ShengShu Technology, in collaboration with Tsinghua University‘s TSAIL Lab, has officially open-sourced TurboDiffusion, a cutting-edge acceleration framework that boosts AI video generation speeds by 100 to 200 times while preserving visual quality. This release marks a pivotal moment for the AI video industry, heralding the arrival of a real-time generation era a “DeepSeek Moment” for video foundation models.
As generative AI rapidly advances in content creation, the focus in video generation is shifting. The industry is no longer asking whether AI can create videos, but whether it can deliver high-quality content faster, more cost-effectively, and at scale for practical applications. To resolve long-standing trade-offs among quality, speed, and computing costs in high-resolution, long-form video generation, ShengShu Technology and TSAIL conducted foundational research in inference efficiency. This work culminated in TurboDiffusion, designed to enhance the scalability and practicality of AI video creation.
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Since its launch, TurboDiffusion has attracted significant attention from the global AI research and developer community. Researchers from Meta and OpenAI, along with teams behind prominent open-source inference acceleration projects like vLLM, have engaged in discussions about its potential impact.
Breaking the Speed Barrier in High-Quality Video Generation
ShengShu Technology had already established a strong foothold in AI video generation. In September 2024, its Vidu platform became the first worldwide solution to introduce subject consistency functionality, setting a new standard for reference-based video generation and gaining widespread adoption among creators.
The subsequent launch of Vidu Q2 further solidified this leadership by offering:
- A complete image generation stack, covering text-to-image, enhanced reference-to-image, and full image editing;
- Advanced reference-based video generation, improving semantic understanding, camera control, and multi-subject consistency;
- High-efficiency image generation, producing 1080p images in just five seconds without compromising visual fidelity.
These achievements reflect Vidu’s strong model architecture and engineering capabilities, proving that speed gains were achieved without sacrificing quality.
As the industry moves toward higher-resolution, longer-duration videos, latency and cost remain key challenges. TurboDiffusion tackles these constraints through a combination of innovative techniques:
- Low-bit attention acceleration: Using SageAttention on low-bit Tensor Cores, it achieves multi-fold, lossless speedups.
- Sparse-Linear Attention (SLA): Sparsifying attention computation delivers an additional 17–20× acceleration.
- Sampling-step distillation: The rCM method enables high-quality video generation in only 3–4 steps.
- Linear layer acceleration: Quantizing weights and activations to 8-bit (W8A8) significantly speeds up linear computations while reducing VRAM usage.
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These technologies, independently developed by TSAIL and ShengShu, enable near-lossless acceleration, making real-time, high-quality video generation practical. SageAttention, in particular, has been integrated into NVIDIA TensorRT and deployed on platforms like Huawei Ascend and Moore Threads S6000. Companies including Tencent Hunyuan, ByteDance Doubao, Alibaba Tora, Baidu PaddlePaddle, Google Veo3, and SenseTime have also adopted this technology in their core products.
Turning Minutes into Seconds
TurboDiffusion delivers remarkable speed improvements. On open-source 1.3B/14B-T2V video generation models, it achieves up to 200× end-to-end acceleration on a single RTX 5090 GPU while maintaining visual quality. Applying the framework to ShengShu’s Vidu model allows an 8-second, 1080p video previously requiring 900 seconds to be generated in roughly 8 seconds.
This breakthrough brings AI video generation closer to real-time interaction and significantly improves usability for creators and enterprises alike.
Looking forward, ShengShu Technology will continue investing in foundational innovations, enhancing efficiency, reducing costs, and driving real-world adoption of generative AI. With ongoing improvements at both system and model levels, the company aims to usher in a more efficient era for creative ecosystems.
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