title: Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse
    Autoencoders
summary: 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
paper_authors._id: 672367df05cb6b4c14721453
paper_authors.name: Viacheslav Surkov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-31T14:05:56.473Z
paper_authors._id: 66144961cc60e73748f546d0
paper_authors.avatarUrl: /avatars/8ae14f7d0f124f81da972906cd9c189b.svg
paper_authors.fullname: Viacheslav Surkov
paper_authors.type: user
paper_authors.user: surokpro2
paper_authors._id: 672367df05cb6b4c14721454
paper_authors.name: Chris Wendler
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T13:42:23.678Z
paper_authors._id: 6481907fcd9bb50c6ad30df5
paper_authors.avatarUrl: /avatars/26a9a7e5f07009ed462bc659c5cb532b.svg
paper_authors.fullname: Chris Wendler
paper_authors.type: user
paper_authors.user: wendlerc
paper_authors._id: 672367df05cb6b4c14721455
paper_authors.name: Mikhail Terekhov
paper_authors._id: 672367df05cb6b4c14721456
paper_authors.name: Justin Deschenaux
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-03T14:22:56.733Z
paper_authors._id: 62d073f2485856cd710ed9fd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62d073f2485856cd710ed9fd/B6dck3fi5X9hhN4ALulMH.jpeg
paper_authors.fullname: Justin Deschenaux
paper_authors.type: user
paper_authors.user: jdeschena
paper_authors._id: 672367df05cb6b4c14721457
paper_authors.name: Robert West
paper_authors._id: 672367df05cb6b4c14721458
paper_authors.name: Caglar Gulcehre
paper_title: Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse
    Autoencoders
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: 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