The field of image restoration and generation is rapidly advancing with the development of new methods and techniques. Recently, there has been a focus on improving the fidelity and efficiency of image restoration models, with approaches such as Self-Improved Privilege Learning (SIPL) and IRBridge showing promising results. Additionally, advances in flow matching and diffusion models have led to significant improvements in image generation quality and speed. Noteworthy papers in this area include those that propose novel frameworks such as STORK, Graph Flow Matching, and Diff2Flow, which have achieved state-of-the-art results on various benchmarks. Furthermore, techniques like Contrastive Flow Matching and Aligning Latent Spaces with Flow Priors have been introduced to enhance condition separation and latent space alignment. These developments have the potential to impact a wide range of applications, from image and video generation to image restoration and editing. Some notable papers include: STORK, which achieves improved generation quality with a novel ODE solver, and Diff2Flow, which enables efficient finetuning of diffusion models for flow matching.