# When I add LPIPS or CLIPScore to a generation regression test, which input conventions, versions and meaning limits must I respect?

- id: art_01M5NQ12BFY2ZENS3BST1J9PEG
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T05, T20 · tags: none
- updated: 2026-10-24T01:04:59.752Z
- url: https://veyrul.com/articles/art_01M5NQ12BFY2ZENS3BST1J9PEG

Problem. SSIM is brittle to harmless shifts; teams add learned metrics but misuse them. Conditions. LPIPS reference implementation (PerceptualSimilarity repo, commit 082bb24f, LPIPS version 0.1) and the CLIPScore paper (arXiv 2104.08718v3). Method. LPIPS: lpips.LPIPS(net='alex') is the default and best as a forward metric; net='vgg' is closer to a traditional perceptual loss for optimisation; inputs must be RGB tensors Nx3xHxW scaled to [-1, +1]; output is a distance (lower means more similar). The README asks publications to state the version used. CLIPScore is defined as CLIP-S(c, v) = w * max(cos(c, v), 0) with w = 2.5, reference-free, between a candidate caption and image embedding; the authors computed it with a ViT-B/32 backbone. Practical regression design (inference): record metric name, backbone, version and input scaling in the test config; use LPIPS for image-to-image change (baseline vs new render) with a tolerance calibrated on your own repeat-run noise, and treat CLIPS...

Problem. SSIM is brittle to harmless shifts; teams add learned metrics but misuse them. Conditions. LPIPS reference implementation (PerceptualSimilarity repo, commit 082bb24f, LPIPS version 0.1) and the CLIPScore paper (arXiv 2104.08718v3). Method. LPIPS: lpips.LPIPS(net='alex') is the default and best as a forward metric; net='vgg' is closer to a traditional perceptual loss for optimisation; inputs must be RGB tensors Nx3xHxW scaled to [-1, +1]; output is a distance (lower means more similar). The README asks publications to state the version used. CLIPScore is defined as CLIP-S(c, v) = w * max(cos(c, v), 0) with w = 2.5, reference-free, between a candidate caption and image embedding; the authors computed it with a ViT-B/32 backbone. Practical regression design (inference): record metric name, backbone, version and input scaling in the test config; use LPIPS for image-to-image change (baseline vs new render) with a tolerance calibrated on your own repeat-run noise, and treat CLIPScore as a coarse prompt-image alignment screen, not a quality score. Failure modes. Feeding [0,1] images to LPIPS silently changes values; CLIPScore was validated for image-captioning judgments (literal descriptions), not for generative-prompt fidelity, and the paper cautions about non-literal captions, so thresholds do not transfer. Limitations. Neither source describes diffusion regression testing; the design advice is mine. Prior item C2-B49-E5-04 covers the definitions; this adds usage rules and limits. Version and date. LPIPS README commit 082bb24f; CLIPScore v3 (2022-03-23).

Curator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.

Limitations: Usage advice is inference; metrics' agreement with human judgment for modern generators is not established by these sources.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/082bb24f84c091ea94de2867d34c4544f68e0963/README.md",
      "note": "Usage; Variants; Section 3"
    },
    {
      "url": "https://arxiv.org/pdf/2104.08718",
      "note": "Usage; Variants; Section 3"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Problem. SSIM is brittle to harmless shifts; teams add learned metrics but misuse them. Conditions. LPIPS reference implementation (PerceptualSimilarity repo, commit 082bb24f, LPIPS version 0.1) and the CLIPScore paper (arXiv 2104.08718v3). Method. LPIPS: lpips.LPIPS(net='alex') is the default and best as a forward metric; net='vgg' is closer to a traditional perceptual loss for optimisation; inputs must be RGB tensors Nx3xHxW scaled to [-1, +1]; output is a distance (lower means more similar). The README asks publications to state the version used. CLIPScore is defined as CLIP-S(c, v) = w * max(cos(c, v), 0) with w = 2.5, reference-free, between a candidate caption and image embedding; the authors computed it with a ViT-B/32 backbone. Practical regression design (inference): record metric name, backbone, version and input scaling in the test config; use LPIPS for image-to-image change (baseline vs new render) with a tolerance calibrated on your own repeat-run noise, and treat CLIPScore as a coarse prompt-image alignment screen, not a quality score. Failure modes. Feeding [0,1] images to LPIPS silently changes values; CLIPScore was validated for image-captioning judgments (literal descriptions), not for generative-prompt fidelity, and the paper cautions about non-literal captions, so thresholds do not transfer. Limitations. Neither source describes diffusion regression testing; the design advice is mine. Prior item C2-B49-E5-04 covers the definitions; this adds usage rules and limits. Version and date. LPIPS README commit 082bb24f; CLIPScore v3 (2022-03-23). · locator: Usage; Variants; Section 3 · source: https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/082bb24f84c091ea94de2867d34c4544f68e0963/README.md

Rights:
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