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stylegan-generation

stylegan-generation

Guides DragGAN and StyleGAN-Human batch generation, interpolation, style mixing, video command planning, and legacy pickle conversion.

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Documentation de la Skill

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name: stylegan-generation description: "Guides DragGAN and StyleGAN-Human batch generation, interpolation, style mixing, video command planning, and legacy pickle conversion." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

StyleGAN Generation

Use this route for non-interactive generation from StyleGAN2/3 pickles, StyleGAN-Human generation, latent interpolation, style-mixing grids/videos, seed/truncation planning, or legacy pickle conversion.

Command planning

Use the bundled command builder; it prints commands and never runs model inference:

python sub-skills/stylegan-generation/scripts/build_generation_command.py \
  --repo-root /path/to/DragGAN gen-images \
  --network checkpoints/model.pkl --seeds 1,2,5-10 --outdir outputs/generated

Read references/generation-workflows.md for each workflow and references/model-loading-and-conversion.md for checkpoint formats and naming. Use references/troubleshooting.md before retrying a failed command.

Main routes

  • Top-level generation: seed ranges, truncation, noise mode, conditional class labels, translation, rotation, and output image names.
  • StyleGAN-Human generation: choose version 1/2/3 explicitly; v2/v3 use the PyTorch path, while v1 needs a TensorFlow 1.x-compatible environment.
  • Interpolation: exactly two useful seeds, GIF frame count, FPS, and optional intermediate frames.
  • Style mixing: row/column seed grids and style-layer ranges.
  • Style-mixing video: expensive and import-gated by the source’s TensorFlow dnnlib.tflib import; use only after checking that dependency.
  • Legacy conversion: convert supported TensorFlow-era StyleGAN2/StyleGAN2-ADA pickles into the native PyTorch pickle format.

Related routes

Bundled files

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