FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention
<|reference_start|>FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention: Video diffusion models have made substantial progress in various video generation applications. However, training models for long video generation tasks require significant computational and data resources, posing a challenge to developing long video diffusion models. This paper investigates a straightforward and training-free approach to extend an existing short video diffusion model (e.g. pre-trained on 16-frame videos) for consistent long video generation (e.g. 128 frames). Our preliminary observation has found that directly applying the short video diffusion model to generate long videos can lead to severe video quality degradation. Further investigation reveals that this degradation is primarily due to the distortion of high-frequency components in long videos, characterized by a decrease in spatial high-frequency components and an increase in temporal high-frequency components. Motivated by this, we propose a novel solution named FreeLong to balance the frequency distribution of long video features during the denoising process. FreeLong blends the low-frequency components of global video features, which encapsulate the entire video sequence, with the high-frequency components of local video features that focus on shorter subsequences of frames. This approach maintains global consistency while incorporating diverse and high-quality spatiotemporal details from local videos, enhancing both the consistency and fidelity of long video generation. We evaluated FreeLong on multiple base video diffusion models and observed significant improvements. Additionally, our method supports coherent multi-prompt generation, ensuring both visual coherence and seamless transitions between scenes.<|reference_end|>
@article{lu2024freelong:,
    title={FreeLong: Training-Free Long Video Generation with SpectralBlend
  Temporal Attention},
    author={Yu Lu, Yuanzhi Liang, Linchao Zhu, Yi Yang},
    journal={arXiv preprint arXiv:2407.19918},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19918},
    primaryClass={cs.CV}
}