  The pre-trained text-to-image diffusion models have been increasingly
employed to tackle the real-world image super-resolution (Real-ISR) problem due
to their powerful generative image priors. Most of the existing methods start
from random noise to reconstruct the high-quality (HQ) image under the guidance
of the given low-quality (LQ) image. While promising results have been
achieved, such Real- ISR methods require multiple diffusion steps to reproduce
the HQ image, increasing the computational cost. Meanwhile, the random noise
introduces uncertainty in the output, which is unfriendly to image restoration
tasks. To address these issues, we propose a one-step effective diffusion
network, namely OSEDiff, for the Real- ISR problem. We argue that the LQ image
contains rich information to restore its HQ counterpart, and hence the given LQ
image can be directly taken as the starting point for diffusion, eliminating
the uncertainty introduced by random noise sampling. We finetune the
pre-trained diffusion network with trainable layers to adapt it to complex
image degradations. To ensure that the one-step diffusion model could yield HQ
Real-ISR output, we apply variational score distillation in the latent space to
conduct KL-divergence regularization. As a result, our OSEDiff model can
efficiently and effectively generate HQ images in just one diffusion step. Our
experiments demonstrate that OSEDiff achieves comparable or even better
Real-ISR results, in terms of both objective metrics and subjective
evaluations, than previous diffusion model based Real-ISR methods that require
dozens or hundreds of steps. The source codes will be released at
https://github.com/cswry/OSEDiff.
