GLM

Today we are excited to introduce GLM-Image, the first open-source, industrial-grade discrete auto-regressive image generation model. GLM-Image adopts a hybrid architecture combining an auto-regressive module with a diffusion decoder. The auto-regressive part is partially based on, and initialized from, [GLM-4-9B-0414][1] with 9 billion parameters, while the diffusion decoder follows [CogView4][2] to adopt a single-stream DiT structure with 7 billion parameters. In general image generation quality, GLM-Image aligns with mainstream latent diffusion approaches, but it shows significant advantages in text-rendering and knowledge-intensive generation scenarios. It performs especially well in tasks requiring precise semantic understanding and complex information expression, while maintaining strong capabilities in high-fidelity and fine-grained detail generation. In addition to text-to-image generation, GLM-Image also supports a rich set of image-to-image tasks including image editing, style transfer, identity-preserving generation, and multi-subject consistency.