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name: schedule-visualization description: "Explain, render, and tune RePaint jump schedules without running full inpainting." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

Schedule Visualization

Use this sub-skill when the task is to inspect, explain, plot, compare, or tune the RePaint diffusion schedule in schedule_jump_params.

Route here for

• Explaining t_T, n_sample, jump_length, jump_n_sample, jump2_*, jump3_*, and start_resampling.
• Rendering a schedule plot or JSON/CSV schedule summary with the bundled headless helper.
• Estimating schedule-driven runtime changes by comparing reverse denoise steps and forward undo steps.
• Explaining how the schedule list feeds the diffusion sampling loop and why resampling makes the noise level move up and down.
• Diagnosing schedule assertions, invalid schedule arguments, missing matplotlib, and headless plotting errors.

Do not handle here

• Dataset layout, masks, checkpoint placement, gt_path, mask_path, model_path, and full config setup: route to inpainting-inference.
• Full inpainting execution and output image inspection: route to inpainting-inference.
• Shared installation, import, Python, Torch, or runtime-environment issues: use root troubleshooting first, then return here for schedule-specific failures.
• Training or finetuning diffusion models: out of scope for this repo skill.

Operating workflow

1. Identify whether the user wants a quick explanation, a rendered plot, a config edit, or a speed/quality tradeoff.
2. For concepts and parameter meanings, read schedule reference.
3. For runnable recipes, use workflows and the bundled scripts/render_schedule.py helper.
4. For function signatures, CLI flags, helper outputs, and source-to-helper provenance, use API reference.
5. If plotting or validation fails, use troubleshooting before changing model/data settings.
6. When schedule edits will be applied to a real inpainting run, hand off to inpainting-inference for asset checks, config execution, and output validation.

Bundled helper

• scripts/render_schedule.py: self-contained headless renderer adapted from RePaint's schedule helper. It can read a RePaint-style YAML config or explicit CLI parameters, writes a PNG plot by default, and can also write JSON/CSV summaries for comparison without a GUI.

Start with workflows for commands, then use schedule reference to interpret the resulting plot and counts.