Abstract
In recent years, diffusion models have been widely adopted for image inpainting tasks due to their powerful generative capabilities, achieving impressive results. Existing multimodal inpainting methods based on diffusion models often require architectural modifications and retraining, resulting in high computational cost. In contrast, context-aware diffusion inpainting methods leverage the model's inherent priors to adjust intermediate denoising steps, enabling high-quality inpainting without additional training and significantly reducing computation. However, these methods lack fine-grained control over the masked regions, often leading to semantically inconsistent or visually implausible content. To address this issue, we propose GuidPaint, a training-free, class-guided image inpainting framework. By incorporating classifier guidance into the denoising process, GuidPaint enables precise control over intermediate generations within the masked areas, ensuring both semantic consistency and visual realism. Furthermore, it integrates stochastic and deterministic sampling, allowing users to select preferred intermediate results and deterministically refine them. Experimental results demonstrate that GuidPaint achieves clear improvements over existing context-aware inpainting methods in both qualitative and quantitative evaluations.
Abstract (translated)
近年来，扩散模型因其强大的生成能力而在图像修复任务中得到了广泛的应用，并取得了令人印象深刻的结果。然而，基于扩散模型的现有多模态修复方法通常需要对架构进行修改和重新训练，导致计算成本高昂。相比之下，感知上下文的扩散修复方法利用了模型本身的先验知识来调整中间去噪步骤，在无需额外训练的情况下实现了高质量的图像修复，并显著降低了计算需求。然而，这些方法缺乏对遮罩区域的精细控制，往往会导致语义不一致或视觉上不可信的内容。 为了解决这个问题，我们提出了GuidPaint，这是一种无须重新训练、基于类别指导的图像修复框架。通过在去噪过程中加入分类器引导，GuidPaint能够在遮盖区域内精确地控制中间生成过程，确保内容既语义连贯又视觉真实。此外，它还结合了随机和确定性采样方法，允许用户选择所需的中间结果，并以确定性方式对其进行优化。 实验结果显示，在定性和定量评估中，GuidPaint在感知上下文的修复方法上均取得了显著改进。
URL
https://arxiv.org/abs/2507.21627
