The recent advancements in text-to-image synthesis have seen a significant shift towards enhancing personalization and fine-grained control over generated images. Researchers are increasingly focusing on methods that not only improve the fidelity and diversity of synthesized images but also address specific challenges such as subject mixing and localized artifacts. A notable trend is the development of training-free or low-parameter models that can be easily integrated into existing frameworks, thereby reducing computational overhead while maintaining high performance. These models often leverage novel attention mechanisms and semantic alignment techniques to refine generated images, ensuring both prompt fidelity and subject consistency. Additionally, there is a growing interest in disentangling content and style from single images, enabling more flexible and creative applications of generative models. This approach allows for independent manipulation of subject and style, opening new possibilities for image customization and recontextualization. Overall, the field is moving towards more sophisticated and efficient methods that offer greater control and versatility in image synthesis, pushing the boundaries of what is possible with generative models.