Phase 9: Evaluation, Production & Capstone · 55 min · Python · PyTorch · CLIP

Image Quality Metrics

FID, CLIPScore, Aesthetic Score, ImageReward — if you can't measure quality, you can't engineer it.

What you will learn

Implement FID (Fréchet Inception Distance): distribution-level quality comparing generated vs real image distributions
Implement CLIPScore: text-image alignment measuring how well image matches prompt
Implement Aesthetic Score: LAION-trained predictor for visual appeal (1-10 scale)
Implement ImageReward/HPS: human preference-trained reward models

Objective

Judge creative outputs with both measurable gates and human critique, then defend the release decision. In the paired studio lab, build an evaluation pipeline that takes a set of generated images with their prompts and scores each image on: (1) CLIPScore (text-image alignment), (2) Aesthetic Score (visual appeal), (3) ImageReward (human preference). Output a ranked list of images by composite score and a quality report.

The Problem

A team generates 100 images with FLUX and 100 with SDXL. Which model produces better images? Without metrics, it's subjective opinion. With metrics, it's data-driven engineering.

FID (Fréchet Inception Distance): distribution-level quality (lower = better)
CLIPScore: text-image alignment (higher = better, 0-1 scale)
Aesthetic Score: visual appeal (1-10 scale, LAION-trained)
ImageReward: human preference model (higher = better)

What you'll build
Implement FID for distribution comparison, CLIPScore for prompt-image alignment, Aesthetic Score for visual appeal, and ImageReward for human preference prediction. Build a multi-metric quality evaluator.

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You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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