PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment (2505.11468v1)

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.

Image Generation through PSDiffusion: A Unified Multi-Layer Approach

The paper "PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment" introduces an advanced framework for generating images with multiple layers, addressing existing limitations in multi-layer image synthesis related to interactions among different layers and maintaining high alpha quality across all components. The proposed PSDiffusion framework aims to deliver coherent and realistic multi-layer compositions through a single feed-forward process, which is a substantial improvement over traditional sequential or post-processing approaches that often suffer from accumulated errors and coherence issues.

Core Contributions

The PSDiffusion framework stands out with its ability to produce multi-layer images that consist of an RGB background and multiple RGBA foregrounds. The approach integrates several novel mechanisms to ensure both the quality of individual layers and their interactions:

1. Global-Layer Interactive Mechanism: PSDiffusion employs a unified diffusion framework capable of generating images with layered structures collaboratively and concurrently. This mechanism improves layer coherence without compromising the individuality of each layer.
2. Attention-Based Layer Generation: The method uses advanced attention mechanisms to align and harmonize the spatial layout of different layers. It extracts layout information from the global text-to-image denoising process, ensuring naturally arranged compositions.
3. Partial Joint Self-Attention Module: This module facilitates coherent content sharing and appearance harmonization among layers, leading to natural visual effects such as shadows and reflections across layers.

Inter-Layer Dataset

The development of the Inter-Layer dataset is another significant contribution of this research, tackling the scarcity of high-quality multi-layer image data. Comprising 30,000 images with 3-6 layers each, this dataset offers meticulously curated samples with professional alpha mattes and interactions. The dataset was constructed using a human-centric workflow, which involved professionals for precise editing, optimizing spatial layouts, and ensuring realistic inter-layer interactions.

Quantitative and Qualitative Analysis

The paper provides an in-depth evaluation comparing PSDiffusion with state-of-the-art methods such as LayerDiffuse and ART. PSDiffusion demonstrates superior performance in generating multi-layer images with realistic spatial layouts, coherent layer interactions, and high alpha quality. The experimental results highlight improvements in metrics such as CLIP Score and Fréchet Inception Distance (FID), confirming the framework’s capacity for enhanced image quality and text alignment.

Implications and Future Directions

The PSDiffusion framework offers significant implications in fields where layered graphical representations are essential, such as digital design, media production, and interactive applications. By improving the synthesis process, PSDiffusion supports better control over individual image components, thereby facilitating precise editing and asset recombination. Future research could explore the extension of PSDiffusion's technology to dynamic content creation, real-time compositional editing, and broader applications across varied visual domains.

In conclusion, the PSDiffusion framework represents a substantial advance in the domain of layered image generation, effectively addressing prior limitations related to layer interactions and alpha quality. Its innovative mechanisms and supporting dataset pave the way for more flexible and coherent multi-layer image synthesis, unlocking new potentials in AI-driven creativity and graphical editing.