SDMatte++: Grafting High-Resolution Diffusion Models for Interactive Matting.

Recent advances in interactive image matting have achieved substantial progress in capturing the primary regions of foreground objects. However, limited generalization capability and inadequate preservation of fine-grained details remain major challenges in interactive image matting. Meanwhile, diffusion models trained on large-scale image-text pairs learn unified visual representations, which enable them to provide strong semantic priors, making them an attractive solution for interactive image matting. To this end, we propose SDMatte, a diffusion-driven interactive matting framework that leverages the strong semantic priors of diffusion models to alleviate the limited generalization capability of existing interactive image matting methods. Specifically, we transform the text-driven interaction capability of diffusion models into a visual prompt-driven interaction capability to enable interactive matting. Furthermore, we integrate coordinate embeddings and opacity embeddings of target objects into U-Net, enhancing the model's sensitivity to spatial position information and opacity information. In addition, we propose a masked self-attention mechanism that enables the model to focus on regions specified by visual prompts, leading to improved interactive performance. Nevertheless, despite the advantages of SDMatte, its large-scale architecture typically necessitates training at low resolutions, which limits its ability to effectively leverage the richer and more pronounced high-frequency information available in high-resolution images for modeling fine-grained details. To this end, we propose a high-resolution detail injection strategy that progressively incorporates high-frequency information extracted from higher-resolution images into low-resolution latent features during the upsampling process, thereby enhancing the model's capability to preserve fine-grained details. Finally, due to the substantial computational overhead of diffusion-based large-scale architectures, which poses challenges for practical deployment, we distill SDMatte and SDMatte++ to obtain lightweight variants, LiteSDMatte and LiteSDMatte++. With only slight performance degradation, the lightweight variants achieve approximately 38% reduction in parameters, around 80% reduction in FLOPs, and roughly 3× faster inference compared to the original models. Extensive experiments on multiple datasets demonstrate the superior performance of our method, validating its generalization capability, fine-grained detail preservation, and accuracy in interactive matting.