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TurboDiffusion recipes

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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

Pick a recipe and runtime

Start with the result you want, then choose one of the runtimes FastVideo actually maintains for it.

Recipe Task and checkpoint
Runtime Maintained paths only

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Exact device and memory details appear only when a recorded run supports them.

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Maintained Source-backed Source config
Model
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Source configuration
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Expected output
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Terminal 
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Open example source View model card

Setup

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.