---
title: "GLM-Image"
kind: "Kit"
category: "Productivity"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/743612c3-c1da-4c92-a1a7-01a5e6ba37c5"
updated: "2026-11-13"
---

# GLM-Image

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Generates and edits images from text and reference pictures, with strong text rendering inside images, using an autoregressive plus diffusion design. For creators and developers who need accurate words in pictures.

GLM-Image is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by zai-org

## About

### What it is

GLM-Image is an image model that pairs a 9B autoregressive generator with a 7B diffusion decoder. The same model can draw from a prompt alone or start from a reference picture. The authors say it matches mainstream latent diffusion models on general quality and is stronger at drawing readable text and at knowledge-heavy scenes.

### Who it suits

It suits creators and developers who need images with accurate text, such as posters and diagrams, or who want editing and style transfer in the same model.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Marketing | Designers | Generate posters and banners with words in the picture | A poster with a headline in Chinese or English |
| Education | Teachers | Make information-dense diagrams from a description | An illustrated study sheet with labels |
| Content creation | Image creators | Edit images, transfer styles and keep subjects consistent | Restyling a photo while keeping the same person |
| Software | Developers | Add image generation to an app through diffusers | A feature that turns a prompt into a picture |

You can skip it if you need only a small model for a modest graphics card, since the full BF16 weights are about 13.9 GB.

### Where to use it

- Python code that uses the diffusers library, in the GLM-Image pipeline
- The transformers library, which also has the model implementation
- Windows, macOS or Linux, with Python for the download
- The vendor's hosted API, which the card links as another way in

| Use case | Who | What they get back |
| --- | --- | --- |
| Text to image | Creators | A detailed image from a written description |
| Image editing and style transfer | Editors | A changed version of a reference image |
| Subject-consistent images | Brand teams | Several images that keep the same person or object |

### How to set it up

- Python on your computer
- A GPU, because the card's example loads the model on CUDA in BF16
- Recent transformers and diffusers, which the card says to install from source
- Disk space, about 13.9 GB for the BF16 weights

1. Install the Hugging Face hub package with pip.
2. Download the repository files.
3. Install transformers and diffusers from source, as the card instructs.
4. Load the model through the GLM-Image pipeline in Python.

```sh
pip install -U huggingface_hub
hf download zai-org/GLM-Image
```

### How to use it

You load the pipeline in Python, then call it with a prompt for text to image, or with a prompt plus a reference image for image to image. The card's example starts like this.

```python
import torch
from diffusers.pipelines.glm_image import GlmImagePipeline

pipe = GlmImagePipeline.from_pretrained("zai-org/GLM-Image", torch_dtype=torch.bfloat16, device_map="cuda")
```

### Example

A person loads the pipeline and writes a prompt for a poster with a short title and several labeled sections. The model returns an image. The card says the model is especially strong when the picture has dense text or needs real knowledge.

### For newcomers and experts

If you are new to this, start with a short prompt that contains a few words you want printed in the image, and check the spelling in the output. Build up to longer prompts afterwards.

If you know the field, the autoregressive part first writes a compact code of about 256 tokens. It then grows that to between 1K and 4K tokens, matching outputs of 1K to 2K resolution. A diffusion decoder with a glyph encoder handles text rendering. Training used decoupled reinforcement learning with GRPO, giving separate feedback to each part.

Write a prompt, with an optional reference image → The autoregressive model drafts a compact code → It expands the code to more tokens → The diffusion decoder renders the image → Read the text and details in the result

### Good to know

- Limits: the setup needs transformers and diffusers installed from source, and the card shows no hardware minimum in the part given here.
- Claims: the quality comparisons with other models come from the authors.
- Languages: the record lists Chinese and English.
- License: MIT.
- Cost to run: no price is named, and the card also links a hosted API from the vendor.
- Data it reaches: it copies the model files to your computer and reads the prompts and images you give it.

## README

````markdown
# GLM-Image

Generates and edits images from text and reference pictures, with strong text rendering inside images, using an autoregressive plus diffusion design. For creators and developers who need accurate words in pictures.

Made by zai-org on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: GLM-Image is an image model that pairs a 9B autoregressive generator with a 7B diffusion decoder. The same model can draw from a prompt alone or start from a reference picture. The authors say it matches mainstream latent diffusion models on general quality and is stronger at drawing readable text and at knowledge-heavy scenes.

Who it suits: It suits creators and developers who need images with accurate text, such as posters and diagrams, or who want editing and style transfer in the same model.

```table
Industry | Who uses it | How they use it | Example
Marketing | Designers | Generate posters and banners with words in the picture | A poster with a headline in Chinese or English
Education | Teachers | Make information-dense diagrams from a description | An illustrated study sheet with labels
Content creation | Image creators | Edit images, transfer styles and keep subjects consistent | Restyling a photo while keeping the same person
Software | Developers | Add image generation to an app through diffusers | A feature that turns a prompt into a picture
```

You can skip it if you need only a small model for a modest graphics card, since the full BF16 weights are about 13.9 GB.

Where to use it:

- Python code that uses the diffusers library, in the GLM-Image pipeline
- The transformers library, which also has the model implementation
- Windows, macOS or Linux, with Python for the download
- The vendor's hosted API, which the card links as another way in

```table
Use case | Who | What they get back
Text to image | Creators | A detailed image from a written description
Image editing and style transfer | Editors | A changed version of a reference image
Subject-consistent images | Brand teams | Several images that keep the same person or object
```

How to set it up:

- Python on your computer
- A GPU, because the card's example loads the model on CUDA in BF16
- Recent transformers and diffusers, which the card says to install from source
- Disk space, about 13.9 GB for the BF16 weights

1. Install the Hugging Face hub package with pip.
2. Download the repository files.
3. Install transformers and diffusers from source, as the card instructs.
4. Load the model through the GLM-Image pipeline in Python.

```sh
pip install -U huggingface_hub
hf download zai-org/GLM-Image
```

How to use it: You load the pipeline in Python, then call it with a prompt for text to image, or with a prompt plus a reference image for image to image. The card's example starts like this.

```python
import torch
from diffusers.pipelines.glm_image import GlmImagePipeline

pipe = GlmImagePipeline.from_pretrained("zai-org/GLM-Image", torch_dtype=torch.bfloat16, device_map="cuda")
```

Example: A person loads the pipeline and writes a prompt for a poster with a short title and several labeled sections. The model returns an image. The card says the model is especially strong when the picture has dense text or needs real knowledge.

For newcomers and experts: If you are new to this, start with a short prompt that contains a few words you want printed in the image, and check the spelling in the output. Build up to longer prompts afterwards.

If you know the field, the autoregressive part first writes a compact code of about 256 tokens. It then grows that to between 1K and 4K tokens, matching outputs of 1K to 2K resolution. A diffusion decoder with a glyph encoder handles text rendering. Training used decoupled reinforcement learning with GRPO, giving separate feedback to each part.

```flow
Write a prompt, with an optional reference image
The autoregressive model drafts a compact code
It expands the code to more tokens
The diffusion decoder renders the image
Read the text and details in the result
```

Good to know:

- Limits: the setup needs transformers and diffusers installed from source, and the card shows no hardware minimum in the part given here.
- Claims: the quality comparisons with other models come from the authors.
- Languages: the record lists Chinese and English.
- License: MIT.
- Cost to run: no price is named, and the card also links a hosted API from the vendor.
- Data it reaches: it copies the model files to your computer and reads the prompts and images you give it.

## On Hugging Face, 14 November 2026

- Task: text to image
- Library: diffusers
- Parameters: 6.9B
- Weights: about 13.9 GB in BF16; about 3.5 GB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 14,352
- Likes: 1,129
- License: mit
- Languages: zh, en
- Quantized versions: 2
- Fine-tunes: 12
- Tags: diffusers, safetensors, text-to-image

## Links

- Model: https://huggingface.co/zai-org/GLM-Image
- Files: https://huggingface.co/zai-org/GLM-Image/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download zai-org/GLM-Image
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 5 KB, downloaded as a zip.

```text
README.md  5 KB
```

## Source

[zai-org/GLM-Image on Hugging Face](https://huggingface.co/zai-org/GLM-Image)

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| GLM-Image (this page) | 14,352 | 1,129 | 6.9B | mit | 2026-01-15 |
| [controlnet-openpose-sdxl-1.0](https://mindket.com/p/e9415382-28bd-4bbf-bf79-4af2e8a6e250) | 294,030 | 334 | 1.3B | apache-2.0 | 2024-07-09 |
| [Qwen-Image-2.1-LoRAs](https://mindket.com/p/a04397f3-266c-41c8-ac1d-ead886d5fa2c) | 28,839 | 138 | — | apache-2.0 | 2026-09-27 |
| [MOSS Sharpness Slider](https://mindket.com/p/2dc4ab48-3d1b-45eb-a595-28600028fa8b) | 1,104 | 26 | — | apache-2.0 | 2026-10-27 |
| [Wan2.1-T2V-1.3B-Diffusers](https://mindket.com/p/366e0512-9024-4a7a-b9bd-35645c681154) | 384,970 | 168 | 1.4B | apache-2.0 | 2025-04-04 |
| [Wan2.1-T2V-14B](https://mindket.com/p/00aa7ba3-e616-4066-817f-ef36df7edbb7) | 111,314 | 1,619 | 14.3B | apache-2.0 | 2025-03-12 |

Numbers as Mindket last read them, on 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions.

- [Hacker News · scosman · 2026-01-14](https://news.ycombinator.com/item?id=46611775): GLM-Image
- [Bluesky · Tom Dörr · 2026-02-24](https://bsky.app/profile/tom-doerr.bsky.social/post/3mfn5wn37jz2u): https://github.com/zai-org/GLM-Image
- [Hacker News · CamperBob2 · 2026-01-15](https://news.ycombinator.com/item?id=46639755): First impressions of Claude Cowork: I don't use a Mac so can't run Cowork, but the normal Claude CLI is pretty good at general automation tasks (as is Codex-CLI and Gemini CLI.) Most recent example: I wanted to try out GLM-image when it dropped the other day, but didn't feel like spending an hour dealing with multifile,…
- [Bluesky · デイリーHuggingFaceトレンド · 2026-01-22](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mcyxugsveu2b): 今日のHuggingFaceトレンド zai-org/GLM-Image GLM-Imageは、テキストと知識を密に組み込んだ高品質な画像生成モデルです。 拡散モデルと自己回帰モデルを組み合わせた独自の構造を持ち、特にテキストレンダリングや複雑な情報表現に優れています。 テキストから画像、画像から画像への生成に加え、編集やスタイル変換も可能です。 transformersとdiffusersライブラリで実装されています。
- [Bluesky · デイリーHuggingFaceトレンド · 2026-01-20](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mctww552vd25): 今日のHuggingFaceトレンド zai-org/GLM-Image GLM-Imageは、テキストと知識を密に組み込んだ高品質な画像生成モデルです。 拡散モデルと自己回帰モデルを組み合わせた独自の構造を持ち、特にテキストレンダリングや複雑な情報表現に優れています。 テキストから画像、画像から画像への生成に加え、編集やスタイル変換も可能です。 transformersとdiffusersライブラリで実装されています。
- [Bluesky · デイリーHuggingFaceトレンド · 2026-01-18](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mcovnhvtuw2o): 今日のHuggingFaceトレンド zai-org/GLM-Image GLM-Imageは、テキストと知識を密に組み込んだ高品質な画像生成モデルです。 拡散モデルと自己回帰モデルを組み合わせた独自の構造を持ち、特にテキストレンダリングや複雑な情報表現に優れています。 テキストから画像、画像から画像への生成に加え、編集やスタイル変換も可能です。 transformersとdiffusersライブラリで実装されています。
- [Bluesky · 猫国防 · 2026-01-17](https://bsky.app/profile/maoguofang.bsky.social/post/3mcls3ywqh22f): GLM-Image Z-ai的新图片生成模型 - 支持文字转图片（T2I） - 支持图片编辑/参考生成图片（I2I） 项目： z.ai/blog/glm-image Hugging Face: huggingface.co/zai-org/GLM-... GitHub: github.com/zai-org/GLM-...
- [Bluesky · デイリーHuggingFaceトレンド · 2026-01-16](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mcjutxa67k2u): 今日のHuggingFaceトレンド zai-org/GLM-Image GLM-Imageは、テキストと知識を密に組み込んだ高品質な画像生成モデルです。 拡散モデルと自己回帰モデルを組み合わせた独自の構造を持ち、特にテキストレンダリングや複雑な情報表現に優れています。 テキストから画像、画像から画像への生成に加え、編集やスタイル変換も可能です。 transformersとdiffusersライブラリで実装されています。
- [Bluesky · meta · 2026-01-14](https://bsky.app/profile/metaend.eth.xyz/post/3mcex34mywy2u): GLM-Image huggingface.co/zai-org/GLM-...
- [Bluesky · デイリーHuggingFaceトレンド · 2026-01-14](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mcetxpkmnk23): 今日のHuggingFaceトレンド zai-org/GLM-Image GLM-Imageは、テキストと知識を密に組み込んだ高品質な画像生成モデルです。 拡散モデルと自己回帰モデルを組み合わせた独自の構造を持ち、特にテキストレンダリングや複雑な情報表現に優れています。 テキストから画像、画像から画像への生成に加え、編集やスタイル変換も可能です。 transformersとdiffusersライブラリで実装されています。

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2026-01-15
- First published: 2026-01-08
- Checked on Hugging Face: 2026-11-13
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open zai-org/GLM-Image on Hugging Face](https://huggingface.co/zai-org/GLM-Image)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/743612c3-c1da-4c92-a1a7-01a5e6ba37c5)
- [Its page on Mindket](https://mindket.com/p/743612c3-c1da-4c92-a1a7-01a5e6ba37c5)