Bilingual Text Rendering: Chinese and English in One Image with GLM-Image
Bilingual text rendering — generating an image whose embedded words are correct in both Chinese and English — is the capability GLM-Image was built around: an autoregressive-plus-diffusion architecture with a dedicated Glyph Encoder, a verified 91.16% text-rendering accuracy on the CVTG-2K benchmark, and stated strength in exactly this Chinese-English combination. For menus, signage, packaging, and posters aimed at audiences in both languages, it turns a multi-tool production job into a single prompt.
This guide covers why the task is hard, how the model handles it, what the verified number does and does not mean, prompt patterns that work, and the limits you should plan around. An earlier version of this article inflated the story with invented market statistics and competitor accuracy tables; they are gone, and what remains is what you can act on.
<!--IMG: A generated bilingual cafe menu board with English dish names and Chinese subtitles, all text correctly spelled-->
Why Is Bilingual Text Rendering So Hard for AI?
Text is the failure mode of image generation. A word in an image is a set of symbols that must be exactly right, positioned meaningfully, and stylistically integrated — and diffusion-based generators, which produce whole images in parallel, tend to render text as texture: shapes that look like writing. The result is the familiar melted lettering of most AI images, acceptable for a background sign, fatal for a headline.
Chinese multiplies the difficulty. English needs 26 letterforms plus digits and punctuation; Chinese needs thousands of distinct characters, each with dense internal stroke structure. There is no fuzzy matching — a stroke too many or too few is a different character with a different meaning. A model that approximate-draws Latin script will produce Chinese that is simply wrong, character by character.
Bilingual layout adds a third layer: two scripts with different visual densities and reading conventions must share one composition, weighted by hierarchy — which language leads, which supports. Traditionally this was solved by a pipeline: generate the artwork, then set the type properly in a design tool, ideally with a bilingual designer reviewing. That pipeline still exists for a reason. GLM-Image's contribution is making a useful slice of it a one-pass operation.
How Does GLM-Image Do It?
GLM-Image, developed by Z.ai (~16B parameters, MIT-licensed, supporting text-to-image and image-to-image), attacks the problem architecturally rather than by brute-force training. The autoregressive stage reads your prompt as language — including a mixed Chinese-and-English prompt, with no translation step in between — and plans the image around it. The Glyph Encoder then conditions rendering on the actual visual forms of characters, so the model is reproducing glyphs, not improvising "writing-like" texture. A diffusion stage handles the continuous image work: surfaces, lighting, materials.
The verified outcome is 91.16% text-rendering accuracy on CVTG-2K, a benchmark that measures how accurately a generated image reproduces the text it was asked to contain. The mechanics are covered in depth in how the AR + diffusion hybrid works, and the model's full verified profile is in GLM-Image specs.
What Does 91.16% Mean in Practice?
Honest interpretation, because benchmark numbers mislead when read as guarantees. It means that on the benchmark's test set, the requested text came out correct in the strong majority of generations. In real use, accuracy varies with the length and complexity of the text: short headlines, product names, prices, and menu items land reliably; longer sentences and dense multi-region layouts take a few retries; full paragraphs remain beyond reliable reach for any current model, this one included.
The professional workflow is therefore: generate, proofread, retry or refine with image-to-image if a character is off. That loop is dramatically faster than the alternative (art generation plus manual typesetting in two scripts), but the proofreading step is not optional — 91.16% is excellent, not perfect, and in Chinese a single wrong character can change meaning.
What Is Bilingual Rendering Actually Good For?
Use cases where the text is short, structured, and load-bearing:
- Menus and signage — a cafe board with English dish names and Chinese subtitles; a storefront sign for a bilingual neighborhood.
- Event posters — a conference flyer carrying the title in both languages, with dates and venues rendered once.
- Product packaging and labels — export packaging needing both scripts on the same label; a tea label reading "Dragon Well Tea" and 龙井茶 with real typographic hierarchy.
- Social and e-commerce creative — campaign graphics for cross-border audiences, sale banners in two languages.
- UI and presentation mockups — interface concepts for products shipping in both markets.
A workable prompt pattern:
A vertical cafe menu board, warm wooden background,
header "SEASONAL MENU" in bold sans-serif at top,
Chinese subtitle 本季精选 directly below at equal weight,
three drink names in English with Chinese translations,
clean layout, generous spacing, legible typography
Quote the exact strings, state placement and hierarchy explicitly, name the typography style, and keep each language's text short. More prompt patterns for text-heavy images are in how to write AI prompts and best AI text rendering; the general technique guide for words-in-images is text in image prompts.
Prompting Best Practices for Two Scripts
Keep text short and load-bearing. Headlines and labels, not paragraphs. If a design needs body copy, generate the artwork and set long text in a design tool — you were going to proofread anyway.
Specify hierarchy in the prompt. "English title at top, Chinese subtitle below, equal size" or "Chinese name prominent, English translation in smaller type underneath" — layout decisions do not happen by default.
Name the typography. "Bold sans-serif," "elegant serif," "traditional calligraphy style" — GLM-Image styles lettering on request, and unspecified text gets unspecified treatment.
Cap the languages at two. Chinese plus English is the model's strongest pairing; adding a third script raises error rates on any current model.
Iterate with image-to-image. When everything is right except one character, a refinement pass that preserves the composition beats rerolling from scratch.
Honest Limits, Stated Plainly
Long text degrades; small print degrades; rare characters and unusual fonts degrade. Three-plus languages in one image is unreliable. And every output that matters should be proofread by someone who reads the language — AI-rendered text can look confident and be wrong. The free daily generations on glmimage.app are enough to learn these limits on your own material before spending anything; paid tiers ($9.90/$29.90/$79.90 monthly for roughly 50/200/600 images at 10 credits per image) are on the pricing page if volume follows. How to use GLMImage covers the full workflow.
Frequently Asked Questions
Can GLM-Image write both Chinese and English in one image?
Yes — that bilingual combination is the model's stated strength, backed by a verified 91.16% text-rendering accuracy on CVTG-2K. Short bilingual layouts (menus, posters, labels) are its sweet spot.
How accurate is the rendered text?
Verified at 91.16% on CVTG-2K; in practice short phrases render reliably while longer text needs retries. Treat every published image to proofreading either way.
What prompts produce the best bilingual text?
Quote exact strings, specify placement and hierarchy for each language, name the typography style, keep text short, and refine imperfect results with image-to-image rather than rerolling.
Does it work for other languages?
The verified strength is Chinese and English. Other scripts are not a stated strength and should be tested on your own prompts before relying on them.
Try it on a real design: generate a bilingual menu or poster →
