Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines

Anonymous

TL;DR: We propose the Diffusion Lens, a method for analyzing the text encoder of T2I models by generating images from its intermediate representations.

Abstract: Text-to-image diffusion models (T2I) use a latent representation of a text prompt to guide the image generation process. However, the encoder that produces the text representation is largely unexplored. We propose the Diffusion Lens, a method for analyzing the text encoder of T2I models by generating images from its intermediate representations. Using the Diffusion Lens, we perform an extensive analysis of two recent T2I models. We find that the text encoder gradually builds prompt representations across multiple scenarios. Complex scenes describing multiple objects are composed progressively and more slowly than simple scenes; earlier layers encode the concepts in the prompts without a clear interaction, which emerges only in later layers. Moreover, the retrieval of uncommon concepts requires further computation until a faithful representation of the prompt is achieved. Concepts are built from coarse to fine, with details being added until the very late layers. Overall, our findings provide valuable insights into the text encoder component in T2I pipelines.

Paper Type: long

Research Area: Interpretability and Analysis of Models for NLP

Contribution Types: Model analysis & interpretability

Languages Studied: English