arXiv:2606.26171v1 Announce Type: cross 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 pr
The increasing sophistication of AI models, particularly in single-image generation, has brought the limitations of multi-image consistency into sharp focus, making advancements in this area critical for next-gen applications.
Achieving long-context consistency in image generation unlocks new possibilities for AI in creative industries, content creation, and interactive experiences, significantly expanding the utility of generative AI.
Previously disjointed AI image outputs can now be linked through a coherent visual narrative, transforming how sequential imagery for comics, storyboards, and visual narratives is produced.
- · AI content creators
- · Creative industries
- · Generative AI model developers
- · Traditional sequential art production pipelines
Improved consistency in AI-generated visual narratives for various media.
Accelerated development of AI tools for complex storytelling and dynamic content generation.
Potential for entirely new forms of interactive visual media where AI maintains creative control over long-form visual coherence.
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Read at arXiv cs.AI