Nv Generate Mr Brain
NVIDIAOfficialCreated by NVIDIA MedTech Team
- Skill
- Content generator
- Science and research
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a standardized wrapper for the NVIDIA NV-Generate-CTMR MR-Brain v1 model, enabling the generation of synthetic T1, T2, FLAIR, SWI, and MRA brain MRI volumes. It manages configuration staging, output path handling, and validation, ensuring that generated volumes are accompanied by metadata. The tool is intended for research and development purposes and is not suitable for clinical deployment, autonomous diagnosis, or as production training data without independent quality review.
What it does
- Generates synthetic 3D brain MRI volumes for various modalities.
- Supports T1, T2, FLAIR, SWI, and MRA modalities, including skull-stripped variants.
- Handles configuration overrides for dimensions, spacing, and inference parameters.
- Validates output volumes against defined engineering invariants.
- Provides a consistent command-line interface for model inference.
- Automates the setup of required model checkpoints and dependencies.
When to use it
- When you need to generate synthetic brain MRI data for research or testing.
- When you need to experiment with different MRI modalities like T1, T2, or FLAIR.
- When you require a standardized, reproducible workflow for MR-Brain v1 inference.
- When you need to verify the output geometry and intensity ranges of synthetic volumes.
Advantages
- Provides a clean, consistent wrapper for complex upstream diffusion models.
- Automates dependency management and model checkpoint verification.
- Ensures reproducible results through controlled configuration and random seeding.
- Includes built-in validation to check the integrity of generated NIfTI volumes.
Usage examples
- “Generate a synthetic T1 brain MRI volume using default settings.”
- The agent executes the wrapper script with the specified modality and output directory, producing a synthetic NIfTI volume and a result JSON.
- “Create a synthetic FLAIR brain MRI with a custom random seed.”
- The agent runs the inference pipeline with the --modality mri_flair and --random-seed flags, ensuring a reproducible synthetic output.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nv-generate-mr-brain/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nv-generate-mr-brain.zip -d .agents/skills
Documentation by NVIDIA MedTech Team
NV-Generate-MR-Brain
Purpose
Used for generating synthetic T1, T2, FLAIR, SWI, or MRA brain MRI volumes with NV-Generate-CTMR rflow-mr-brain v1. Not for production training data.
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, and validation.
Manifest I/O: inputs are
model_config_override; outputs aresynthetic_mr_brain_volumesandresult_json.
Instructions
Read
skill_manifest.yamlbefore changing arguments, side effects, or validation gates.Run
scripts/run_mr_brain.pythrough the documented command below; keep outputs under a caller-provided run directory.If a host agent exposes
run_script, userun_script("scripts/run_mr_brain.py", args=[...]); otherwise run the Bash/Python command shown below.For a command-shape review, do not install packages, clone repositories, download weights, or start GPU inference. Emit only the exact wrapper command with the supplied config path plus explicit
--output-dir,--modality, and--random-seedvalues.For an executable run, 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 withoutnibabel/MONAI, so dropping the install fails withModuleNotFoundError.If
--modality mri_mrais selected, state that upstream reports sparse MRA training coverage and that output quality is not guaranteed.Do not add
rm,mkdir, or any cleanup of--output-dir; the wrapper creates it. Use a fresh--output-dirinstead of deleting one.Check the emitted JSON and paired verifier guidance before treating the run as evidence.
Examples
Command-shape review only (no setup or execution):
python skills/nv-generate-mr-brain/scripts/run_mr_brain.py \
PATH_TO_MR_BRAIN_CONFIG.json \
--output-dir runs/nv_generate_mr_brain_demo \
--modality mri_t1 \
--random-seed 1234For an executable run, use the setup-aware command under Usage.
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/run_mr_brain.py | Primary entrypoint declared by skill_manifest.yaml. | MODEL_CONFIG.json --output-dir OUT_DIR --modality mri_t1 [--random-seed N] [--yes] |
Prerequisites
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 contacthttps://huggingface.coorhttps://github.comduring 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.diff_model_infer. Do not modify code under $NV_GENERATE_ROOT or the repo-local fallback at .workbench_data/upstreams/NV-Generate-CTMR.rflow-mr-brain generates image-only synthetic brain MRI volumes. It does not emit paired segmentation masks.
Output volumes are synthetic. They are not safe as training data for production medtech models without independent quality review.
Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission.
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
| Missing dependency or import error | Runtime package drift from skill_manifest.yaml. | Install the packages declared in the manifest or use the documented setup command. |
| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |
| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |
Wraps the upstream
NVIDIA-Medtech/NV-Generate-CTMR
MR brain image-only generation workflow. The wrapper does not reimplement
diffusion sampling or autoencoder decoding. It stages config overrides, runs
the documented python -m scripts.diff_model_infer command for
rflow-mr-brain, then summarizes the generated NIfTI volume.
Exact Runnable Surface
For user run commands, use this repo-root wrapper path exactly:
export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-da438fe}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-mr-brain/scripts/run_mr_brain.py PATH_TO_MR_BRAIN_CONFIG.json --output-dir OUT_DIR --modality mri_t1 --random-seed 1234Do not invent generate.sh, infer.py, Medical AI Skills run, or python -m nv_generate_mr_brain commands. PATH_TO_MR_BRAIN_CONFIG.json must be the user's supplied request path.
Preconditions
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 once:
if [ -z "${NV_GENERATE_ROOT:-}" ]; then
export NV_GENERATE_COMMIT=da438fec6484cdb6f421f8c7051d954ebefff730
export NV_GENERATE_ROOT="$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-da438fe"
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
pip install -r "$NV_GENERATE_ROOT/requirements.txt"The wrapper executes upstream code only when NV_GENERATE_ROOT is at the exact
manifest commit and its tracked files are clean. Keep model weights untracked
under models/, and use the wrapper override JSON instead of editing upstream
configs. Child processes receive only an allowlist of runtime, CUDA, locale,
and certificate variables; API keys, tokens, passwords, and unrelated parent
environment values are not forwarded.
Download the reused autoencoder and MR-Brain v1 checkpoint from their exact manifest revisions:
python -m huggingface_hub.commands.huggingface_cli download \
nvidia/NV-Generate-CT models/autoencoder_v1.pt \
--revision 75ac080fb1083c403793563477724c038e7d430c \
--local-dir "$NV_GENERATE_ROOT"
python -m huggingface_hub.commands.huggingface_cli download \
nvidia/NV-Generate-MR-Brain models/diff_unet_3d_rflow-mr-brain_v1.pt \
--revision ef9759bf221265b2704569cdeeac20bbf03b62ee \
--local-dir "$NV_GENERATE_ROOT"The wrapper verifies both downloaded files against their published Git LFS SHA-256 object IDs before launching inference.
Runtime needs an NVIDIA GPU with at least 16 GB VRAM. There is no CPU fallback in the upstream path.
The wrapper also searches .workbench_data/upstreams/NV-Generate-CTMR if
NV_GENERATE_ROOT is unset or does not have the required upstream layout.
For agent-generated user run commands, use the command in Usage. Do not prepend
clone or model-download setup steps when the repo-local
upstream cache already exists. In a fresh Python environment, still include
pip install -r "$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. If setup requires cd "$NV_GENERATE_ROOT", return to the Medical AI Skills repo before invoking
skills/nv-generate-mr-brain/scripts/run_mr_brain.py.
Usage
export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-da438fe}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-mr-brain/scripts/run_mr_brain.py \
PATH_TO_MR_BRAIN_CONFIG.json \
--output-dir runs/nv_generate_mr_brain_demo \
--modality mri_t1 \
--random-seed 1234Replace PATH_TO_MR_BRAIN_CONFIG.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 request is at
runs/.../default_mri_t1.json", that exact path is the first positional
argument to scripts/run_mr_brain.py.
Supported MR-brain modality names are mri, mri_t1, mri_t2,
mri_flair, mri_mra, mri_swi, mri_t1_skull_stripped,
mri_t2_skull_stripped, mri_flair_skull_stripped,
mri_mra_skull_stripped, and mri_swi_skull_stripped. These map to the upstream
configs/modality_mapping.json IDs documented in the README.
For FOV and setup details, see references/fov-and-downloads.md.
The pinned v1 config ships axial T1w defaults of dim=[256,256,128],
spacing=[0.94,0.94,1.36], 30 inference steps, and
cfg_guidance_scale=2. Keep the staged config value unless a model-specific
validation justifies an override; older examples may describe the v0
256^3/1 mm geometry or guidance scale 10. MRA is supported by v1, but the
upstream training-data report contains few MRA scans, so output quality is not
guaranteed.
The fixture argument is a small JSON override for
configs/config_maisi_diff_model_rflow-mr-brain.json. Pass default to use
the upstream defaults plus the CLI modality and random seed. Common override
keys are dim, spacing, num_inference_steps, cfg_guidance_scale, and
modality.
Each run records the staged config, model inventory, upstream command, output geometry, spacing, affine, intensity range, and non-constant / finite-data checks. Output volumes are synthetic and are not safe as production training data without independent review.
Not for clinical interpretation, production deployment, autonomous diagnosis, or regulatory submission.
Files
- ATTRIBUTION.mdAdded by AutoGineer931 B
- BENCHMARK.md8.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md10.1 KiB
- evals/evals.json2.1 KiB
- fixtures/README.md1.1 KiB
- fixtures/default_mri_t1.json298 B
- fixtures/mri_flair_skull_stripped.json295 B
- fixtures/mri_mra.json357 B
- fixtures/mri_t2.json272 B
- references/fov-and-downloads.md2.0 KiB
- scripts/run_mr_brain.pyScript29.3 KiB
- skill-card.md4.0 KiB
- skill.oms.sig6.8 KiB
- skill_manifest.yaml8.3 KiB
- tests/test_run_mr_brain.pyScript9.2 KiB
- validators/output_schema.json6.2 KiB
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 an API key, 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-mr-brain · 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-mr-brain.zip