The field of image generation and diffusion models is rapidly advancing, with a focus on improving the accuracy and efficiency of these models. Recent developments have centered around enhancing the performance of diffusion-based models, particularly in terms of image super-resolution and generative capabilities. Notable trends include the use of latent space super-resolution, uncertainty-guided perturbation, and consistency trajectory matching to achieve higher-quality image generation. These innovations have significant implications for downstream applications such as image reconstruction, editing, and fusion. Noteworthy papers in this area include:

• The introduction of the ABM-Solver, which leverages the Adams-Bashforth-Moulton predictor-corrector method to enhance the accuracy of ODE solving in rectified flow models.
• The proposal of LSRNA, a novel framework for higher-resolution image generation using diffusion models by leveraging super-resolution directly in the latent space.
• The development of Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model, which improves the utilization of low-resolution information to enhance performance.