Object-Centric Diffusion for Efficient Video Editing
(2401.05735)Abstract
Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts. However, such solutions typically incur heavy memory and computational costs to generate temporally-coherent frames, either in the form of diffusion inversion and/or cross-frame attention. In this paper, we conduct an analysis of such inefficiencies, and suggest simple yet effective modifications that allow significant speed-ups whilst maintaining quality. Moreover, we introduce Object-Centric Diffusion, coined as OCD, to further reduce latency by allocating computations more towards foreground edited regions that are arguably more important for perceptual quality. We achieve this by two novel proposals: i) Object-Centric Sampling, decoupling the diffusion steps spent on salient regions or background, allocating most of the model capacity to the former, and ii) Object-Centric 3D Token Merging, which reduces cost of cross-frame attention by fusing redundant tokens in unimportant background regions. Both techniques are readily applicable to a given video editing model \textit{without} retraining, and can drastically reduce its memory and computational cost. We evaluate our proposals on inversion-based and control-signal-based editing pipelines, and show a latency reduction up to 10x for a comparable synthesis quality.
Overview
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The paper addresses computational inefficiencies in video editing with diffusion models, proposing Object-Centric Diffusion to reduce the workload.
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Object-Centric Sampling and 3D Token Merging are introduced as methods to focus edits on crucial areas and streamline memory usage.
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The proposed techniques significantly reduce computation time and memory demands, with up to 10x latency reduction and 17x memory savings, while maintaining quality.
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The research suggests using existing optimizations like efficient samplers and token reduction to improve the speed of video editing processes.
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The study demonstrates how applying object-centric techniques to current frameworks results in substantial improvements in efficiency and quality.
Efficiency Improvements in Video Editing with Object-Centric Diffusion
Introduction to the Efficiency Challenge
Video editing powered by diffusion models has made great strides in quality and capability. Such models can now incorporate textual edit prompts to modify the global style, local structure, and attributes of video footage. Nonetheless, these advancements come with a significant computational load. Traditional techniques, including diffusion inversion and cross-frame self-attention, ensure temporal coherence but are computationally intense. This paper seeks to address these inefficiencies by proposing modifications that conserve quality while drastically speeding up the editing process.
Breaking Down Inefficiencies
Investigations into the current frameworks for video editing have identified major points of inefficiency, particularly in memory and computational demands. These problems largely stem from attention-based guidance and a high volume of diffusion steps during the video generation process. This research identifies ways to leverage existing optimizations, such as efficient samplers and token reduction in attention layers, to amplify speed without deterring the quality of edited content.
Object-Centric Techniques
The core innovation of the paper revolves around Object-Centric Diffusion (OCD). This method focuses computational efforts on the foreground, harnessing the concept that edits are often most crucial where the action is. Two novel techniques are introduced:
- Object-Centric Sampling: This method differentiates diffusion processes between edited and background areas, allowing for a more computationally efficient focus on the regions of interest.
- Object-Centric 3D Token Merging: This approach streamlines cross-frame attention by merging tokens in the less significant background regions, exploiting redundancies to reduce workload.
These techniques can be applied to existing video editing models swiftly, without retraining, while significantly lowering memory usage and computational costs.
Demonstrated Results and Contributions
Applying the proposed techniques to existing inversion-based and ControlNet-based video editing frameworks, the researchers attained impressive results. Notably, they achieved a latency reduction by a factor of 10x in inversion-based models and 6x in ControlNet-based models, with memory savings up to 17x, all the while maintaining comparative synthesis quality.
The contributions can be summarized as follows:
- An analysis and suggestions for acceleration in current video editing models.
- Introduction of Object-Centric Sampling for focused diffusion processing.
- Introduction of Object-Centric 3D Token Merging which reduces the number of cross-frame attention tokens.
- Optimization of two recent video editing models, showcasing rapid editing speeds without sacrificing quality.
Through extensive experiments, this paper affirms that focusing computational resources on imperative regions using object-centric solutions enhances the quality and efficiency of video editing. This work presents a meaningful step toward more efficient, high-quality video editing that can be of great benefit in various applications.
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