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name: setup-and-models description: "Guides Interactive Deep Colorization installation, backend choice, model artifact checks, and Docker or display setup before running colorization workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

setup-and-models

Use this sub-skill when a task is about preparing Interactive Deep Colorization rather than applying color hints: installing legacy dependencies, choosing Caffe versus PyTorch, checking required model files, deciding whether Docker/PyQt5 is a better route, or diagnosing setup failures.

Route first

• Read references/setup-reference.md to choose a backend, dependency path, and model/runtime prerequisites.
• Read references/model-artifacts.md before downloading, validating, or explaining model weights and expected filenames.
• Read references/docker-reference.md for the repository's Docker/PyQt5 variant and display-server caveats.
• Read references/troubleshooting.md when Caffe, PyTorch, Qt, display, OpenCV, or missing-model errors block a workflow.

Safe bundled script

• Run scripts/check_model_artifacts.py to validate whether a checkout or staged artifact directory has the expected Caffe and PyTorch weight files. The script never downloads; it only checks file presence, size, and known paths.

Boundaries

• For local-hints GUI, notebook-style API, mask/ab tensor, CLI-default, suggested-color, or saved-output questions, route to ../interactive-colorization/SKILL.md.
• For global histogram/reference-image transfer, route to ../global-histogram-transfer/SKILL.md.
• This sub-skill documents setup and safe validation. It does not certify that PyCaffe, PyQt GUI launch, Docker build, downloaded model inference, or network downloads work on the user's host.

Key setup decisions

1. Decide backend intent:
   • caffe is the original SIGGRAPH 2017 backend and is required for the global histogram transfer notebook.
   • pytorch uses converted weights for the local-hints GUI path and avoids PyCaffe, but still needs the PyTorch weight file.
2. Decide UI path:
   • The root GUI script is PyQt4-based.
   • The Docker entry script is PyQt5-based and defaults to the PyTorch backend.
3. Validate assets before launching:
   • Use the bundled model checker for expected paths and missing files.
   • Do not launch GUI/notebooks first when model-weight errors are likely; missing weights usually fail only after expensive imports.
4. Treat training as out of scope:
   • The repository README points training to a separate PyTorch reimplementation repository; this generated skill covers inference/demo usage only.