Diffusion models have recently achieved remarkable progress in generating realistic images. However, challenges remain in accurately understanding and synthesizing the layout requirements in the textual prompts. To align the generated image with layout instructions, we present a training-free layout calibration system SimM that intervenes in the generative process on the fly during inference time. Specifically, following a "check-locate-rectify" pipeline, the system first analyses the prompt to generate the target layout and compares it with the intermediate outputs to automatically detect errors. Then, by moving the located activations and making intra- and inter-map adjustments, the rectification process can be performed with negligible computational overhead. To evaluate SimM over a range of layout requirements, we present a benchmark SimMBench that compensates for the lack of superlative spatial relations in existing datasets. And both quantitative and qualitative results demonstrate the effectiveness of the proposed SimM in calibrating the layout inconsistencies.

翻译：扩散模型近期在生成逼真图像方面取得了显著进展。然而，在准确理解和合成文本提示中的布局要求方面仍存在挑战。为使生成图像与布局指令对齐，我们提出了一种无需训练的布局校准系统SimM，该推理时系统在生成过程中动态介入。具体而言，遵循"检查-定位-修正"流程，系统首先分析提示以生成目标布局，并将其与中间输出进行比较以自动检测错误。随后通过移动已定位的激活并进行图内与图间调整，修正过程能以极低的计算开销完成。为评估SimM在多种布局要求下的性能，我们提出了基准测试集SimMBench，以弥补现有数据集中缺乏超常空间关系的不足。定量与定性结果均证明了所提SimM在校正布局不一致性方面的有效性。