Efficient Temporal Consistency in Diffusion-Based Video Editing with Adaptor Modules: A Theoretical Framework

2025-04-22

AI Summary

Temporal inconsistency across frames remains a critical challenge in diffusion-based video editing. Method: This paper introduces the first theoretical adapter framework tailored for the DDIM sampler, featuring a lightweight adapter module that jointly learns shared and frame-specific prompts, coupled with a differentiable temporal consistency loss. We formally prove the Lipschitz continuity of this loss’s gradient and derive stability bounds for DDIM inversion alongside monotonic convergence guarantees for gradient descent. Contribution/Results: Our analysis bridges a fundamental gap—prior adapter methods for generative video lack rigorous temporal consistency guarantees. Experiments demonstrate that the proposed framework significantly improves inter-frame coherence under low-overhead editing, maintains controllable inversion error, and achieves both computational efficiency and reliability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Adapter-based methods are commonly used to enhance model performance with minimal additional complexity, especially in video editing tasks that require frame-to-frame consistency. By inserting small, learnable modules into pretrained diffusion models, these adapters can maintain temporal coherence without extensive retraining. Approaches that incorporate prompt learning with both shared and frame-specific tokens are particularly effective in preserving continuity across frames at low training cost. In this work, we want to provide a general theoretical framework for adapters that maintain frame consistency in DDIM-based models under a temporal consistency loss. First, we prove that the temporal consistency objective is differentiable under bounded feature norms, and we establish a Lipschitz bound on its gradient. Second, we show that gradient descent on this objective decreases the loss monotonically and converges to a local minimum if the learning rate is within an appropriate range. Finally, we analyze the stability of modules in the DDIM inversion procedure, showing that the associated error remains controlled. These theoretical findings will reinforce the reliability of diffusion-based video editing methods that rely on adapter strategies and provide theoretical insights in video generation tasks.

Problem

• Maintaining temporal coherence in diffusion-based video editing
• Differentiable temporal consistency under bounded feature norms
• Stability analysis of adapter modules in DDIM inversion

Innovation

• Adapter modules maintain temporal coherence efficiently
• Prompt learning with shared and specific tokens
• Theoretical framework for DDIM-based consistency adapters