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.

本文介绍了零样本文本引导下的开放领域图像超分辨率探索问题，提出了两种零样本文本引导超分辨率的方法，涉及生成过程、扩散模型和恢复方法，实现了匹配语义意义并保持数据一致性的多样解决方案，优化了极端超分辨率任务的恢复质量、多样性和可探索性。