---

name: "repaint" description: "Routes RePaint inpainting, config, output-layout, and jump-schedule workflows." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

RePaint

Use this skill when the task is about the RePaint diffusion inpainting repository: configs, masks, checkpoints, inference runs, output directories, or the resampling schedule.

What this skill covers

• Running or adapting RePaint-style inpainting with the bundled helper in sub-skills/inpainting-inference/.
• Inspecting and tuning jump schedules with the bundled helper in sub-skills/schedule-visualization/.
• Choosing the right config family for faces, ImageNet-style content, or Places2-style scenes.
• Diagnosing missing checkpoints, mask polarity mistakes, output-path problems, and schedule-parameter failures.

When to use each route

inpainting-inference

Use this route when the user asks about:

• model_path, gt_path, mask_path, paths.srs, paths.lrs, paths.gts, or paths.gt_keep_masks
• copying or adapting a RePaint config for custom images
• checkpoint placement or dataset/mask layout
• running the inpainting sampler or inspecting its outputs
• troubleshooting config, layout, label, or output-directory problems

schedule-visualization

Use this route when the user asks about:

• schedule_jump_params
• t_T, n_sample, jump_length, jump_n_sample, jump2_*, jump3_*, or start_resampling
• rendering or comparing schedules
• explaining the speed/quality tradeoff of resampling
• diagnosing schedule assertions or plotting issues

Read first

• references/configuration.md for shared config terms and family mapping.
• references/troubleshooting.md for cross-cutting import, runtime, and config failures.
• references/repo-provenance.md for the source snapshot and staleness baseline.
• references/repo-routing-metadata.json for router placement metadata.

Then read the route-specific files:

• sub-skills/inpainting-inference/SKILL.md
• sub-skills/schedule-visualization/SKILL.md

Runtime and install guidance

• Use a Python 3.11 environment with torch, numpy, pillow, pyyaml, blobfile, tqdm, and matplotlib available.
• The repository has no packaging metadata, so use the checkout directly instead of expecting an editable install.
• Keep the helper scripts inside this skill tree; do not rely on the original repository's scripts as runtime instructions.
• For a minimal smoke check, import the source packages from the checkout: conf_mgt, guided_diffusion, and utils.

Typical entry points

• sub-skills/inpainting-inference/scripts/run_inpainting.py
• sub-skills/schedule-visualization/scripts/render_schedule.py

Safe defaults

• Treat download.sh as reference-only; it performs network downloads and is not a runtime helper.
• Keep mask_loader: true, return_dict: true, return_dataloader: true, and random_crop: false for the example inpainting path.
• Use --dry_run in the inpainting helper before a heavy sampler run.
• Render schedules before changing a full inpainting config when the request is about speed or harmonization.

If you are unsure

• If the question mentions output images, model assets, or mask directories, it belongs in inpainting-inference.
• If the question mentions resampling counts or schedule plots, it belongs in schedule-visualization.
• If the question is only about generic diffusion training or another image-generation library, this skill is not the best fit.