SRPO introduces a sampling strategy for diffusion fine-tuning that improves optimization stability and computational efficiency when aligning the full diffusion trajectory with human preference signals. It applies a novel direct alignment approach to restore highly noisy images during training, targeting improved image generation quality. The project provides code and trained models for the Flux diffusion model.