TurboDiffusion: 100–200× Acceleration for Video Diffusion Models (github.com)

TurboDiffusion, a new video generation acceleration framework, has been announced, promising to drastically speed up end-to-end diffusion generation by 100-200 times. Utilizing innovative techniques like SageAttention and Sparse-Linear Attention (SLA) for attention acceleration as well as rCM for timestep distillation, TurboDiffusion achieves an impressive end-to-end generation time of just 1.9 seconds, compared to the original model's 184 seconds. This leap in performance makes it a promising tool for the AI/ML community, particularly in video synthesis applications where speed and efficiency are critical. The repository, which includes essential installation and usage instructions for popular environments, supports the generation of high-quality videos at 480p and 720p resolutions. Model checkpoints provide users with immediate access to various configurations optimized for different GPU capabilities. The integration of adaptive models and real-time inference capabilities further underscores TurboDiffusion's significance, potentially lowering barriers for developers, researchers, and content creators to harness advanced video generation technologies effectively. As the project continues to evolve, including updates to checkpoints and documentation, it could redefine standards in video generation efficiency within the AI landscape.