SAEdit: Token-level control for continuous image editing via Sparse
  AutoEncoder
Authors: name: Ronen Kamenetsky, name: Sara Dorfman, name: Daniel Garibi, name: Roni Paiss, name: Or Patashnik, name: Daniel Cohen-Or

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.