  Recent advancements in large-scale pre-trained text-to-image models have led
to remarkable progress in semantic image synthesis. Nevertheless, synthesizing
high-quality images with consistent semantics and layout remains a challenge.
In this paper, we propose the adaPtive LAyout-semantiC fusion modulE (PLACE)
that harnesses pre-trained models to alleviate the aforementioned issues.
Specifically, we first employ the layout control map to faithfully represent
layouts in the feature space. Subsequently, we combine the layout and semantic
features in a timestep-adaptive manner to synthesize images with realistic
details. During fine-tuning, we propose the Semantic Alignment (SA) loss to
further enhance layout alignment. Additionally, we introduce the Layout-Free
Prior Preservation (LFP) loss, which leverages unlabeled data to maintain the
priors of pre-trained models, thereby improving the visual quality and semantic
consistency of synthesized images. Extensive experiments demonstrate that our
approach performs favorably in terms of visual quality, semantic consistency,
and layout alignment. The source code and model are available at
https://github.com/cszy98/PLACE/tree/main.
