Trans-Adapter: A Plug-and-Play Framework for Transparent Image Inpainting

2025-08-01

AI Summary

Existing image inpainting methods operate solely on RGB inputs, rendering them inadequate for RGBA images requiring precise alpha-channel control; conventional two-stage approaches (inpainting followed by matting) suffer from alpha inconsistency and jagged alpha edges. This paper introduces Trans-Adapter, a plug-and-play adapter framework enabling end-to-end RGBA image inpainting for the first time using diffusion models, with native support for ControlNet-based conditional guidance. Key contributions include: (1) unified joint modeling of RGB and alpha channels to ensure semantic coherence and edge fidelity in transparent regions; (2) LayerBench—the first benchmark dedicated to transparent-image inpainting; and (3) α-FID, a reference-free metric quantifying alpha-edge quality. Extensive experiments demonstrate that Trans-Adapter significantly outperforms prior methods in both visual quality and alpha-boundary fidelity, while seamlessly integrating with mainstream open-source diffusion models.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Transparent, Interpretable, Explainable ML
• Humans and AI: AI for Accessibility

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

RGBA images, with the additional alpha channel, are crucial for any application that needs blending, masking, or transparency effects, making them more versatile than standard RGB images. Nevertheless, existing image inpainting methods are designed exclusively for RGB images. Conventional approaches to transparent image inpainting typically involve placing a background underneath RGBA images and employing a two-stage process: image inpainting followed by image matting. This pipeline, however, struggles to preserve transparency consistency in edited regions, and matting can introduce jagged edges along transparency boundaries. To address these challenges, we propose Trans-Adapter, a plug-and-play adapter that enables diffusion-based inpainting models to process transparent images directly. Trans-Adapter also supports controllable editing via ControlNet and can be seamlessly integrated into various community models. To evaluate our method, we introduce LayerBench, along with a novel non-reference alpha edge quality evaluation metric for assessing transparency edge quality. We conduct extensive experiments on LayerBench to demonstrate the effectiveness of our approach.

Problem

• Existing inpainting methods only handle RGB images, not RGBA.
• Current RGBA inpainting struggles with transparency consistency and edges.
• Trans-Adapter enables direct transparent image inpainting via diffusion models.

Innovation

• Plug-and-play adapter for transparent image inpainting
• Direct RGBA processing via diffusion-based models
• ControlNet integration for controllable editing