Reinforcement learning (RL) has proven its potential in complex decision-making tasks. Yet, many RL systems rely on manually crafted state representations, requiring effort in feature engineering. Visual Reinforcement Learning (VRL) offers a way to address this challenge by enabling agents to learn directly from raw visual input. Nonetheless, VRL continues to face generalization issues, as models often overfit to specific domain features.To tackle this issue, we propose Diffusion Guided Adaptive Augmentation (DGA2), an augmentation method that utilizes Stable Diffusion to enhance domain diversity.We introduce an Adaptive Domain Shift strategy that dynamically adjusts the degree of domain shift according to the agent’s learning progress for effective augmentation with Stable Diffusion.Additionally, we employ saliency as the mask to preserve the semantics of data.Our experiments on the DMControl-GB, Adroit, and Procgen environments demonstrate that DGA2 improves generalization performance compared to existing data augmentation and generalization methods.