Cross-Frame Representation Alignment for Fine-Tuning Video Diffusion
  Models
Authors: name: Sungwon Hwang, name: Hyojin Jang, name: Kinam Kim, name: Minho Park, name: Jaegul choo

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.
Project page: https://crepavideo.github.io