LCG: Long-Context Consistent Image Generation with Sparse Relational Attention

2026-06-24 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

Long-Context Generation (LCG) is a new framework designed for long-context multi-image text-to-image generation, addressing the challenge of maintaining consistency across sequential outputs in applications like comics and storyboards. LCG integrates the Sparse Relational Attention (SRA) mechanism, which enables selective attention to core features across extended visual contexts, ensuring computational tractability for semantic and layout information propagation. To further enforce semantic alignment and prevent appearance drift, especially in complex multi-character scenes, LCG introduces the Routing Consistency Constraint (RCC), utilizing identity-aware masks. Supporting this framework, the Long-Context Consistency Dataset (LCCD) was constructed, comprising 600K training sequences and a 1K test set, with each sequence containing 6 to 20 character-centric images. Experiments show LCG surpasses existing baselines in prompt alignment and character consistency for long-context image generation.

Key takeaway

For Computer Vision Engineers developing visual narrative tools, LCG offers a robust solution for maintaining character and semantic consistency across sequential image outputs. You should consider integrating Sparse Relational Attention and identity-aware consistency constraints to prevent appearance drift in your multi-image generation pipelines. This approach significantly improves prompt alignment and character stability, crucial for applications like comics and storyboards.

Key insights

LCG uses sparse attention and consistency constraints for scalable, coherent multi-image generation.

Principles

• Consistency across sequential images is critical.
• Sparse attention can manage long visual contexts.
• Identity-aware masks align structural patterns.

Method

LCG employs Sparse Relational Attention (SRA) for selective feature attention and the Routing Consistency Constraint (RCC) with identity-aware masks to align structural patterns across generation branches.

In practice

• Generate consistent comics or storyboards.
• Create visual narratives with stable characters.
• Mitigate appearance drift in multi-character scenes.

Topics

• Long-Context Generation
• Sparse Relational Attention
• Multi-Image Generation
• Character Consistency
• Visual Narratives
• LCCD Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.