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TurboDiffusion is an advanced open-source framework designed to dramatically accelerate video diffusion model generation, aiming for performance improvements on the order of 100–200× compared with traditional implementations while retaining high output quality. It achieves this by combining a suite of algorithmic and engineering optimizations, including attention acceleration techniques, efficient step distillation methods, and quantization strategies that reduce computational overhead. The project targets large video models and enables developers to run accelerated generation even on single high-end GPUs, making fast video synthesis more practical for research and creative workflows. TurboDiffusion is structured to integrate with existing diffusion model architectures and provides tools for experimenting with and benchmarking speed and quality trade-offs.
Features
- 100–200× acceleration for video diffusion generation
- Low-bit attention and sparse linear acceleration techniques
- Step distillation support for efficient training/inference workflows
- Quantized parameter and activation optimization
- Compatibility with existing diffusion model pipelines
- Tools for benchmarking speed vs quality
Project Samples
