LimeCross: Context-Conditioned Layered Image Editing with Structural Consistency

TL;DR

LimeCross is a training-free framework for context-aware layered image editing that preserves structural consistency and layer integrity during text-guided modifications.

Contribution

It introduces a novel attention-based method for editing RGBA layers with context-awareness, avoiding layer collapse and background leakage.

Findings

LimeCross outperforms existing baselines in layer purity.

It maintains high composite realism in edited images.

The approach effectively preserves layer structure and transparency.

Abstract

Layered image assets are widely used in real-world creative workflows, enabling non-destructive iteration and flexible re-composition. Recent advances in layered image generation and decomposition synthesize or recover layered representations, yet controllable editing of layered images remains challenging. Manual editing requires careful coordination across layers to maintain consistent illumination and contact, while AI-based pipelines collapse layers into a flattened image for editing, then decompose them again, introducing background-to-foreground leakage and unstable transparency. To address these limitations, we propose LimeCross, a training-free context-conditioned layered image editing framework that edits user-selected RGBA layers according to text while keeping the remaining layers unchanged. It leverages contextual cues from other layers using a bi-stream attention mechanism to preserve cross-layer consistency, while explicitly maintaining layer integrity to prevent the contamination of edited layers. To evaluate our approach, we introduce LayerEditBench, a benchmark of 1500 layered scenes with paired source/target prompts, along with evaluation protocols that assess both edit fidelity and alpha channel stability. Extensive experiments demonstrate that LimeCross improves layer purity and composite realism over strong editing baselines, establishing context-conditioned layered editing as a principled framework for controllable generative creation.