让扩散模型直接修复带透明通道的图像，效果更自然。
Trans-Adapter: A Plug-and-Play Framework for Transparent Image Inpainting
- 用插件式模块让扩散模型直接处理RGBA图像
- 新评估指标显示边缘更平滑，透明度更一致
- 可配合ControlNet实现可控编辑，适配主流模型
RGBA图像因包含额外的透明通道，在合成、蒙版和透明效果应用中比标准RGB图像更具优势。然而，现有图像修复方法均针对RGB图像设计。传统透明图像修复通常采用先修复后抠图的两阶段流程，但难以保持编辑区域的透明一致性，且抠图易产生锯齿状边缘。为此，我们提出Trans-Adapter——一个即插即用的适配器，使基于扩散的修复模型能直接处理透明图像。该模块支持通过ControlNet进行可控编辑，并可无缝集成至多种社区模型中。为评估方法效果，我们引入LayerBench数据集及一种新的非参考透明边缘质量评估指标，用于量化透明边界清晰度。在LayerBench上开展的大量实验验证了本方法的有效性。
原文摘要 · Abstract (English)
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.
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