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name: upsampling_evaluation_protocol description: Preserve guided diffusion target metadata, proxy declarations, classifier scale, and evaluation protocol consistency.

Upsampling and Evaluation Protocol

Use this skill when preparing recovery_result.json, experiment plans, or validation checks for guided diffusion experiments. It keeps full and proxy targets aligned with the module plan and paper metrics.

Inputs

• Dataset, split, metric, and paper target value.
• Resolution and optional upsampling stage.
• Sample count, classifier scale, and checkpoint or proxy declaration.
• Recovery mode: full, reduced, or proxy.

Outputs

• Normalized protocol object.
• ok boolean plus warnings/errors.
• Target metadata suitable for recovery_result.json.paper_target.

Workflow

1. Copy target metadata from module_plan.json.fast_recovery_target rather than inventing a new metric.
2. Require classifier scale for guided full or reduced sampling claims.
3. Distinguish base-resolution guided samples from cascaded upsampling results.
4. For proxy recovery, require proxy: true, rationale, metric name, and mechanism checks.
5. Reject metric or dataset drift during analysis.

Validation

Run python scripts/evaluation_protocol.py --mode proxy. The tests verify that a proxy protocol passes with declared metadata and that full guided claims without classifier scale fail.

Limitations

This skill does not compute FID or precision/recall. It validates evaluation metadata and prevents unsupported comparisons to the paper tables.