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In recent years, conditional image synthesis has attracted growing attention due to its controllability in the image generation process. Although recent works have achieved realistic results, most of them fail to handle fine-grained styles with subtle details. To address this problem, a novel normalization module, named DRAN, is proposed. It learns fine-grained style representation, while maintaining the robustness to general styles. Specifically, we first introduce a multi-level structure,arXiv:2109.14525v3 fatcat:iayrldmpevfzbaenhlejh74icy