Instruction-Based Video Editing by Repurposing an Image Editing Model

• URL: http://arxiv.org/abs/2608.14790v3
• Date: Fri, 21 Aug 2026 16:17:37 GMT
• Title: Instruction-Based Video Editing by Repurposing an Image Editing Model
• Abstract summary: We show that a strong instruction-based image editing model can edit videos by operating directly on video-VAE latents.<n>Our results suggest that, despite the large investment in training video latent spaces, per-frame video latents remain close enough to the image domain that mature image editing priors transfer with minimal adaptation.
• Score: 55.9214783600876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instruction-based video editing is commonly built on video-pretrained generative backbones: a video diffusion transformer is adapted, at considerable cost, to condition on a source video and an editing instruction. In this report we explore a different route and show that a strong instruction-based image editing model can edit videos by operating directly on video-VAE latents. Starting from Qwen-Image-Edit, we arrange the latent frames of a Wan~2.1 video VAE as tiles of one large virtual image, reuse the editor's image positional encoding for every tile, and bridge the two latent spaces with a pair of lightweight input/output projections warm-started from the editor's own patchify and unpatchify layers, so that at initialization a (static) video is embedded exactly as an image the model already understands. The whole system is then fine-tuned on the public Ditto-1M editing triplets, and a few denoising steps of Wan~2.2 serve as an optional temporal enhancer. We motivate the design with a chain of zero-training observations: the stock image editor already edits a video presented as a contact sheet; it is indifferent to whether the sheet's tokens come from one joint encode or from per-frame encodes stitched in latent space; and it even edits genuine video latents zero-shot to a clearly recognizable degree, leaving fine-tuning only a fidelity gap to close. Our results suggest that, despite the large investment in training video latent spaces, per-frame video latents remain close enough to the image domain that mature image editing priors transfer with minimal adaptation. Project Page: https://yunpeng1998.github.io/Qwen-Video-Edit-Page Code: https://github.com/yunpeng1998/Qwen-Video-Edit Model: https://huggingface.co/yunpeng1998/Qwen-Video-Edit

Related papers

• ViFeEdit: A Video-Free Tuner of Your Video Diffusion Transformer [74.61793196579036]
  We propose a video-free tuning framework termed ViFeEdit for video diffusion transformers.<n>ViFeEdit achieves versatile video generation and editing, adapted solely with 2D images.<n>Our method delivers promising results of controllable video generation and editing with only minimal training on 2D image data.
  arXiv  Detail & Related papers  (2026-03-16T16:10:46Z)
• FREE-Edit: Using Editing-aware Injection in Rectified Flow Models for Zero-shot Image-Driven Video Editing [12.549184989151135]
  Image-driven video editing aims to propagate edit contents from the modified first frame to the rest frames.<n>Current methods usually invert the source video to noise using a pre-trained image-to-video (I2V) model and then guide the sampling process using the edited first frame.<n>We propose an Editing-awaRE (REE) injection method to modulate injection intensity of each token.
  arXiv  Detail & Related papers  (2026-03-01T16:01:44Z)
• EasyV2V: A High-quality Instruction-based Video Editing Framework [108.78294392167017]
  captionemphEasyV2V is a framework for instruction-based video editing.<n>EasyV2V works with flexible inputs, e.g., video+text, video+mask+reference+, and state-of-the-art video editing results.
  arXiv  Detail & Related papers  (2025-12-18T18:59:57Z)
• I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models [18.36472998650704]
  We introduce a novel and generic solution that extends the applicability of image editing tools to videos by propagating edits from a single frame to the entire video using a pre-trained image-to-video model. Our method, dubbed I2VEdit, adaptively preserves the visual and motion integrity of the source video depending on the extent of the edits.
  arXiv  Detail & Related papers  (2024-05-26T11:47:40Z)
• Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models [65.268245109828]
  Ground-A-Video is a video-to-video translation framework for multi-attribute video editing. It attains temporally consistent editing of input videos in a training-free manner. Experiments and applications demonstrate that Ground-A-Video's zero-shot capacity outperforms other baseline methods in terms of edit-accuracy and frame consistency.
  arXiv  Detail & Related papers  (2023-10-02T11:28:37Z)
• VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing [18.24307442582304]
  We introduce VidEdit, a novel method for zero-shot text-based video editing. Our experiments show that VidEdit outperforms state-of-the-art methods on DAVIS dataset.
  arXiv  Detail & Related papers  (2023-06-14T19:15:49Z)
• Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models [68.31777975873742]
  Recent attempts at video editing require significant text-to-video data and computation resources for training. We propose vid2vid-zero, a simple yet effective method for zero-shot video editing. Experiments and analyses show promising results in editing attributes, subjects, places, etc., in real-world videos.
  arXiv  Detail & Related papers  (2023-03-30T17:59:25Z)
• Edit-A-Video: Single Video Editing with Object-Aware Consistency [49.43316939996227]
  We propose a video editing framework given only a pretrained TTI model and a single text, video> pair, which we term Edit-A-Video. The framework consists of two stages: (1) inflating the 2D model into the 3D model by appending temporal modules tuning and on the source video (2) inverting the source video into the noise and editing with target text prompt and attention map injection. We present extensive experimental results over various types of text and videos, and demonstrate the superiority of the proposed method compared to baselines in terms of background consistency, text alignment, and video editing quality.
  arXiv  Detail & Related papers  (2023-03-14T14:35:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.