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name: continuous_score_loss description: Build continuous denoising score-matching targets and deterministic optimizer checks for score-SDE recovery.

Continuous Score Loss

Use this skill when training or validating a score-SDE mechanism on perturbed data. It constructs analytical VE perturbation scores and a weighted mean-squared denoising score-matching objective. It is suitable for tiny reduced recovery and unit tests, not for claiming full image-model performance.

Inputs

• Clean scalar samples, continuous times, and deterministic noise values.
• A schedule implementation that supplies sigma(t).
• Linear score-model parameters or explicit predictions.
• Optional loss weighting mode.

Outputs

• Perturbed samples and target scores.
• Mean score-matching loss.
• Gradients and updated parameters for a tiny linear score model.

Workflow

1. Pair each clean sample with a time and noise value.
2. Perturb with the VE kernel and compute target_score=-noise/sigma(t).
3. Predict score with a*x_t + b*t + c for reduced recovery.
4. Compute mean squared error and gradients.
5. Apply a bounded optimizer step and record before/after loss and parameters.

Validation

Run python scripts/continuous_score_loss.py --self-test or the generated skill validator with tests enabled.

Limitations

The included optimizer is intentionally tiny and standard-library only. It proves the denoising-score objective can execute under bounded constraints but is not a substitute for full neural network training.