GLM-Image combines a 9B parameter autoregressive module with a 7B parameter diffusion decoder. This hybrid architecture excels at text rendering and knowledge-intensive generation, delivering high-fidelity images with precise semantic understanding.
GLM Image represents a breakthrough in AI image generation technology. Unlike traditional diffusion models, GLM-Image employs a unique hybrid architecture that combines the semantic understanding power of autoregressive models with the visual quality of diffusion decoders. This innovative approach makes GLM Image the first open-source, industrial-grade discrete autoregressive image generation model.
The GLM Image model consists of two powerful components: a 9 billion parameter autoregressive generator initialized from GLM-4-9B-0414, and a 7 billion parameter diffusion decoder based on a single-stream DiT architecture. This dual-stage process allows GLM Image to excel where other models struggle—particularly in text rendering and knowledge-intensive generation tasks.
What sets GLM Image apart is its exceptional ability to understand and render text within images. While mainstream latent diffusion models often struggle with accurate text generation, GLM Image significantly outperforms them, making it the ideal choice for creating posters, infographics, educational materials, and any visual content that requires precise text rendering. The model's knowledge-intensive generation capabilities also make GLM Image perfect for technical diagrams, scientific illustrations, and content requiring deep semantic understanding.
Unique hybrid architecture combining the best of autoregressive and diffusion models.
Combines 9B autoregressive module (initialized from GLM-4-9B) with 7B diffusion decoder for superior semantic understanding and visual quality.
Significantly outperforms mainstream diffusion models in text rendering tasks, making it ideal for posters, signage, and text-heavy designs.
Excels at generating images requiring precise semantic understanding and complex information expression, from technical diagrams to educational content.
Maintains strong capabilities in high-fidelity and fine-grained detail generation, aligning with mainstream latent diffusion quality.
Beyond text-to-image, supports image editing, style transfer, identity-preserving generation, and multi-subject consistency.
Available on HuggingFace and GitHub. Industrial-grade model accessible to researchers and developers worldwide.
Advanced hybrid architecture for superior image generation.
Describe your vision with complex details. GLM-Image's autoregressive module excels at understanding knowledge-intensive prompts and text-heavy descriptions.
The 9B parameter autoregressive generator creates a compact encoding (256-4K tokens), capturing semantic meaning and text elements with precision.
The 7B parameter diffusion decoder transforms the encoding into high-resolution images (1K-2K), maintaining fine-grained details and text fidelity.
Get high-fidelity outputs with accurate text rendering, perfect for posters, infographics, and knowledge-rich visual content.
Common questions about the GLM-Image model.
Unique hybrid architecture combining the best of autoregressive and diffusion models.
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