Generated banner for Nv Generate Ct Rflow
Generated logo for Nv Generate Ct Rflow

Nv Generate Ct Rflow

NVIDIAOfficialCreated by NVIDIA MedTech Team

  • Skill
  • Tool integration
  • Science and research
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Used for generating synthetic CT volumes and masks with NV-Generate-CTMR rflow-ct. Not for production training data without review.
— the description by NVIDIA MedTech Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The nv-generate-ct-rflow skill provides a standardized interface for the NVIDIA-Medtech NV-Generate-CTMR rectified-flow synthesis pipeline. It manages configuration staging, output path handling, and validation for generating synthetic CT volumes and paired masks. The skill includes helper utilities for anatomy lookup, diagnostic mask generation, and image-only synthesis, while delegating core inference and sampling to the upstream repository.

What it does

  • Generates synthetic CT volumes and corresponding label masks.
  • Supports controllable anatomy generation for specific tumor types.
  • Provides diagnostic tools for raw mask generation and image-from-mask synthesis.
  • Validates input configurations against schema bounds and anatomy constraints.
  • Produces summary cards for visual inspection of generated outputs.
  • Maps paired output labels back to standard MAISI label IDs.

When to use it

  • When you need to generate synthetic CT image data for research purposes.
  • When you require paired CT volumes and segmentation masks.
  • When you need to perform controllable synthesis of specific anatomical regions or tumors.
  • When you need to validate configuration files before executing large-scale inference.

Advantages

  • Automates environment setup and dependency management for the upstream pipeline.
  • Ensures consistent output structure and metadata recording.
  • Provides built-in preflight checks to prevent common configuration errors.
  • Simplifies complex inference workflows through standardized wrapper commands.

Usage examples

“Generate a synthetic CT volume using the chest lung tumor controllable configuration.”
The agent executes the wrapper script with the specified configuration, producing synthetic CT volumes and masks in the designated output directory.
“List all available chest anatomies for generation.”
The agent runs the anatomy lookup tool and returns a list of 132-class labels grouped by the chest region.
“Validate the abdomen liver and spleen configuration without running full inference.”
The agent performs a preflight check on the configuration file and reports any schema or constraint violations without generating image data.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/nv-generate-ct-rflow/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip nv-generate-ct-rflow.zip -d .agents/skills

Documentation by NVIDIA MedTech Team

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

ScriptPurposeArguments
scripts/_anatomy.pyInternal helper used by the primary entrypoint.Imported only; do not call directly.
scripts/_summary_card.pyInternal helper used by the primary entrypoint.Imported only; do not call directly.
scripts/list_anatomies.pyHelper command for catalog or anatomy lookup.[--region REGION] [--filter TEXT] [--controllable]
scripts/run_rflow_ct.pyPrimary entrypoint declared by skill_manifest.yaml.CONFIG_INFER.json --output-dir OUT_DIR [--random-seed N] [--version rflow-ct] [--yes]
scripts/run_ct_mask.pyAdvanced diagnostic helper for standalone raw MAISI mask generation.REQUEST.json --output-dir OUT_DIR [--random-seed N] [--preflight-only] [--yes]
scripts/run_ct_from_mask.pyAdvanced helper for CT image generation from a MAISI label mask.REQUEST.json --output-dir OUT_DIR [--random-seed N] [--yes]
scripts/run_ct_image.pyAdvanced helper for CT image-only generation without paired labels.MODEL_CONFIG.json --output-dir OUT_DIR [--version rflow-ct] [--random-seed N] [--yes]

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

ErrorCauseFix
Missing dependency or import errorRuntime package drift from skill_manifest.yaml.Install the packages declared in the manifest or use the documented setup command.
Empty or schema-invalid outputWrong input path, unsupported modality, or upstream failure.Re-run with a known fixture and inspect the wrapper JSON plus stderr.
Validation gate failureOutput violated a declared engineering invariant.Keep the failed evidence pack and use the gate message to repair inputs or wrapper code.

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.

Usage

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-ct-rflow/scripts/run_rflow_ct.py \
  PATH_TO_CONFIG_INFER.json \
  --output-dir runs/nv_generate_ct_rflow_demo \
  --random-seed 0 \
  --version rflow-ct

Replace PATH_TO_CONFIG_INFER.json with the user's actual request/config path. Do not copy the fixture path from this document unless the user explicitly asked to run that fixture. If the user says "the case request is at runs/.../chest_lung_tumor_controllable.json", that exact path is the first positional argument to scripts/run_rflow_ct.py.

The fixture argument is a config_infer.json override file: it can replace num_output_samples, body_region, anatomy_list, controllable_anatomy_size, output_size, and spacing. Pass default to use the upstream config verbatim. The wrapper stages the override into the upstream tree before running.

Fixture catalog

fixtures/ ships curated configs for common paired synthesis use cases: chest lung lobes, chest with controllable lung tumor, abdomen solid organs, abdomen with controllable hepatic tumor, head + cervical spine, pelvis. See fixtures/README.md for the full table.

Helper commands
# Browse the 132-class label_dict grouped by body region.
python skills/nv-generate-ct-rflow/scripts/list_anatomies.py --region chest
python skills/nv-generate-ct-rflow/scripts/list_anatomies.py --controllable
python skills/nv-generate-ct-rflow/scripts/list_anatomies.py --filter tumor

# Validate a fixture and preview cost without launching inference.
NV_GENERATE_ROOT=$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-61c4ec7 \
  python skills/nv-generate-ct-rflow/scripts/run_rflow_ct.py \
    skills/nv-generate-ct-rflow/fixtures/abdomen_liver_spleen.json \
    --output-dir runs/preview --preflight-only

Advanced helpers stay inside this skill for debugging and less-common CT generation modes. Use them only when the user explicitly asks for that mode:

# Raw MAISI mask diagnostic, useful for checking lung tumor -> label 23.
python skills/nv-generate-ct-rflow/scripts/run_ct_mask.py \
  skills/nv-generate-ct-rflow/fixtures/ct_mask_lung_tumor.json \
  --output-dir runs/ct_mask_debug --preflight-only

# CT image from an existing MAISI label mask with body label 200.
python skills/nv-generate-ct-rflow/scripts/run_ct_from_mask.py \
  skills/nv-generate-ct-rflow/fixtures/ct_from_mask_request_example.json \
  --output-dir runs/ct_from_mask_demo

# CT image-only generation without paired labels.
python skills/nv-generate-ct-rflow/scripts/run_ct_image.py \
  skills/nv-generate-ct-rflow/fixtures/ct_image_only_default.json \
  --output-dir runs/ct_image_only_demo --version rflow-ct

The wrapper runs preflight on every invocation (regardless of --preflight-only): config-schema bounds, anatomy names matched against the upstream label_dict, body_region in the supported set, controllable_anatomy_size constraints, upstream CT output-size/spacing contracts, body-region-aware x/y FOV minimums, dataset presence under $NV_GENERATE_ROOT/datasets/, CUDA available, and an estimated peak VRAM / wall-time. Runs estimated to exceed 5 min wall-time or 30 GB VRAM peak require --yes to proceed.

Each invocation runs python -m scripts.inference -t configs/config_network_rflow.json -i configs/config_infer.json -e configs/environment_rflow-ct.json --random-seed <s> --version rflow-ct. Output evidence records the upstream git commit, model checkpoint hashes, the rendered config, per-sample image/mask geometry, mask label set, image HU range summary, and per-class voxel volumes.

When controllable_anatomy_size is non-empty, upstream ignores the broader anatomy_list for the saved paired label map and filters labels to the controllable anatomy names. The saved paired label values are local 1..N ordinals, not raw MAISI label IDs. Read output.output_label_mapping in result_json to map saved output labels back to source labels; for example, output label 1 can represent MAISI label 23 (lung tumor). For curated lung-tumor examples, prefer a controllable size around 0.5 or larger; smaller requests such as 0.2 can produce absent or extremely small label-23 components for some seeds.

For FOV and setup details, see references/fov-and-downloads.md. For advanced helper label-space details, see references/ct-mask-label-space.md and references/ct-from-mask-format.md.

Visual sample card

Alongside the NIfTI pairs, the wrapper writes summary.html to the output directory: a per-sample mid-slice triptych (axial / coronal / sagittal) with label overlay, plus a table of the rendered config and verifier-facing aggregates. Lets you eyeball the result without firing up 3D Slicer. Pass --no-summary-card to skip.

Anatomy plausibility (label-set sanity, voxel HU range as CT, image/mask geometry match, declared output labels present, lung-lobe HU floor) is checked by verifiers/ct_synthesis_quality_v1.

Not for clinical interpretation, training data for production deployment, or any non-synthetic-research use.

Files

LICENSE

Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs a command-line tool, Needs Python

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA MedTech Team
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/nv-generate-ct-rflow · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA MedTech Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nv-generate-ct-rflow.zip