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

• URL: http://arxiv.org/abs/2505.11468v1
• Date: Fri, 16 May 2025 17:23:35 GMT
• Title: PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment
• Authors: Dingbang Huang, Wenbo Li, Yifei Zhao, Xinyu Pan, Yanhong Zeng, Bo Dai,
• Abstract summary: PSDiffusion is a unified diffusion framework for simultaneous multi-layer text-to-image generation.<n>Our model can automatically generate multi-layer images with one RGB background and multiple RGBA foregrounds.<n>Our method introduces a global-layer interactive mechanism that generates layered-images concurrently and collaboratively.
• Score: 24.964578950380947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have made remarkable advancements in generating high-quality images from textual descriptions. Recent works like LayerDiffuse have extended the previous single-layer, unified image generation paradigm to transparent image layer generation. However, existing multi-layer generation methods fail to handle the interactions among multiple layers such as rational global layout, physics-plausible contacts and visual effects like shadows and reflections while maintaining high alpha quality. To solve this problem, we propose PSDiffusion, a unified diffusion framework for simultaneous multi-layer text-to-image generation. Our model can automatically generate multi-layer images with one RGB background and multiple RGBA foregrounds through a single feed-forward process. Unlike existing methods that combine multiple tools for post-decomposition or generate layers sequentially and separately, our method introduces a global-layer interactive mechanism that generates layered-images concurrently and collaboratively, ensuring not only high quality and completeness for each layer, but also spatial and visual interactions among layers for global coherence.

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