The field of text-to-image generation is moving towards improving the consistency and creativity of generated images. Researchers are exploring new approaches to enhance the alignment between text prompts and image semantics, aesthetics, and human preferences. One notable direction is the development of novel frameworks for prompt-level fine-tuning, which enable gradient-based optimization of injected tokens while enforcing value, orthonormality, and conformity constraints. Another area of focus is the creation of consistent image sequences from given storylines, with an emphasis on maintaining character consistency. Training-free methods are also gaining attention, as they allow for continuous generation of new characters and storylines without re-tuning. Additionally, there is a growing interest in enhancing the creative capability of text-to-image generative models, with approaches that selectively amplify features during the denoising process. Noteworthy papers include: IPGO, which introduces a novel framework for prompt-level fine-tuning that consistently matches or outperforms cutting-edge benchmarks. Object Isolated Attention, which proposes an enhanced Transformer module that improves character consistency and outperforms current methods. C3, which enhances creativity in Stable Diffusion-based models without extensive computational costs. CoCoIns, which effectively synthesizes consistent subjects across multiple independent creations using contrastive learning. UNO, which achieves high consistency while ensuring controllability in both single-subject and multi-subject driven generation.