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name: model-internals description: "Explain MUNIT trainer, generator, discriminator, and porting internals for safe architecture modification." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MUNIT model internals

Use this sub-skill when a Researcher is modifying, debugging, or porting the MUNIT/UNIT model code: trainer class contracts, AdaIN generator wiring, UNIT VAE generator behavior, multi-scale discriminator losses, normalization/activation/padding options, checkpoint compatibility, and legacy PyTorch migration issues.

This sub-skill is self-contained operating knowledge distilled from the repository trainer, network, utility, config, tutorial, and CLI evidence. It intentionally avoids depending on the original source checkout for runtime guidance.

Boundaries

Use this sub-skill for:

• Understanding MUNIT_Trainer(hyperparameters) and UNIT_Trainer(hyperparameters) object graphs, update methods, losses, save/resume state, and CUDA assumptions.
• Editing or reimplementing AdaINGen(input_dim, params), VAEGen(input_dim, params), MsImageDis(input_dim, params), encoders, decoder, blocks, AdaIN, LayerNorm, or related helpers.
• Checking whether a config is architecture-compatible before changing gen, dis, input_dim_*, loss, or style-code settings.
• Planning a modern PyTorch port without accidentally changing style-code semantics, GAN loss semantics, InstanceNorm checkpoint loading, or VGG side effects.

Reroute instead:

• End-user training commands, resume commands, run outputs, and long GPU jobs: ../training/.
• Single-image inference, example-guided translation, batch inference, and metrics: ../inference-and-evaluation/.
• Runtime installation, legacy CUDA/PyTorch setup, dependency probes, and Docker/conda decisions: ../environment-and-setup/.
• Dataset folder/list layouts and YAML path repair: ../data-and-configuration/.

Start here

1. Read references/architecture.md for the model graph and dataflow before editing code.

2. Read references/api-reference.md for class signatures, expected config keys, method behavior, and return shapes.

3. For ports or extensions, read references/porting-and-extension.md before touching deprecated PyTorch APIs or checkpoint loading.

4. For failure diagnosis, use references/troubleshooting.md to map symptoms to causes and safe fixes.

5. Run the static helper against a target checkout or config when available. It does not import MUNIT modules, allocate CUDA tensors, download weights, or instantiate trainers:

   python scripts/inspect_munit_architecture.py --help
   python scripts/inspect_munit_architecture.py \
     --repo-root /path/to/user/munit-checkout \
     --config configs/demo_edges2handbags_folder.yaml \
     --trainer MUNIT
   

Critical operating facts

• MUNIT is two-domain: domain A and domain B each get a generator and discriminator. Checkpoints store generator/discriminator state as dictionaries with a and b entries.
• MUNIT_Trainer uses AdaIN generators and a sampled style code with shape [batch, gen.style_dim, 1, 1]; UNIT_Trainer uses VAE-style content generators and needs UNIT KL loss weights not present in the MUNIT demo configs.
• AdaIN decoder calls require dynamic AdaIN weight/bias assignment before every decode. Calling the decoder path before assigning these parameters triggers an assertion.
• The original trainer and inference paths call .cuda() directly. Treat CPU-only inspection as static verification, not proof that unmodified training or inference will run.
• When vgg_w > 0, the legacy utility may try to create a model directory, download a VGG .t7 file, convert it with load_lua, and save converted weights. Keep that side effect out of static checks.

Reference map

• references/api-reference.md - verified class signatures, trainer methods, network constructors, config keys, and selected utility helpers.
• references/architecture.md - MUNIT/UNIT model graph, AdaIN parameter flow, discriminator scales, and loss/update sequence.
• references/porting-and-extension.md - safe extension and modernization guidance for style dimensions, devices, PyTorch APIs, YAML, VGG, and state dicts.
• references/troubleshooting.md - symptom-driven fixes for AdaIN, checkpoints, unsupported options, GAN loss choices, legacy APIs, InstanceNorm conversion, CUDA assumptions, and VGG download side effects.
• scripts/inspect_munit_architecture.py - self-contained static inspector for source text and config files.