hao-ai-lab-FastVideo/docs/cookbook/turbodiffusion.md

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TurboDiffusion recipes

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Turbo

Maintained family · Inference

TurboDiffusion inference recipes

TurboDiffusion profiles accelerate Wan checkpoints with step-distilled sampling and the SLA attention backend. These recipes follow the registry's `turbodiffusion` model family.

Inference live Distillation planned Fine-tuning planned Training planned Evaluation planned Optimization planned Deployment planned

Choose a model and GPU count

Choose a model, then pick a GPU count. Each selection maps to one checked-in recipe. If that recipe does not use your chosen count, the page says so instead of guessing.

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Before you run

The generated commands expect a local clone:

git clone https://github.com/hao-ai-lab/FastVideo.git
cd FastVideo

Use Configuration for supported Python and CLI settings, Optimizations for attention and memory tradeoffs, and the support matrix for the supported model and optimization surface.

Troubleshooting

• TurboDiffusion paths load community-published loayrashid/TurboWan* checkpoints; availability is governed by those repos.
• The SLA attention backend used by the I2V recipe is selected inside the example source; do not combine it with another FASTVIDEO_ATTENTION_BACKEND override in the same shell.

Evidence status

All recipes on this page are Source-backed: their commands, model IDs, and flags were validated against the checked-in FastVideo sources listed above (static validation). No runtime GPU validation is recorded for these recipes, so GPU model fit, memory use, throughput, and runtime duration are Unknown and deliberately not claimed. Runtime buttons show only the GPU counts configured in checked-in sources.