---

name: alae description: "Use the ALAE repository for Adversarial Latent Autoencoder data preparation, CUDA training, checkpoint-backed generation, latent editing, and legacy metrics." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

ALAE repo skill

Use this skill when the user names ALAE, StyleALAE, adversarial latent autoencoders, the podgorskiy/ALAE repository, train_alae.py, interactive_demo.py, style_mixing/stylemix.py, ALAE TFRecords, principal directions, or ALAE FID/PPL/LPIPS metrics.

ALAE is an older, un-packaged script repository. Future agents normally need a local ALAE checkout, must run native scripts from that checkout root, and should set PYTHONPATH to the checkout root for subdirectory scripts.

First reads

• Read repository provenance before trusting version-sensitive guidance or deciding whether to refresh this skill.
• Read setup and environment before installing dependencies, choosing CUDA/TensorFlow variants, or launching native scripts.
• Read configuration before choosing a config, overriding YACS options, or diagnosing path/checkpoint mismatches.
• Read cross-cutting troubleshooting when import, CUDA, TensorFlow, checkpoint, data path, or stale README issues appear.
• Router metadata for import tooling lives in repo-routing-metadata.json.

Safe root helpers

Run these generated helpers from this skill directory, or use their full paths, while pointing --repo-root at the user's ALAE checkout:

python scripts/check_alae_environment.py --repo-root <ALAE-checkout>
python scripts/download_alae_artifacts.py --dataset all

check_alae_environment.py imports safe dependencies/source modules and checks PyTorch CUDA with a tiny tensor by default. It does not download models, import metric scripts, run training, or open a GUI.

download_alae_artifacts.py lists pretrained model IDs/URLs by default. It downloads only when both --download and --yes are supplied.

Route by task

User intent	Read
Prepare TFRecords, validate data/sample/style paths, align faces, or adapt dataset paths away from /data/datasets	data-preparation
Train or resume ALAE/StyleALAE, build a train_alae.py command, inspect configs, debug DDP/NCCL/dareblopy/checkpoints	training
Run pretrained demo, generate images, make reconstruction/style-mixing/interpolation/traversal figures, check principal directions	generation
Prepare or troubleshoot FID, reconstruction FID, PPL, or LPIPS legacy metric scripts	metrics

Operating sequence for most tasks

1. Confirm the user has or can create a local ALAE checkout; do not rely on the checkout used to build this skill.

2. Read provenance if the checkout differs from the recorded commit or contains new/removed scripts.

3. Run or adapt the root environment checker. Core training/generation requires CUDA-visible PyTorch; CPU-only import is not enough.

4. Use the configuration reference and sub-skill-specific checkers before launching any long GPU, GUI, data conversion, metric, or network operation.

5. For native ALAE commands, run from the checkout root:

   cd <ALAE-checkout>
   export PYTHONPATH="$PYTHONPATH:$(pwd)"
   python train_alae.py -c ffhq
   

6. Ask for explicit approval before large downloads, raw dataset conversion, full training, GUI launch, or 10k-50k sample metric runs.

Important constraints

• The README's ablation/separate-model routes are stale in this checkout: train_alae_separate.py, model_separate.py, and celeba_ablation_*.yaml were not present. Do not route users to them unless a newer checkout actually contains them.
• metrics/fid_sep.py imports absent separate-model code and is not an executable route for this skill.
• Many original scripts have hard-coded assumptions (/data/datasets, training_artifacts/<dataset>/last_checkpoint, fixed sample filenames, FFHQ-specific direction vectors). Prefer generated checkers before native execution.
• TensorFlow/dnnlib metric workflows are optional legacy routes; they may import in a TF1 environment while still lacking a compatible TensorFlow GPU CUDA/cuDNN stack.