UniFL is a project designed to enhance the quality of generative models and accelerate inference speeds. It effectively addresses the existing issues of image quality, aesthetic appeal, and inference speed in current diffusion models through three key components: perceptual feedback learning, decoupled feedback learning, and adversarial feedback learning. Through experimental validation and user studies, UniFL has demonstrated significant performance improvements and strong generalization capabilities across multiple diffusion models.