How to Evaluate a Model Without Fooling Yourself

피파 한 줄 정리: 7-axis 평가: 일관성·editability·prompt 충실도·text·anatomy·motion·failure rate. 데모 cherry-pick에 속지 말고 *내 prompt*로 직접 테스트해.

Here's a trap nearly everyone falls into: you see a stunning image from a new model on social media, decide it's "the best," and switch your entire workflow. Two weeks later, you discover the model can't handle text, breaks on multi-character scenes, and produces inconsistent results with your actual prompts. You were fooled by a cherry-picked demo.

Evaluating a model properly is like test-driving a car. You don't just look at the showroom beauty shot — you drive it in rain, check the trunk space, try parallel parking, and see how it handles potholes. Here's how to test-drive a media model honestly.

The Seven-Axis Evaluation

1. Consistency — Generate the same prompt 10 times. How similar are the results? Do faces, styles, and compositions remain coherent? Inconsistent models waste time through unpredictable outputs.

2. Editability — Take a generated image and try to modify one element. Can you change a background without the subject shifting? Can you adjust colors without layout changes? Models that resist editing force you into generate-from-scratch loops.

3. Prompt Adherence — Write a prompt with 5 specific requirements (subject, action, setting, lighting, style). Count how many the model delivers. Prompt adherence is often more important than raw beauty.

4. Text Handling — Ask for an image with a specific word written on a sign. Check spelling, kerning, and legibility. This is where models diverge dramatically.

5. Anatomy & Physics — Generate people in complex poses, hands holding objects, reflections in mirrors, transparent materials. These stress-test the model's physical understanding.

6. Motion Realism (video models) — Watch for body drift, rubbery movement, physics violations, and temporal flickering. Pause at random frames — do they hold up individually?

7. Failure Rate — Out of 20 generations, how many are usable? A model that produces stunning results 10% of the time and garbage 90% of the time is less useful than one producing good (not stunning) results 80% of the time.

Key Takeaways

Never evaluate a model based on cherry-picked demos. Test it systematically across multiple axes with your own prompts.
Consistency and failure rate often matter more than peak quality.
Give yourself a learning curve — every model has prompt patterns that work better than others.

Code

예시 코드·python

# Simple model evaluation scorecard
def evaluate_model(model_name, test_results):
    """Score a model across seven axes (1-10 each)"""
    axes = [
        "consistency", "editability", "prompt_adherence",
        "text_handling", "anatomy_physics",
        "motion_realism", "failure_rate_inv"  # inverted: lower failure = higher score
    ]
    scores = {}
    for axis in axes:
        scores[axis] = test_results.get(axis, 5)

    weighted = (
        scores["consistency"] * 1.5 +      # high weight: saves iteration time
        scores["prompt_adherence"] * 1.5 +  # high weight: core functionality
        scores["failure_rate_inv"] * 1.2 +  # high weight: workflow reliability
        scores["editability"] * 1.0 +
        scores["text_handling"] * 0.8 +
        scores["anatomy_physics"] * 0.8 +
        scores["motion_realism"] * 0.7
    )
    max_possible = 10 * (1.5 + 1.5 + 1.2 + 1.0 + 0.8 + 0.8 + 0.7)
    return round(weighted / max_possible * 100, 1)  # percentage score

How to Evaluate a Model Without Fooling Yourself

The Seven-Axis Evaluation

Code

External links

Exercise

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