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name: metrics description: "Prepare and route safe ALAE FID, reconstruction FID, PPL, and LPIPS metric workflows." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

ALAE metrics

Use this sub-skill when a user asks how to evaluate ALAE/StyleALAE with FID, reconstruction FID, PPL, or LPIPS, or when they need to decide whether a legacy metric run is ready, too expensive, or blocked by dependencies.

Routing contract

• Use references/metrics-workflows.md for commands, required artifacts, metric outputs, sample-count costs, and config caveats.
• Use references/troubleshooting.md for TensorFlow/dnnlib, metric pickle, CUDA/cuDNN, checkpoint, TFRecord, and stale-script failures.
• Run scripts/check_metrics_stack.py before recommending a native metric run. The checker is safe: it does not import metrics/*.py, does not download files, and does not run evaluation.
• Route TFRecord creation, raw dataset conversion, and sample-image layout questions to ../data-preparation/SKILL.md.
• Route checkpoint download, pretrained artifact readiness, generation, reconstruction, or style-mixing asset questions to ../generation/SKILL.md or the root setup guidance.
• Route training launch, checkpoint semantics, or architecture/checkpoint compatibility questions to ../training/SKILL.md.

Non-negotiable safety notes

• Do not import metrics/fid.py, metrics/fid_rec.py, metrics/ppl.py, or metrics/lpips.py merely to inspect them. Their top-level module code initializes the legacy TensorFlow/dnnlib stack and calls a metric pickle download helper.
• Treat metrics as optional legacy workflows. The inspected environment proved TensorFlow 1.15-style APIs and dnnlib.tflib imports, but TensorFlow GPU libraries for the old CUDA/cuDNN stack were missing or unverified.
• Do not run bundled full metric evaluations. Native metric scripts process 10k to 50k samples and require explicit user approval, local data, checkpoints, metric pickle files, CUDA, and the legacy TensorFlow/dnnlib stack.
• Do not route metrics/fid_sep.py as executable. It depends on the absent separate-model implementation and a missing separate-model default config.

Default operating sequence

1. Confirm the user has a checkout root and intends to run an expensive optional metric, not just inspect readiness.
2. From the ALAE repository root, ensure PYTHONPATH includes the checkout root before running native repository scripts.
3. From this sub-skill directory (or by using the generated skill's script path), run the safe stack checker with an explicit config name or path, for example python scripts/check_metrics_stack.py --repo-root <ALAE-checkout> --config ffhq.
4. If the checker reports missing TensorFlow 1.x APIs, dnnlib, metric pickle files, a checkpoint pointer, CUDA visibility, or required TFRecords, resolve those first through the routed sub-skills.
5. Only then present the native metric command and its expected output file or stdout result from references/metrics-workflows.md.