Generating high-quality labeled image datasets is crucial for training
accurate and robust machine learning models in the field of computer vision.
However, the process of manually labeling real images is often time-consuming
and costly. To address these challenges associated with dataset generation, we
introduce "DiffuGen," a simple and adaptable approach that harnesses the power
of stable diffusion models to create labeled image datasets efficiently. By
leveraging stable diffusion models, our approach not only ensures the quality
of generated datasets but also provides a versatile solution for label
generation. In this paper, we present the methodology behind DiffuGen, which
combines the capabilities of diffusion models with two distinct labeling
techniques: unsupervised and supervised. Distinctively, DiffuGen employs prompt
templating for adaptable image generation and textual inversion to enhance
diffusion model capabilities.

通过使用稳定的扩散模型，DiffuGen 是一种简单且适应性强的方法，用于高效生成标记的图像数据集，以解决手动标记真实图像的耗时和成本昂贵的问题。论文介绍了 DiffuGen 的方法学，该方法将扩散模型的能力与无监督和监督的两种标签技术相结合，通过提示模板和文本倒置来增强扩散模型的能力。