The field of diffusion-based models and inverse problem solvers is experiencing significant growth, with a focus on improving the accuracy and efficiency of these methods. Recent developments have highlighted the importance of understanding the posterior distribution in blind deconvolution and the role of diffusion-based priors in capturing realistic image distributions. Additionally, researchers are exploring new approaches to inject measurement information into diffusion-based inverse problem solvers, leading to faster and more noise-robust solutions. Convergence guarantees for diffusion-based generative models are also being established, providing a foundation for further advancements. Noteworthy papers in this area include: Injecting Measurement Information Yields a Fast and Noise-Robust Diffusion-Based Inverse Problem Solver, which proposes a new approach to estimate the conditional posterior mean, resulting in a fast and memory-efficient inverse solver. Convergence of Deterministic and Stochastic Diffusion-Model Samplers, which provides new convergence guarantees in Wasserstein distance for diffusion-based generative models, covering both stochastic and deterministic sampling methods.