Decoding Diffusion: A Scalable Framework for Unsupervised Analysis of Latent Space Biases and Representations Using Natural Language Prompts

TL;DR

This paper introduces a scalable, unsupervised framework that uses natural language prompts to analyze and interpret the semantic latent spaces of diffusion models, revealing biases and representations without manual intervention.

Contribution

It presents a novel method leveraging natural language prompts for automatic, broad exploration of diffusion latent spaces, improving interpretability and scalability over prior approaches.

Findings

Uncovered hidden patterns in diffusion latent spaces

Demonstrated broad applicability across domains

Enhanced understanding of model biases and representations

Abstract

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.