
Educational Infographics: Visualizing Data with GLM-Image
How to create complex educational visuals that require precise labels and layout logic.
Infographics are notoriously difficult for AI. They require a specific flow of information.
The GLM-Image Difference
GLM-Image can handle "step-by-step" layouts. You can prompt for "Step 1", "Step 2", and "Step 3" with consistent styling and legible text.
Case Study: Solar System Diagram
Prompt: "A scientific diagram of the solar system. Labels: 'SUN', 'EARTH', 'MARS'. Minimalist style, white background, precise arrows pointing to each planet."
Outcome
Unlike other models that might create a beautiful cluster of planets, GLM-Image places the text exactly where the arrow points.
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Recreate the key “text-in-image” tests (CVTG-2K style) with prompts you can copy, run, and compare across models.


Transformers AR Stage Deep Dive: What Are the 256→4K Tokens?
GLM-Image generates image tokens autoregressively—starting from ~256 tokens and expanding to 1K–4K. Here's what that means for layouts, typography, and control.


Menu Test: Why GLM-Image Beats Diffusion Models at Legible Pricing
A practical menu benchmark you can run at home—testing price readability, alignment, and typography using GLM-Image with a clear scoring rubric.
