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

Michael Toker, Hadas Orgad, Mor Ventura, Dana Arad, Yonatan Belinkov

Abstract
Text-to-image diffusion models (T2I) use a latent representation of a text prompt to guide the image generation process. However, the process by which the encoder produces the text representation is unknown. 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. Exploring compound prompts, we find that complex scenes describing multiple objects are composed progressively and more slowly compared to simple scenes; Exploring knowledge retrieval, we find that representation of uncommon concepts require further computation compared to common concepts, and that knowledge retrieval is gradual across layers. Overall, our findings provide valuable insights into the text encoder component in T2I pipelines.
Anthology ID:
2024.acl-long.524
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9713–9728
Language:
URL:
https://aclanthology.org/2024.acl-long.524
DOI:
Bibkey:
Cite (ACL):
Michael Toker, Hadas Orgad, Mor Ventura, Dana Arad, and Yonatan Belinkov. 2024. Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9713–9728, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines (Toker et al., ACL 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.acl-long.524.pdf
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