Runnable, self-contained inference examples for the Wan2.1-1.3B (recipe, runtime) card on the v2 runtime. CPU + numpy only — the architecture (cards, driven loops, scheduler, caches, parity) is real; the neural forwards are numpy toys (see ../../v2/README.md and ../../design_summary.md). On a GPU box you swap ComponentSpec.factory for the torch Wan adapter and none of these scripts change.

Each script bootstraps the repo onto sys.path, so run it from anywhere:

python3 v2_examples/inference/01_basic_t2v.py

Script	What it shows
`01_basic_t2v.py`	the minimal path: build engine → typed T2V request → `run` → read the video artifact
`02_params_and_reproducibility.py`	`DiffusionParams` knobs (steps / CFG guidance / resolution) + seeded bit-identical reproducibility
`03_streaming.py`	per-denoise-step preview chunks via `OutputSpec(stream=...)` (off by default)
`04_concurrent_interleaved.py`	step-interleaved batching, the interleave parity gate (serial == interleaved), shared-prompt feature-cache reuse
`05_async_serving.py`	`AsyncEngine`: concurrent generate, live event streaming, step-boundary cancellation

All five use only the public API (v2.recipes.build_default_engine, v2.core.request.make_request, v2.runtime.AsyncEngine). For the OpenAI HTTP server, disaggregated pools, and the fleet/Dynamo path, see python3 -m v2.examples (example h).