Infinite-Story: A Training-Free Consistent Text-to-Image Generation

• URL: http://arxiv.org/abs/2511.13002v1
• Date: Mon, 17 Nov 2025 05:46:16 GMT
• Title: Infinite-Story: A Training-Free Consistent Text-to-Image Generation
• Authors: Jihun Park, Kyoungmin Lee, Jongmin Gim, Hyeonseo Jo, Minseok Oh, Wonhyeok Choi, Kyumin Hwang, Jaeyeul Kim, Minwoo Choi, Sunghoon Im,
• Abstract summary: We present Infinite-Story, a training-free framework for consistent text-to-image (T2I) generation.<n>Our method addresses two key challenges in consistent T2I generation: identity inconsistency and style inconsistency.<n>Our method achieves state-of-the-art generation performance, while offering over 6X faster inference (1.72 seconds per image) than the existing fastest consistent T2I models.
• Score: 21.872330710303036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Infinite-Story, a training-free framework for consistent text-to-image (T2I) generation tailored for multi-prompt storytelling scenarios. Built upon a scale-wise autoregressive model, our method addresses two key challenges in consistent T2I generation: identity inconsistency and style inconsistency. To overcome these issues, we introduce three complementary techniques: Identity Prompt Replacement, which mitigates context bias in text encoders to align identity attributes across prompts; and a unified attention guidance mechanism comprising Adaptive Style Injection and Synchronized Guidance Adaptation, which jointly enforce global style and identity appearance consistency while preserving prompt fidelity. Unlike prior diffusion-based approaches that require fine-tuning or suffer from slow inference, Infinite-Story operates entirely at test time, delivering high identity and style consistency across diverse prompts. Extensive experiments demonstrate that our method achieves state-of-the-art generation performance, while offering over 6X faster inference (1.72 seconds per image) than the existing fastest consistent T2I models, highlighting its effectiveness and practicality for real-world visual storytelling.

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