arXiv:2509.12046v2 Announce Type: replace-cross Abstract: Although autoregressive (AR) models have demonstrated remarkable success in image generation, extending these models to layout-conditioned generation remains challenging due to the sparse nature of layout conditions and the risk of feature entanglement. We present \textbf{S}tructured \textbf{M}asking for \textbf{AR}-based \textbf{L}ayout-to-\textbf{I}mage (SMARLI), a novel framework that effectively integrates spatial layout constraints into the AR generation process. To equip AR models with layout control, a structured masking strategy
The continuous advancements in AI research, particularly in generative models, are pushing the boundaries of what these models can achieve, necessitating more nuanced control mechanisms.
This development improves control over text-to-image generation, making AI-generated content more practical and useful for specific design and creative applications, reducing the need for extensive post-processing.
The ability to integrate spatial layout constraints into autoregressive image generation models will lead to more precise and controllable AI-powered creative tools and design workflows.
- · AI researchers
- · Generative AI companies
- · Design and creative industries
- · Content creators
- · Manual graphic designers (for routine tasks)
- · Less sophisticated image generation models
Improved efficiency and accuracy in AI-driven image and design generation.
Accelerated development of AI tools that can interpret complex user instructions for visual content creation.
Potential for AI systems to generate entire visual campaigns or product designs from high-level textual descriptions and layout specifications, disrupting traditional creative agencies.
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Read at arXiv cs.AI