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TokenFlow: Consistent diffusion features for consistent video editing

TokenFlow Editing results

Hover over the videos to see the original video and text prompts.

a car made of ice on an icy road

a robot spinning a silver ball


Input video


             Text-to-video [1]

Tune-a-video [2]

     Gen-1 [3]

        Per frame PnP[4]


        title = {TokenFlow: Consistent Diffusion Features for Consistent Video Editing},
        author = {Geyer, Michal and Bar-Tal, Omer and Bagon, Shai and Dekel, Tali},
        journal={arXiv preprint arxiv:2307.10373},

[1] Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan, Roberto Henschel, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. Text2video-zero: Text-to-image diffusion models are zero-shot video generators. arXiv preprint arXiv:2303.13439, 2023.

[2] Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian
Lei, Yuchao Gu, Wynne Hsu, Ying Shan, Xiaohu Qie, and
Mike Zheng Shou. Tune-a-video: One-shot tuning of image
diffusion models for text-to-video generation. arXiv preprint
arXiv:2212.11565, 2022

[3] Patrick Esser, Johnathan Chiu, Parmida Atighehchian,
Jonathan Granskog, and Anastasis Germanidis. Structure
and content-guided video synthesis with diffusion models.
arXiv preprint arXiv:2302.03011, 2023

[4] Narek Tumanyan, Michal Geyer, Shai Bagon, and
Tali Dekel. Plug-and-play diffusion features for text-
driven image-to-image translation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

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