Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style
  Transfer
Nisha Huang Yuxin Zhang Weiming Dong
Large-scale text-to-video diffusion models have demonstrated an exceptional
ability to synthesize diverse videos. However, due to the lack of extensive
text-to-video datasets and the necessary computational resources for training,
directly applying these models for video stylization remains difficult. Also,
given that the noise addition process on the input content is random and
destructive, fulfilling the style transfer task's content preservation criteria
is challenging. This paper proposes a zero-shot video stylization method named
Style-A-Video, which utilizes a generative pre-trained transformer with an
image latent diffusion model to achieve a concise text-controlled video
stylization. We improve the guidance condition in the denoising process,
establishing a balance between artistic expression and structure preservation.
Furthermore, to decrease inter-frame flicker and avoid the formation of
additional artifacts, we employ a sampling optimization and a temporal
consistency module. Extensive experiments show that we can attain superior
content preservation and stylistic performance while incurring less consumption
than previous solutions. Code will be available at
https://github.com/haha-lisa/Style-A-Video.