image-evaluator

image-evaluator is a lightweight CLI utility for generative AI practitioners and researchers. It evaluates ten core quality dimensions of synthetic images: predicted visual aesthetics, text-to-image semantic alignment, image editing directional change (Directional CLIP), facial identity preservation, pairwise image fidelity (deep perceptual distance LPIPS, structural similarity SSIM, and peak signal-to-noise ratio PSNR), dataset distribution fidelity (Fréchet Inception Distance FID and Kernel Inception Distance KID), and human preference alignment (PickScore).

Evaluation Workflow

flowchart LR
    A[Task Goal] --> B{Choose --metrics}
    B -->|aesthetic| C[LAION Aesthetic]
    B -->|clip| D[CLIP Similarity]
    B -->|directional_clip| U[Directional CLIP]
    B -->|arcface| E[ArcFace Distance]
    B -->|lpips| F[LPIPS Distance]
    B -->|ssim| G[SSIM Similarity]
    B -->|psnr| H[PSNR Ratio]
    B -->|fid| O[FID Distance]
    B -->|kid| P[KID Distance]
    B -->|pickscore| S[PickScore Preference]
    C --> I[Float, Higher Better]
    D --> J[Cosine Sim, Higher Better]
    U --> V[Directional Sim, Higher Better]
    E --> K[Cosine Dist, Lower Better]
    F --> L[Distance, Lower Better]
    G --> M[Index, Higher Better]
    H --> N[dB, Higher Better]
    O --> Q[Wasserstein-2, Lower Better]
    P --> R[MMD, Lower Better]
    S --> T[Calibrated Likelihood, Higher Better]

Metric Selection

The toolkit registers 10 core metrics backed by an immutable MetricRegistry. Each metric provides formal input contracts, task and objective discovery tags, and execution protocols:

Quick Start

Installation

image-evaluator requires Python 3.11–3.14. Install the release via pip:

python -m pip install image-evaluator==0.4.0

The supported runtime path is macOS or Linux with CPU ONNX Runtime. Linux users who want the GPU runtime can replace onnxruntime with onnxruntime-gpu after installation. Windows is currently unverified.

CLI Metric Discovery & Inspection

image-evaluator provides instant metric discovery and specification inspection with zero deep-model loading overhead (<20ms response):

1. List all registered metrics:

   image-evaluator list
   

2. Filter metrics by task or objective:

   image-evaluator list --task image_editing
   image-evaluator list --objective pixel_fidelity
   image-evaluator list -t image_reconstruction -o structural_similarity
   

3. Inspect detailed metric specification:

   image-evaluator show directional_clip
   

4. Structured JSON output for pipeline discovery:

   image-evaluator list --format json
   image-evaluator show directional_clip --format json
   

Tutorial

The CLI enforces explicit metric selection via --metrics and initializes only selected models:

• --metrics (required): One or more of aesthetic, clip, directional_clip, arcface, lpips, ssim, psnr, fid, kid, pickscore.
• --image (required): Path to an image file or directory (must be a directory when fid or kid is selected).
• --prompt: Required when clip, pickscore, or directional_clip is selected; prohibited otherwise.
• --prompt-src: Required when directional_clip is selected; describes the source image before editing.
• --reference: Required when reference-based metrics (directional_clip, arcface, lpips, ssim, psnr, fid, kid) are selected; prohibited otherwise (must be a directory when fid or kid is selected).
• --format: Output format, either text (default) or json (for CI/CD and jq pipelines).

1. Aesthetic evaluation only:

   image-evaluator --metrics aesthetic --image path/to/image.png
   

2. CLIP text alignment only:

   image-evaluator --metrics clip --image path/to/image.png --prompt "a cat in oil painting style"
   

3. Pairwise fidelity triad (LPIPS, SSIM, PSNR):

   image-evaluator --metrics lpips ssim psnr --image path/to/image.png --reference path/to/ref.png
   

4. Structured JSON output for CI/CD automation:

   image-evaluator --metrics ssim psnr --image img.png --reference ref.png --format json | jq .metrics.ssim
   

5. Dataset distribution metrics (FID and KID):

   image-evaluator --metrics fid kid \
       --reference path/to/real_images/ \
       --image path/to/generated_images/
   

6. PickScore human preference evaluation:

   image-evaluator --metrics pickscore \
       --image path/to/image.png \
       --prompt "a cat in oil painting style"
   

7. Multi-metric evaluation across folders:

   image-evaluator --metrics aesthetic clip arcface lpips ssim psnr fid kid pickscore \
       --image path/to/images/ \
       --prompt "a cat in oil painting style" \
       --reference path/to/refs/
   

Python SDK (Zero-Disk-I/O Memory Stream)

For single-image and pairwise metrics (aesthetic, clip, directional_clip, arcface, lpips, ssim, psnr, pickscore), evaluate in-memory PIL.Image, torch.Tensor, or numpy.ndarray objects directly without saving to disk (dataset metrics fid and kid require directory paths):

import torch
from image_evaluator import evaluate, evaluate_detailed

t_img = torch.rand(3, 256, 256)
t_ref = torch.rand(3, 256, 256)

# 1. Standard Dictionary Return (100% backward compatible)
scores = evaluate(
    metrics=["ssim", "psnr"],
    image=t_img,
    reference=t_ref,
    device="cpu",
)
print(scores)  # {'ssim': 0.0012, 'psnr': 8.142}

# 2. Detailed Result API (structured result with timing & specs)
result = evaluate_detailed(
    metrics=["ssim", "psnr"],
    image=t_img,
    reference=t_ref,
    device="cpu",
)
print(result["ssim"])              # 0.0012 (Mapping protocol transparent access)
print(result.scores)               # {'ssim': 0.0012, 'psnr': 8.142}
print(result.duration_seconds)     # float: precise execution duration
print(result.to_json())            # RFC 8259 compliant JSON string

Programmatic Metric Registry Discovery

Inspect registered metric specifications directly without importing heavy model backends:

from image_evaluator.registry import filter_metrics, get_metric, list_metrics

# List all 10 registered metrics (<20ms response, zero heavy imports)
all_metrics = list_metrics()

# Filter metrics by task and objective
editing_metrics = filter_metrics(task="image_editing")
fidelity_metrics = filter_metrics(objective="pixel_fidelity")

# Inspect specification details
spec = get_metric("directional_clip")
print(spec.display_name)       # "Directional CLIP"
print(spec.score_direction)    # "higher_is_better"
print(spec.inputs.required)    # ("image", "reference_image", "prompt", "source_prompt")

Batch Evaluation & Performance Best Practices

When evaluating multiple images, avoid running the CLI in shell loops (for f in *.png; do ...), which re-initializes deep models ($N$ times) and introduces massive cold-start friction (~25-35s for 10 images).

Instead, choose between two high-throughput native paradigms:

1. Native CLI Directory Mode (Single model initialization, ~2-3s for 10 images):

   image-evaluator --metrics clip --image ./generated/ --prompt "a golden retriever on a sunny lawn"
   image-evaluator --metrics lpips ssim psnr --image ./generated/ --reference ./ground_truth/
   

2. Python Predictor Reuse (Zero reloading overhead, memory-resident tensors/PIL, ~1.5-2.0s for 10 images):

   from PIL import Image
   from image_evaluator import ClipScorePredictor, SSIMPredictor
   
   # Instantiate once; weights stay resident in memory/VRAM
   clip_pred = ClipScorePredictor(device="cpu")
   ssim_pred = SSIMPredictor()
   
   # High-throughput in-memory loop
   samples = [("img1.png", "prompt1"), ("img2.png", "prompt2")]
   for path, prompt in samples:
       score = clip_pred.evaluate_clip_score(path, prompt)
   

See the full Task Selection Guide and Batch Performance Guide on the documentation site.

Interpretation & Protocol Guidelines

1. Protocol Consistency: Always compare scores under identical model backbones and preprocessing pipelines.
2. Relative Comparison: Avoid universal absolute thresholds; interpret scores relative to a baseline control.
3. Fail-Fast Spatial Dimension Policy: Pairwise metrics (lpips, ssim, psnr) strictly reject mismatched image dimensions with ValueError to prevent artificial interpolation distortion. Align sizes beforehand via downsampling or super-resolution.
4. SSIM Minimum Size: SSIM requires both image dimensions to be at least 11 pixels because it uses the documented 11 × 11 Gaussian window. Smaller inputs fail with ValueError.
5. Dataset Distribution Contract: Both fid and kid require existing directories on both sides; passing single files is rejected immediately. Neither metric requires filename stem matching ($N_{\text{ref}} \ne N_{\text{gen}}$ is allowed).
6. Sample Size Sensitivity & Unbiasedness: FID is a biased estimator with finite-sample bias that increases significantly on smaller sample sizes (triggering a UserWarning reminding users of finite-sample sensitivity). KID is an unbiased U-statistic estimator that can produce small negative values near zero; these reflect normal statistical fluctuations and must not be truncated. KID defaults to deterministic seed=0 for bit-exact reproducibility.
7. Human Preference Alignment: PickScore assesses text-image alignment against trained human preference choices from the Pick-a-Pic dataset (yuvalkirstain/PickScore_v1). Scores are uncalibrated logits where higher scores indicate stronger preference; win rate is evaluated strictly via the sigmoid difference between candidate pairs conditioned on identical prompts. PickScore strictly requires an explicit text prompt (--prompt).

Release Status

See CHANGELOG.md for the accepted user-facing changes and known limitations.