Direct preference optimization (DPO) has shown success in aligning diffusion models with human preference. However, We identify two potential risks for existing DPO algorithms: First, current DPO methods for estimating the rewards of step-wise intermediate samples are biased, leading to inaccurate preference ordering for step-wise optimization. Second, existing DPO methods may inadvertently increase the sampling probabilities of dispreferred samples, potentially introducing application risks. To address these issues, we propose Revised Direct Preference Optimization (RDPO), a simple but effective step-wise DPO-based text-to-image diffusion model alignment method. By designing a more theoretically grounded and efficient intermediate-step reward estimation and introducing an additional regularization terms to constrain the sampling probability of dispreferred samples, RDPO can achieve more effective and stable text-to-image alignment performance. Our experiments on two datasets, with base models including Stable Diffusion v1.5 and SDXL, demonstrate that RDPO can effectively learn and construct reward signals for each step of the model, improving alignment performance while ensuring better generalization.