title: FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers
summary: Rectified flow models have emerged as a dominant approach in image
  generation, showcasing impressive capabilities in high-quality image synthesis.
  However, despite their effectiveness in visual generation, rectified flow
  models often struggle with disentangled editing of images. This limitation
  prevents the ability to perform precise, attribute-specific modifications
  without affecting unrelated aspects of the image. In this paper, we introduce
  FluxSpace, a domain-agnostic image editing method leveraging a representation
  space with the ability to control the semantics of images generated by
  rectified flow transformers, such as Flux. By leveraging the representations
  learned by the transformer blocks within the rectified flow models, we propose
  a set of semantically interpretable representations that enable a wide range of
  image editing tasks, from fine-grained image editing to artistic creation. This
  work offers a scalable and effective image editing approach, along with its
  disentanglement capabilities.
paper_authors._id: 675ca1bf2905333f65f374c6
paper_authors.name: Yusuf Dalva
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-16T09:41:55.826Z
paper_authors._id: 65454d7c117ecae648892170
paper_authors.avatarUrl: /avatars/83a7091a24bf86801176ca85234b417a.svg
paper_authors.fullname: Yusuf Dalva
paper_authors.type: user
paper_authors.user: ydalva
paper_authors._id: 675ca1bf2905333f65f374c7
paper_authors.name: Kavana Venkatesh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-17T08:04:19.865Z
paper_authors._id: 6760ac76f39b78ef539b236a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/jHIjeR9sf1tnuT4rYllfc.png
paper_authors.fullname: Kavana Venkatesh
paper_authors.type: user
paper_authors.user: Kavanavnlp
paper_authors._id: 675ca1bf2905333f65f374c8
paper_authors.name: Pinar Yanardag
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:54:51.594Z
paper_authors._id: 683b9c7d4568ffd33e7334e1
paper_authors.avatarUrl: /avatars/046bd5a78a24f3bc4e1f6cb6076970e0.svg
paper_authors.fullname: Pinar Yanardag
paper_authors.type: user
paper_authors.user: Pinguar
paper_title: FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers
paper_summary: Rectified flow models have emerged as a dominant approach in image
  generation, showcasing impressive capabilities in high-quality image synthesis.
  However, despite their effectiveness in visual generation, rectified flow
  models often struggle with disentangled editing of images. This limitation
  prevents the ability to perform precise, attribute-specific modifications
  without affecting unrelated aspects of the image. In this paper, we introduce
  FluxSpace, a domain-agnostic image editing method leveraging a representation
  space with the ability to control the semantics of images generated by
  rectified flow transformers, such as Flux. By leveraging the representations
  learned by the transformer blocks within the rectified flow models, we propose
  a set of semantically interpretable representations that enable a wide range of
  image editing tasks, from fine-grained image editing to artistic creation. This
  work offers a scalable and effective image editing approach, along with its
  disentanglement capabilities.
paper_ai_summary: FluxSpace uses rectified flow transformers to enable precise, attribute-specific image editing by leveraging learned semantic representations.
paper_ai_keywords: rectified flow models image generation disentangled editing Flux transformer blocks semantically interpretable representations image editing artistic creation