Dreamix: Video Diffusion Models are General Video Editors

Eyal Molad*,1, Eliahu Horwitz*,1,2, Dani Valevski*,1, Alex Rav Acha1, Yossi Matias1, Yael Pritch1, Yaniv Leviathan†,1, Yedid Hoshen†,1,2
1Google Research, 2The Hebrew University of Jerusalem
*Indicates Equal Contribution, Indicates Equal Advising


Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very few works have done so for video editing. We present the first diffusion-based method that is able to perform text-based motion and appearance editing of general videos. Our approach uses a video diffusion model to combine, at inference time, the low-resolution spatio-temporal information from the original video with new, high resolution information that it synthesized to align with the guiding text-prompt. As obtaining high-fidelity to the original video requires retaining some of its high-resolution information, we add a preliminary stage of finetuning the model on the original video, significantly boosting fidelity. We propose to improve motion editability by a new, mixed objective that jointly finetunes with full temporal attention and with temporal attention masking. We further introduce a new framework for image animation. We first transform the image into a coarse video by simple image processing operations such as replication and perspective geometric projections, and then use our general video editor to animate it. As a further application, we can use our method for subject-driven video generation. Extensive qualitative and numerical experiments showcase the remarkable editing ability of our method and establish its superior performance compared to baseline methods.

Video Editing


Subject Driven Video Generation

Method Overview: Mixed Video-Image Finetuning

Mixed Video-Image Finetuning

Finetuning the video diffusion model on the input video alone limits the extent of motion change. Instead, we use a mixed objective that beside the original objective (bottom left) also finetunes on the unordered set of frames. This is done by using “masked temporal attention”, preventing the temporal attention and convolution from being finetuned (bottom right). This allows adding motion to a static video.

Inference Overview

Inference Overview

Our method supports multiple applications by application dependent pre-processing (left), converting the input content into a uniform video format. For image-to-video, the input image is duplicated and transformed using perspective transformations, synthesizing a coarse video with some camera motion. For subject driven video generation, the input is omitted - finetuning alone take care of the fidelity. This coarse video is then edited using our general ``Dreamix Video Editor`` (right): we first corrupt the video by downsampling followed by adding noise. We then apply the finetuned text-guided video diffusion model, which upscales the video to the final spatio-temporal resolution

Video Presentation


  title={Dreamix: Video Diffusion Models are General Video Editors},
  author={Molad, Eyal and Horwitz, Eliahu and Valevski, Dani and Acha, Alex Rav and Matias, Yossi and Pritch, Yael and Leviathan, Yaniv and Hoshen, Yedid},
  journal={arXiv preprint arXiv:2302.01329},


We thank Ely Sarig for creating the video, Jay Tenenbaum for the video narration, Amir Hertz for the implementation of our eval baseline, Daniel Cohen-Or, Assaf Zomet, Eyal Segalis, Matan Kalman and Emily Denton for their valuable inputs that helped improve this work.