thu-ml/TurboDiffusion

Health v2 · maintenance only

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3851 stars · 277 forks observed · 2026-10-01

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TurboDiffusion is a Python framework that accelerates end-to-end video diffusion model generation by 100-200x using SageAttention, Sparse-Linear Attention, and rCM timestep distillation. It ships accelerated TurboWan checkpoints for text-to-video and image-to-video generation on consumer GPUs like the RTX 5090.

Use cases

• generate videos from text prompts much faster
• speed up video diffusion model inference
• run video generation on a single consumer GPU
• accelerate Wan video models with distilled checkpoints
• reduce diffusion sampling steps for video generation
• generate 480p or 720p videos in seconds instead of minutes

When to choose

• you need fast video generation with minimal quality loss on limited GPU hardware
• you want to use Wan 2.1/2.2 video models with dramatically reduced inference time
• you are doing research or building apps around accelerated video diffusion

When to avoid

• you need video models other than the provided TurboWan checkpoints
• you require maximum video quality over generation speed
• you work with non-English prompts without prompt augmentation
• you need a finalized, production-hardened release (checkpoints and paper are still being updated)

Facets

library · maturity active

llm-inference machine-learning video-processing gpu-computing deep-learning artificial-intelligence gpu-computing python diffusion-models inference-acceleration video-generation sageattention sparse-linear-attention consistency-model timestep-distillation ai-infra video gpu linux

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