{"id":2253,"date":"2026-05-14T09:00:00","date_gmt":"2026-05-14T09:00:00","guid":{"rendered":"https:\/\/wiro.ai\/blog\/?p=2253"},"modified":"2026-09-27T21:53:39","modified_gmt":"2026-09-27T21:53:39","slug":"glm-image-text-rendering-in-6-prompt-tests","status":"publish","type":"post","link":"https:\/\/wiro.ai\/blog\/glm-image-text-rendering-in-6-prompt-tests\/","title":{"rendered":"GLM-Image: Text Rendering in 6 Prompt Tests"},"content":{"rendered":"

GLM-Image text rendering<\/strong> is the point of this six-prompt test. The goal was not to make six attractive pictures. It was to see whether one text-to-image model could keep words, prices, labels, columns, punctuation, and small interface strings usable inside finished-looking images.<\/p>\n

The test used GLM-Image on Wiro<\/a> with six square 1024 by 1024 outputs. Each run used 30 inference steps, guidance scale 1.5, one sample, and seed 0. Those are the model page defaults used for this test. No per-output price was shown in the Wiro documentation, so this post does not assign a cost to these six results. The documentation includes one illustrative completed task with a six-second elapsed time, but that is not a timed measurement of these particular images.<\/p>\n

GLM-Image combines an autoregressive generator with a diffusion decoder. Its model card describes a Glyph Encoder for text, which helps explain why it is a sensible candidate for layouts that need more than a single decorative word. The open model card is available on Hugging Face<\/a>. That source also notes that output dimensions must be divisible by 32 and recommends putting text intended for rendering inside quotation marks. The prompts below used exact strings, lists, or explicit labels for that reason.<\/p>\n

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