Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion

• URL: http://arxiv.org/abs/2501.05484v1
• Date: Wed, 08 Jan 2025 05:49:39 GMT
• Title: Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion
• Authors: Yongjia Ma, Junlin Chen, Donglin Di, Qi Xie, Lei Fan, Wei Chen, Xiaofei Gou, Na Zhao, Xun Yang,
• Abstract summary: We propose GLC-Diffusion, a tuning-free method for long video generation.<n>It models the long video denoising process by establishing Global-Local Collaborative Denoising.<n>We also propose a Video Motion Consistency Refinement (VMCR) module that computes the gradient of pixel-wise and frequency-wise losses.
• Score: 22.988212617368095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating high-fidelity, coherent long videos is a sought-after aspiration. While recent video diffusion models have shown promising potential, they still grapple with spatiotemporal inconsistencies and high computational resource demands. We propose GLC-Diffusion, a tuning-free method for long video generation. It models the long video denoising process by establishing denoising trajectories through Global-Local Collaborative Denoising to ensure overall content consistency and temporal coherence between frames. Additionally, we introduce a Noise Reinitialization strategy which combines local noise shuffling with frequency fusion to improve global content consistency and visual diversity. Further, we propose a Video Motion Consistency Refinement (VMCR) module that computes the gradient of pixel-wise and frequency-wise losses to enhance visual consistency and temporal smoothness. Extensive experiments, including quantitative and qualitative evaluations on videos of varying lengths (\textit{e.g.}, 3\times and 6\times longer), demonstrate that our method effectively integrates with existing video diffusion models, producing coherent, high-fidelity long videos superior to previous approaches.

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