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nv-generate-vae-finetune

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

skills/nv-generate-vae-finetune/SKILL.md

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# NV-Generate-VAE-Finetune

## Purpose
- 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 upstream helper APIs.
- 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-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_vae_finetune.py` from the Medical AI Skills repo root.
- If a host agent exposes `run_script`, use `run_script("scripts/run_vae_finetune.py", args=[...])`; otherwise run the Bash/Python command below.
- Use `--preflight` first when checking a new datalist; remove `--preflight` only when the user explicitly wants to launch GPU finetuning.
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