  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.
