The field of text-to-image generation is witnessing significant advancements, particularly in fine-grained text-image alignment and generative models. Researchers are exploring innovative methods to improve the precision of text-image alignment, which is crucial for precise control over visual tokens. Noteworthy developments include the use of reinforcement learning algorithms to emphasize fine-grained semantic differences and the introduction of new benchmarks to evaluate the performance of text-to-image models. Another area of focus is the evaluation of image-text alignment in text-to-image models, with studies highlighting the limitations of existing evaluation frameworks and proposing recommendations for improvement. Diffusion models are also being investigated for their potential in text-to-image generation, with research aimed at calibrating pixel-level text-image alignment and addressing misalignment issues. Text-guided image editing is becoming increasingly widespread, and there is a growing need for comprehensive frameworks to verify and assess the quality of text-guided edits. The development of zero-shot generative model adaptation methods is another significant trend, with researchers proposing novel approaches to adapt pre-trained generators to target domains using text guidance. Notable papers include: FocusDiff, which proposes a novel reinforcement learning algorithm to enhance fine-grained text-image semantic alignment. ELBO-T2IAlign, which introduces a simple yet effective method to calibrate pixel-text alignment in diffusion models. AIR, which proposes an iterative refinement approach for zero-shot generative model adaptation.