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.
Abstract (translated)
RGBA图像，由于其额外的alpha通道，在需要混合、遮罩或透明度效果的应用中至关重要。这使得它们比标准RGB图像更具灵活性和适用性。然而，现有的图像修复方法（即图像插值技术）主要是针对RGB图像设计的，并未专门考虑包含透明信息的RGBA图像。 对于处理含有透明度的图像时，传统的做法通常是将背景图放置在RGBA图像下方，并采用两阶段流程：首先是图像修复（inpainting），其次是图像遮罩(matting)。这种方案的问题在于它难以保持编辑区域中透明度的一致性，并且可能在透明边界处引入锯齿状边缘。 为了解决这些问题，我们提出了Trans-Adapter——一种即插即用的适配器，它可以使得基于扩散模型（diffusion-based models）的图像修复技术能够直接处理包含透明信息的RGBA图像。此外，Trans-Adapter还支持通过ControlNet进行可控编辑，并且可以无缝集成到各种社区模型中。 为了评估我们提出的方法的有效性，我们引入了LayerBench这一全新的基准测试框架以及一种新颖的非参考alpha边缘质量评价指标来专门衡量透明度边缘的质量水平。我们在LayerBench上进行了广泛实验，以证明我们的方法在处理包含透明信息的图像修复任务中的优越性能。
URL
https://arxiv.org/abs/2508.01098
