  In this paper, we introduce the problem of zero-shot text-guided exploration
of the solutions to open-domain image super-resolution. Our goal is to allow
users to explore diverse, semantically accurate reconstructions that preserve
data consistency with the low-resolution inputs for different large
downsampling factors without explicitly training for these specific
degradations. We propose two approaches for zero-shot text-guided
super-resolution - i) modifying the generative process of text-to-image
\textit{T2I} diffusion models to promote consistency with low-resolution
inputs, and ii) incorporating language guidance into zero-shot diffusion-based
restoration methods. We show that the proposed approaches result in diverse
solutions that match the semantic meaning provided by the text prompt while
preserving data consistency with the degraded inputs. We evaluate the proposed
baselines for the task of extreme super-resolution and demonstrate advantages
in terms of restoration quality, diversity, and explorability of solutions.
