FastBlend: a Powerful Model-Free Toolkit Making Video Stylization Easier
Zhongjie Duan Chengyu Wang Cen Chen Weining Qian Jun Huang Mingyi Jin
With the emergence of diffusion models and rapid development in image
processing, it has become effortless to generate fancy images in tasks such as
style transfer and image editing. However, these impressive image processing
approaches face consistency issues in video processing. In this paper, we
propose a powerful model-free toolkit called FastBlend to address the
consistency problem for video processing. Based on a patch matching algorithm,
we design two inference modes, including blending and interpolation. In the
blending mode, FastBlend eliminates video flicker by blending the frames within
a sliding window. Moreover, we optimize both computational efficiency and video
quality according to different application scenarios. In the interpolation
mode, given one or more keyframes rendered by diffusion models, FastBlend can
render the whole video. Since FastBlend does not modify the generation process
of diffusion models, it exhibits excellent compatibility. Extensive experiments
have demonstrated the effectiveness of FastBlend. In the blending mode,
FastBlend outperforms existing methods for video deflickering and video
synthesis. In the interpolation mode, FastBlend surpasses video interpolation
and model-based video processing approaches. The source codes have been
released on GitHub.