DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models
Junhao Xia, Chaoyang Zhang, Yecheng Zhang, Chengyang Zhou, Zhichang Wang, Bochun Liu, Dongshuo Yin
TL;DR
DAPE introduces a two-stage, parameter-efficient fine-tuning framework for video editing with diffusion models, significantly enhancing temporal consistency and visual quality while reducing computational costs.
Contribution
The paper proposes a novel two-stage PEFT framework for video editing that improves temporal coherence and visual quality, and introduces a new comprehensive benchmark dataset.
Findings
DAPE outperforms previous methods in temporal coherence.
Enhanced text-video alignment achieved with DAPE.
Benchmark dataset enables more comprehensive evaluation.
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…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
* Extensive experiments (baseline comparisons & ablation studies) support the claims. * The paper is well-structured, easy to follow, and clearly written. * The new benchmark enables more comprehensive evaluation of video editing methods.
* The framework (dual-stage tuning + adapters) appears as an incremental extension of existing diffusion adaptation techniques rather than a fundamentally new paradigm. * The method assumes spatial and temporal features can be optimized independently, which may not hold for highly dynamic scenes. This may limit applicability to complex video content. * Depth-wise 5×5 convolutions in visual adapters are empirically motivated, but there is no theoretical or architectural justification for the choi
- The writing is easy to understand, and the painting is well-drawn. - The motivation of reducing computational costs and improve the performance for video editing task is effectiveness.
1. The fourth case in Fig.1 change the appearance of the cat, however, the appearance of the edited squirrel is weird. The result also lacks dynamic motion of the changed subjects. 2. In DAPE architecture, how does the model ensure both the quality of the temporal and visual feature learning if the framework uses a two-stage learning? 3. In Adjustable Norm-tuning, the finetune of normalization parameters has been proved in previous methods, the proposed adjustable norm-tuning actually belongs to
- The DAPE framework is not a standalone model but a "plug-and-play" tuning method. The authors demonstrate this strength by applying DAPE to several existing baseline models (like Tune-A-Video, RAVE, and CCEdit) and showing consistent improvements. - The quantitative results in Table 1 are convincing. They clearly show that applying DAPE ("DAPE (TAV)", "DAPE (RAVE)", etc.) almost universally improves the performance of the baseline models across a wide range of metrics, including temporal cons
- The primary weakness of the evaluation is the complete absence of video results. Video editing is an inherently dynamic task, and key artifacts like temporal flickering, incoherence, or unnatural motion can only be judged by viewing the output videos. The paper relies entirely on static frames (e.g., in Figure 7 and Figure 8) and aggregated user study scores. While the user study (Figure 11) asked participants to rate "overall smoothness (no distortion, flicker, etc.)", the reader cannot indep
1. The paper introduces the DAPE Dataset, a large-scale, well-curated, and richly annotated benchmark that addresses clear limitations of prior datasets and will be a valuable resource for the community. 2. The framework is shown to be a general-purpose enhancer that can be applied on top of various existing video editing models to improve their performance (as shown in Table 1).
1. The paper empirically demonstrates that "One-stage" joint training is suboptimal (Table 3) and attributes this to "negative interactions." However, it lacks a deeper analysis or intuition as to *why* this conflict occurs. Exploring the optimization dynamics (e.g., gradient conflicts) between the norm-tuning and the visual adapter would make the motivation even stronger. 2. While DAPE is a "parameter-efficient" method, the paper focuses primarily on performance (quality, consistency) gains. A
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Multimodal Machine Learning Applications · Video Analysis and Summarization
MethodsDiffusion · Adapter