/cdf-event-video-generation

name: cdf-event-video-generation
description: Run the CDF Orchestration Event Video Generation DAG on Kubernetes or OSMO - image-to-video anomaly generation, auto-labeling, and anomaly dataset generation. Select for requests about event video generation, anomaly video generation, image-to-video synthesis, Cosmos3 image2video, anomaly dataset creation, safety/surveillance SDG, or generating person-falling, person-climbing, person-running, fighting, smoking/vaping, fire/smoke, or shoplifting video clips from a seed image. Covers both event_video_generation_dag_k8s and the external-endpoints-only event_video_generation_dag_osmo. Runs environment setup first when controller readiness is unknown. Not for person-crop clothing/attribute augmentation (that is cdf-image-attribute-augmentation) and not for video style transfer.
version: "1.0.2"
license: CC-BY-4.0 AND Apache-2.0
metadata:
  owner: NVIDIA
  service: cosmos-data-factory
  version: "1.0.2"
  reviewed: "2026-10-09"
  author: NVIDIA
  tags:
    - physical-ai
    - cdf-orchestration
    - event-video-generation
    - cosmos

CDF Orchestration — Event Video Generation

Run the Event Video Generation DAG end to end: seed-image input preparation, Cosmos3 image-to-video anomaly augmentation, auto-labeling (detection and tracking, captioning, anomaly visual QA, person-attribute visual QA, person attribute search), anomaly dataset generation, and result retrieval.

Component details

This workflow skill and its references are the primary guide for configuring and running the workflow. Consult a component skill only when the requested configuration or diagnosis requires component-specific details not covered here. Routine payload authoring with documented fields, preflight, triggering, monitoring, and result retrieval do not require component skill access.

When additional component guidance is needed, consult that component's documentation, and do not invent keys or values. The links below are pinned to the latest `main` commit of each component repository as of 2026-10-20; a component skill installed separately may describe a different version.

| Component | Covers | Documentation at that commit | |---|---|---| | `cdf-augmentation` | Image-to-video configuration, generation prompts, model parameters, evaluators, and augmentation errors | [`cdf-augmentation` skill][augmentation-skill] | | `cdf-auto-labeling` | Detection/tracking, captioning, visual QA, question banks, attribute-search configuration, and auto-labeling errors | [`cdf-auto-labeling` skill][auto-labeling-skill] |

[augmentation-skill]: https://github.com/NVIDIA/cdf-augmentation/tree/73c861c43a4952b6060c87265bf39f4abc48199a/skills/cdf-augmentation [auto-labeling-skill]: https://github.com/NVIDIA/cdf-auto-labeling/tree/6c75c76c52a6c76c3866a38d30885e11123b0981/skills/cdf-auto-labeling

Use only documentation at these commits; do not substitute a moving branch or another version.

If the repository is inaccessible, continue tasks supported by this workflow's guidance. Identify the unavailable reference and the unresolved component-specific detail only when that missing guidance prevents completing the requested work; do not invent component settings or treat component access as a prerequisite for ordinary workflow use.

Apply component guidance through this workflow's supported payload fields and DAG constraints. Component configuration and the Airflow trigger payload are different contracts; component capabilities do not automatically become supported DAG options. Keep execution through this workflow's existing procedure. If the pinned guidance differs from the deployed component version or DAG integration, report the mismatch before proposing the affected settings.

DAG selection

The workflow builds one DAG per compute platform from `airflow/dags/workflows/event_video_generation_dag/`:

| Platform | DAG ID | Manifest | |---|---|---| | Kubernetes | `event_video_generation_dag_k8s` | `event_video_generation_k8s_manifest.yaml` | | OSMO | `event_video_generation_dag_osmo` | `event_video_generation_osmo_manifest.yaml` |

Both manifests are checked in, so both DAGs are registered. A DAG is registered only if its manifest exists; a missing manifest means the DAG is absent from Airflow rather than broken. List the DAGs Airflow actually loaded before triggering, and never name a DAG ID that is not in that list.

**`event_video_generation_dag_osmo` supports external inference endpoints only.** Its manifest defines OSMO batch tasks but no VLM/image-to-video/Cosmos-Evaluator service components, so an internal-services payload cannot deploy what it needs:

• Always set top-level `external_services: true` (and `cosmos.external_services` to `true`, or

omit so it inherits the top-level value). Any OSMO payload with `external_services: false` is invalid for the deployed manifest even though the shared payload schema can parse it — never submit one.

• Supply `cosmos.vlm_service_url`, `cosmos.llm_service_url`, and `cosmos.image2video_service_url`.
• Leave `service_lifecycle.vlm_service.enabled` / `service_lifecycle.image2video_service.enabled`

as `false`, or omit `service_lifecycle` entirely.

• The OSMO DAG has no managed Cosmos Evaluator either — if any quality check stays enabled, supply

an external Arbitrator URL via `cosmos.cosmos_evaluator_service_url` reachable from OSMO tasks, or explicitly disable both checks. Never reuse a K8s-DAG internal-mode starter payload for the OSMO DAG.

• Pools differ from the K8s DAG. Derive them from the OSMO manifest rather than assuming: in the

currently checked-in `event_video_generation_osmo_manifest.yaml` the OSMO tasks (`detection_and_tracking`, `captioning`, `visual_qa`, `augmentation`) run on the `osmo_rtx6000` profile → pool `osmo_rtx6000_1`, while `event_and_person_attribute_search` stays on `k8s_cpu_task` → `default_pool`. (`osmo_h100_1` exists in `deploy/values.yaml` but this manifest does not reference it.) The OS