ReDiStory: Region-Disentangled Diffusion for Consistent Visual Story Generation

• URL: http://arxiv.org/abs/2602.01303v1
• Date: Sun, 01 Feb 2026 16:04:40 GMT
• Title: ReDiStory: Region-Disentangled Diffusion for Consistent Visual Story Generation
• Authors: Ayushman Sarkar, Zhenyu Yu, Chu Chen, Wei Tang, Kangning Cui, Mohd Yamani Idna Idris,
• Abstract summary: ReDiStory is a training-free framework that improves multi-frame story generation via inference-time prompt embedding reorganization.<n>It reduces cross-frame interference without modifying diffusion parameters or requiring additional supervision.<n> Experiments on the ConsiStory+ benchmark show consistent gains over 1Prompt1Story on multiple identity consistency.
• Score: 6.4611000755192585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating coherent visual stories requires maintaining subject identity across multiple images while preserving frame-specific semantics. Recent training-free methods concatenate identity and frame prompts into a unified representation, but this often introduces inter-frame semantic interference that weakens identity preservation in complex stories. We propose ReDiStory, a training-free framework that improves multi-frame story generation via inference-time prompt embedding reorganization. ReDiStory explicitly decomposes text embeddings into identity-related and frame-specific components, then decorrelates frame embeddings by suppressing shared directions across frames. This reduces cross-frame interference without modifying diffusion parameters or requiring additional supervision. Under identical diffusion backbones and inference settings, ReDiStory improves identity consistency while maintaining prompt fidelity. Experiments on the ConsiStory+ benchmark show consistent gains over 1Prompt1Story on multiple identity consistency metrics. Code is available at: https://github.com/YuZhenyuLindy/ReDiStory

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