Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse data. Its quality strongly depends on the choice of known data. Optimising their spatial location -- the inpainting mask -- is challenging. A commonly used tool for this task are stochastic optimisation strategies. However, they are slow as they compute multiple inpainting results. We provide a remedy in terms of a learned mask generation model. By emulating the complete inpainting pipeline with two networks for mask generation and neural surrogate inpainting, we obtain a model for highly efficient adaptive mask generation. Experiments indicate that our model can achieve competitive quality with an acceleration by as much as four orders of magnitude. Our findings serve as a basis for making diffusion-based inpainting more attractive for applications such as image compression, where fast encoding is highly desirable.
翻译:基于扩散的绘图是利用稀有数据重建图像的有力工具。 其质量在很大程度上取决于已知数据的选择。 优化它们的空间位置 -- -- 油漆面罩 -- -- 具有挑战性。 这项任务常用的工具是随机优化战略。 但是,它们在计算多重油漆结果时速度缓慢。 我们从学习的遮罩生成模型的角度提供了一种补救措施。 通过以两个掩罩生成网络和神经代孕涂漆网络模拟完整的油漆管道,我们获得了高效的适应性掩罩生成模型。 实验显示,我们的模型可以达到竞争质量,加速达到四个级的强度。 我们的发现作为基础,使基于扩散的油漆对图像压缩等应用更具吸引力, 快速编码非常可取。