DiscoArt Repo Skill

Use this skill when the task involves DiscoArt, the Python package for creating Disco Diffusion artworks through a one-line API, YAML/CLI workflows, and optional Jina service deployment.

DiscoArt is CUDA-oriented: config and CLI planning can be checked safely, but actual create() runs may download large diffusion/CLIP/secondary-model weights and need GPU memory. Do not start generation, model downloads, Docker builds, or persistent servers unless the user explicitly wants that runtime action.

Start here

1. Install/import check. Use scripts/check_discoart_environment.py --check-cuda to verify package imports, version, default config access, and CUDA visibility without loading models.
2. Pick the right route. Use the sub-skill map below; most tasks should immediately move to a focused sub-skill.
3. Normalize before running. Validate YAML/kwargs through discoart.config.load_config or the bundled validators before calling create() or starting a service.
4. Preserve names and paths. Prefer explicit name_docarray, DISCOART_OUTPUT_DIR, and DISCOART_CACHE_DIR so outputs and cache behavior are predictable.
5. Check provenance if stale. Read references/repo-provenance.md before refreshing this skill or comparing it with a newer checkout.

Sub-skill routes

If the user asks to...	Read
Generate or plan artwork with Python create(), choose model/runtime settings, handle DocumentArray outputs, recover local/cloud results, or use go_big()	sub-skills/artwork-generation/SKILL.md
Write, validate, repair, or explain configs, prompt schema v1, legacy prompt weights, schedule strings, CLIP guidance routing, cut schedule groups, cheatsheets, or config import/export helpers	sub-skills/configuration-and-prompts/SKILL.md
Use python -m discoart, export/run YAML from CLI, serve through Jina Flow, call /create//result//skip//stop, or plan Docker/Jupyter GPU execution	sub-skills/cli-and-serving/SKILL.md
Debug package-wide install/import, CUDA/model-cache, network/version-check, output/cache, or optional dependency issues before choosing a workflow	references/troubleshooting.md

Minimal safe environment check

python scripts/check_discoart_environment.py --check-cuda

This helper imports DiscoArt with remote model lookup disabled, checks the default configuration can be loaded, prints key dependency versions, and optionally probes torch.cuda without running diffusion.

Public install and runtime facts

pip install discoart

For local development against a checkout, use an editable install only in a disposable environment:

python -m pip install -e .

• Package/distribution/import name: discoart.
• Skill baseline package version: 0.12.2.
• Public import examples: from discoart import create, cheatsheet, load_config, save_config, show_config, go_big and from discoart.config import save_config_svg, export_python.
• The primary API is create(**kwargs) -> Optional[DocumentArray]; static overload/docstring facts are generated from the packaged YAML resources.
• Python 3.7+ is declared by the package, but older ML dependencies often require conservative Python and PyTorch choices. For new environments, prefer a supported Python version with compatible CUDA PyTorch rather than the newest Python available.
• Primary generation is practical only with CUDA-enabled PyTorch and enough VRAM. CPU can import and validate configs but is not a practical generation backend.

For a concise package overview, outputs, environment variables, and evidence-backed constraints, read references/package-overview.md.

Guardrails

• Do not depend on opening this repository's original README, notebook, tests, resources, or scripts at runtime. This generated skill is self-contained.
• Do not run repository-native tests or examples until after task-specific planning; full generation may download large models and take minutes even on GPU.
• Do not run python -m discoart serve as a default check; it blocks as a persistent service.
• Do not expose local environment paths, cache paths, or private construction reports in user-facing answers.
• Maintainer release automation, CI linting, and packaging publication workflows are outside this operating skill.

References

• references/package-overview.md — package purpose, APIs, outputs, runtime prerequisites, and environment variables.
• references/troubleshooting.md — cross-cutting install/import, CUDA/cache, network, DocArray, and service setup failures.
• references/repo-provenance.md — source repository snapshot and refresh baseline.
• references/repo-routing-metadata.json — structured scenario metadata consumed by the repo-skills router importer.