What is TurboDiffusion
TurboDiffusion is jointly introduced by Tsinghua University, Shengshu Technology, and UC Berkeleyvideo generationacceleration framework. Through technologies such as SageAttention, SLA (Sparse Linear Attention), and rCM (timestep distillation), the framework increases video generation speed by 100~200 times, and can shorten a generation task that originally takes 184 seconds to 1.9 seconds on a single RTX 5090 GPU. The framework lowers the barrier to video generation, drives industry transformation, and makes creativity the core competitive advantage.
Main features of TurboDiffusion
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Significantly accelerate video generation: Increase video generation speed by 100-200 times, for example, on a single RTX 5090 GPU, shortening a generation task that originally takes 184 seconds to 1.9 seconds.
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High-quality video output: Maintain video quality while accelerating, and support generating 480p and 720p high-definition videos.
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Support multiple models: Provide multiple pre-trained models (such as Wan2.1 and Wan2.2), suitable for different tasks such as text-to-video (T2V) and image-to-video (I2V).
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Low resource requirements: Through quantization and optimization, reduce the demand for hardware resources, making video generation more efficient and economical.
Technical principles of TurboDiffusion
- SageAttention (low-bit attention mechanism): Replace the traditional attention mechanism with low-bit SageAttention, reducing computational complexity while maintaining high-quality output. Combined with SLA (Sparse Linear Attention), it further optimizes long-sequence processing and reduces the computational burden.
- rCM (timestep distillation): Through timestep distillation technology, compress the multi-step diffusion process of traditional diffusion models into a few steps, significantly reducing the computational steps required for generation.
- Model quantization (W8A8): TurboDiffusion adopts W8A8 quantization technology to quantize the model's weights and activations to 8 bits. Quantization reduces the model's storage requirements and computational complexity, while improving inference efficiency and reducing VRAM usage.
- Sparse activation and optimization: Introduce a sparse activation strategy to reduce computation by selectively activating neurons. Combined with dynamic activation checkpointing technology, it further optimizes memory usage and computational efficiency, and is especially suitable for high-dimensional data processing.
TurboDiffusion project address
- GitHub repository：https://github.com/thu-ml/TurboDiffusion
- arXiv technical paper：https://arxiv.org/pdf/2512.16093
Application scenarios of TurboDiffusion
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Video content creation: TurboDiffusion can quickly generate high-quality videos, suitable for fields such as advertising, film and television, and short videos, significantly shortening the creation cycle and supporting real-time interactive scenarios.
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Film and television production: Used for special effects production and video previews, helping to quickly generate complex visual effects and script concept validation, reducing production time and cost.
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Advertising and marketing: The framework can quickly generate personalized advertising videos, meet the needs of different scenarios, and improve advertising appeal and production efficiency.
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Education and training: The framework can generate educational videos and virtual training scenarios, enrich teaching content, and improve learning outcomes and training experience.
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Games and entertainment: Generate dynamic videos in game development and interactive entertainment, enhancing the visual experience and sense of immersion.