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inpainting-inference

inpainting-inference

Routes RePaint config selection, custom dataset and mask layout, checkpoint placement, inference execution, output inspection, and inference troubleshooting.

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Documentation de la Skill

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name: "inpainting-inference" description: "Routes RePaint config selection, custom dataset and mask layout, checkpoint placement, inference execution, output inspection, and inference troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

inpainting-inference

Use this sub-skill to run or adapt RePaint's main inpainting workflow. It covers config choice, dataset and mask layout, checkpoint placement, inference, output inspection, and common runtime failures.

Use this route for

  • Face, ImageNet, and Places2 style inference configs.
  • Adapting the example inference pipeline to custom images and masks.
  • Checking config, checkpoint, dataset, and output paths before a heavy run.
  • Inspecting written outputs under the configured paths.* directories.
  • Debugging missing checkpoints, bad mask polarity, YAML mistakes, and count mismatches.

Do not use this route for

  • Schedule rendering or resampling tuning. Use ../schedule-visualization/.
  • Low-level diffusion derivations. Keep those in references only.
  • Network download automation. download.sh is reference-only.

Read first

  • ../../references/configuration.md for shared config terminology.
  • ../../references/troubleshooting.md for shared failure modes.
  • references/configuration.md for inpainting-specific config families and key meanings.
  • references/assets.md for checkpoint and dataset layout.
  • references/workflows.md for the dry-run and run sequence.
  • references/api-reference.md for verified helper and upstream signatures.
  • references/troubleshooting.md for inpainting-specific failures.

Skill-owned script

  • scripts/run_inpainting.py — dry-run-capable wrapper adapted from the upstream inpainting sampler.

Typical workflow

  1. Pick the closest base config: face, ImageNet, or Places2.
  2. Point model_path, gt_path, and mask_path at your local assets.
  3. Run the bundled helper with --dry_run to confirm pair counts, mask polarity, and output directories.
  4. Fix any layout or config errors before running the sampler.
  5. Run the helper without --dry_run to execute the bundled sampler path.
  6. Inspect paths.srs, paths.lrs, paths.gts, and paths.gt_keep_masks.

Cross-links

  • Resampling schedule questions belong to ../schedule-visualization/.
  • Shared terminology belongs to ../../references/configuration.md and ../../references/troubleshooting.md.
  • If you need schedule tuning details, do not edit this sub-skill; switch routes instead.
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