Question 1

What is GLM-Image and how is it different from other AI image generators?

Accepted Answer

GLM-Image is ZhipuAI's flagship image generation model featuring a hybrid 9B autoregressive + 7B diffusion decoder architecture. Unlike pure diffusion models like DALL-E or Stable Diffusion, GLM-Image excels at text rendering (91.16% word accuracy) and knowledge-intensive scenarios like posters and infographics. It's the world's first open-source industrial-grade discrete autoregressive image model.

Question 2

What are the technical specifications?

Accepted Answer

Architecture: 9B-parameter autoregressive generator (from GLM-4-9B-0414) + 7B-parameter single-stream DiT diffusion decoder with Glyph Encoder. Resolution: 512px-2048px (multiples of 32). Aspect ratios: 1:1, 3:4, 4:3, 16:9, and custom. The AR module generates ~256 tokens expanding to 1K-4K for high-resolution output.

Question 3

How accurate is the text rendering?

Accepted Answer

GLM-Image ranks #1 among open-source models on text rendering benchmarks. CVTG-2K: 91.16% word accuracy, 95.57% NED score. LongText-Bench: 95.24% English, 97.88% Chinese. The Glyph Encoder module significantly reduces character errors and the 'forgetting while writing' problem common in diffusion models.

Question 4

What tasks does GLM-Image support?

Accepted Answer

Text-to-Image: Generate images from text descriptions with precise text rendering. Image-to-Image: Image editing, style transfer, background replacement, multi-subject fusion, and identity-preserving generation. All tasks are supported in a unified model.

Question 5

How can I use GLM-Image?

Accepted Answer

Three options: 1) ZhipuAI Cloud API at $0.014/image via chatglm.cn or z.ai. 2) Local deployment with Hugging Face transformers + diffusers (requires 80GB+ VRAM). 3) SGLang serve for optimized inference. Install: pip install git+https://github.com/huggingface/transformers.git diffusers

Question 6

What are the hardware requirements for self-hosting?

Accepted Answer

Single GPU with 80GB+ VRAM, or multi-GPU setup. The model uses bfloat16 precision. SGLang and vLLM-Omni support is being integrated for AR speedup. Model weights are available on Hugging Face (zai-org/GLM-Image) and ModelScope.

Question 7

Is GLM-Image open source? What's the license?

Accepted Answer

Yes, GLM-Image is fully open-source under the MIT license. You can use it for commercial purposes, modify it, and distribute it. Available on Hugging Face (zai-org/GLM-Image) and ModelScope. The model was fully trained on domestic chips.

Question 8

What are the recommended generation settings?

Accepted Answer

Resolution: Multiples of 32, recommended sizes 1280×1280, 1568×1056, 1056×1568, 1472×1088. Inference steps: 50. Guidance scale: 1.5-7.5 (lower for creative, higher for prompt adherence). Temperature: 0.9, top_p: 0.75 for the AR module. Use quotation marks for text content in prompts.

Question 9

How does GLM-Image compare to GPT-Image, DALL-E, and Stable Diffusion?

Accepted Answer

GLM-Image outperforms on text rendering: 91.16% vs GPT-Image's 85.69% word accuracy on CVTG-2K. It matches mainstream latent diffusion in general quality while excelling in knowledge-intensive scenarios. Key advantage: native Chinese support (97.88% accuracy) and open-source availability.

Question 10

Can I use GLM-Image for commercial projects?

Accepted Answer

Yes. The MIT license allows commercial use without restrictions. Paid API plans through ZhipuAI include full commercial usage rights. For self-hosted deployments, you retain all rights to generated images.

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