Fix GLM-Image Text Errors: Garbled Characters, Wrong Spacing, Bad Fonts

Hello, I'm Anna, a writer and life experimenter. I didn't set out to debug GLM-Image text error oddities, I was just trying to make a simple poster with a short line in Chinese and a small English tagline. Nothing fancy. The kind of thing I'd usually do in Canva in eight minutes. But I was already in a chat window, so I asked GLM-Image to generate it. And well… that’s how the fun began.

What pushed me over the edge: the first result looked great from afar… until I zoomed in. The Chinese headline had a swapped character and stray strokes, the spacing was off like it had inhaled too much air, and the font shifted mid-line. I tested this over a few evenings in December 2025 and again in January 2026, across roughly 40 attempts. Here's what kept breaking, why it happens, and the prompt tweaks that actually reduced friction enough to keep me from opening another tab.

Common Text Errors in GLM-Image

Garbled Chinese Characters

My most consistent GLM-Image text error was garbled or "almost right" Chinese. On short phrases like "早起打卡" or "新春礼包," I'd get lookalike radicals, simplified/traditional mix-ups, or a character that felt like a cousin wearing the right jacket. About 1 in 4 images were obviously wrong: another 1 in 4 were subtly off enough that I only noticed when I read it aloud — classic “wait, what?” moment.

What it looks like in practice:

• Correct stroke count but wrong component (e.g., replacing a left-side radical with a near neighbor)
• Characters that read fine at thumbnail size but break under zoom
• Traditional vs. simplified inconsistency in the same word

Wrong Spacing

The second pattern was spacing that didn't respect the language. Chinese text often doesn't use spaces between characters, but the model sometimes inserted little gaps or over-loosened kerning. On mixed-language lines (Chinese + English), it tended to pad everything like it was laying out a runway— seriously, why tho?

Telltale signs:

• Evenly distributed micro-gaps between Chinese characters
• English words squeezed or stretched to match the Chinese rhythm
• Centered text drifting a few pixels off-center after upscaling

Font Style Mismatch

I also saw font style drift inside a single line: a clean sans tiptoeing into a pseudo-calligraphic stroke, or a bold weight fading to regular by the last character. It's subtle, but if you're printing or posting at any real size, you can feel the wobble.

Common flavors:

• Serif-like terminals showing up in a sans description
• Inconsistent stroke width from left to right
• "Handwritten" texture bleeding into only part of the phrase

Why These Happen

I can't see inside the model, but the patterns line up with how image generators usually work: they draw what text should look like rather than typeset actual glyphs. So, no real fonts, no literal Unicode handling, just pixels that resemble letters.

From what I observed and cross-checked against general guidance in image model docs, a few practical causes stack up:

• Rendering by imitation: The model learns what text tends to look like in photos and posters. That's great for style: less great for exact spelling.
• Mixed-language confusion: When you combine Chinese and English, layout rules conflict. The model smooths the difference by spacing everything "nicely," which often means incorrectly.
• Style over accuracy: If you ask for a dynamic, textured look, the model will prioritize vibes. That's when strokes drift and weights shift mid-line.
• Data bias: There's more training data of English signage than clean, print-ready Chinese. That shows up as higher accuracy for English and "close enough" for Chinese.

None of this makes GLM-Image unusable, it just means if you need precise text, you have to guide it more carefully, or handle the text outside the generation step — yep, sometimes the manual step wins.

Prompt Fixes That Work

I tried a lot of small changes. Most didn't help immediately, but a few consistently nudged results from "almost" to "usable." Here's what actually reduced GLM-Image text error issues for me.

1. Lock the language and the exact phrase

• Be explicit: "Use Chinese (Simplified). The exact text: ‘早起打卡'. No other characters."
• Add a boundary: "Place the phrase exactly, no extra symbols, no duplicates."
• Avoid mixing languages on the same line. If you need English, put it on a separate line and describe that line separately.

Result: This cut character-level mistakes by roughly a third in my tests (not magic, but noticeable).

1. Ask for a neutral, print-like style first

• Describe the text like typesetting, not art: "clean printed sans-serif, uniform stroke width, consistent weight, flat color, no brush texture."
• If you need a handwritten look, get a clean base first, then iterate style in small steps. Jumping straight to calligraphic strokes caused more mis-shapes.

Result: Font drift dropped a lot when I kept style requests plain.

1. Control spacing with simple rules

• For Chinese: "no spaces between characters, tight but readable spacing, balanced center alignment."
• For mixed-language layouts: "Chinese headline on first line, English subheading on separate line below, smaller size, standard English kerning."
• Add: "keep lines truly centered relative to the canvas." It helped with tiny drift after upscaling.

Result: Kerning still wasn't perfect, but the obvious gaps mostly disappeared.

1. Give the text a quiet environment

• Specify background simplicity: "solid light background, no textures behind text." Busy backgrounds seemed to pull strokes out of shape.
• Limit competing elements near the text: "no stickers, no overlapping objects near the headline."

Result: Fewer garbles, especially on the last character of short phrases (which weirdly broke most often for me).

1. Use quotes sparingly, but consistently

• Wrapping the phrase in quotes sometimes improved fidelity, but sometimes the model drew the quotes. What worked better: "The exact text is: 早起打卡. No quotation marks." Stating it plainly helped more than literal quotes.

1. Retry with small seeds and upscales

• If your tool exposes seeds or "variations," reroll before you rewrite the whole prompt. I often got a correct version within 3–5 rerolls once the base prompt was solid.
• Upscale after you confirm the text is correct at normal size. Upscalers can introduce tiny warps that aren't fixable later.

1. Keep it short

• Shorter phrases were much more reliable. If you have a sentence, consider breaking it into two lines with line-by-line instructions.

1. When accuracy matters, add text later

• The low-drama path: generate background and layout with a placeholder ("TEXT HERE"), then add the real words in a design tool. I know, it's not pure. It's also faster and correct.

A prompt that gave me the most consistent results (about 70% usable outcomes):

"Minimal poster. Solid light background. Centered Chinese headline. Use Chinese (Simplified). The exact text is: 早起打卡. No spaces between characters. Clean printed sans-serif, uniform stroke width, flat color, no texture. Balanced center alignment. Second line: small English subheading below, standard English kerning, simple sans-serif. No extra symbols, no quotes."

Is it wordy? Yes. Did it save me from opening Canva half the time? Also yes.

When to Use Alternatives

I like pushing a tool a little, but there's a point where you stop negotiating with pixels. I switched to alternatives when:

• The phrase had to be exact (names, dates, addresses). I generated the layout in GLM-Image, then added text in Figma or Canva. It took me 3–6 minutes and removed the worry.

• I needed consistent brand typography. Image models don't honor your actual font files. If you care about brand weight or spacing, add the text afterward.
• The background was complex. When text sits on texture or gradients, models tend to "blend" the letters. I'd composite: generate the background, export, then overlay real text.
• I wanted stylized lettering but exact characters. I sometimes used a two-step: clean base text in a design tool, then a gentle texture overlay (either via an image editor or a very light generative fill) to keep the letters intact.

If you're set on staying inside the model, try a hybrid: generate multiple clean crops of the same phrase, pick the best, and composite. It's not elegant, but neither is squinting at a misdrawn radical.

By the way, let me introduce a super handy AI tool—our Macaron. It helps you handle those little daily hassles, letting AI proactively solve problems for you instead of making you tweak prompts every time

→ Give Macaron a try.

For more technical details about GLM-Image's architecture and how it handles text rendering, check out the official technical blog and the GLM-Image documentation— geek mode on.