color-lock-edit is a CLI tool that provides deterministic pixel protection, measurable seam diagnostics, and a model-agnostic pipeline for verifying and improving local AI image edits.

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License

MIT

Last updated

2026-07-28

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FR-AI / ANALYSIS

Why it is worth attention

It separates semantic prompt constraints from hard pixel constraints, offers a quality gate that automatically rejects color matches that worsen boundary artifacts, and provides machine-readable reports for auditing edit integrity.

Who it is for

  • AI image editing developers integrating local edit validation
  • Quality assurance engineers testing generative image models
  • Researchers evaluating boundary artifacts in local edits
  • Artists and designers who need pixel-level confidence in AI-assisted retouching

Use cases

  • Verifying that a local AI edit (e.g., object replacement) did not alter protected background pixels
  • A/B benchmarking different AI models or prompts for seam quality and color stability
  • Preparing crop packages with explicit coordinate restoration for models that accept single-image references
  • Automatically falling back to a no-match candidate when local color matching degrades seam metrics

Strengths

  • Deterministic pixel protection: decoded protected pixels are proven identical to the original after PNG save/reload
  • Model-agnostic pipeline works with any image generator that outputs PNGs
  • Measurable seam diagnostics (Delta E, luminance, boundary discontinuities) and a quality gate with PASS/REVIEW/FAIL labels
  • Support for explicit object, interaction, and protect masks with local crop generation and coordinate restoration

Considerations

  • Does not generate images, infer correct masks, or automatically align arbitrary compositions
  • Mask design remains a human or upstream-tool responsibility; incomplete masks can still pass pixel tests
  • Explicit resize is limited to near-equal aspect ratios and requires opt-in; crop generation is preferred

README quick start

Installation

Description

Deterministic pixel protection and verification for local AI image edits

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