Why AI Image Models Fail at 'Song Dynasty Aesthetics': A Deep Test Reveals the Limits of Composite Visual Judgment

sujingshen · x · 2026-07-30

This is an in-depth test record on whether AI image generation models can reliably translate 'Song Dynasty aesthetics'. The author progresses from material, color, and line experiments to wireframe constraints, character narratives, knowledge covers, and product posters, trying methods like universal color cards, direction samples, structural wireframes, content routing, and negative constraints, with A/B comparison protocols for real articles.

Key findings: Models can hit local features like blue-green, fine lines, and silk texture, but fail to consistently perform composite visual judgments—making lines follow objects, colors adhere to forms, whitespace create space, main subject respond to content, and maintain coherent yet varied series.

Methodology: Decompose aesthetic requirements into judgeable tasks (object & line, color & material, space & whitespace, theme & main subject, serial usability), change one variable at a time, test with real use cases, and turn failures into rules rather than longer prompts.

Typical failure modes: Repeated dark backgrounds and gold lines, title fixed at upper right, no explanatory relationship between traditional symbols and article theme, over-saturated and cluttered commercial-style images.

Related event: Deep Tests Reveal Why AI Image Models Struggle with Song Dynasty Aesthetics(2 posts)→

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