The sophistication of GLM-Image is further enhanced by its unique post-training regimen utilizing decoupled reinforcement learning, specifically the GRPO algorithm. This modular feedback strategy tailors learning signals based on the component they influence: the autoregressive module receives low-frequency feedback to refine aesthetic quality and semantic adherence to the prompt, ensuring better instruction following. Concurrently, the diffusion decoder receives high-frequency feedback specifically to improve detail fidelity and the accuracy of rendered text within the image, leading to exceptionally realistic textures and sharp textual elements.
Beyond standard text-to-image generation, GLM-Image is designed for versatility, seamlessly supporting a comprehensive suite of image-to-image capabilities within the same framework. This encompasses nuanced image editing, complex style transfer operations, and critical tasks like identity-preserving generation for maintaining consistent appearances of subjects across multiple outputs. The model’s ability to excel in information-intensive scenarios, such as creating complex layouts with accurate embedded text, positions it as a powerful tool for applications requiring both high visual fidelity and semantic precision.
