SAeUron: Interpretable Concept Unlearning in Diffusion Models with
  Sparse Autoencoders
Diffusion models, while powerful, can inadvertently generate harmful or
undesirable content, raising significant ethical and safety concerns. Recent
machine unlearning approaches offer potential solutions but often lack
transparency, making it difficult to understand the changes they introduce to
the base model. In this work, we introduce SAeUron, a novel method leveraging
features learned by sparse autoencoders (SAEs) to remove unwanted concepts in
text-to-image diffusion models. First, we demonstrate that SAEs, trained in an
unsupervised manner on activations from multiple denoising timesteps of the
diffusion model, capture sparse and interpretable features corresponding to
specific concepts. Building on this, we propose a feature selection method that
enables precise interventions on model activations to block targeted content
while preserving overall performance. Evaluation with the competitive
UnlearnCanvas benchmark on object and style unlearning highlights SAeUron's
state-of-the-art performance. Moreover, we show that with a single SAE, we can
remove multiple concepts simultaneously and that in contrast to other methods,
SAeUron mitigates the possibility of generating unwanted content, even under
adversarial attack. Code and checkpoints are available at:
https://github.com/cywinski/SAeUron.
67a07bbb8e344720ae1a6008
Bartosz Cywiński
claimed_verified
2025-02-03T11:07:48.609Z
6422f416a73327caad9d1d86
/avatars/aa3639277cd1732504402fc64a57eff8.svg
Bartosz Cywiński
user
bcywinski
67a07bbb8e344720ae1a6009
Kamil Deja
SAeUron: Interpretable Concept Unlearning in Diffusion Models with
  Sparse Autoencoders
Diffusion models, while powerful, can inadvertently generate harmful or
undesirable content, raising significant ethical and safety concerns. Recent
machine unlearning approaches offer potential solutions but often lack
transparency, making it difficult to understand the changes they introduce to
the base model. In this work, we introduce SAeUron, a novel method leveraging
features learned by sparse autoencoders (SAEs) to remove unwanted concepts in
text-to-image diffusion models. First, we demonstrate that SAEs, trained in an
unsupervised manner on activations from multiple denoising timesteps of the
diffusion model, capture sparse and interpretable features corresponding to
specific concepts. Building on this, we propose a feature selection method that
enables precise interventions on model activations to block targeted content
while preserving overall performance. Evaluation with the competitive
UnlearnCanvas benchmark on object and style unlearning highlights SAeUron's
state-of-the-art performance. Moreover, we show that with a single SAE, we can
remove multiple concepts simultaneously and that in contrast to other methods,
SAeUron mitigates the possibility of generating unwanted content, even under
adversarial attack. Code and checkpoints are available at:
https://github.com/cywinski/SAeUron.
SAeUron uses sparse autoencoders to remove unwanted concepts from diffusion models in text-to-image tasks, improving safety and performance.
diffusion models sparse autoencoders feature selection UnlearnCanvas activation denoising adversarial attack