PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment

• URL: http://arxiv.org/abs/2505.11468v2
• Date: Sat, 08 Nov 2025 20:54:08 GMT
• Title: PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment
• Authors: Dingbang Huang, Wenbo Li, Yifei Zhao, Xinyu Pan, Chun Wang, Yanhong Zeng, Bo Dai,
• Abstract summary: Transparent image layer generation plays a significant role in digital art and design.<n>Existing methods typically decompose transparent layers from a single RGB image using a set of tools or generate multiple transparent layers sequentially.<n>We propose PSDiffusion, a unified diffusion framework that leverages image composition priors from pre-trained image diffusion model for simultaneous multi-layer text-to-image generation.
• Score: 23.67447416568964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transparent image layer generation plays a significant role in digital art and design workflows. Existing methods typically decompose transparent layers from a single RGB image using a set of tools or generate multiple transparent layers sequentially. Despite some promising results, these methods often limit their ability to model global layout, physically plausible interactions, and visual effects such as shadows and reflections with high alpha quality due to limited shared global context among layers. To address this issue, we propose PSDiffusion, a unified diffusion framework that leverages image composition priors from pre-trained image diffusion model for simultaneous multi-layer text-to-image generation. Specifically, our method introduces a global layer interaction mechanism to generate layered images collaboratively, ensuring both individual layer quality and coherent spatial and visual relationships across layers. We include extensive experiments on benchmark datasets to demonstrate that PSDiffusion is able to outperform existing methods in generating multi-layer images with plausible structure and enhanced visual fidelity.

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