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nv-generate-vae-finetune

separate

Used for finetuning the NV-Generate-CTMR MAISI VAE from CT/MRI NIfTI datalists. Not for clinical or production data approval.

When to use

• Used for finetuning the NV-Generate-CTMR MAISI VAE/autoencoder from user-supplied CT or MRI NIfTI training volumes.
• Not for clinical interpretation, regulatory use, or approving synthetic data for production training.
• Upstream currently documents VAE training in `train_vae_tutorial.ipynb` and provides configs/helpers, but not a `scripts.train_vae` CLI. This skill does not execute the notebook; it stages the required config/datalist glue locally and uses…
• Manifest I/O: inputs are `datalist` and `data_base_dir`; outputs are `autoencoder_checkpoint`, `discriminator_checkpoint`, and `result_json`.
• The underlying training contract is the upstream config/env JSON (`config_maisi_vae_train.json` + `environment_maisi_vae_train.json`, as used in `train_vae_tutorial.ipynb`). The wrapper stages those JSON files for you and exposes the most-…

When not to use

• Requires a current upstream `NV-Generate-CTMR` checkout with VAE configs and helper APIs. The skill owns the runner glue and does not depend on the notebook.
• 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, reconstruction quality, or downstream model utility.
• Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission, or production training-data approval.

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