NV-Generate-MR

Purpose

Used for generating synthetic body MRI volumes with NV-Generate-CTMR rflow-mr. Not for paired masks or 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 are

synthetic_mr_volumes

and

result_json

Instructions

Read
```
skill_manifest.yaml
```
before changing arguments, side effects, or validation gates.
Run
```
scripts/run_mr.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_mr.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

Script	Purpose	Arguments
`scripts/run_mr.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 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.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 generates image-only synthetic MRI volumes. It does not emit paired segmentation masks.
The upstream README recommends
```
rflow-mr-brain
```
instead for brain MRI synthesis; use
```
skills/nv-generate-mr-brain
```
for that path.
NV-Generate-MR weights are listed by upstream as NVIDIA Non-Commercial. Do not use outputs as production training data without legal and 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 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

, then summarizes the generated NIfTI volume.

Exact Runnable Surface

For user run commands in a fresh benchmark environment, use this setup plus repo-root wrapper command exactly:

bash

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-.workbench_data/upstreams/NV-Generate-CTMR}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-mr/scripts/run_mr.py PATH_TO_MR_CONFIG.json --output-dir OUT_DIR --modality mri_t1 --random-seed 0

Do not invent

generate.sh

infer.py

Medical AI Skills run

, or

python -m nv_generate_mr

commands.

PATH_TO_MR_CONFIG.json

must be the user's supplied request path.

Preconditions

Clone and install the upstream repo once. In this Medical AI Skills checkout, prefer the repo-local cache path when it exists:

bash

mkdir -p .workbench_data/upstreams
test -d .workbench_data/upstreams/NV-Generate-CTMR/.git || \
  git clone https://github.com/NVIDIA-Medtech/NV-Generate-CTMR.git \
    .workbench_data/upstreams/NV-Generate-CTMR
export NV_GENERATE_ROOT=.workbench_data/upstreams/NV-Generate-CTMR
pip install -r "$NV_GENERATE_ROOT/requirements.txt"

Download the MR weights:

bash

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

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

NV_GENERATE_ROOT

is unset or points at a stale clone.

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/scripts/run_mr.py

Usage

bash

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-.workbench_data/upstreams/NV-Generate-CTMR}" && \
python -m pip install -r "$NV_GENERATE_ROOT/requirements.txt" && \
python skills/nv-generate-mr/scripts/run_mr.py \
  PATH_TO_MR_CONFIG.json \
  --output-dir runs/nv_generate_mr_demo \
  --modality mri_t1 \
  --random-seed 0

Replace

PATH_TO_MR_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.py

Supported rflow-mr modality names are

mri

mri_t1

mri_t2

, and

mri_flair

, matching the upstream MR image-generation guide. The upstream README recommends

rflow-mr-brain

instead when synthesizing brain images; use

skills/nv-generate-mr-brain

for that path. For FOV and setup details, see

references/fov-and-downloads.md

The fixture argument is a small JSON override for

configs/config_maisi_diff_model_rflow-mr.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.

nv-generate-mr

NPX Install

Tags

SKILL.md Content

NV-Generate-MR

Purpose

Instructions

Available Scripts

Prerequisites

Limitations

Troubleshooting

Exact Runnable Surface

Preconditions

Usage