Skiparse Attention makes full 3D video generation practical without sacrificing quality
Open-Sora Plan introduces an open-source video generation model that creates high-resolution, long-duration videos from various inputs. It uses a Wavelet-Flow VAE, Joint Image-Video Skiparse Denoiser, and condition controllers, achieving impressive generation quality while maintaining computational efficiency.
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https://arxiv.org/abs/2412.00131
🎯 Original Problem:
Generating high-quality, long-duration videos has been challenging due to computational costs and data requirements. Current models struggle with low resolution and short frame lengths.
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🔧 Solution in this Paper:
→ The architecture combines three key components: Wavelet-Flow VAE for efficient compression, Joint Image-Video Skiparse Denoiser for spatiotemporal modeling, and condition controllers for various inputs.
→ Skiparse Attention mechanism balances computation efficiency with modeling capability by alternating between Single Skip and Group Skip operations.
→ Min-Max Token Strategy aggregates data of different resolutions within same buckets for efficient computation.
→ Adaptive Gradient Clipping prevents outlier data from skewing model gradients.
→ Multi-dimensional data curation pipeline filters and annotates visual data automatically.
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💡 Key Insights:
→ Full 3D attention, while powerful, is computationally expensive; Skiparse Attention provides similar benefits at lower cost
→ Multi-stage training from images to videos enables better visual understanding
→ Efficient data curation is crucial for high-quality video generation
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📊 Results:
→ Achieves video generation at 256x256 resolution with 25-49 frames
→ Demonstrates stable motion and visual quality comparable to Full 3D Attention
→ Reduces attention computation complexity by factor of k while maintaining quality
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