  Current large-scale generative models have impressive efficiency in
generating high-quality images based on text prompts. However, they lack the
ability to precisely control the size and position of objects in the generated
image. In this study, we analyze the generative mechanism of the stable
diffusion model and propose a new interactive generation paradigm that allows
users to specify the position of generated objects without additional training.
Moreover, we propose an object detection-based evaluation metric to assess the
control capability of location aware generation task. Our experimental results
show that our method outperforms state-of-the-art methods on both control
capacity and image quality.
