LCG: Long-Context Consistent Image Generation with Sparse Relational Attention
This paper proposes LCG, a framework for long-context multi-image generation using Sparse Relational Attention (SRA) and Routing Consistency Constraint (RCC), along with a large synthetic dataset LCCD. Experiments show LCG outperforms baselines in prompt alignment and character consistency.
[2606.26171] LCG: Long-Context Consistent Image Generation with Sparse Relational Attention
[Submitted on 24 Jun 2026]
Title:LCG: Long-Context Consistent Image Generation with Sparse Relational Attention
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Abstract:Recent image generation models achieve impressive quality in single-image synthesis, but often fail to maintain consistency across sequential outputs, as required in comics, storyboards, and visual narratives. We propose Long-Context Generation (LCG), a framework for long-context multi-image text-to-image generation, to improve consistency and scalability in long-context multi-image generation. LCG employs the Sparse Relational Attention (SRA) mechanism to selectively attend to core features across extended visual contexts, ensuring that the propagation of semantic and layout information remains computationally tractable. To enforce semantic alignment, we introduce the Routing Consistency Constraint (RCC), which leverages identity-aware masks to align structural patterns across generation branches, effectively mitigating drift in appearance even in complex multi-character scenes. To support training and evaluation in this setting, we construct the Long-Context Consistency Dataset (LCCD), a large-scale synthetic dataset comprising character-centric multi-image sequences spanning varied situational contexts. LCCD contains 600K training sequences and a separate 1K test set, with each sequence containing 6 to 20 images. The experiments demonstrate that LCG outperforms the compared baselines in prompt alignment and character consistency for long-context image generation, including multi-character scenes.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.26171 [cs.CV]
(or arXiv:2606.26171v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.26171
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zihao Wang [view email] [v1] Wed, 24 Jun 2026 09:34:10 UTC (15,980 KB)
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