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

TL;DR

PSDiffusion is a unified diffusion-based framework that generates multi-layer transparent images with coherent layout and visual effects, improving over existing methods in quality and realism.

Contribution

The paper introduces PSDiffusion, a novel multi-layer image generation method that models global layout and interactions using a diffusion model with a global layer interaction mechanism.

Findings

Outperforms existing methods in multi-layer image quality

Produces images with more plausible structure and visual effects

Demonstrates effectiveness on benchmark datasets

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.