"Lumina-Video: Efficient and Flexible Video Generation with Multi-scale Next-DiT"
Below podcast on this paper is generated with Google's Illuminate.
https://arxiv.org/abs/2502.06782
The challenge lies in efficiently generating high-quality videos due to the complex spatiotemporal nature of video data, which existing models like Next-DiT struggle with. This paper proposes Lumina-Video to address these challenges by improving efficiency and flexibility in video generation.
This is achieved through a Multi-scale Next-DiT architecture and incorporating motion score conditioning.
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📌 Multi-scale Next-DiT smartly allocates compute. It uses smaller patches for initial global structure and larger patches for detailed refinement. This hierarchical approach boosts inference speed without major quality loss.
📌 Motion score conditioning offers explicit video dynamic control. By tuning positive and negative classifier-free guidance motion scores, users gain intuitive command over video dynamism, a key feature for creative control.
📌 Progressive and multi-source training enhances Lumina-Video's robustness. Joint image-video training and synthetic data integration improve generalization and data efficiency, crucial for large video models.
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Methods Explored in this Paper 🔧:
→ Lumina-Video introduces Multi-scale Next-DiT architecture. This architecture uses multiple patch sizes within a single model. It shares a common backbone across different patch sizes for efficiency.
→ Multi-scale Next-DiT uses smaller patch sizes for initial denoising steps to capture global structure efficiently. Larger patch sizes are used in later steps for finer details. This multi-stage approach balances quality and computation.
→ The method incorporates motion scores derived from optical flow as an explicit condition. This motion score conditioning allows direct control over the dynamic degree of generated videos. It manipulates motion conditioning during classifier-free guidance to control dynamics.
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Key Insights 💡:
→ Multi-scale patchification improves inference efficiency with minimal quality sacrifice. It enables dynamic adjustment of computational cost based on needs.
→ Analysis reveals larger patch sizes are suitable for early denoising stages and smaller sizes for later stages. This justifies their multi-stage inference strategy.
→ Controlling the *difference* between positive and negative motion conditioning effectively adjusts video dynamics. Synthetic data shows promise for training stable video generation models.
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Results 📊:
→ Lumina-Video achieves a Total Quality Score of 82.94% on VBench benchmark.
→ It attains a Semantic Score of 78.39% and a Motion Smoothness score of 98.92% on VBench.
→ Ablation studies show combining multiple patch sizes achieves better quality-efficiency balance compared to single patch sizes.