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name: munit description: "Route NVlabs MUNIT legacy multimodal image-to-image translation setup, data, training, inference, evaluation, and model-internals tasks." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MUNIT Repo Skill

Use this repo skill for NVlabs/MUNIT, the legacy PyTorch implementation of Multimodal Unsupervised Image-to-Image Translation. It helps future agents operate the codebase without rereading the original docs or source: choose a legacy runtime, validate datasets/configs, construct training or inference commands, diagnose checkpoints and outputs, and understand the trainer/network internals.

Critical Context

• MUNIT is an old CUDA/PyTorch project. The documented runtime is Python 2.7 or 3.6, PyTorch 0.4.1, TorchVision 0.2.x, CUDA 9.x, cuDNN 7.x, PyYAML, tensorboard, and tensorboardX.
• The repository is not an installable Python distribution. User workflows normally run scripts from a user-provided MUNIT checkout or a deliberately copied/ported equivalent.
• Unmodified training and inference call .cuda() directly. CPU-only import or parser checks are useful preflight checks, but they do not prove real training or translation will run.
• Pretrained checkpoints, VGG weights, Inception classifiers, and full datasets are external assets. Do not download them without explicit user approval.
• This skill is self-contained guidance and bundled helpers; do not ask future agents to open the source README, tutorial, configs, or scripts to complete ordinary tasks.

Quick Setup Check

For an existing user checkout, start with the environment checker:

python sub-skills/environment-and-setup/scripts/check_munit_environment.py --repo-root /path/to/user/munit-checkout

Use --expect-cuda only when the user asks to probe CUDA availability. The checker avoids CUDA tensor allocation and does not run training or inference.

Route Map

User need	Read this sub-skill	Why
Install, dependency, Docker, CUDA, checkpoint/data download boundary, or modern-PyTorch import failure	sub-skills/environment-and-setup/	Owns legacy runtime and setup troubleshooting.
YAML config editing, data_root, list files, train/test domain folders, demo-data validation, dataset preparation	sub-skills/data-and-configuration/	Owns config and data-loader semantics plus safe validators.
Training command construction, MUNIT vs UNIT, resume, logs, sample grids, checkpoints, long-run planning	sub-skills/training/	Owns train.py CLI and output/checkpoint behavior.
Single-image translation, example-guided style, batch translation, direction flags, output folders, IS/CIS metrics	sub-skills/inference-and-evaluation/	Owns test.py and test_batch.py command builders and failure modes.
AdaIN generator, UNIT VAE generator, multi-scale discriminator, losses, state dicts, code porting, architecture edits	sub-skills/model-internals/	Owns trainer/network APIs and migration pitfalls.

Common Workflow Order

1. Use environment-and-setup to choose the legacy runtime and identify blocked CUDA or modern-PyTorch issues.
2. Use data-and-configuration to validate the config and dataset paths.
3. Use training to construct a dry-run training or resume command.
4. Use inference-and-evaluation to construct checkpointed translation and optional metric commands.
5. Use model-internals only when changing architecture, porting the code, or explaining trainer/network behavior.

Public Install Baseline

A historically faithful conda-style setup follows the repository documentation:

conda create -n munit-legacy python=3.6
conda activate munit-legacy
conda install pytorch=0.4.1 torchvision cuda91 -c pytorch
conda install -c anaconda pyyaml pip
pip install tensorboard tensorboardX

The Docker evidence uses a CUDA 9.1/cuDNN 7 Ubuntu 16.04 base. On modern GPUs or modern PyTorch, expect compatibility work; route that to environment-and-setup and model-internals.

Repo-Level References

• references/repo-provenance.md records the source snapshot, dirty-state note, package/import facts, and evidence paths used to create this skill. Read it before deciding whether to refresh this skill for a changed checkout.
• references/troubleshooting.md summarizes cross-cutting MUNIT failures and routes each symptom to the right sub-skill.
• references/repo-routing-metadata.json is structured metadata for DisCo's managed repo-skill router when this skill is imported.

Operating Boundaries

• Do not run train.py, test.py, test_batch.py, or demo dataset scripts as a casual check. Use the bundled dry-run helpers first.
• Do not treat the current working directory, the generation checkout, or any private inspection environment as part of this skill.
• Do not replace real CUDA/checkpoint/dataset verification with synthetic prompt checks when the task requires an actual model result.
• For modernizing the code, preserve MUNIT semantics first: two domains, style/content decomposition, AdaIN parameter assignment, loss weights, checkpoint a/b dictionaries, and output naming.