SAEdit: Token-level control for continuous image editing via Sparse
  AutoEncoder
Large-scale text-to-image diffusion models have become the backbone of modern
image editing, yet text prompts alone do not offer adequate control over the
editing process. Two properties are especially desirable: disentanglement,
where changing one attribute does not unintentionally alter others, and
continuous control, where the strength of an edit can be smoothly adjusted. We
introduce a method for disentangled and continuous editing through token-level
manipulation of text embeddings. The edits are applied by manipulating the
embeddings along carefully chosen directions, which control the strength of the
target attribute. To identify such directions, we employ a Sparse Autoencoder
(SAE), whose sparse latent space exposes semantically isolated dimensions. Our
method operates directly on text embeddings without modifying the diffusion
process, making it model agnostic and broadly applicable to various image
synthesis backbones. Experiments show that it enables intuitive and efficient
manipulations with continuous control across diverse attributes and domains.
68e4cb1ee4e093a7044e4e5f
Ronen Kamenetsky
68e4cb1ee4e093a7044e4e60
Sara Dorfman
68e4cb1ee4e093a7044e4e61
Daniel Garibi
claimed_verified
2025-10-07T12:25:48.450Z
65fbf768980d4143b0d3ab52
/avatars/018a26ce944860e9ab97ca43765e7d76.svg
Daniel Garibi
user
garibida
68e4cb1ee4e093a7044e4e62
Roni Paiss
68e4cb1ee4e093a7044e4e63
Or Patashnik
claimed_verified
2025-10-16T06:42:24.344Z
62853516e483e0d37b354ce1
https://cdn-avatars.huggingface.co/v1/production/uploads/62853516e483e0d37b354ce1/t5Tyd3E07w26B9Z3XpZWI.jpeg
Or Patashnik
user
orpatashnik
68e4cb1ee4e093a7044e4e64
Daniel Cohen-Or
SAEdit: Token-level control for continuous image editing via Sparse
  AutoEncoder
Large-scale text-to-image diffusion models have become the backbone of modern
image editing, yet text prompts alone do not offer adequate control over the
editing process. Two properties are especially desirable: disentanglement,
where changing one attribute does not unintentionally alter others, and
continuous control, where the strength of an edit can be smoothly adjusted. We
introduce a method for disentangled and continuous editing through token-level
manipulation of text embeddings. The edits are applied by manipulating the
embeddings along carefully chosen directions, which control the strength of the
target attribute. To identify such directions, we employ a Sparse Autoencoder
(SAE), whose sparse latent space exposes semantically isolated dimensions. Our
method operates directly on text embeddings without modifying the diffusion
process, making it model agnostic and broadly applicable to various image
synthesis backbones. Experiments show that it enables intuitive and efficient
manipulations with continuous control across diverse attributes and domains.
A method for disentangled and continuous text-to-image editing uses token-level manipulation of text embeddings with sparse autoencoders to control image attributes smoothly.
diffusion models text-to-image disentanglement continuous control token-level manipulation text embeddings sparse autoencoder sparse latent space semantically isolated dimensions model agnostic