FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers
Authors: name: Yusuf Dalva, name: Kavana Venkatesh, name: Pinar Yanardag

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.