paidf-cosmos-predict

name	paidf-cosmos-predict
description	Prepare and run PAIDF Cosmos Predict video generation for DEFT media samples.
license	Apache-2.0
compatibility	Requires docker + nvidia-container-toolkit, a reachable OpenAI-compatible VLM captioning endpoint, and access to the PAIDF augmentation image.
metadata	{"author":"NVIDIA Corporation","version":"0.1.0"}
allowed-tools	Read Bash Write
tags	["paidf","cosmos-predict","video","vlm-captioning","deft","data-generation"]

PAIDF Cosmos Predict Generation

Prepare and run PAIDF Cosmos Predict generation for media samples. The skill emits a JSONL handoff that maps each input id to the original media path and generated video path.

Purpose

Use this skill when a DEFT workflow already has media samples and needs synthetic/generated videos from PAIDF Cosmos Predict. This skill does not start the VLM captioning service. A reachable OpenAI-compatible base URL for the model used to caption input media must be provided at runtime.

Prerequisites

• Docker with NVIDIA GPU support and nvidia-container-toolkit.
• Access to the PAIDF augmentation image declared by images.metropolis_sdg.paidf_augmentation in versions.yaml.
• A running VLM captioning service with an OpenAI-compatible API base URL. The base URL must be provided by the user or upstream workflow at runtime; reuse that exact base URL for every --vlm-captioning-endpoint argument. Do not include /models in VLM_CAPTIONING_ENDPOINT.
• HF_TOKEN in the run environment when Cosmos model downloads require HuggingFace access — exported, or in a user-approved env file (bare KEY=value lines) that the run block sources.
• VLM_API_KEY in the run environment the same way when the VLM captioning endpoint requires authentication.
• Input media paths that are absolute paths on the host under the required media directory. Pass the host media directory with --media-dir; the skill mounts it into the PAIDF container at the exact same path.

Inputs

Input	Required	Notes
Input JSONL	Yes	Path to the generic media JSONL. The user or upstream workflow must provide it. Each row must include string fields id and media_path; id values must be unique.
Output directory	Yes	Host directory for prepared PAIDF config, generated videos, captions, metadata, logs, and final handoff. The user or upstream workflow must provide it.
VLM captioning endpoint base URL	Yes	The user or upstream workflow must provide this OpenAI-compatible base URL, for example a URL ending in /v1. Do not include /models. Pass the exact same base URL to verify_vlm_captioning_base_url.py before any other step and to prepare_paidf_config.py when writing config.yaml.
Generation settings	No	If the user provides a generation settings JSON, use it. Otherwise set GENERATION_SETTINGS to skills/data/paidf-cosmos-predict/assets/default_generation_settings.json. Always pass the resolved path to prepare_paidf_config.py with --generation-settings.
PAIDF GPU count	Yes	Number of GPUs for PAIDF augmentation. Pass it to prepare_paidf_config.py with --paidf-num-gpus N and to Docker with --gpus "$PAIDF_NUM_GPUS".
Media directory	Yes	Host directory containing the input media referenced by media_path. Pass it as --media-dir /path/to/media_dir to prepare_paidf_config.py and mount it into Docker 1:1.
VLM captioning prompt	Yes	Prompt text file for VLM captioning. Pass it to prepare_paidf_config.py with --caption-prompt-file; the script inlines the prompt into config.yaml.
HF_TOKEN	Yes for Cosmos model downloads	The agent checks that HF_TOKEN is already set in the run environment, whether exported or sourced from a user-approved env file, and forwards it to Docker with -e HF_TOKEN.

Input JSONL row shape:

{"id": "stable-sample-id", "media_path": "/abs/input.mp4"}

The input JSONL may contain duplicate media_path values, but every id must be unique. This lets an upstream workflow attach multiple logical samples to the same source video while avoiding repeated PAIDF generation for that video.

Example:

{"id": "sample-a-question-1", "media_path": "/abs/video_a.mp4"}
{"id": "sample-a-question-2", "media_path": "/abs/video_a.mp4"}
{"id": "sample-b-question-1", "media_path": "/abs/video_b.mp4"}

prepare_paidf_config.py converts media_path values to absolute paths and deduplicates them before writing config.yaml, so each unique video appears only once in PAIDF data[] and goes through captioning/generation only once. write_paidf_handoff.py then expands back to the input row level so every original id appears in either generated_videos.jsonl or failed_videos.jsonl, with duplicate media rows pointing to the same generated or expected-generated video path.

Runtime Image

The PAIDF augmentation image is resolved from images.metropolis_sdg.paidf_augmentation in versions.yaml; users do not need to provide it for the standard workflow.

Agent Run Procedure

Before running, determine these runtime values from the user request or upstream workflow output:

• INPUT_JSONL: path to the media JSONL.
• OUTPUT_DIR: path to the PAIDF output directory.
• VLM_CAPTIONING_ENDPOINT: user-provided VLM captioning OpenAI-compatible base URL. Do not include /models.
• GENERATION_SETTINGS: user-provided generation settings JSON, or skills/data/paidf-cosmos-predict/assets/default_generation_settings.json when the user did not provide one.
• PAIDF_NUM_GPUS: PAIDF GPU count.
• MEDIA_DIR: media directory to mount 1:1 into Docker.
• CAPTION_PROMPT_FILE: VLM captioning prompt file.

If required values are missing, ask the user for them before launching PAIDF.

Set the runtime values:

INPUT_JSONL="<user-provided-input-jsonl>"
OUTPUT_DIR="<user-provided-output-dir>"
VLM_CAPTIONING_ENDPOINT="<user-provided-vlm-captioning-base-url>"
GENERATION_SETTINGS="skills/data/paidf-cosmos-predict/assets/default_generation_settings.json"
PAIDF_NUM_GPUS="<user-provided-paidf-gpu-count>"
MEDIA_DIR="<user-provided-media-dir>"
CAPTION_PROMPT_FILE="<user-provided-caption-prompt-file>"

If the user provides a generation settings JSON, replace GENERATION_SETTINGS with that path.

Check the VLM captioning base URL before preparing PAIDF inputs. This sends a preflight request to <VLM_CAPTIONING_ENDPOINT>/models only to verify that the base URL is reachable and OpenAI-compatible. PAIDF still receives VLM_CAPTIONING_ENDPOINT itself in config.yaml, not the derived /models URL.

python skills/data/paidf-cosmos-predict/scripts/verify_vlm_captioning_base_url.py \
  --vlm-captioning-endpoint "$VLM_CAPTIONING_ENDPOINT"

If this fails, stop the PAIDF workflow and issue an error indicating that the user-provided base URL did not pass the OpenAI-compatible /models preflight check.

Prepare the PAIDF output directory:

python skills/data/paidf-cosmos-predict/scripts/prepare_paidf_config.py \
  --input-jsonl "$INPUT_JSONL" \
  --output-dir "$OUTPUT_DIR" \
  --vlm-captioning-endpoint "$VLM_CAPTIONING_ENDPOINT" \
  --media-dir "$MEDIA_DIR" \
  --generation-settings "$GENERATION_SETTINGS" \
  --paidf-num-gpus "$PAIDF_NUM_GPUS" \
  --caption-prompt-file "$CAPTION_PROMPT_FILE"

prepare_paidf_config.py performs a writeability preflight, writes config.yaml, writes path_map.jsonl, writes run_metadata.json, and creates container-writable output directories. If an existing mounted directory or file is not writable by the agent, it fails early with a permission-fix command.

The generated config uses VLM-only captioning plus Cosmos Predict text-to-world generation. PAIDF's separate LLM prompt-augmentation step is disabled by omission: captioning.llm is not present. prepare_paidf_config.py inlines CAPTION_PROMPT_FILE into captioning.vlm.user_prompt.

Prepared helper files:

• path_map.jsonl maps each unique absolute input media_path to the deterministic generated video path, caption path, and metadata path. write_paidf_handoff.py uses it to produce one generated or failed handoff row per input row.
• run_metadata.json records the VLM captioning endpoint, captioning model, and PAIDF GPU count used when config.yaml was prepared. It is provenance only.

Run PAIDF:

set -a; source /path/to/.env; set +a   # omit if already exported

[ -n "${HF_TOKEN:-}" ] || {
  echo "MISSING: HF_TOKEN is not set. Export it, or point the loader above at a user-approved env file."
  exit 1
}

if [ -z "${VLM_API_KEY:-}" ]; then
  echo "WARNING: VLM_API_KEY is not set. Continue only if the VLM captioning endpoint does not require authentication."
fi

PAIDF_IMAGE="$(scripts/resolve_versions_key.py images.metropolis_sdg.paidf_augmentation)"

set -o pipefail
docker run --rm \
  --gpus "$PAIDF_NUM_GPUS" \
  --ipc=host \
  --network host \
  -e HF_TOKEN \
  -e VLM_API_KEY \
  -v "$MEDIA_DIR:$MEDIA_DIR:ro" \
  -v "$OUTPUT_DIR:$OUTPUT_DIR" \
  "$PAIDF_IMAGE" \
  --config "$OUTPUT_DIR/config.yaml" \
  2>&1 | tee "$OUTPUT_DIR/paidf_docker.log"

The Docker command resolves the PAIDF image from versions.yaml, mounts MEDIA_DIR 1:1 read-only, mounts OUTPUT_DIR 1:1 writable, forwards HF_TOKEN and VLM_API_KEY, and keeps a copy of Docker stdout/stderr in $OUTPUT_DIR/paidf_docker.log via tee. set -o pipefail preserves the Docker exit status when using tee.

Create the handoff after PAIDF writes the generated videos:

python skills/data/paidf-cosmos-predict/scripts/write_paidf_handoff.py \
  --input-jsonl "$INPUT_JSONL" \
  --path-map "${OUTPUT_DIR}/path_map.jsonl" \
  --generated-jsonl "${OUTPUT_DIR}/generated_videos.jsonl" \
  --failed-jsonl "${OUTPUT_DIR}/failed_videos.jsonl"

Workflow

1. Verify the user-provided VLM captioning base URL with verify_vlm_captioning_base_url.py. If this fails, stop and report the OpenAI-compatible /models preflight error.
2. Run prepare_paidf_config.py on the input media JSONL using that same endpoint. It reads VLM captioning and PAIDF generation values from the generation settings, preflights write access, writes the PAIDF config and path map, and dedupes PAIDF generation to one entry per unique media path.
3. Run the PAIDF Docker command. The agent should monitor Docker output, Docker exit status, and generated output counts.
4. Run write_paidf_handoff.py to produce generated_videos.jsonl and failed_videos.jsonl.

Long-Running Run Updates

PAIDF generation can run for a long time. During an agent-driven run, keep the user informed without requiring them to ask for status.

• Before launching PAIDF, report the output directory, unique media count from path_map.jsonl, PAIDF GPU count, and media directory.
• While PAIDF is running, provide a short progress update every 30 minutes. Include elapsed time, generated video count under generated/videos/, expected unique media count, and the latest useful PAIDF log signal if available.
• If using a tool environment where one foreground Docker command would block user-facing updates, launch PAIDF in a monitorable way, then poll Docker status/logs and filesystem output counts between updates.
• On completion, report whether the container exited successfully, generated video count, missing output count if any, and the generated_videos.jsonl and failed_videos.jsonl paths after running write_paidf_handoff.py.
• On failure, report the container exit code, the relevant Docker output, and whether any partial generated videos were produced.

Reference Files

assets/default_generation_settings.json is the default VLM captioning and Cosmos Predict generation settings file. If the user provides a different generation settings JSON, set GENERATION_SETTINGS to that path; otherwise set it to the default asset path. Always pass the resolved path with --generation-settings. Use assets/paidf_config_template.yaml only as a readable shape for the generated PAIDF config.

Deterministic Mapping

The helper converts each media_path to an absolute path, hashes that absolute path with SHA-256, and uses the first 16 hex characters as the sample id.

For each unique media path:

generated video: <output_dir>/generated/videos/<hash16>.mp4
caption:         <output_dir>/captions/<hash16>.txt
metadata:        <output_dir>/generated/metadata/<hash16>.json

Duplicate input media paths share one generated path. The handoff still writes one row per input row, so multiple unique input ids may point to the same generated video.

Outputs

Output	Producer	Notes
config.yaml	prepare_paidf_config.py	PAIDF config with one entry per unique absolute media path.
path_map.jsonl	prepare_paidf_config.py	Internal helper file mapping each unique absolute media path to its expected generated video, caption, and metadata paths. write_paidf_handoff.py uses this file to emit one generated or failed row per input JSONL row. Host/container paths are identical.
run_metadata.json	prepare_paidf_config.py	Internal provenance file recording the VLM captioning endpoint/model and PAIDF GPU count used to prepare the config.
paidf_docker.log	Docker command	Docker stdout/stderr captured with tee during PAIDF generation.
generated/videos/*.mp4	PAIDF	One generated video per unique absolute media path.
generated_videos.jsonl	write_paidf_handoff.py	JSONL for successful PAIDF outputs; one row per input row whose generated video exists.
failed_videos.jsonl	write_paidf_handoff.py	JSONL audit for missing generated videos; one row per input row whose expected generated video is absent.

Each generated_videos.jsonl row has exactly:

{"id": "stable-sample-id", "original_media_path": "/abs/input.mp4", "generated_video_path": "/abs/generated.mp4"}

Each failed_videos.jsonl row has exactly:

{"id": "stable-sample-id", "original_media_path": "/abs/input.mp4", "expected_generated_video_path": "/abs/generated.mp4", "error": "missing_generated_video"}

Troubleshooting

Symptom	Likely Cause	Agent Response
VLM captioning base URL preflight fails	The captioning service is not running, the base URL is wrong, /v1 is missing, the OpenAI-compatible /models probe is unavailable, or the service is not reachable from the agent host	Report the base URL, the derived /models probe URL, and error detail. The agent does not have enough context to start or repair the service; ask the user whether to provide a different base URL, start the service externally, or stop the run.
PAIDF fails after launch	The PAIDF container exited non-zero	Report the exit status, the relevant Docker output, and the count of generated videos under <output_dir>/generated/videos/ versus expected unique media count from path_map.jsonl. Then ask the user how to proceed before retrying, deleting outputs, changing settings, or continuing with partial results.
Cosmos checkpoint download fails	HF_TOKEN is unset, expired, or lacks access to the gated Cosmos repo	Report the authentication/download error. Suggest that the user accept the model license if needed and make HF_TOKEN available before retrying, by exporting it or adding it to a user-approved env file the run sources. Do not ask the user to paste the token into chat; never create the token value yourself and never print it.
VLM captioning returns authentication errors	VLM_API_KEY is unset, expired, or not authorized for the provided VLM captioning endpoint	Report the authentication error from paidf_docker.log. Ask the user to export VLM_API_KEY or add it to a user-approved env file the run sources before retrying. After the user says to proceed, double-check that VLM_API_KEY is set before launching PAIDF again. Do not ask the user to paste the key into chat; never create the key value yourself and never print it.
User has not provided PAIDF GPU count	The skill cannot infer the correct PAIDF GPU count from hardware alone because the desired allocation depends on the run plan and shared resources	Ask the user for PAIDF_NUM_GPUS before prepare/run. Do not guess.
PAIDF appears to use the wrong GPU count	PAIDF_NUM_GPUS was set incorrectly for the run	Report the value used for --paidf-num-gpus and Docker . Ask the user for the corrected GPU count; if they provide it, rerun and then the Docker command with the same corrected value.