---

name: stylegan-training description: "Guides StyleGAN-Human SHHQ training with the bundled StyleGAN2/StyleGAN3 command builders, dataset layout checks, GPU planning, dry runs, resumes, and training troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

StyleGAN-Human Training

Use this route when the user wants to train or resume StyleGAN2-ADA or StyleGAN3 on SHHQ, adapt the paper-scale eight-GPU recipes, validate dataset/output paths, or diagnose training command failures.

Safety and scope

Training is expensive and asset-gated. Do not launch a long run from a generic request. First confirm the user has lawful access to SHHQ, a prepared dataset directory or zip, enough GPU memory/storage, and a desired run budget.

The bundled builder only prints a command:

python sub-skills/stylegan-training/scripts/build_training_command.py \
  --repo-root /path/to/DragGAN --version sg2 \
  --data data/SHHQ-1.0 --outdir training_results/sg2 \
  --gpus 1 --batch 4 --kimg 10 --dry-run-flag

The training_scripts/sg2 and training_scripts/sg3 files in StyleGAN-Human are modified training entry points and network files, not always complete standalone training roots. If the builder warns about missing support modules such as metrics/, training_loop.py, torch_utils/, or dnnlib/, apply the StyleGAN-Human modifications to a complete StyleGAN2-ADA or StyleGAN3 training tree and pass --training-root <prepared-root> before executing.

Use references/training-workflows.md for SG2/SG3 recipes, references/dataset-and-config.md for SHHQ layout and legal/config constraints, and references/troubleshooting.md before retrying a failed run.

Workflow decisions

• SG2-ADA: --cfg shhq, --data, --gpus, --aug, --mirror, --snap, and --square False; --batch, --gamma, and --kimg can override the base config.
• SG3: --cfg stylegan3-r (or stylegan3-t), --data, --gpus, --batch, --gamma, --mirror, --aug, --square False, and --snap are required/planned controls.
• Debug run: reduce GPUs, batch, and kimg; keep a separate output directory; use --dry-run first.
• Paper-scale reproduction: the README examples use eight GPUs and long schedules. Match the paper recipe only when the hardware, data, and budget are explicitly available.
• Resume: pass a compatible network pickle with --resume; record the source run, config, data version, and changed options.

Related routes

• Batch inference and generation: ../stylegan-generation/SKILL.md.
• Alignment, PTI, and human attribute editing: ../stylegan-human-manipulation/SKILL.md.
• DragGAN interactive editing: ../draggan-ui/SKILL.md.

Bundled files

• scripts/build_training_command.py prints SG2/SG3 commands and can validate the data path without launching training.
• references/training-workflows.md explains command construction, dry runs, snapshots, and resumes.
• references/dataset-and-config.md explains SHHQ access, directory/zip expectations, rectangle geometry, and output conventions.
• references/troubleshooting.md covers CUDA, OOM, dataset, resume, metrics, and multi-GPU problems.