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name: setup-and-checkpoints description: "Prepares AnyDoor environments, checkpoints, and config paths." metadata: disco-role: operating disable-model-invocation: true license: MIT

Setup and Checkpoints

Use this sub-skill when the task is about installing AnyDoor, verifying the runtime environment, locating or patching checkpoint paths, or explaining why a fresh clone still cannot generate images.

What this sub-skill owns

• Environment setup for AnyDoor’s torch/CUDA stack.
• Repo layout checks and import readiness.
• Placeholder checkpoint and DINOv2 path patching.
• Optional dependency awareness for xformers and demo mask refinement.
• The preflight stage before any inference, demo, or training route.

What this sub-skill does not own

• Mask preprocessing for inference inputs.
• Gradio, Cog, or prediction input/output handling.
• Dataset-specific path formats or training recipes.

Route those topics to the sibling sub-skills.

Read first

• ../../references/environment-and-installation.md
• ../../references/checkpoints-and-configs.md
• ../../references/troubleshooting.md
• references/checkpoint-checklist.md
• references/troubleshooting.md

Use these scripts

• ../../scripts/check_anydoor_environment.py for a safe repo-root preflight.
• ../../scripts/patch_anydoor_configs.py to replace checkpoint placeholders.

Trigger phrases

This is usually the right branch when the user says things like:

• “I just cloned the repo.”
• “Why does import or CUDA fail?”
• “Where do I put the AnyDoor checkpoint?”
• “What should I do about the DINOv2 weight?”
• “Can I skip xformers?”
• “The demo says it needs a model but I only see placeholders.”

Minimum operational checklist

1. Confirm the repo root contains cldm/, ldm/, datasets/, configs/, and dinov2/.
2. Confirm the Python environment imports torch and sees CUDA when generation is expected.
3. Check that the placeholder values in configs/inference.yaml, configs/demo.yaml, and configs/anydoor.yaml have been replaced.
4. Verify the DINOv2 checkpoint exists at the configured path.
5. If the demo refinement toggle is enabled, verify the optional iseg weight.

Common decisions

• xformers missing: usually record and continue. The attention code has a fallback path.
• share package unavailable: only the source weight-conversion helper is affected; the rest of the repo can still be documented and checked.
• CPU import only: useful for support checks, but not proof that AnyDoor can generate images.
• Placeholder config values remain: stop and patch before routing to any generation workflow.

Troubleshooting focus

This branch owns installation and backend problems such as:

• ModuleNotFoundError from running outside the repo root.
• CUDA wheel or driver mismatches.
• Optional dependency gaps like xformers.
• Missing checkpoint or DINOv2 weights.
• Demo refinement toggle failures due to a missing iseg weight.
• The stale conversion-script config path issue.

Output expected from a future agent

A future agent should be able to say:

• which paths are ready,
• which placeholders are still unresolved,
• whether CUDA is actually available,
• which optional dependencies were skipped,
• and what remains blocked before any generation route.

Cross-links

When this branch is complete, pass the task to:

• inference-and-demo for mask/image generation tasks.
• data-and-training for dataset and training tasks.

Quality bar

The branch is ready when the future agent can prepare a repo root, patch the configs, and explain any remaining backend or checkpoint limitations without reopening the source files.