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name: instance-features description: "Use pix2pixHD's instance-aware feature encoding, clustering, feature caches, and feature-conditioned train/test workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Instance Features

Use this sub-skill when a pix2pixHD task depends on encoded instance features, label-level feature variants, precomputed feature maps, or the clustered .npy cache that drives feature-conditioned training and inference.

Route Here For

• encode_features.py and precompute_feature_maps.py
• --instance_feat, --label_feat, --load_features, --feat_num, --n_clusters
• features.npy, features_clustered_*.npy, and *_feat/ dataset folders
• checkpoint requirements for source encoders and feature-conditioned inference
• KMeans / scikit-learn availability
• safe construction of the feature-aware commands from scripts/*feat*.sh
• UIModel-style interactive feature editing that reuses the same clustered feature bank

Route Elsewhere

• Dataset layout, label/instance pairing, and basic preprocessing: setup-and-data
• Plain training, VRAM planning, or checkpoint resume behavior: training
• Plain test-time synthesis, HTML output, or non-feature inference: inference

Core Rules

1. instance_feat and label_feat both enable the feature path. The published helper recipes default to instance_feat; label_feat redirects the feature source to labels in the encoder path and is mainly relevant when you are reasoning about the model/UI code.
2. load_features means the dataset supplies precomputed feature maps from phase_feat/ and the model does not build netE.
3. feat_num is the channel width of the feature vectors. Changing it invalidates old features.npy, features_clustered_*.npy, and train_feat/ caches.
4. n_clusters controls the per-label KMeans count and the cluster filename suffix. The test-time default is features_clustered_010.npy.
5. Use the bundled helper scripts first; they print commands and validate caches without launching long jobs.

Fast Path

From this sub-skill directory, pass the actual pix2pixHD checkout as --repo-root:

python scripts/check_feature_cache.py --repo-root <repo-root> --name label2city_512p_feat --mode full --check-generator --check-encoder
python scripts/build_feature_command.py --repo-root <repo-root> --recipe train_1024p_feat_24G

Boundaries

• Do not use this sub-skill for label-only training or plain inference without feature conditioning.
• Do not route TensorRT, vendor runtimes, or other accelerator deployment paths here.
• Do not promise an encode/clustering step unless the required feature checkpoint and scikit-learn / KMeans are available.

References

• Workflows: read for encode, precompute, feature-train, and feature-test sequences.
• CLI reference: read for feature flags and defaults.
• Feature layout: read for cache files, *_feat/ folders, and checkpoint expectations.
• Troubleshooting: read for missing KMeans, cache, checkpoint, and CUDA failures.

Bundled Scripts

• build_feature_command.py: run to print feature workflow commands without launching long jobs.
• check_feature_cache.py: run to validate feature caches and precomputed feature-map folders.