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name: programmatic-api description: "Guides Python ModelLoader, Trainer, and StyleGAN2 API usage for loading stylegan2_pytorch checkpoints and generating image tensors." disable-model-invocation: true metadata: disco-role: operating license: MIT

Programmatic API and Checkpoint Sampling

Use this sub-skill when the user wants Python code instead of the stylegan2_pytorch CLI, especially for loading a trained checkpoint with ModelLoader, converting latent noise to styles, generating image tensors, and saving samples.

Start here

1. Confirm the environment with the root checker: ../../scripts/check_install.py. The package requires CUDA at import time.
2. Read references/api-reference.md for verified signatures and how ModelLoader, Trainer, and checkpoint files relate.
3. Read references/workflows.md for loading and sampling recipes.
4. Use references/troubleshooting.md if a checkpoint is missing, base_dir is wrong, tensor shapes/devices are wrong, or a package-version mismatch prevents loading.
5. For CLI training or checkpoint creation, route to training.

Common task routes

• Load the latest trained generator: use ModelLoader(base_dir=..., name=...) with the directory where the CLI was invoked, assuming the default models/<name>/ layout exists.
• Load a specific checkpoint: pass load_from=<checkpoint_number> to ModelLoader.
• Generate images from random noise: create a CUDA noise tensor with latent dimension 512, call noise_to_styles, then call styles_to_images.
• Save image tensors: use torchvision.utils.save_image; outputs are clamped to [0, 1] by styles_to_images.
• Custom checkpoint layout: if training used a non-default --models_dir, either restore the default base_dir/models/<name>/ layout for ModelLoader or use Trainer directly with matching models_dir.

Bundled helper script

scripts/sample_from_checkpoint.py adapts the README ModelLoader snippet into a reusable checker that loads a checkpoint from the default layout and writes a sample grid. Run it only after a checkpoint exists:

python sub-skills/programmatic-api/scripts/sample_from_checkpoint.py \
  --base-dir /path/to/run-base \
  --name my-project \
  --output-dir /tmp/sg2-samples

Boundaries

• Do not use this sub-skill to build full training commands, tune augmentation, or plan multi-GPU runs; route those to training.
• Do not claim CPU-only support. ModelLoader.noise_to_styles and styles_to_images move data to CUDA and the package import already requires CUDA.
• Do not tell future agents to open original source files. API facts are distilled into this sub-skill's references and helper script.