The field of text-to-image synthesis is rapidly advancing with a focus on improving the quality and controllability of generated images. Recent developments have led to the creation of novel frameworks and models that enable the generation of high-quality images with unprecedented fidelity. Autoregressive models have shown promising results in generating styled text images, while diffusion models have achieved state-of-the-art performance in ultra-high-resolution image synthesis. Additionally, multimodal autoregressive models have been proposed to address the challenge of generating long-form text in images. Noteworthy papers include Emuru, which proposes a novel framework for text image generation using a variational autoencoder and an autoregressive Transformer, and Diffusion-4K, which achieves impressive performance in high-quality image synthesis and text prompt adherence. Other notable papers include Beyond Words, which introduces a novel text-focused tokenizer and a multimodal autoregressive model for long-text image generation, and BizGen, which advances article-level visual text rendering for infographics generation.