The field of image generation is rapidly advancing with the development of new diffusion models. These models have shown impressive results in generating high-quality images, and recent research has focused on improving their efficiency and effectiveness. One of the key trends in this area is the development of novel architectures and techniques that enable faster and more accurate image generation. For example, research has explored the use of entropy-based time schedulers, which can improve the inference performance of trained models. Additionally, new guidance mechanisms, such as Entropy Rectifying Guidance, have been proposed to improve the quality and consistency of generated images. Other notable developments include the introduction of unified frameworks for photorealistic reconstruction and intrinsic scene modeling, as well as the use of discrete diffusion timestep tokens for multimodal pretraining. Noteworthy papers in this area include U-Shape Mamba, which proposes a novel diffusion model that achieves state-of-the-art results in image generation while reducing computational overhead. Entropic Time Schedulers for Generative Diffusion Models is another notable paper, which presents a time scheduler that selects sampling points based on entropy rather than uniform time spacing. Overall, the field of image generation is rapidly evolving, and these advances have the potential to enable new applications and use cases in areas such as computer vision, robotics, and artificial intelligence.