Recent advances in image generation have made diffusion models powerful tools for creating high-quality images. However, their iterative denoising process makes understanding and interpreting their semantic latent spaces more challenging than other generative models, such as GANs. Recent methods have attempted to address this issue by identifying semantically meaningful directions within the latent space. However, they often need manual interpretation or are limited in the number of vectors that can be trained, restricting their scope and utility. This paper proposes a novel framework for unsupervised exploration of diffusion latent spaces. We directly leverage natural language prompts and image captions to map latent directions. This method allows for the automatic understanding of hidden features and supports a broader range of analysis without the need to train specific vectors. Our method provides a more scalable and interpretable understanding of the semantic knowledge encoded within diffusion models, facilitating comprehensive analysis of latent biases and the nuanced representations these models learn. Experimental results show that our framework can uncover hidden patterns and associations in various domains, offering new insights into the interpretability of diffusion model latent spaces.

本文解决了扩散模型在图像生成中的潜在空间理解与解释的困难，提出了一种新的框架，通过自然语言提示和图像描述无监督地探索潜在空间。该方法无需训练特定向量，能够自动理解隐藏特征，从而实现对潜在偏见和模型学习出细微表征的更广泛分析。实验结果表明，该框架可以揭示各领域中的隐藏模式和关联，为扩散模型潜在空间的可解释性提供了新的见解。

解码扩散：一种可扩展的无监督分析潜在空间偏见和表征的框架，利用自然语言提示