The field of image generation is rapidly advancing, with a focus on developing more controllable and diverse models. Recent research has explored various approaches to improve the fidelity and alignment of generated images with text prompts, including the use of salient concept-aware image embedding models and region-controllable data augmentation frameworks. Another area of focus is on developing more interpretable and interactive models, such as those that allow users to control the image generation process through parametric activation functions or personalized image filters. Noteworthy papers in this area include ReCon, which introduces a novel augmentation framework that enhances the capacity of structure-controllable generative models for object detection, and LayerComposer, which presents an interactive framework for personalized, multi-subject text-to-image generation. Additionally, papers such as Class-N-Diff and CBDiff have made significant contributions to the field of image generation, with Class-N-Diff proposing a classification-induced diffusion model for fair skin cancer diagnosis and CBDiff introducing a conditional Bernoulli diffusion model for image forgery localization.