FastBlend: a Powerful Model-Free Toolkit Making Video Stylization Easier (2311.09265v1)

Abstract: 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.

• The paper introduces a model-free approach using patch matching to address video stylization consistency by blending frames and interpolating keyframes.
• It employs a sliding window blending mode and keyframe-based interpolation, reducing flicker and achieving efficient processing (e.g., 200 frames in 8 minutes on an NVIDIA 3060 GPU).
• Empirical results and open-source release demonstrate FastBlend's practical effectiveness in enhancing video-to-video translation pipelines and reducing artifacts.

FastBlend: A Toolkit for Consistent Video Stylization

The paper "FastBlend: a Powerful Model-Free Toolkit Making Video Stylization Easier" introduces an innovative approach to address consistency issues in video stylization, improving coherence across individual frames processed by diffusion models. FastBlend, as proposed by the authors, offers a model-free toolkit leveraging a patch matching algorithm and designed for both blending and interpolation modes to ensure seamless video transitions.

Key Contributions

1. Model-Free Approach: FastBlend operates solely in image space without altering the generation process of diffusion models, ensuring compatibility with existing methods. This design choice allows FastBlend to serve as a post-processing tool in various video-to-video translation pipelines.
2. Blending and Interpolation Modes: The toolkit introduces two inference modes to manage video stylization:
   • Blending Mode: It addresses video flicker by blending frames within a sliding window, using a patch matching algorithm for consistency.
   • Interpolation Mode: This mode renders entire videos using keyframes, facilitating smooth transitions and enhanced video coherence.
3. Algorithmic Efficiency: FastBlend integrates several optimizing techniques, including compiled kernel functions and tree-like data structures, which significantly enhance computational efficiency. This is particularly evident in its ability to transform 200 flickering frames on an NVIDIA 3060 GPU within just eight minutes.
4. Strong Empirical Results: In the blending mode, FastBlend surpasses previous methods in video deflickering and synthesis. Similarly, in the interpolation mode, it achieves superior results compared to other interpolation and model-based video processing techniques, as demonstrated through extensive experiments and human evaluation.

Implications and Future Directions

FastBlend's design promotes seamless integration with diffusion models, offering a robust solution to the video consistency problem that currently plagues diffusion-driven approaches. The practical implications extend to numerous video processing applications, particularly in enhancing the efficiency and quality of video stylization tasks. By maintaining coherence and efficiently handling computational demands, FastBlend sets a new standard for future developments in video processing pipelines.

The paper hints at potential expansions of FastBlend's capabilities, such as combining it with other advanced video processing methods. Future research could explore deep integration with various AI models, broadening its application scope while simultaneously refining the algorithm's speed and accuracy.

Concluding Remarks

FastBlend represents a notable advancement in video processing, bridging the gap between image and video stylization in diffusion-based models. Its effective handling of video flicker and consistency, combined with impressive computational efficiency, marks it as a valuable tool for researchers and practitioners in the video processing domain. The release of its source codes on GitHub further underscores a commitment to community-driven development and continued innovation.