Trans-Adapter: A Plug-and-Play Framework for Transparent Image Inpainting
This addresses a specific technical bottleneck in image editing for applications requiring transparency effects, representing an incremental but practical improvement over existing methods.
The paper tackles the problem of transparent image inpainting for RGBA images, which existing methods handle poorly with a two-stage pipeline that compromises transparency consistency and edge quality. The proposed Trans-Adapter framework enables diffusion-based inpainting models to process transparent images directly, achieving improved results as validated on the introduced LayerBench dataset with a new evaluation metric.
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.