/cdf-image-attribute-augmentation

name: cdf-image-attribute-augmentation
description: Run the CDF Orchestration Image Attribute Augmentation DAG on Kubernetes - person-crop clothing augmentation, attribute search, and augmented dataset generation. Select for requests about image attribute augmentation, person attribute search, person re-identification data, clothing augmentation, attribute captions, augmentation payloads, run status, or result retrieval. Runs environment setup first when controller readiness is unknown. Not for video or defect-image generation.
version: "1.0.1"
license: CC-BY-4.0 AND Apache-2.0
metadata:
  owner: NVIDIA
  service: cosmos-data-factory
  version: "1.0.1"
  reviewed: "2026-10-09"
  author: NVIDIA
  tags:
    - physical-ai
    - cdf-orchestration
    - image-attribute-augmentation
    - cosmos

CDF Orchestration — Image Attribute Augmentation

Run the Image Attribute Augmentation DAG end to end: person-crop input preparation, cosmos image-edit augmentation, cosmos post-processing, event and person attribute search, augmented 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-edit configuration, clothing variables and distributions, captioning, verification, and augmentation errors | [`cdf-augmentation` skill][augmentation-skill] | | `cdf-auto-labeling` | Event and person attribute-search configuration and 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/image_attribute_augmentation_dag/`:

| Platform | DAG ID | Manifest | |---|---|---| | Kubernetes | `image_attribute_augmentation_dag_k8s` | `image_attribute_augmentation_k8s_manifest.yaml` |

Kubernetes is the only platform whose manifest is checked in, so `image_attribute_augmentation_dag_k8s` is the only DAG this repository registers. 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.

There is a **single end-to-end pipeline** — there are no augmentation-only or labeling-only DAG variants. If a user asks for augmentation without attribute search, tell them the checked-in DAG does not offer that flow rather than inventing a DAG ID.

Manual payload entry in the Airflow UI

If the user wants to enter their own payload directly in the Airflow UI rather than have you construct and trigger one, your job is limited to getting them to the UI: confirm controller readiness, ensure `make port-forward` is running (see [airflow-direct-api.md](references/airflow-direct-api.md#port-forward-ui-access)), and report the reachable URL. Do not render a payload, run preflight, or trigger a run yourself in this case — the user is doing that from the UI. Resume monitoring (step 6 below) once they tell you a run has been triggered; you can find it via the Airflow API without needing the payload they used.

Scope

**Before building any payload, collect all of the following from the user.** Never invent an endpoint URL or bucket path — use only values the user supplied.

| Required | Field | What to ask | |---|---|---| | Always | `input_path` | S3 (or HTTP/HTTPS) URL whose immediate subdirectories are person-ID folders | | Always | `output_directory` | Writable S3 URL where results should be written | | Always | service mode | `external` (user provides endpoint URLs) or `internal` (DAG deploys services in-cluster) | | External mode | `cosmos.vlm_service_url` | Full HTTPS URL for the VLM inference endpoint | | External mode | `cosmos.llm_service_url` | Full HTTPS URL for the LLM inference endpoint | | External mode | `cosmos.image_edit_service_url` | Full HTTPS URL for the image-edit inference endpoint | | Optional | `max_imgs` | Number of person-ID folders to process (default: 1; 0 or negative = all) | | Optional | `cosmos.num_augmentation` | Clothing variants per person (default: 1) | | Optional | `cosmos.variable_distribution` | Clothing attri