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name: video-inference description: "Build and validate one-shot TurboDiffusion Wan T2V/I2V inference commands without running models." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TurboDiffusion video inference

Use this sub-skill when the task is to plan or review a one-shot TurboDiffusion video generation command for:

• Wan2.1 text-to-video (T2V) using a single DiT checkpoint.
• Wan2.2 image-to-video (I2V) using separate high-noise and low-noise DiT checkpoints.
• Prompt, image, model/checkpoint, quantization, attention, output-path, and no-download preflight decisions.

Do not use this sub-skill for interactive multi-turn generation; route that to interactive-serving. Route checkpoint conversion, quantization, rCM/SLA training, and checkpoint merging to training-and-checkpoints. Route CUDA extension, Sparse/SageSLA dependency, custom-op, and low-level acceleration failures to acceleration-backends.

Read first

• references/video-inference.md — one-shot T2V/I2V workflow, preflight checklist, and output validation.
• references/cli-reference.md — verified CLI flags and bundled command-builder options.
• references/model-assets.md — model catalog, checkpoint roles, quantized/unquantized selection, and required asset checklist.
• references/troubleshooting.md — failure matrix for quant flags, high/low I2V paths, adaptive resolution, attention backends, missing assets, output suffixes, and prompt quality.

Bundled helpers

These helpers only render shell commands. They do not import TurboDiffusion, download assets, run models, train, convert checkpoints, or access credentials.

• scripts/build_t2v_command.py — render a Wan2.1 T2V command and catch common checkpoint/--quant_linear mistakes.
• scripts/build_i2v_command.py — render a Wan2.2 I2V command and catch high/low checkpoint swaps, image-path omissions, and quantization flag mistakes.

Source-layout command quirk

TurboDiffusion's public one-shot scripts are usually launched from a source layout with helper packages such as imaginaire, rcm, serve, SLA, and ops importable from the inner turbodiffusion source directory. If a command fails with an import such as No module named imaginaire, add a public source-layout PYTHONPATH pointing at that inner source directory, for example PYTHONPATH=turbodiffusion, or run from an environment/layout where those helper packages are already importable.