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

• URL: http://arxiv.org/abs/2403.06269v2
• Date: Mon, 18 Nov 2024 23:08:21 GMT
• Title: FastVideoEdit: Leveraging Consistency Models for Efficient Text-to-Video Editing
• Authors: Youyuan Zhang, Xuan Ju, James J. Clark,
• Abstract summary: Existing approaches relying on image generation models for video editing suffer from time-consuming one-shot fine-tuning, additional condition extraction, or DDIM inversion. We propose FastVideoEdit, an efficient zero-shot video editing approach inspired by Consistency Models (CMs) Our method enables direct mapping from source video to target video with strong preservation ability utilizing a special variance schedule.
• Score: 8.907836546058086
• License:
• 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 inspired by Consistency Models (CMs). By leveraging the self-consistency property of CMs, we eliminate the need for time-consuming inversion or additional condition extraction, reducing editing time. Our method enables direct mapping from source video to target video with strong preservation ability utilizing a special variance schedule. This results in improved speed advantages, as fewer sampling steps can be used while maintaining comparable generation quality. Experimental results validate the state-of-the-art performance and speed advantages of FastVideoEdit across evaluation metrics encompassing editing speed, temporal consistency, and text-video alignment.

Related papers

• VideoDirector: Precise Video Editing via Text-to-Video Models [45.53826541639349]
  Current video editing methods rely on text-to-video (T2V) models, which inherently lack temporal-coherence generative ability. We propose a spatial-temporal decoupled guidance (STDG) and multi-frame null-text optimization strategy to provide pivotal temporal cues for more precise pivotal inversion. Experimental results demonstrate that our method effectively harnesses the powerful temporal generation capabilities of T2V models.
  arXiv  Detail & Related papers  (2024-11-26T16:56:53Z)
• Taming Rectified Flow for Inversion and Editing [57.3742655030493]
  Rectified-flow-based diffusion transformers, such as FLUX and OpenSora, have demonstrated exceptional performance in the field of image and video generation. Despite their robust generative capabilities, these models often suffer from inaccurate inversion, which could limit their effectiveness in downstream tasks such as image and video editing. We propose RF-r, a novel training-free sampler that enhances inversion precision by reducing errors in the process of solving rectified flow ODEs.
  arXiv  Detail & Related papers  (2024-11-07T14:29:02Z)
• COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing [57.76170824395532]
  Video editing is an emerging task, in which most current methods adopt the pre-trained text-to-image (T2I) diffusion model to edit the source video. We propose COrrespondence-guided Video Editing (COVE) to achieve high-quality and consistent video editing. COVE can be seamlessly integrated into the pre-trained T2I diffusion model without the need for extra training or optimization.
  arXiv  Detail & Related papers  (2024-06-13T06:27:13Z)
• Zero-Shot Video Editing through Adaptive Sliding Score Distillation [51.57440923362033]
  This study proposes a novel paradigm of video-based score distillation, facilitating direct manipulation of original video content. We propose an Adaptive Sliding Score Distillation strategy, which incorporates both global and local video guidance to reduce the impact of editing errors.
  arXiv  Detail & Related papers  (2024-06-07T12:33:59Z)
• 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)
• EffiVED:Efficient Video Editing via Text-instruction Diffusion Models [9.287394166165424]
  We introduce EffiVED, an efficient diffusion-based model that supports instruction-guided video editing. We transform vast image editing datasets and open-world videos into a high-quality dataset for training EffiVED.
  arXiv  Detail & Related papers  (2024-03-18T08:42:08Z)
• 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)
• Edit Temporal-Consistent Videos with Image Diffusion Model [49.88186997567138]
  Large-scale text-to-image (T2I) diffusion models have been extended for text-guided video editing. T achieves state-of-the-art performance in both video temporal consistency and video editing capability.
  arXiv  Detail & Related papers  (2023-08-17T16:40:55Z)
• 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)
• 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.