  This paper introduces ConStyle v2, a strong plug-and-play prompter designed
to output clean visual prompts and assist U-Net Image Restoration models in
handling multiple degradations. The joint training process of IRConStyle, an
Image Restoration framework consisting of ConStyle and a general restoration
network, is divided into two stages: first, pre-training ConStyle alone, and
then freezing its weights to guide the training of the general restoration
network. Three improvements are proposed in the pre-training stage to train
ConStyle: unsupervised pre-training, adding a pretext task (i.e.
classification), and adopting knowledge distillation. Without bells and
whistles, we can get ConStyle v2, a strong prompter for all-in-one Image
Restoration, in less than two GPU days and doesn't require any fine-tuning.
Extensive experiments on Restormer (transformer-based), NAFNet (CNN-based),
MAXIM-1S (MLP-based), and a vanilla CNN network demonstrate that ConStyle v2
can enhance any U-Net style Image Restoration models to all-in-one Image
Restoration models. Furthermore, models guided by the well-trained ConStyle v2
exhibit superior performance in some specific degradation compared to ConStyle.
