The field of video and image generation is moving towards more advanced and robust methods for selecting noise and aligning priors. Recent developments have focused on improving the quality and temporal coherence of generated videos and images, with a particular emphasis on addressing the issues of semantic drift and temporal incoherence. Novel approaches have been proposed to quantify uncertainty and select high-quality noise seeds, as well as to align textual and visual priors in a more effective manner. These advancements have the potential to significantly improve the performance of video and image generation models, enabling the creation of more realistic and customized content. Notably, some papers have introduced innovative frameworks for corruption-aware training, cross-modality prior alignment, and dual-level feature decoupling, which have shown promising results in boosting personalized image generation and preserving subject identity. Some particularly noteworthy papers include: AlignGen, which proposes a Cross-Modality Prior Alignment mechanism to enhance personalized image generation, and ANSE, which introduces a Bayesian Active Noise Selection via Attention framework to select high-quality noise seeds for video diffusion models.