Efficient Semantic Image Synthesis via Class-Adaptive Normalization

TL;DR

This paper introduces CLADE, a lightweight normalization method for semantic image synthesis that maintains high quality while reducing computational overhead compared to SPADE, by focusing on semantic class-awareness.

Contribution

The paper proposes CLADE, a novel class-adaptive normalization technique that simplifies spatial adaptiveness, and introduces CLADE-ICPE with intra-class positional encoding for improved spatial modulation.

Findings

CLADE achieves comparable quality to SPADE with fewer parameters.

CLADE-ICPE further enhances spatial adaptiveness.

The methods are validated on multiple challenging datasets.

Abstract

Spatially-adaptive normalization (SPADE) is remarkably successful recently in conditional semantic image synthesis \cite{park2019semantic}, which modulates the normalized activation with spatially-varying transformations learned from semantic layouts, to prevent the semantic information from being washed away. Despite its impressive performance, a more thorough understanding of the advantages inside the box is still highly demanded to help reduce the significant computation and parameter overhead introduced by this novel structure. In this paper, from a return-on-investment point of view, we conduct an in-depth analysis of the effectiveness of this spatially-adaptive normalization and observe that its modulation parameters benefit more from semantic-awareness rather than spatial-adaptiveness, especially for high-resolution input masks. Inspired by this observation, we propose class-adaptive normalization (CLADE), a lightweight but equally-effective variant that is only adaptive to semantic class. In order to further improve spatial-adaptiveness, we introduce intra-class positional map encoding calculated from semantic layouts to modulate the normalization parameters of CLADE and propose a truly spatially-adaptive variant of CLADE, namely CLADE-ICPE.Through extensive experiments on multiple challenging datasets, we demonstrate that the proposed CLADE can be generalized to different SPADE-based methods while achieving comparable generation quality compared to SPADE, but it is much more efficient with fewer extra parameters and lower computational cost. The code and pretrained models are available at \url{https://github.com/tzt101/CLADE.git}.