Nv Generate Ct Rflow

NV-Generate-CT (rflow-ct)

Purpose

• Used for generating synthetic CT volumes and masks with NV-Generate-CTMR rflow-ct. Not for production training data without review.
• Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
• Do not write custom inference code for normal runs. The wrapper owns config staging, output paths, label mapping evidence, and validation.
• Manifest I/O: inputs are config_infer_override; outputs are synthetic_ct_volumes and result_json.

Instructions

• Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
• Run scripts/run_rflow_ct.py through the documented command below; keep outputs under a caller-provided run directory.
• If a host agent exposes run_script, use run_script("scripts/run_rflow_ct.py", args=[...]); otherwise run the Bash/Python command shown below.
• Emit a single bash code block, and keep the python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" step in that same command — the runtime may be a fresh environment without nibabel/MONAI, so dropping the install fails with ModuleNotFoundError.
• Do not add rm, mkdir, or any cleanup of --output-dir; the wrapper creates it. Use a fresh --output-dir instead of deleting one.
• Check the emitted JSON and paired verifier guidance before treating the run as evidence.

Available Scripts

Prerequisites

• Required environment variables: NV_GENERATE_ROOT.
• Runtime requirements: GPU/CUDA when declared by the manifest; Python packages listed in runtime.side_effects.pip_packages.
• Side effects: writes generated outputs under the caller's --output-dir, may cache model assets under ~/.cache/huggingface/, and may contact https://huggingface.co or https://github.com during setup.
• Run commands from the repository root unless an existing section below says otherwise.

Limitations

• This is a thin wrapper. Inference, sampling, and decoding are delegated entirely to NVIDIA-Medtech/NV-Generate-CTMR's scripts.inference. Do not modify code under $NV_GENERATE_ROOT.
• rflow-ct requires CUDA and ≈ 16 GB VRAM minimum for the default 256³ output_size. Larger output_size (e.g. 512×512×768) needs an A100/H100.
• Output volumes are synthetic. They are not safe to use as training data for production medtech models without an independent quality review.
• Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission.

Troubleshooting

Wraps the upstream NVIDIA-Medtech/NV-Generate-CTMR rectified-flow synthesis pipeline. The wrapper does not reimplement diffusion, sampling, or autoencoder decoding — it shells out to the upstream scripts.inference entry point exactly as the project's README documents and inspects the produced image/mask pairs.

Preconditions

1. If NV_GENERATE_ROOT already names a local checkout, the wrapper uses it and records its current commit in the result. Otherwise, create the recommended pinned default checkout (one-time):

   if [ -z "${NV_GENERATE_ROOT:-}" ]; then
     export NV_GENERATE_COMMIT=61c4ec709b84cad468852243c48e250bec732074
     export NV_GENERATE_ROOT="$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7"
     if [ ! -d "$NV_GENERATE_ROOT/.git" ]; then
       git clone https://github.com/NVIDIA-Medtech/NV-Generate-CTMR.git "$NV_GENERATE_ROOT"
       git -C "$NV_GENERATE_ROOT" checkout --detach "$NV_GENERATE_COMMIT"
     fi
   fi
   python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt"

2. Download the rflow-ct weights and the mask-candidate datasets into the clone (one-time, ≈ 5.5 GB):

   cd "$NV_GENERATE_ROOT"
   python -m scripts.download_model_data --version rflow-ct --root_dir "./"

   The mask candidates (datasets/all_masks_flexible_size_and_spacing_4000) condition the diffusion sampler; omitting them via --model_only will make the inference script fail with a missing-file error at startup. The anatomy-size condition file is also part of the full CT download and is needed for controllable mask generation.

3. NVIDIA GPU with ≥ 16 GB VRAM and CUDA. There is no CPU fallback.

For agent-generated user run commands, prefer the short wrapper command in Usage. Do not prepend clone or model-download setup steps when NV_GENERATE_ROOT or the repo-local upstream cache is already present. In a fresh Python environment, still include python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" before the wrapper unless the active environment has already proven those imports are available; cached weights do not imply cached Python packages. Run the wrapper from the medical-AI-skills repo root. If setup requires cd "$NV_GENERATE_ROOT", return to the Medical AI Skills repo before invoking skills/nv-generate-ct-rflow/scripts/run_rflow_ct.py.