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nv-generate-vae-finetune

Stage and execute NV-Generate-CTMR MAISI VAE finetuning from MONAI-style datalists.

Updated Apr 16, 2026
Search Tags:#finetune#vae#ct#training-workflow#upstream-api#monai#mri
One-click install
npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nv-generate-vae-finetune
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nv-generate-vae-finetune
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/nv-generate-vae-finetune
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill nv-generate-vae-finetune

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nibabel, numpy, torch, monai, scipy, scikit-image, einops, huggingface_hub, tqdm, fire, tensorboard, lpips, PyYAML, and includes scripts (resource) components.

What problem does it solve?

Train and tailor the NV-Generate-CTMR MAISI VAE for CT/MRI datasets by wrapping upstream configs, validating datasets, and staging runs, enabling engineering teams to run finetuning without duplicating boilerplate.

Core Features & Use Cases

  • Stages upstream VAE finetuning workflows and datalist contracts, providing ready-to-run configuration scaffolding.
  • Validates MONAI-style datalists (training and validation), resolves absolute image paths, and prepares staged artifacts for training.
  • Use Case: researchers can quickly prototype finetuning on CT and MRI volumes and compare results across different settings using a single, repeatable wrapper.

Quick Start

Invoke this skill with a MONAI-style datalist and a data-base directory to stage the VAE finetuning workflow and run the training pipeline.

Frequently Asked Questions about nv-generate-vae-finetune

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I finetune a VAE on CT and MRI datasets using MONAI?▼

To finetune a VAE on CT and MRI datasets, provide a MONAI-style datalist and data-base directory to stage upstream configs and execute the training workflow, generating autoencoder and discriminator checkpoints.

What is the NV-Generate-CTMR MAISI VAE finetuning process?▼

The MAISI VAE finetuning process wraps upstream configs, validates MONAI-style datalists by resolving absolute image paths, stages artifacts, and delegates GPU-backed training to upstream APIs to output workflow summaries.

Do I need a GPU to run VAE finetuning for medical imaging data?▼

Yes, this VAE finetuning workflow for medical imaging data is applied in research and development environments where GPU-backed finetuning is available to execute the upstream MONAI training APIs.

Can I use this wrapper to validate MONAI datalists before staging training?▼

Yes, the wrapper validates MONAI-style datalists for training and validation, resolves absolute image paths, and runs an optional preflight validation before preparing staged artifacts for the training workflow.

What is the best way to avoid boilerplate when training a medical imaging VAE?▼

The best way to avoid boilerplate when training a medical imaging VAE is using a wrapper that provides deterministic staging contracts and ready-to-run configuration scaffolding, enabling teams to run finetuning without duplicating setup code.

What dependencies are required to stage upstream VAE finetuning workflows?▼

Staging upstream VAE finetuning workflows requires dependencies including torch, MONAI, nibabel, numpy, scipy, scikit-image, einops, huggingface_hub, tensorboard, and lpips to support environment wiring and training execution.

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