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arXiv · 2409.19624

Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection

Yuhang Ma·Wenting Xu·Chaoyi Zhao·Keqiang Sun·Qinfeng Jin·Zeng Zhao·Changjie Fan·Zhipeng Hu
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Abstract

Recent advances in text-to-image diffusion models have spurred significant interest in continuous story image generation. In this paper, we introduce Storynizor, a model capable of generating coherent stories with strong inter-frame character consistency, effective foreground-background separation, and diverse pose variation. The core innovation of Storynizor lies in its key modules: ID-Synchronizer and ID-Injector. The ID-Synchronizer employs an auto-mask self-attention module and a mask perceptual loss across inter-frame images to improve the consistency of character generation, vividly representing their postures and backgrounds. The ID-Injector utilize a Shuffling Reference Strategy (SRS) to integrate ID features into specific locations, enhancing ID-based consistent character generation. Additionally, to facilitate the training of Storynizor, we have curated a novel dataset called StoryDB comprising 100, 000 images. This dataset contains single and multiple-character sets in diverse environments, layouts, and gestures with detailed descriptions. Experimental results indicate that Storynizor demonstrates superior coherent story generation with high-fidelity character consistency, flexible postures, and vivid backgrounds compared to other character-specific methods.

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Yuhang Ma, Wenting Xu, Chaoyi Zhao, Keqiang Sun, Qinfeng Jin, Zeng Zhao, Changjie Fan, Zhipeng Hu. 2024-09-29. Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection. https://arxiv.org/abs/2409.19624

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