GLM-Image Auto-Regressive for Dense-Knowledge and High-Fidelity Image Generation (z.ai)

Today marks the launch of GLM-Image, an open-source discrete auto-regressive model designed for high-fidelity image generation. Distinct in its hybrid architecture, GLM-Image combines an auto-regressive module (with 9 billion parameters) initialized from GLM-4-9B-0414 and a 7 billion-parameter diffusion decoder inspired by CogView4. This innovative model excels particularly in text-rendering and knowledge-intensive tasks, outperforming traditional diffusion methods by merging robust semantic understanding with intricate detail generation. Its versatile capabilities extend beyond text-to-image generation to encompass image editing, style transfer, and identity consistency, making it a powerful tool for creative applications. The significance of GLM-Image lies in its dual focus: ensuring accurate semantic expression while maintaining high visual fidelity. By employing semantic-VQ tokenization that enhances training efficiency and leveraging a progressive generation strategy, GLM-Image achieves remarkable image clarity without sacrificing detail. Furthermore, its integration of semantics via a modified diffusion decoder allows it to handle complex instructions and dense knowledge scenarios better than its predecessors. With its promising architecture and training methodologies, GLM-Image sets a new standard for image generation, bridging artistic creativity with technological precision in AI/ML.