lora-evaluate

Evaluating a LoRA for overfitting and prompt fidelity requires generating a structured test grid and scoring image outputs across multiple axes. This automated assessment analyzes subject consistency and visual artefacts to return a pass, review, or fail quality verdict before promotion.

Concept leakage detection in LoRA evaluation uses a negative-control prompt that should not mention the trained subject. It checks the generated test grid to ensure the LoRA does not inadvertently bleed the trained concept into unrelated prompts, which helps prevent overfitting.

Yes, you can automatically score LoRA visual artefacts and subject consistency using multi-axis automated evaluation. The process generates an evaluation report and an image grid, providing evidence-based scores for prompt fidelity and overfitting risk to support a quality verdict.

Running a LoRA quality check requires absolute input paths for the LoRA model and training run metadata, plus an output directory. These inputs allow the evaluation process to write the image test grid and the evaluation.json report containing the pass, review, or fail status.

No, the LoRA evaluation process operates as a pipeline-safe, read-only assessment and does not train, promote, delete, or publish images. It strictly returns an advisory result with a pass, review, or fail status to guide human decision-making before model versioning.