Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models
📰 ArXiv cs.AI
Learn to compress video diffusion models using collaborative few-step distillation and low-bit quantization for efficient deployment
Action Steps
- Apply few-step distribution-matching distillation to reduce the number of denoising steps
- Use low-bit quantization to compress the model's parameters
- Calibrate the high-noise and low-noise routes in the dual-expert denoising pipeline
- Evaluate the compressed model's performance using metrics such as visual quality and parameter footprint
- Deploy the compressed model in a real-world application to test its efficiency
Who Needs to Know This
AI engineers and researchers working on video diffusion models can benefit from this technique to reduce deployment costs and improve efficiency
Key Insight
💡 Collaborative few-step distillation and low-bit quantization can significantly reduce the deployment costs of video diffusion models while maintaining strong visual quality
Share This
Compress video diffusion models with collaborative few-step distillation and low-bit quantization! #AI #VideoDiffusion
Key Takeaways
Learn to compress video diffusion models using collaborative few-step distillation and low-bit quantization for efficient deployment
Full Article
Title: Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models
Abstract:
arXiv:2606.00658v1 Announce Type: cross Abstract: Large video diffusion models achieve strong visual quality but remain expensive to deploy because each sample requires many denoising steps and a large resident parameter footprint. This paper studies a deployment-oriented compression pipeline for Wan2.2-T2V-A14B by combining few-step distribution-matching distillation with low-bit quantization. The pipeline follows the model's dual-expert denoising route, calibrates the high-noise and low-noise
Abstract:
arXiv:2606.00658v1 Announce Type: cross Abstract: Large video diffusion models achieve strong visual quality but remain expensive to deploy because each sample requires many denoising steps and a large resident parameter footprint. This paper studies a deployment-oriented compression pipeline for Wan2.2-T2V-A14B by combining few-step distribution-matching distillation with low-bit quantization. The pipeline follows the model's dual-expert denoising route, calibrates the high-noise and low-noise
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