  Generative Artificial Intelligence image models have achieved outstanding
performance in text-to-image generation and other tasks, such as inpainting
that completes images with missing fragments. The performance of inpainting can
be accurately measured by taking an image, removing some fragments, performing
the inpainting to restore them, and comparing the results with the original
image. Interestingly, inpainting can also be applied recursively, starting from
an image, removing some parts, applying inpainting to reconstruct the image,
and then starting the inpainting process again on the reconstructed image, and
so forth. This process of recursively applying inpainting can lead to an image
that is similar or completely different from the original one, depending on the
fragments that are removed and the ability of the model to reconstruct them.
Intuitively, stability, understood as the capability to recover an image that
is similar to the original one even after many recursive inpainting operations,
is a desirable feature and can be used as an additional performance metric for
inpainting. The concept of stability is also being studied in the context of
recursive training of generative AI models with their own data. Recursive
inpainting is an inference-only recursive process whose understanding may
complement ongoing efforts to study the behavior of generative AI models under
training recursion. In this paper, the impact of recursive inpainting is
studied for one of the most widely used image models: Stable Diffusion. The
results show that recursive inpainting can lead to image collapse, so ending
with a nonmeaningful image, and that the outcome depends on several factors
such as the type of image, the size of the inpainting masks, and the number of
iterations.
