DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models (2505.07057v1)

Abstract: Video generation based on diffusion models presents a challenging multimodal task, with video editing emerging as a pivotal direction in this field. Recent video editing approaches primarily fall into two categories: training-required and training-free methods. While training-based methods incur high computational costs, training-free alternatives often yield suboptimal performance. To address these limitations, we propose DAPE, a high-quality yet cost-effective two-stage parameter-efficient fine-tuning (PEFT) framework for video editing. In the first stage, we design an efficient norm-tuning method to enhance temporal consistency in generated videos. The second stage introduces a vision-friendly adapter to improve visual quality. Additionally, we identify critical shortcomings in existing benchmarks, including limited category diversity, imbalanced object distribution, and inconsistent frame counts. To mitigate these issues, we curate a large dataset benchmark comprising 232 videos with rich annotations and 6 editing prompts, enabling objective and comprehensive evaluation of advanced methods. Extensive experiments on existing datasets (BalanceCC, LOVEU-TGVE, RAVE) and our proposed benchmark demonstrate that DAPE significantly improves temporal coherence and text-video alignment while outperforming previous state-of-the-art approaches.

Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing

The paper "DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models" presents an insightful exploration into video editing using diffusion models, addressing current limitations in computational efficiency and output quality. Video generation, particularly within the scope of diffusion models, poses significant challenges due to the complexity of ensuring temporal coherence and precise text-video alignment.

Methodology

DAPE is introduced as a dual-stage parameter-efficient fine-tuning framework specifically designed for video editing tasks. The core innovation lies in its two distinct stages: norm-tuning and visual-adapter tuning.

• Stage 1: Norm-Tuning - This stage focuses on improving temporal consistency. It leverages a learnable norm-tuning method that adjusts normalization parameters to optimally balance features from original and normalized latent representations. The evidence from previous studies suggests that normalization scales play a crucial role in managing temporal dynamics in video diffusion models.
• Stage 2: Visual-Adapter Tuning - The subsequent stage is dedicated to enhancing visual quality. It employs an adapter module integrated into specific layers of the model, facilitating robust visual feature comprehension. This design choice aligns with existing research indicating adapters can significantly improve model adaptability in few-shot scenarios.

The authors also adopt a novel loss function—Huber loss—which offers robustness against outliers, accommodating the inherent distribution discrepancies between pretrained data and individual video samples.

Dataset Development

A substantial aspect of the work involves addressing shortcomings in existing benchmarks for video editing. The authors propose the DAPE Dataset, a curated collection of 232 videos featuring diverse content, standard resolution, and consistent frame counts. This dataset facilitates comprehensive evaluation across various editing scenarios, enabling objective comparisons.

Experimental Results

Extensive experiments conducted on established datasets like BalanceCC, LOVEU-TGVE, RAVE, alongside the DAPE Dataset, demonstrate DAPE's effectiveness. It significantly outperforms leading methods in enhancing temporal coherence, reducing interpolation errors, and aligning video outputs closely with input prompts.

Quantitative metrics reveal notable improvements:

• Enhanced CLIP-Frame similarity illustrates superior cross-frame consistency.
• Reduced Interpolation Error and increased PSNR confirm improved internal video continuity.
• Higher CLIP-Text scores signify better semantic fidelity in relation to given prompts.

Qualitative assessments and user studies further affirm DAPE's capacity to produce visually coherent and semantically relevant video edits.

Implications and Future Directions

The dual-stage framework posits a practical approach to video editing, balancing computational efficiency and output quality. This method not only suggests pathways for enhancing diffusion models but also underscores the potential of PEFT techniques in generative tasks.

In future developments, exploring adaptive strategies to dynamically configure stage parameters based on specific editing tasks or video characteristics may further optimize performance. Additionally, integrating multi-modal inputs for more intricate editing tasks, leveraging audio cues, or adapting models across broader application domains such as augmented reality or virtual environments, may yield insightful advancements in AI-driven video manipulation.

In conclusion, the paper makes substantive contributions to the domain of video editing via diffusion models, presenting a robust framework and dataset that set the stage for continued innovation in parameter-efficient model tuning and generative video tasks.