  We propose Generative Probabilistic Image Colorization, a diffusion-based
generative process that trains a sequence of probabilistic models to reverse
each step of noise corruption. Given a line-drawing image as input, our method
suggests multiple candidate colorized images. Therefore, our method accounts
for the ill-posed nature of the colorization problem. We conducted
comprehensive experiments investigating the colorization of line-drawing
images, report the influence of a score-based MCMC approach that corrects the
marginal distribution of estimated samples, and further compare different
combinations of models and the similarity of their generated images. Despite
using only a relatively small training dataset, we experimentally develop a
method to generate multiple diverse colorization candidates which avoids mode
collapse and does not require any additional constraints, losses, or
re-training with alternative training conditions. Our proposed approach
performed well not only on color-conditional image generation tasks using
biased initial values, but also on some practical image completion and
inpainting tasks.
