  Stable Diffusion is a popular Transformer-based model for image generation
from text; it applies an image information creator to the input text and the
visual knowledge is added in a step-by-step fashion to create an image that
corresponds to the input text. However, this diffusion process can be corrupted
by errors from the underlying hardware, which are especially relevant for
implementations at the nanoscales. In this paper, the dependability of Stable
Diffusion is studied focusing on soft errors in the memory that stores the
model parameters; specifically, errors are injected into some critical layers
of the Transformer in different blocks of the image information creator, to
evaluate their impact on model performance. The simulations results reveal
several conclusions: 1) errors on the down blocks of the creator have a larger
impact on the quality of the generated images than those on the up blocks,
while the errors on middle block have negligible effect; 2) errors on the
self-attention (SA) layers have larger impact on the results than those on the
cross-attention (CA) layers; 3) for CA layers, errors on deeper levels result
in a larger impact; 4) errors on blocks at the first levels tend to introduce
noise in the image, and those on deep layers tend to introduce large colored
blocks. These results provide an initial understanding of the impact of errors
on Stable Diffusion.
