This is a simplified guide to an AI model called GLM-Image maintained by zai-org. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.
Model overview
GLM-Image combines an autoregressive generator with a diffusion decoder to create images from text and perform sophisticated image editing tasks. The 9B autoregressive component generates a compact visual token sequence, which the 7B diffusion decoder then expands into high-resolution outputs. This hybrid architecture delivers competitive image quality while excelling in scenarios demanding precise text rendering and knowledge-intensive details. Unlike pure diffusion models that struggle with text accuracy, this approach handles complex information expression with greater fidelity. The model maintains the semantic understanding capabilities of GLM-4.1V-9B-Thinking while specializing in visual generation rather than multimodal reasoning.
Model inputs and outputs
GLM-Image processes both textual prompts and image inputs to generate or modify visual content. The system accepts text descriptions of arbitrary length and complexity, along with optional reference images for conditional generation tasks. The model outputs high-resolution images ranging from 1024x1024 to 2048x1024 pixels, maintaining visual coherence and detail quality across diverse generation scenarios.
Inputs
- Text prompts describing desired image content, including detailed specifications for layout, composition, and visual elements
- Reference images for image-to-image tasks such as editing and style transfer
- Multiple reference images for maintaining consistency across subjects in multi-image generation scenarios
- Guidance parameters controlling the strength of prompt adherence and generation diversity
Outputs
- Generated images in PNG format at specified dimensions
- High-fidelity visual content with accurate text rendering and fine-grained details
- Edited or transformed images preserving identity and compositional elements from source images
Capabilities
The model generates images from text descriptions with particular strength in information-dense layouts. Recipe illustrations with ingredient lists, numbered steps, and visual hierarchies render with precise text and accurate proportions. Image-to-image capabilities include background replacement, style transfer between images, identity-preserving generation for people and objects, and multi-subject consistency maintenance. The decoupled reinforcement learning training using the GRPO algorithm enhances both semantic alignment and visual detail quality, with the autoregressive module improving aesthetic understanding and the diffusion decoder refining texture fidelity and text accuracy. Post-training feedback signals target low-frequency features like composition and high-frequency elements like texture, creating nuanced control over output characteristics.
What can I use it for?
Design professionals can generate detailed marketing materials with embedded text and complex layouts without manual typography adjustments. Educational content creators produce recipe cards, instructional guides, and infographics with embedded text that renders correctly on the first attempt. E-commerce businesses create product photography variations with consistent branding and identity. Visual artists explore style transfer to apply artistic treatments while preserving subject identity. Publishers generate magazine layouts and illustrated guides combining dense information with high visual quality. The model's accuracy with text-based content opens applications previously requiring manual post-processing or graphic design expertise. Compare this capability to Z-Image-Turbo for efficiency-focused workflows or visualglm-6b for combined understanding and generation tasks.
Things to try
Test the model's text rendering accuracy with prompts containing typography-heavy content like posters, menus, or certificates. Generate illustrated recipes or instructional manuals with step-by-step visual guides and embedded text descriptions. Perform background replacement while preserving foreground subjects, then compare results when providing multiple reference images for consistency constraints. Apply artistic styles to photographs while maintaining face identity for personal portrait variations. Create magazine-style layouts with multiple content sections, experimenting with guidance scale values to balance creative interpretation against strict prompt adherence. The Glyph Encoder text module distinguishes this model from standard diffusion approaches, making typography-intensive generation a particularly revealing test case.