GuidPaint: Class-Guided Image Inpainting with Diffusion Models
This work addresses the issue of lack of control in image inpainting for users, though it is incremental as it builds on existing context-aware diffusion methods.
The paper tackles the problem of fine-grained control in training-free diffusion-based image inpainting, where existing methods often produce semantically inconsistent content, and proposes GuidPaint, which achieves clear improvements in qualitative and quantitative evaluations over context-aware inpainting methods.
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.