility for large-scale practical applications. To address this issue, this study proposes a modular cloud-based distributed system built on Stable Diffusion. By separating the character generation and story generation processes, and integrating multi-feature control techniques, a caching mechanism, and an asynchronous task queue architecture, the system enhances generation efficiency and scalability. The experimental design includes both automated and human evaluations of character consistency, performance testing, and multi-node simulation. The results show that the proposed system outperforms the baseline model StoryGen in both CLIP-I and human evaluation metrics. In terms of performance, under the experimental environment of this study, dual-node deployment reduces average waiting time by approximately 19%, while the four-node simulation further reduces it by up to 65%. Overall, this study demonstrates the advantages of cloud-distributed GenAI in maintaining character consistency and reducing generation latency, highlighting its potential value in multi-user collaborative story visualization applications.