Cross-Frame Representation Alignment for Fine-Tuning Video Diffusion Models (2506.09229v2)

Abstract: Fine-tuning Video Diffusion Models (VDMs) at the user level to generate videos that reflect specific attributes of training data presents notable challenges, yet remains underexplored despite its practical importance. Meanwhile, recent work such as Representation Alignment (REPA) has shown promise in improving the convergence and quality of DiT-based image diffusion models by aligning, or assimilating, its internal hidden states with external pretrained visual features, suggesting its potential for VDM fine-tuning. In this work, we first propose a straightforward adaptation of REPA for VDMs and empirically show that, while effective for convergence, it is suboptimal in preserving semantic consistency across frames. To address this limitation, we introduce Cross-frame Representation Alignment (CREPA), a novel regularization technique that aligns hidden states of a frame with external features from neighboring frames. Empirical evaluations on large-scale VDMs, including CogVideoX-5B and Hunyuan Video, demonstrate that CREPA improves both visual fidelity and cross-frame semantic coherence when fine-tuned with parameter-efficient methods such as LoRA. We further validate CREPA across diverse datasets with varying attributes, confirming its broad applicability.

• The paper introduces CREPA, a method that fine-tunes Video Diffusion Models by aligning adjacent frame features to maintain semantic continuity.
• CREPA outperforms traditional approaches by significantly improving metrics such as motion smoothness, background consistency, and subject coherence.
• Empirical results on models like CogVideoX-5B and Hunyuan Video demonstrate enhanced Fréchet Video Distance and Inception Scores, indicating superior video synthesis quality.

Cross-Frame Representation Alignment for Fine-Tuning Video Diffusion Models: An Analytical Overview

The paper "Cross-Frame Representation Alignment for Fine-Tuning Video Diffusion Models" provides an extensive investigation into the enhancement of Video Diffusion Models (VDMs) through advanced fine-tuning techniques. This paper primarily revolves around the introduction and validation of Cross-frame Representation Alignment (CREPA), a novel regularization methodology, designed to address the challenge of maintaining semantic consistency across video frames during model fine-tuning.

Context and Motivation

Video Diffusion Models represent a burgeoning frontier in generative artificial intelligence, with the capacity to synthesize high-fidelity video sequences from text prompts. Nevertheless, fine-tuning these models to distill specific attributes from training datasets remains computationally intensive and challenging. Existing regularization approaches such as Representation Alignment (REPA), predominantly deployed for image diffusion models, exhibit limitations when directly applied to VDMs—most notably, a failure to ensure cross-frame semantic coherence, which is crucial for generating temporally consistent video content.

Contributions and Methodology

The proposal of CREPA seeks to fill the aforementioned gap by introducing a cross-frame mechanism into the fine-tuning process. Unlike its predecessor, REPA*, which aligns the hidden states of VDMs frame-by-frame to individual pretrained visual features, CREPA broadens the scope of alignment. It not only aligns the hidden states to the corresponding frames but also incorporates adjacent frame information. This cross-frame alignment leverages pretrained features from neighboring frames as a means to preserve temporal context and semantic continuity, effectively constraining the hidden states to align more faithfully with the learned semantic trajectory over time.

Empirically, the paper demonstrates that CREPA significantly improves both visual fidelity and semantic coherence in videos generated by two large-scale VDMs: CogVideoX-5B and Hunyuan Video. These models, when fine-tuned with CREPA, displayed marked advancements in key evaluation metrics, such as Motion Smoothness, Background Consistency, and Subject Consistency, as part of the VBench benchmark assessments. Furthermore, CREPA showcases improved Fréchet Video Distance (FVD) and Inception Score (IS), underscoring its enhanced capability in producing videos that are both perceptually and qualitatively superior to those generated via traditional methods.

Practical and Theoretical Implications

Practically, CREPA's introduction provides a versatile and computationally efficient framework for fine-tuning VDMs across diverse application domains, ranging from entertainment to educational content generation. By enhancing the ability of VDMs to semantically align with intricate stylistic and narrative patterns present in training datasets, CREPA empowers creators and developers to leverage large-scale generative models more effectively and with reduced computational overhead.

Theoretically, the paper contributes a nuanced understanding of the interplay between diffusion-based generative processes and cross-frame semantic alignment, evolving the discourse on how temporal representations can be harnessed to refine generative outcomes. This could spur further research into adaptive alignment techniques that dynamically modulate the extent of cross-frame regularization based on video content dynamics and generation context.

Future Directions

Future research could focus on extending CREPA's principles to enhance pre-training paradigms for VDMs, aiming to further collapse the gap between model generalization and task-specific specialization. Additionally, investigating CREPA's integration within emergent domains such as World Foundational Models, which facilitate 3D scene understanding and synthesis from video, presents a promising avenue for advancing VDM capabilities within spatially consistent scenarios.

In summary, the paper provides a comprehensive exploration of cross-frame semantic alignment, illustrating its efficacy and potential in refining video synthesis through diffusion models. CREPA stands out as a meaningful contribution to the toolkit for generative model optimization, with substantial implications for both current applications and future advancements in video-based AI systems.