FastVideoEdit: Leveraging Consistency Models for Efficient Text-to-Video Editing

arXiv:2403.06269v1 Announce Type: new
Abstract: Diffusion models have demonstrated remarkable capabilities in text-to-image and text-to-video generation, opening up possibilities for video editing based on textual input. However, the computational cost associated with sequential sampling in diffusion models poses challenges for efficient video editing. Existing approaches relying on image generation models for video editing suffer from time-consuming one-shot fine-tuning, additional condition extraction, or DDIM inversion, making real-time applications impractical. In this work, we propose FastVideoEdit, an efficient zero-shot video editing approach …

abstract arxiv capabilities challenges computational cost cs.cv diffusion diffusion models editing however image image generation image generation models sampling text text-to-image text-to-video textual type video video generation