  We propose a text-guided variational image generation method to address the
challenge of getting clean data for anomaly detection in industrial
manufacturing. Our method utilizes text information about the target object,
learned from extensive text library documents, to generate non-defective data
images resembling the input image. The proposed framework ensures that the
generated non-defective images align with anticipated distributions derived
from textual and image-based knowledge, ensuring stability and generality.
Experimental results demonstrate the effectiveness of our approach, surpassing
previous methods even with limited non-defective data. Our approach is
validated through generalization tests across four baseline models and three
distinct datasets. We present an additional analysis to enhance the
effectiveness of anomaly detection models by utilizing the generated images.
