Anomaly detection (including classification and segmentation) is a practical and challenging task due to the scarcity of anomaly samples in industrial inspection. Some existing anomaly detection methods address this issue by synthesizing anomalies with noise or external data. However, there is always a large semantic gap between synthetic anomalies and real-world anomalies, resulting in weak performance in anomaly detection. To solve the above problem, we propose a few-shot anomaly-driven generation method, which guides the diffusion model to generate more realistic and diverse anomalies with only a few real anomalies, thereby benefiting training anomaly detection models. Specifically, our work is divided into three stages. In the first stage, we learn the anomaly distribution based on a few given real anomalies and inject the learned knowledge into an embedding. In the second stage, we use the embedding and given bounding boxes to guide the diffusion model to generate realistic and diverse anomalies on specific objects (or textures). In the final stage, we propose a weakly-supervised anomaly detection method to train a more powerful model with generated anomalies. Our method builds upon DRAEM and DesTSeg as the foundation model and conducts experiments on the commonly used industrial anomaly detection dataset, MVTec. The experiments demonstrate that our generated anomalies effectively improve the model performance of both anomaly classification and segmentation tasks simultaneously, eg., DRAEM and DseTSeg achieved a 5.8% and 1.5% improvement in AU-PR metric on segmentation task, respectively. We will make the code and the generated anomalous images (70,760) available for reproducibility.