  Test-time adaptation (TTA) addresses the unforeseen distribution shifts
occurring during test time. In TTA, performance, memory consumption, and time
consumption are crucial considerations. A recent diffusion-based TTA approach
for restoring corrupted images involves image-level updates. However, using
pixel space diffusion significantly increases resource requirements compared to
conventional model updating TTA approaches, revealing limitations as a TTA
method. To address this, we propose a novel TTA method that leverages an image
editing model based on a latent diffusion model (LDM) and fine-tunes it using
our newly introduced corruption modeling scheme. This scheme enhances the
robustness of the diffusion model against distribution shifts by creating
(clean, corrupted) image pairs and fine-tuning the model to edit corrupted
images into clean ones. Moreover, we introduce a distilled variant to
accelerate the model for corruption editing using only 4 network function
evaluations (NFEs). We extensively validated our method across various
architectures and datasets including image and video domains. Our model
achieves the best performance with a 100 times faster runtime than that of a
diffusion-based baseline. Furthermore, it is three times faster than the
previous model updating TTA method that utilizes data augmentation, making an
image-level updating approach more feasible.
