As demand accelerates for scalable, high-quality AI video generation, the industry is reaching a critical turning point where speed and practicality matter as much as creative capability. ShengShu Technology and Tsinghua University TSAIL Lab have jointly announced the open-source release of TurboDiffusion, a new acceleration framework that delivers up to 100 to 200 times faster AI video generation with minimal to no loss in visual quality. The release is widely being described by researchers as a “DeepSeek Moment” for video foundation models, marking the arrival of real-time AI video creation.
While recent advances have proven that AI video generation is possible, real-world adoption has remained constrained by latency, cost, and compute intensity. TurboDiffusion directly addresses these long-standing limitations by fundamentally rethinking inference efficiency. Designed to support high-resolution, long-form video, the framework enables dramatic speed improvements while preserving visual stability, consistency, and fidelity, making AI video generation viable for enterprise and interactive use cases.
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Following its release, TurboDiffusion quickly gained traction across global research and developer communities. Researchers from organizations such as Meta and OpenAI, along with contributors to leading inference acceleration projects like vLLM, have actively discussed its implications for the future of generative video systems.
The breakthrough builds on ShengShu Technology’s earlier leadership in AI video. In September 2024, its Vidu model became the first globally to introduce subject consistency, enabling reference-based video generation. Subsequent updates with Vidu Q2 expanded these capabilities with advanced image generation, enhanced multi-subject consistency, improved camera control, and high-efficiency 1080p image output in seconds. These advances demonstrated that performance gains could be achieved through architectural and engineering innovation rather than sacrificing quality.
TurboDiffusion represents the next leap forward. Instead of relying on a single optimization, the framework combines multiple advanced acceleration techniques, including low-bit attention computation, sparse-linear attention, sampling-step distillation, and 8-bit quantization of linear layers. Together, these methods reduce computational overhead while maintaining near-lossless output, allowing diffusion-based models to approach real-time responsiveness.
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The impact on performance is substantial. On open-source text-to-video models, TurboDiffusion delivers up to a 200x end-to-end speedup on a single high-end GPU. When applied to ShengShu Technology’s proprietary Vidu model, a 1080p, eight-second video that previously required roughly 900 seconds to generate can now be produced in approximately eight seconds. What once took minutes is now achieved in seconds, fundamentally changing how creators and enterprises can interact with AI video systems.
Several of TurboDiffusion’s core techniques have already demonstrated real-world viability. SageAttention, which enables low-bit attention acceleration, has been integrated into NVIDIA TensorRT and deployed across platforms such as Huawei Ascend and Moore Threads GPUs. The technology has also been adopted by major AI systems including Tencent Hunyuan, ByteDance Doubao, Alibaba Tora, Baidu PaddlePaddle, SenseTime, and Google Veo.
By making TurboDiffusion fully open source, ShengShu Technology and Tsinghua University TSAIL Lab are accelerating the transition of AI video generation from experimental research into scalable, commercial deployment.
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