---

name: training description: "Guides command-line stylegan2_pytorch training, checkpoint resume/restart, image generation, interpolation, augmentation, logging, and CUDA troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training and CLI Workflows

Use this sub-skill for the installed stylegan2_pytorch command. It covers training from image folders, checkpoint behavior, sample generation, interpolation GIFs, low-data augmentation, optional logging/FID features, transparent images, and memory/backend choices.

Start here

1. Confirm the package imports and CUDA is available with the root checker: ../../scripts/check_install.py.
2. Read references/data-layout.md before building a command, especially when the user's data folder may be empty, nested, or have unsupported file extensions.
3. Use references/cli-reference.md to translate user intent into exact CLI flags and defaults.
4. Use references/workflows.md for complete recipes: new training, resume/restart, generation, interpolation, FID/logging, and multi-GPU variants.
5. Use references/troubleshooting.md when the command fails, diverges, runs out of memory, cannot find images, cannot load a checkpoint, or hits an optional dependency issue.

Common task routes

• Train a new model: choose --data, --name, --image_size, --batch_size, --gradient_accumulate_every, --network_capacity, output directories, and whether --new is needed.
• Resume training: keep the same project --name and output directories; omit --new so the latest model_<n>.pt is loaded.
• Restart with changed architecture/data settings: add --new and make the output path decision explicit because the command clears the existing project model/results directories.
• Generate still samples: run with --generate after a checkpoint exists; use --load_from and --trunc_psi when the user requests a specific checkpoint or truncation.
• Generate interpolation: run with --generate_interpolation, tune --interpolation_num_steps, and add --save_frames only when individual frame files are desired.
• Low-data training: use --aug_prob and --aug_types; document that supported differentiable augmentation types come from the package's DiffAugment map.
• Memory-sensitive training: reduce --batch_size, raise --gradient_accumulate_every, and lower --network_capacity before changing core architecture flags.
• Optional add-ons: FID (--calculate_fid_every) needs pytorch-fid; Aim logging (--log) needs an Aim service/UI setup for visualization; Apex fp16 (--fp16) needs a compatible Apex install.

Bundled helper scripts

• scripts/make_tiny_fixture.py creates a small deterministic RGB or RGBA image folder for smoke commands when the user has no safe local fixture.
• scripts/train_smoke.py wraps the installed CLI with one-step, low-capacity defaults and temporary output directories. Use --dry-run first when explaining or reviewing commands; execute it only in a CUDA environment.

Boundaries

• Do not use this sub-skill for Python ModelLoader checkpoint sampling; route that to programmatic-api.
• Do not use this sub-skill for source-code refactors, release publishing, or general GAN theory detached from this package.
• Do not direct future agents to read or run files from the original repository checkout. Use the installed package command and bundled references/scripts in this skill.