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運用・デプロイ · SKILL.md

applications-and-deployment

applications-and-deployment

Guides MMGeneration latent editing, interpolation, projection, StyleCLIP, and TorchServe deployment workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

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GitHub

name: "applications-and-deployment" description: "Guides MMGeneration latent editing, interpolation, projection, StyleCLIP, and TorchServe deployment workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Applications and Deployment

Use this sub-skill when the user wants to manipulate a StyleGAN-style latent space or package a model for serving.

Typical triggers

  • "How do I interpolate between two latent codes?"
  • "How do I project an image into StyleGAN space?"
  • "How do I run SeFa or StyleCLIP?"
  • "How do I export a checkpoint for TorchServe?"
  • "How do I explain the advanced app scripts?"

Include here

  • apps/interpolate_sample.py
  • apps/conditional_interpolate.py
  • apps/stylegan_projector.py
  • apps/modified_sefa.py
  • apps/styleclip.py
  • tools/deployment/mmgen2torchserver.py
  • tools/deployment/mmgen_unconditional_handler.py
  • tools/deployment/test_torchserver.py
  • StyleGAN latent-space assumptions and TorchServe packaging behavior

Exclude here

  • Basic sampling and translation -> inference-and-sampling
  • Training and resume behavior -> training-and-distribution
  • Metric evaluation -> evaluation-and-metrics
  • Config and registry editing -> configuration-and-extension

Read these files first

  • references/workflows.md
  • references/troubleshooting.md
  • ../../references/model-overview.md
  • ../../references/api-reference.md
  • ../../references/cli-reference.md

What good guidance looks like

A future agent should be able to:

  1. Tell the user which latent space or model family the script assumes.
  2. Explain which apps need a StyleGAN-family generator versus a translation model.
  3. Distinguish safe help/shape checks from expensive image-generation runs.
  4. Describe the TorchServe packaging workflow without starting the server.
  5. Warn about optional dependencies such as CLIP.

Common failure modes

  • The checkpoint is not StyleGAN-like, so the latent projection or SeFa route does not apply.
  • The script expects w or w+ latents, but the caller passes a different representation.
  • StyleCLIP fails because the optional clip dependency is missing.
  • TorchServe packaging fails because the external archiver/service is not installed.

For concrete recovery steps, read references/troubleshooting.md.

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