Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse
  Autoencoders
Authors: name: Viacheslav Surkov, name: Chris Wendler, name: Mikhail Terekhov, name: Justin Deschenaux, name: Robert West, name: Caglar Gulcehre

Sparse autoencoders (SAEs) have become a core ingredient in the reverse
engineering of large-language models (LLMs). For LLMs, they have been shown to
decompose intermediate representations that often are not interpretable
directly into sparse sums of interpretable features, facilitating better
control and subsequent analysis. However, similar analyses and approaches have
been lacking for text-to-image models. We investigated the possibility of using
SAEs to learn interpretable features for a few-step text-to-image diffusion
models, such as SDXL Turbo. To this end, we train SAEs on the updates performed
by transformer blocks within SDXL Turbo's denoising U-net. We find that their
learned features are interpretable, causally influence the generation process,
and reveal specialization among the blocks. In particular, we find one block
that deals mainly with image composition, one that is mainly responsible for
adding local details, and one for color, illumination, and style. Therefore,
our work is an important first step towards better understanding the internals
of generative text-to-image models like SDXL Turbo and showcases the potential
of features learned by SAEs for the visual domain.
  Code is available at https://github.com/surkovv/sdxl-unbox