TurboDiffusion
TurboDiffusion: Bringing Video Diffusion into the Seconds Era
Listed in categories:
DiffusionVideo
Description
TurboDiffusion is an acceleration framework for video generation models, designed to bring video diffusion into the seconds era. Developed by Tsinghua University's ML group, it achieves 100-200x end-to-end speedups on a single RTX 5090 while maintaining high video quality. It supports both text-to-video and image-to-video pipelines, making it suitable for practical deployment in various production platforms.
How to use TurboDiffusion?
To use TurboDiffusion, install the required Python and Torch versions, create a conda environment, and install the TurboDiffusion package. You can also build it from source by cloning the GitHub repository and installing the necessary dependencies.
Core features of TurboDiffusion:
1️⃣
Attention acceleration with SageAttention2++
2️⃣
Step distillation for high-quality video in 3-4 steps
3️⃣
Low-bit quantization (W8A8) for improved throughput
4️⃣
SLA sparse attention for additional speedup
5️⃣
Support for multiple video generation models (text-to-video and image-to-video)
Why could be used TurboDiffusion?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Rapid video generation for content creators | ✅ | |
| # 2 | Real-time video processing in gaming and streaming applications | ✅ | |
| # 3 | High-fidelity video production for commercial use | ✅ | |
Who developed TurboDiffusion?
TurboDiffusion is developed by a research team from Tsinghua University, UC Berkeley, and industry partners, led by Jun Zhu. The project aims to enhance video generation capabilities through innovative acceleration techniques.