The field of text-to-image generation is witnessing significant advancements with a focus on improving image quality, diversity, and efficiency. Recent developments have led to the introduction of novel frameworks and techniques that enhance the performance of existing models. One of the key areas of research is the improvement of visual autoregressive models, which have shown promising results in generating high-quality images. Additionally, there is a growing interest in developing methods that can generate diverse and high-fidelity images, addressing the issue of mode collapse and improving the overall quality of generated images. Another important direction is the development of test-time optimization frameworks that can refine generated images and improve their quality without requiring significant computational resources. Noteworthy papers in this area include FVAR, which introduces a next-focus prediction paradigm to improve image quality, and TPSO, which enhances generative diversity through prompt semantic space optimization. LoTTS is also notable for its localized test-time scaling approach, which adaptively resamples defective regions in images to improve quality while reducing computational cost. Overall, these advancements are pushing the boundaries of text-to-image generation and opening up new possibilities for applications in computer vision and graphics.