git:20260810.d4417f7 to git:20260814.cfc74ba

14 added, 11 removed. Audit A to A.

---

name: wan2-2

description: Use when packaging, running, reviewing, or extending the Alibaba Wan 2.2 TI2V-5B BYOF solution, its official video artifacts, or its verified Rerun evidence.

---

# Wan 2.2 Workbench support

Use this skill for the public Wan 2.2 registry candidate and its verified video

evidence. Read these files before changing behavior:

- `npa/workflows/workbench/npa-workflows/byof-wan2.2.yaml`

- `npa/workflows/workbench/npa-workflows/byof-wan2.2-multigpu.yaml`

- `npa/src/npa/workflows/wan_rerun.py`

- `docs/workbench/wan2.2.md`

Also load `byof-onboard`, `oss-solution-registry-onboard`,

`author-npa-workflow`, `real-components`, `solution-licensing`, `gpu-selection`,

`nebius-infra`, `testing-conventions`, `npa-agent`, and

`agent-visual-feedback` when their surfaces are involved.

## Ground truth

- Official source: `https://github.com/Wan-Video/Wan2.2.git`, pinned to

`42bf4cfaa384bc21833865abc2f9e6c0e67233dc`.

- Official model: `Wan-AI/Wan2.2-TI2V-5B`, pinned to

`921dbaf3f1674a56f47e83fb80a34bac8a8f203e`.

- TI2V-5B is a stock generative-video model supporting text and image inputs.

- - A private validation record accepted the real single-GPU

+ - A historical private validation record accepted the real single-GPU

text-to-video path on RTX PRO 6000 Blackwell (`sm_120`) from immutable image

digest `sha256:1baa4e2e89999ea26df81891ac786fa99c7498cbf173e5c5abad54c6f1dd1d13`,

including exact MP4/RRD byte identity.

- - A private validation record accepted one shared official

+ - A historical private validation record accepted one shared official

generation from that same observed image digest on four B200s (`sm_100`) with

world size 4, NCCL, T5 and DiT FULL_SHARD FSDP, Ulysses size 4, and exact

MP4/RRD byte identity.

+ - Those records used Torch 2.7.1/CUDA 12.8 and NCCL 2.27.7. The current

+ acceptance gate is Torch 2.13.0/CUDA 13.0 and NCCL 2.29.7; it requires fresh

+ operator-accepted single- and four-GPU evidence before publication.

- I2V, A14B, speech-to-video, Animate, and training are separate capabilities.

- Stock Wan does not predict robot actions. Never claim that it is

action-conditioned.

For changing facts, use only the official Wan repository, official Wan-AI model

cards, and primary framework documentation.

## Packaging contract

Use `workbench.byof.repo`; do not add a fake Wan toolRef. Keep the repo and all

model inputs immutable. The image may contain pinned source and dependencies but

no checkpoint weights, credentials, private code, or user data. The runtime

must remain non-root, with `/opt/byof` and its venv readable and executable.

The single-GPU baseline requests one RTX PRO 6000 Blackwell (`sm_120`), uses the

- official PyTorch 2.7.1 CUDA 12.8 wheel line, and binds pinned Wan attention to

- native PyTorch SDPA instead of FlashAttention. Record the device, compute

- capability, driver, CUDA, torch version, compiled arch list, and finite SDPA

- probe.

+ security-fixed PyTorch 2.13.0 CUDA 13.0 wheel line, and binds pinned Wan

+ attention to native PyTorch SDPA instead of FlashAttention. Record the device,

+ compute capability, driver, CUDA, torch version, compiled arch list, and finite

+ SDPA probe.

The distributed spec uses `byof-solution-smoke-wan22-b200-4gpu.yaml` and exactly

four ranks. Invoke the official path with:

```text

/opt/byof/.venv/bin/python -m torch.distributed.run --standalone \

--nnodes=1 --nproc_per_node=4 wan22_distributed_wrapper.py

# The generated wrapper instruments all ranks, then executes pinned official

# /opt/byof/generate.py as __main__ with --dit_fsdp --t5_fsdp --ulysses_size 4.

```

Fail closed unless every rank proves NCCL initialization/all-reduce, a unique

local B200, T5 and DiT FULL_SHARD wrappers, live Ulysses distributed-attention

and all-to-all calls, the upstream final barrier, observer terminal

synchronization, and compute capability 10.0 with `sm_100` support.

The single-GPU smoke writes:

- `wan2_2_ti2v_5b.mp4`

- `wan2_2_ti2v_5b_text_to_video.json`

- `wan2_2_runtime_inventory.json`

The distributed smoke additionally writes:

- `wan2_2_ti2v_5b_multigpu.mp4`

- `wan2_2_ti2v_5b_multigpu.json`

- `wan2_2_multigpu_topology.json`

- `wan2_2_multigpu_runtime_inventory.json`

- `wan2_2_multigpu_rank_0.json` through `wan2_2_multigpu_rank_3.json`

Decode every frame and fail on invalid dimensions/count/FPS, a corrupt or empty

container, an implausibly small file, or uniform content. Keep

`capabilities_exercised` exact and `deferred` empty for hard-gated runs.

## Rerun evidence contract

Every successful named Wan solution smoke is postprocessed by

`npa.workflows.wan_rerun`. The postprocessor runs after the existing BYOF S3

upload and must fail the parent command if source validation, RRD generation,

local parsing, `rerun rrd verify`, upload, S3 byte verification, remote parsing,

or manifest verification fails.

Use Rerun SDK `0.31.4`, matching the agent-compatible `npa[viz]` extra. Embed

the exact MP4 at `/wan2_2/video/asset`, log one timestamped

`VideoFrameReference` per decoded frame at `/wan2_2/video/frame`, and use the

`video_time` duration timeline. Static JSON facts belong in the summary,

validation, runtime, distributed, rank, and metric entities; do not invent a

time series.

The distributed filenames are:

- `wan2_2_ti2v_5b_multigpu.rrd`

- `wan2_2_ti2v_5b_multigpu_rrd_manifest.json`

The manifest must contain source object URIs plus ETags, byte sizes, and

SHA-256 values; RRD URI/hash/size/version/entities; embedded-video identity;

and local plus remote verification. Only a successfully uploaded and remotely

verified manifest may name `wan2.2_verified_rerun_recording`.

## Capability status

| Capability | Status |

| --- | --- |

- | `wan2.2_ti2v_5b_text_to_video` | accepted; real RTX PRO run |

- | `wan2.2_decoded_mp4_validation` | accepted; 17 decoded 1280×704 frames at 24 fps |

- | `wan2.2_ti2v_5b_text_to_video_multigpu_fsdp_ulysses` | accepted; real 4×B200 official path |

- | `wan2.2_distributed_rank_topology_validation` | accepted; four unique ranks/devices and collective/barrier evidence |

- | `wan2.2_verified_rerun_recording` | accepted only with the uploaded verified RRD manifest |

+ | `wan2.2_ti2v_5b_text_to_video` | accepted historical evidence; current runtime needs a fresh RTX PRO run |

+ | `wan2.2_decoded_mp4_validation` | accepted historical evidence; 17 decoded 1280×704 frames at 24 fps |

+ | `wan2.2_ti2v_5b_text_to_video_multigpu_fsdp_ulysses` | accepted historical evidence; current runtime needs a fresh 4×B200 official run |

+ | `wan2.2_distributed_rank_topology_validation` | accepted historical evidence; four unique ranks/devices and collective/barrier evidence |

+ | `wan2.2_verified_rerun_recording` | accepted historical evidence only for the prior runtime; current acceptance still requires an uploaded verified RRD manifest |

| `wan2.2_ti2v_5b_image_to_video` | deferred |

| A14B / S2V / Animate | deferred |

| official TI2V fine-tuning | deferred; no pinned-source entrypoint |

| stock Wan action prediction | rejected as an upstream capability |

## Licensing

Track official source, baked dependencies, runtime-fetched CUDA software,

run-time model/tokenizer, and data separately. Source/model declarations do not

classify a built image. The promoted first-class `npa-wan2-2` contract is public

eligible only when a pushed digest proves all `nvidia-*`, CUDA/cuDNN/NCCL,

checkpoint, credential, and cache bytes absent from every layer and history via

`npa/scripts/scan_image_wan_payload.py`. The operator must explicitly accept

current NVIDIA terms before `wan-runtime` writes the CUDA stack to its volume;

model/tokenizer acquisition remains runtime-only. Do not treat automation as

human legal approval and never publish merely because the Dockerfile looks clean.

Carry both the acceptance gate and Hugging Face token only as submission secrets

(`--secret-env NPA_WAN_ACCEPT_NVIDIA_RUNTIME_TERMS --secret-env HF_TOKEN`), never

as workflow config, task env literals, or image metadata.

## Validation

Use the repository venv, never bare Python:

```bash

npa/.venv/bin/npa workbench workflow validate-spec \

npa/workflows/workbench/npa-workflows/byof-wan2.2.yaml

npa/.venv/bin/npa workbench workflow plan-spec \

npa/workflows/workbench/npa-workflows/byof-wan2.2.yaml --run-id wan22-plan

npa/.venv/bin/npa workbench workflow validate-spec \

npa/workflows/workbench/npa-workflows/byof-wan2.2-multigpu.yaml

npa/.venv/bin/npa workbench workflow plan-spec \

npa/workflows/workbench/npa-workflows/byof-wan2.2-multigpu.yaml \

--run-id wan22-multigpu-plan

npa/.venv/bin/python -m pytest npa/tests/workflows/test_wan_rerun.py -q

npa/.venv/bin/python -m pytest npa/tests/workflows/test_byof_solution_smokes.py -q

npa/.venv/bin/python -m pytest npa/tests/guardrails/test_skills_index.py -q

npa/.venv/bin/python -m pytest npa/tests/smoke/test_all_workflow_yamls.py -q

```

The gated live tests are `npa/tests/e2e/test_byof_wan22_live_e2e.py` and

`npa/tests/e2e/test_byof_wan22_multigpu_live_e2e.py`. Future compatibility

changes require fresh live evidence rather than inference from an older run.