visual-diff

To compare a website replica screenshot against a reference capture, you generate pixel-level diffs to expose exact divergences. This process produces a visual drift map, a pixelmatch diff image, and an aggregate fidelity score to pinpoint mismatched regions.

Visual regression testing with LLM critique uses vision prompts to interpret layout and token-level mismatches that pixel diffs miss. It maps visual drift regions back to design tokens, providing structural feedback and a composite fidelity score for replicas.

You can automate stopping an iterative design refinement loop by applying plateau detection rules alongside blocking-failure thresholds. This enforces automated iteration control by halting the process when visual fidelity scores stop improving or when critical design tokens mismatch.

Yes, you can trace pixel mismatch regions back to specific design tokens during replica validation. The structural critique mechanism maps drift regions to design tokens, flagging blocking failures like incorrect font families or hero colors for prioritized fixes.

Yes, pixelmatch works for generating red and yellow visualizations of UI screenshot differences. It computes exact pixel divergences between replica and reference captures, outputting diff images that expose specific areas of visual drift for refinement.

Pixel diffs alone cannot explain structural layout or token-level mismatches during design validation. Without LLM critique, you miss semantic context, lacking the ability to interpret why visual drift occurred or enforce blocking-failure thresholds tied to specific design tokens.