用生成再拆解方法实现图像分层合成，无需训练即可控细节。
LayeringDiff: Layered Image Synthesis via Generation, then Disassembly with Generative Knowledge
- 先生成整体图，再拆解为前景背景层，避免逐层训练。
- 利用预训练模型生成多样内容与尺度，支持复杂场景。
- 高频率对齐模块提升细节精度，适合创意设计应用。
层次结构是专业艺术家的重要工具，可实现对视觉元素的独立控制。本文提出 LayeringDiff，一种新的分层图像合成方法：首先使用现成的图像生成模型生成复合图像，随后将其拆解为前景和背景层。通过从复合图像中提取层而非从零生成，LayeringDiff 避免了为单个图层开发生成能力所需的大量训练。同时，借助预训练的通用生成模型，该方法可生成多样化的内容和物体尺度。为实现有效的层分解，我们采用大规模预训练生成先验来估计前景与背景层，并提出高频对齐模块以优化估计层的细节。全面实验表明，该方法能有效合成分层图像，并支持多种实际应用场景。
原文摘要 · Abstract (English)
Layers have become indispensable tools for professional artists, allowing them to build a hierarchical structure that enables independent control over individual visual elements. In this paper, we propose LayeringDiff, a novel pipeline for the synthesis of layered images, which begins by generating a composite image using an off-the-shelf image generative model, followed by disassembling the image into its constituent foreground and background layers. By extracting layers from a composite image, rather than generating them from scratch, LayeringDiff bypasses the need for large-scale training to develop generative capabilities for individual layers. Furthermore, by utilizing a pretrained off-the-shelf generative model, our method can produce diverse contents and object scales in synthesized layers. For effective layer decomposition, we adapt a large-scale pretrained generative prior to estimate foreground and background layers. We also propose high-frequency alignment modules to refine the fine-details of the estimated layers. Our comprehensive experiments demonstrate that our approach effectively synthesizes layered images and supports various practical applications.
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