Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse
  Autoencoders
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
672367df05cb6b4c14721453
Viacheslav Surkov
claimed_verified
2024-10-31T14:05:56.473Z
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Viacheslav Surkov
user
surokpro2
672367df05cb6b4c14721454
Chris Wendler
claimed_verified
2025-04-16T13:42:23.678Z
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/avatars/26a9a7e5f07009ed462bc659c5cb532b.svg
Chris Wendler
user
wendlerc
672367df05cb6b4c14721455
Mikhail Terekhov
672367df05cb6b4c14721456
Justin Deschenaux
claimed_verified
2024-11-03T14:22:56.733Z
62d073f2485856cd710ed9fd
https://cdn-avatars.huggingface.co/v1/production/uploads/62d073f2485856cd710ed9fd/B6dck3fi5X9hhN4ALulMH.jpeg
Justin Deschenaux
user
jdeschena
672367df05cb6b4c14721457
Robert West
672367df05cb6b4c14721458
Caglar Gulcehre
Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse
  Autoencoders
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
Sparse autoencoders are applied to learn interpretable features from intermediate updates in text-to-image diffusion models, such as SDXL Turbo, enhancing understanding of these models' internal mechanisms.
sparse autoencoders large-language models reverse engineering interpretable features few-step text-to-image diffusion models SDXL Turbo transformer blocks denoising U-net image composition local details color illumination style