SAWNA: Space-Aware Text to Image Generation
Abstract
Layout-aware text-to-image generation allows users to synthesize images by specifying object positions through text prompts and layouts. This has proven useful in a variety of creative fields such as advertising, UI design, and animation, where structured scene control is essential. In real-world workflows, however, certain regions are often intentionally left empty—for instance, for headlines in advertisements, buttons in interface prototypes, or subtitles and speech bubbles in animation frames. Existing models lack the ability to explicitly preserve such negative spaces, often resulting in unwanted content and complicating downstream editing. We introduce Space-Controllable Text-to-Image Generation, a task that treats reserved areas as first-class constraints. To address this, we propose SAWNA (Space-Aware Text-to-Image Generation), a training-free diffusion framework that injects nonreactive noise into user-defined masked regions, ensuring they remain empty throughout generation. Our method maintains semantic integrity and visual fidelity without retraining and integrates seamlessly into layout-sensitive workflows in design, advertising, and animation. Experiments demonstrate that SAWNA reliably enforces spatial constraints and improves the practical usability of generated content.
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