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数据与研究 · SKILL.md

model-management

model-management

【数据与研究】Manage InvokeAI model taxonomy, records, install/load/cache diagnostics, LoRA/GGUF format triage, and safe metadata checks without full generation runs.

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技能说明

GitHub

name: model-management description: "Manage InvokeAI model taxonomy, records, install/load/cache diagnostics, LoRA/GGUF format triage, and safe metadata checks without full generation runs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Model Management

Use this sub-skill when the task involves InvokeAI model identity, registration, installation, search, deletion semantics, loading/cache behavior, model metadata diagnostics, LoRA or quantized format triage, or external provider model records.

Read First

Safe Helper Scripts

  • Run python scripts/summarize_model_taxonomy.py --json to print bundled taxonomy values without importing InvokeAI.
  • Run python scripts/summarize_model_taxonomy.py --live --configs only when InvokeAI is importable and you need live enum/config tags.
  • Run python scripts/classify_model_metadata.py <metadata.json|metadata.yaml|model_index.json|config.json> to classify safe metadata/config files without loading weights.
  • Run python scripts/inspect_model_file.py <model-file-or-dir> for size, extension, safetensors header metadata, GGUF header hints, and pickle-risk warnings.

Routing Boundaries

  • Stay here for taxonomy, config records, model install/register/import/search/delete semantics, missing-file diagnostics, loader/cache behavior, LoRA format mismatch, GGUF/quantized model triage, and external provider model-record issues.
  • Route model loader invocation field authoring to workflow nodes.
  • Route default workflows or queue behavior involving model nodes to workflows and queues.
  • Route server config, root/model/cache path settings, device selection, RAM/VRAM limits, and external provider API key/base URL setup to operations config.

Safety Rules

  • Do not delete model files, unregister records, clear caches, or run destructive cleanup unless the user explicitly authorizes the exact action.
  • Prefer metadata/config inspection before any operation that loads weights; never load pickle-based .ckpt, .pt, .pth, or .bin files just to identify a model unless the user accepts the risk.
  • Treat full generation, regression image comparisons, downloads, conversions, and GPU-heavy smoke runs as out of scope unless explicitly authorized.
  • Distinguish unregister from delete: unregister removes only the database record; delete may remove files managed under InvokeAI's models directory; unconditional deletion is a stronger destructive operation.
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