FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers
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.
675ca1bf2905333f65f374c6
Yusuf Dalva
claimed_verified
2024-12-16T09:41:55.826Z
65454d7c117ecae648892170
/avatars/83a7091a24bf86801176ca85234b417a.svg
Yusuf Dalva
user
ydalva
675ca1bf2905333f65f374c7
Kavana Venkatesh
claimed_verified
2024-12-17T08:04:19.865Z
6760ac76f39b78ef539b236a
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/jHIjeR9sf1tnuT4rYllfc.png
Kavana Venkatesh
user
Kavanavnlp
675ca1bf2905333f65f374c8
Pinar Yanardag
claimed_verified
2025-06-02T07:54:51.594Z
683b9c7d4568ffd33e7334e1
/avatars/046bd5a78a24f3bc4e1f6cb6076970e0.svg
Pinar Yanardag
user
Pinguar
FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers
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.
FluxSpace uses rectified flow transformers to enable precise, attribute-specific image editing by leveraging learned semantic representations.
rectified flow models image generation disentangled editing Flux transformer blocks semantically interpretable representations image editing artistic creation