Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection
<|reference_start|>Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection: 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.<|reference_end|>
@article{ma2024storynizor:,
    title={Storynizor: Consistent Story Generation via Inter-Frame Synchronized and
  Shuffled ID Injection},
    author={Yuhang Ma, Wenting Xu, Chaoyi Zhao, Keqiang Sun, Qinfeng Jin, Zeng
  Zhao, Changjie Fan, Zhipeng Hu},
    journal={arXiv preprint arXiv:2409.19624},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19624},
    primaryClass={cs.CV cs.AI}
}