  We propose an efficient diffusion-based text-to-video super-resolution (SR)
tuning approach that leverages the readily learned capacity of pixel level
image diffusion model to capture spatial information for video generation. To
accomplish this goal, we design an efficient architecture by inflating the
weightings of the text-to-image SR model into our video generation framework.
Additionally, we incorporate a temporal adapter to ensure temporal coherence
across video frames. We investigate different tuning approaches based on our
inflated architecture and report trade-offs between computational costs and
super-resolution quality. Empirical evaluation, both quantitative and
qualitative, on the Shutterstock video dataset, demonstrates that our approach
is able to perform text-to-video SR generation with good visual quality and
temporal consistency. To evaluate temporal coherence, we also present
visualizations in video format in
https://drive.google.com/drive/folders/1YVc-KMSJqOrEUdQWVaI-Yfu8Vsfu_1aO?usp=sharing .
