In recent years, Stable Diffusion has significantly advanced the quality of text-to-image generation. However, accurately interpreting and representing Spatial Layouts specified by text prompts remains challenging. Existing approaches typically rely either on extra grounding information or utilize LLMs (large language models) combined with layout-controllable models which suffer from high computational costs. To address these limitations, we propose a novel method SamLayGe, a lightweight and efficient layout generation model designed for seamless integration into existing text-to-image pipelines. SamLayGe autonomously generates comprehensive layouts without requiring explicit user inputs, thus surpassing current LLM-based and layout-controllable approaches in terms of versatility and efficiency. Furthermore, we propose LayGeBench, a benchmark dataset addressing ambiguities in spatial descriptions of prior datasets. Extensive evaluations demonstrate that SamLayGe consistently produces images that accurately adhere to textual layout descriptions, achieving superior performance in terms of both accuracy and computational efficiency. Code is available at https://github.com/shenlanzhuanshu/caption-to-positional-layout . Position-Aware Text-to-Image Generation with Efficient Controllability.