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name: py-torch-studio-gan description: "Use PyTorch-StudioGAN for GAN image-synthesis training, YAML configuration, checkpoint sampling and analysis, and IS/FID/PRDC evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PyTorch-StudioGAN repo skill

Use this skill when a task names PyTorch-StudioGAN, StudioGAN, src/main.py, src/evaluate.py, BigGAN/StyleGAN StudioGAN YAML configs, GAN image-synthesis training, checkpoint image analysis, or StudioGAN IS/FID/PRDC metric workflows.

This is a repo-specific operating guide for a script-first project. StudioGAN does not expose a normal Python package or console entry point; practical runs use a separate StudioGAN checkout and its src/main.py / src/evaluate.py scripts. The bundled helpers in this skill accept --repo-root /path/to/PyTorch-StudioGAN so they can validate or build commands for whatever checkout the user is actually using.

Choose the route

User task	Read next
Choose or edit YAML configs, validate custom ImageFolder data, build training/resume/freezeD/DDP/HDF5 commands	training and configuration
Evaluate real/fake image folders with IS, FID, PRDC, cache features/moments, choose eval backbones or clean/friendly resizers	evaluation metrics
Use trained checkpoints for fake/real image saving, visualization, KNN, interpolation, frequency analysis, t-SNE, iFID, CAS, SeFa, truncation, or Langevin sampling	sampling and analysis
Check install/runtime assumptions, CUDA, script-first layout, W&B, pretrained weights, or custom CUDA op issues	root troubleshooting
Confirm what source version this skill was distilled from before refreshing	repository provenance

Minimum operating prerequisites

• A StudioGAN checkout with README.md, src/main.py, src/evaluate.py, src/config.py, src/configs/, src/models/, src/metrics/, and src/utils/.
• Python with PyTorch and TorchVision installed for the user's backend. StudioGAN's real training, sampling, and metric paths are CUDA-oriented; CPU-only environments are suitable for command/config checks only.
• Runtime Python dependencies used by the selected workflows: tqdm, ninja, h5py, kornia, matplotlib, pandas, scikit-learn (imports as sklearn), scipy, seaborn, wandb, PyYAML (imports as yaml), click, requests, pyspng, imageio-ffmpeg, and timm.
• Dataset, checkpoint, pretrained metric weights/cache, W&B login/offline policy, and CUDA toolkit/compiler availability when the selected workflow needs them.

Fast environment and checkout check

Run the root helper before suggesting a long training or metric job:

python scripts/check_studiogan_environment.py \
  --repo-root /path/to/PyTorch-StudioGAN \
  --require-cuda \
  --run-cli-help

The helper imports the expected dependencies, checks CUDA when requested, verifies that the checkout has the two public scripts, and can run -h help checks without training, downloading data, or contacting W&B.

Common safe workflow pattern

1. Start with training and configuration to validate the YAML and data layout.
2. Build a dry-run training command; do not launch long runs until GPU count, W&B policy, save directory, and dataset availability are clear.
3. After a checkpoint exists, use sampling and analysis for checkpoint-driven outputs or evaluation metrics for standalone folder metrics.
4. If a command fails, prefer the nearest troubleshooting reference before changing configs by guesswork.

Do not over-claim verification

• CLI help, config validation, and bundled command-builder checks prove wiring and option compatibility; they do not prove benchmark-quality GAN training or meaningful metric values.
• Tiny image folders are useful for input-shape smoke tests only; they do not produce meaningful FID/PRDC/IS.
• Long training, model-weight downloads, W&B network login, TensorFlow 1.x legacy metrics, and first-time StyleGAN custom-op compilation require explicit runtime budget and environment approval.

Router metadata

Structured router metadata for managed import lives in repo routing metadata. Do not hand-edit a live router from this skill tree.