Recent advancements in generative models have significantly enhanced their capacity for image generation, enabling a wide range of applications such as image editing, completion and video editing. A specialized area within generative modeling is layout-to-image (L2I) generation, where predefined layouts of objects guide the generative process. In this study, we introduce a novel regional cross-attention module tailored to enrich layout-to-image generation. This module notably improves the representation of layout regions, particularly in scenarios where existing methods struggle with highly complex and detailed textual descriptions. Moreover, while current open-vocabulary L2I methods are trained in an open-set setting, their evaluations often occur in closed-set environments. To bridge this gap, we propose two metrics to assess L2I performance in open-vocabulary scenarios. Additionally, we conduct a comprehensive user study to validate the consistency of these metrics with human preferences.

本研究解决了布局到图像生成中，现有方法在复杂文本描述场景下表现不佳的问题。提出了一种新颖的区域交叉注意力模块，以增强生成过程，并提出了评估开放词汇情景下生成性能的新指标。研究发现，这些指标与人类偏好高度一致，具有重要的应用潜力。

重新思考丰富上下文的布局到图像生成的训练和评估