Nv Generate Mr Brain Finetune

NV-Generate-MR-Brain-Finetune

Purpose

• Used for finetuning the NV-Generate-CTMR rflow-mr-brain v1 diffusion UNet from user-supplied T1, T2, FLAIR, SWI, or MRA NIfTI training volumes.

• Not for clinical interpretation, regulatory use, or approving synthetic data for production training.

• The wrapper stages the config glue locally and delegates execution to existing upstream scripts: scripts.diff_model_create_training_data, scripts.diff_model_train, and optionally scripts.diff_model_infer. It does not execute the notebook.

• Manifest I/O: inputs are datalist and data_base_dir; outputs are finetuned_checkpoint, optional inference_outputs, and result_json.

• The underlying training contract is the upstream config/env JSON (the same one driven from cell [10] of train_diff_unet_tutorial.ipynb). The wrapper stages those JSON files for you and exposes the most-tuned fields as CLI flags; the sections below document the fields, their defaults, and how to monitor/tune a run.

Instructions

• Read skill_manifest.yaml before changing arguments, side effects, or validation gates.

• Run scripts/run_mr_brain_finetune.py from the Medical AI Skills repo root.

• If a host agent exposes run_script, use run_script("scripts/run_mr_brain_finetune.py", args=[...]); otherwise run the Bash/Python command below.

• For a command-shape review, do not install packages, clone repositories, download weights, or start GPU training. Emit only the wrapper command with the supplied datalist, an explicit --data-base-dir, an explicit --output-dir, and the requested modality.

• When the user explicitly asks for a training-launch command, do not silently replace it with --preflight; include --preflight only for a preflight request.

• Use --preflight first when checking a new datalist; remove --preflight only when the user explicitly wants to launch GPU finetuning.

• For a staged preflight input bundle directory, use BUNDLE/preflight_datalist.json as the datalist and BUNDLE/preflight_dataset as --data-base-dir when those files are present.

Examples

Validate and stage a preflight finetune check from an input bundle (the recommended first step — no GPU, no training). This is the single canonical command; replace INPUT_BUNDLE and OUT_DIR with your paths:

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-da438fe}" && \
python skills/nv-generate-mr-brain-finetune/scripts/run_mr_brain_finetune.py \
  INPUT_BUNDLE/preflight_datalist.json \
  --data-base-dir INPUT_BUNDLE/preflight_dataset \
  --output-dir OUT_DIR \
  --modality mri_t1 \
  --preflight

For real GPU finetuning and other variations, see Usage below.

Command-shape review for a requested training launch (no setup or execution):

python skills/nv-generate-mr-brain-finetune/scripts/run_mr_brain_finetune.py \
  PATH_TO_DATALIST.json \
  --data-base-dir PATH_TO_DATA_ROOT \
  --output-dir runs/nv_generate_mr_brain_finetune \
  --epochs 2 \
  --modality mri_t1

Available Scripts

Prerequisites

• An explicit NV_GENERATE_ROOT may point to the caller's local checkout and must contain scripts/diff_model_create_training_data.py, scripts/diff_model_train.py, and scripts/diff_model_infer.py. The result records its current commit.

• If NV_GENERATE_ROOT is unset, the wrapper searches .workbench_data/upstreams/NV-Generate-CTMR.

• CUDA_VISIBLE_DEVICES is optional and can be used to select the GPU for real training.

• Runtime requirements: NVIDIA CUDA GPU for real training, Python packages from the upstream requirements.txt, and downloaded MR-brain weights.

• Side effects: writes staged configs, embeddings, checkpoints, optional inference images, and logs under the caller-provided --output-dir; may write model caches under the upstream checkout and ~/.cache/huggingface/; may contact https://huggingface.co for model assets and https://github.com for the upstream checkout.

• The datalist is a MONAI-style JSON object with training[].image paths relative to --data-base-dir. training[].modality is optional and defaults to mri_t1.

When no local checkout is supplied, 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

The wrapper executes upstream code only when NV_GENERATE_ROOT is at the exact manifest commit and its tracked files are clean. Supply custom training and inference settings through the documented config flags rather than editing the checkout. 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. The public v1 assets do not require a credential; pre-download them if your network setup requires separate tooling.

Before a GPU run, download the exact autoencoder and MR-Brain v1 checkpoint revisions declared by the manifest. Passing --download-model-data performs these same two pinned downloads:

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"

1. Config and environment JSON (adapt to your data)

This is a thin wrapper around the upstream train_diff_unet_tutorial.ipynb flow. Each run performs four steps, delegating the heavy lifting to the model author's scripts:

1. Stage configs — copy the three config JSONs and rewrite only the run-specific paths and n_epochs (notebook cell 15).

2. python -m scripts.diff_model_create_training_data → latent *_emb.nii.gz embeddings (cell 17).

3. Write embedding sidecars — a <emb>.nii.gz.json per embedding with spacing/modality (and body-region indices when the model uses them). This is the one piece of glue that lives in the notebook (cell 19), not in upstream scripts/, and diff_model_train requires it; the skill owns it.

4. python -m scripts.diff_model_train (cell 21), optionally python -m scripts.diff_model_infer.

Tune by editing the config JSON, not by adding flags. All training/inference hyperparameters (lr, batch_size, cache_rate, inference dim/spacing/num_inference_steps/cfg_guidance_scale, …) live in config_maisi_diff_model_rflow-mr-brain.json. Edit the upstream copy, or pass your own with --model-config FILE (and --env-config / --model-def for the other two). The wrapper only ever rewrites the fields below.

Environment JSON (environment_maisi_diff_model_rflow-mr-brain.json) — fields the wrapper rewrites per run:

Model config (config_maisi_diff_model_rflow-mr-brain.json) — the only fields the wrapper touches:

Everything else in that file (lr, batch_size, cache_rate, the rest of diffusion_unet_inference) is left exactly as written — edit the JSON to change it.

The pinned v1 inference block defaults to dim=[256,256,128], spacing=[0.94,0.94,1.36], and cfg_guidance_scale=2. The wrapper preserves those fields. Older v0 examples may show 256^3, 1 mm spacing, and guidance scale 10; use the staged v1 JSON as the execution source of truth.

Runtime flags (not config fields): --num-gpus N (>1 launches torch.distributed.run), --no-amp (disable mixed precision, passed through to diff_model_train).

--modality selects the integer code from configs/modality_mapping.json. Supported brain values include mri (8), mri_t1 (9, default), mri_t2 (10), mri_flair (11), mri_mra (16), mri_swi (20), and the skull-stripped values mri_t1_skull_stripped (29), mri_t2_skull_stripped (30), mri_flair_skull_stripped (31), mri_swi_skull_stripped (32), and mri_mra_skull_stripped (33). Per-case training[].modality overrides --modality. The modality also feeds the step-3 embedding sidecars. Upstream reports sparse MRA training coverage, so MRA output quality is not guaranteed.

For an end-to-end reference including example data download and checkpoint loading, see the upstream tutorial train_diff_unet_tutorial.ipynb.

2. Usage (one-line training)

Preflight only:

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-da438fe}" && \
python skills/nv-generate-mr-brain-finetune/scripts/run_mr_brain_finetune.py \
  PATH_TO_DATALIST.json \
  --data-base-dir PATH_TO_DATA_ROOT \
  --output-dir runs/nv_generate_mr_brain_finetune_preflight \
  --preflight

Preflight bundle input:

export NV_GENERATE_ROOT="${NV_GENERATE_ROOT:-$HOME/.cache/nvidia-skills/upstreams/NV-Generate-CTMR-da438fe}" && \
python skills/nv-generate-mr-brain-finetune/scripts/run_mr_brain_finetune.py \
  PATH_TO_INPUT_BUNDLE/preflight_datalist.json \
  --data-base-dir PATH_TO_INPUT_BUNDLE/preflight_dataset \
  --output-dir runs/nv_generate_mr_brain_finetune_preflight \
  --preflight

GPU finetuning:

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-finetune/scripts/run_mr_brain_finetune.py \
  PATH_TO_DATALIST.json \
  --data-base-dir PATH_TO_DATA_ROOT \
  --output-dir runs/nv_generate_mr_brain_finetune \
  --epochs 2 \
  --modality mri_t1 \
  --run-inference

Replace PATH_TO_DATALIST.json and PATH_TO_DATA_ROOT with the user's actual paths. Do not use the fixture datalist for real training; it is a preflight-only placeholder.

3. Monitor training (TensorBoard)

scripts.diff_model_train writes TensorBoard event files under the staged model_dir (OUT_DIR/artifacts/models). Launch TensorBoard against the output directory and watch the loss curve:

python -m pip install tensorboard && \
tensorboard --logdir runs/nv_generate_mr_brain_finetune/artifacts

The run summary is written to OUT_DIR/artifacts/workflow_summary.json (checkpoint path, embedding sidecars, inference outputs); the JSON the wrapper prints to stdout mirrors the same paths plus exit_code and a stderr_tail for quick triage.

4. Hyperparameter tuning and common pitfalls

• Loss not decreasing / unstable — lower diffusion_unet_train.lr (default 1e-5) in the model-config JSON, or keep AMP on (default); --no-amp is slower but more numerically stable on older GPUs.

• Out-of-memory — keep diffusion_unet_train.batch_size at 1 and cache_rate at 0 in the config JSON, and confirm the autoencoder/UNet fit your GPU before scaling. Multi-GPU (--num-gpus N) shards the batch via torch.distributed.run.

• Few cases / quick check — keep --epochs small (the wrapper default 2 is for verification, not convergence; the upstream config ships 1000).

• Wrong modality conditioning — set --modality or per-case training[].modality to a value present in configs/modality_mapping.json; a mismatch produces a clear error rather than silently mislabeling latents.

• Slow startup on first run — diff_model_create_training_data precomputes latent embeddings once; reuse the same --output-dir to avoid recomputing them.

5. Evaluate the finetuned model

Use the staged checkpoint (OUT_DIR/artifacts/models/<model_filename>) as the diffusion UNet for generation, then inspect the synthesized volumes:

• Pass --run-inference here for a quick built-in sanity render, or

• Point the nv-generate-mr-brain inference skill at the finetuned checkpoint to generate fresh brain MRI volumes for qualitative review.

This skill gates file accounting and command provenance only — anatomical realism and downstream utility must be judged by a domain expert on the generated images.

Limitations

• Requires a current upstream NV-Generate-CTMR checkout with the existing diffusion training scripts. The skill itself stages the required config and datalist glue locally and does not depend on the notebook or PR #33.

• Full training can be expensive and is not deterministic across hardware, CUDA, and package versions.

• The wrapper gates file accounting and command provenance, not anatomical realism or downstream model utility.

• Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission, or production training-data approval.

Troubleshooting