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FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors (2501.08225v1)

Published 14 Jan 2025 in cs.CV

Abstract: Interactive image editing allows users to modify images through visual interaction operations such as drawing, clicking, and dragging. Existing methods construct such supervision signals from videos, as they capture how objects change with various physical interactions. However, these models are usually built upon text-to-image diffusion models, so necessitate (i) massive training samples and (ii) an additional reference encoder to learn real-world dynamics and visual consistency. In this paper, we reformulate this task as an image-to-video generation problem, so that inherit powerful video diffusion priors to reduce training costs and ensure temporal consistency. Specifically, we introduce FramePainter as an efficient instantiation of this formulation. Initialized with Stable Video Diffusion, it only uses a lightweight sparse control encoder to inject editing signals. Considering the limitations of temporal attention in handling large motion between two frames, we further propose matching attention to enlarge the receptive field while encouraging dense correspondence between edited and source image tokens. We highlight the effectiveness and efficiency of FramePainter across various of editing signals: it domainantly outperforms previous state-of-the-art methods with far less training data, achieving highly seamless and coherent editing of images, \eg, automatically adjust the reflection of the cup. Moreover, FramePainter also exhibits exceptional generalization in scenarios not present in real-world videos, \eg, transform the clownfish into shark-like shape. Our code will be available at https://github.com/YBYBZhang/FramePainter.

Summary

  • The paper reframes interactive image editing as an image-to-video generation task, harnessing video diffusion priors for enhanced transformation handling.
  • It introduces a sparse control encoder with matching attention to seamlessly manage editing signals and ensure token consistency during large visual edits.
  • FramePainter achieves superior performance with fewer training samples, indicating both efficient resource utilization and robust generalization across diverse editing tasks.

Overview of "FramePainter: Endowing Interactive Image Editing with Video Diffusion Priors"

The paper introduces FramePainter, an innovative approach for interactive image editing utilizing video diffusion priors. The method fundamentally transforms traditional image editing paradigms by reformulating the task as an image-to-video generation problem. This novel perspective allows the integration of dynamic knowledge from pre-trained video diffusion models, specifically harnessing the powerful priors within these models to improve image editing in both training efficiency and output consistency.

FramePainter builds off the Stable Video Diffusion (SVD) framework, introducing a sparse control encoder to manage editing signals, such as sketches or drag-based points. The system is fortified by leveraging matching attention mechanisms, facilitating enhanced correspondence between source and edited image tokens. This is particularly critical for managing large transformations within visual content, which temporal attention models often find challenging due to reduced receptive fields. By incorporating these advancements, FramePainter requires substantially fewer training samples than previous state-of-the-art methods while achieving improved performance and compatibility with dynamic real-world editing applications.

Key Contributions

  1. Reformulation of Image Editing Tasks: The task is reframed from image-to-image transformation to an image-to-video generation problem, capitalizing on video diffusion models' strengths in processing real-world dynamic scenarios.
  2. Introduction of Matching Attention: By proposing a matching attention mechanism, the paper addresses the limitations of temporal attention models, enhancing image consistency by promoting dense token correspondences.
  3. Efficiency and Data Utilization: The technique considerably reduces the need for extensive datasets and complex model architectures through the infusion of video diffusion priors, offering a more resource-efficient alternative to conventional methods.
  4. Empirical Superiority and Generalization: FramePainter's empirical evaluation demonstrates superior performance across various editing tasks with less training data. It also generalizes effectively to out-of-domain applications, such as novel shape transformations.

Implications and Prospects

The implications of this work are twofold. Practically, FramePainter enhances the utility of image editing software, making it more accessible to users by simplifying the editing process and reducing computational overhead. Theoretically, this approach validates the integration of video diffusion models into image processing tasks, suggesting a broader applicability of these techniques across other domains, including augmented reality and automated design systems.

Furthermore, the paper hints at future developments in artificial intelligence, particularly in evolving cross-modal applications where the integration of video knowledge into static image models could become a norm. The dynamic nature of video data offers rich, underutilized potentials for improving static image manipulations, positioning FramePainter as a foundational reference for such future explorations.

Conclusion

In summary, FramePainter introduces a paradigm shift in how interactive image editing tasks are approached. Through video diffusion priors and innovative attention mechanisms, it offers a significant advancement in editing accuracy and efficiency, adapting to complex visual transformations with unmatched ease. This research not only enriches the current methodologies available for image editing but also inspires the exploration of video-informed models across diverse applications in AI.

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