title: Semantic Image Inversion and Editing using Rectified Stochastic
    Differential Equations
summary: Generative models transform random noise into images; their inversion aims to
  transform images back to structured noise for recovery and editing. This paper
  addresses two key tasks: (i) inversion and (ii) editing of a real image using
  stochastic equivalents of rectified flow models (such as Flux). Although
  Diffusion Models (DMs) have recently dominated the field of generative modeling
  for images, their inversion presents faithfulness and editability challenges
  due to nonlinearities in drift and diffusion. Existing state-of-the-art DM
  inversion approaches rely on training of additional parameters or test-time
  optimization of latent variables; both are expensive in practice. Rectified
  Flows (RFs) offer a promising alternative to diffusion models, yet their
  inversion has been underexplored. We propose RF inversion using dynamic optimal
  control derived via a linear quadratic regulator. We prove that the resulting
  vector field is equivalent to a rectified stochastic differential equation.
  Additionally, we extend our framework to design a stochastic sampler for Flux.
  Our inversion method allows for state-of-the-art performance in zero-shot
  inversion and editing, outperforming prior works in stroke-to-image synthesis
  and semantic image editing, with large-scale human evaluations confirming user
  preference.
paper_authors._id: 670ddc05d6ac6394419dc0ca
paper_authors.name: Litu Rout
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-15T14:13:51.573Z
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paper_authors.fullname: Litu Rout
paper_authors.type: user
paper_authors.user: LituRout
paper_authors._id: 670ddc05d6ac6394419dc0cb
paper_authors.name: Yujia Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-15T21:18:04.582Z
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paper_authors.fullname: Issac Chen
paper_authors.type: user
paper_authors.user: issaccyj
paper_authors._id: 670ddc05d6ac6394419dc0cc
paper_authors.name: Nataniel Ruiz
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-15T21:18:02.380Z
paper_authors._id: 6318c6e6cb116eab31d70a16
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6318c6e6cb116eab31d70a16/yAw5XtvqaPUkPj2B6JOzz.jpeg
paper_authors.fullname: Nataniel
paper_authors.type: user
paper_authors.user: flavoredquark
paper_authors._id: 670ddc05d6ac6394419dc0cd
paper_authors.name: Constantine Caramanis
paper_authors._id: 670ddc05d6ac6394419dc0ce
paper_authors.name: Sanjay Shakkottai
paper_authors._id: 670ddc05d6ac6394419dc0cf
paper_authors.name: Wen-Sheng Chu
paper_title: Semantic Image Inversion and Editing using Rectified Stochastic
    Differential Equations
paper_summary: Generative models transform random noise into images; their inversion aims to
  transform images back to structured noise for recovery and editing. This paper
  addresses two key tasks: (i) inversion and (ii) editing of a real image using
  stochastic equivalents of rectified flow models (such as Flux). Although
  Diffusion Models (DMs) have recently dominated the field of generative modeling
  for images, their inversion presents faithfulness and editability challenges
  due to nonlinearities in drift and diffusion. Existing state-of-the-art DM
  inversion approaches rely on training of additional parameters or test-time
  optimization of latent variables; both are expensive in practice. Rectified
  Flows (RFs) offer a promising alternative to diffusion models, yet their
  inversion has been underexplored. We propose RF inversion using dynamic optimal
  control derived via a linear quadratic regulator. We prove that the resulting
  vector field is equivalent to a rectified stochastic differential equation.
  Additionally, we extend our framework to design a stochastic sampler for Flux.
  Our inversion method allows for state-of-the-art performance in zero-shot
  inversion and editing, outperforming prior works in stroke-to-image synthesis
  and semantic image editing, with large-scale human evaluations confirming user
  preference.
paper_ai_keywords: generative models diffusion models DMs inversion editing stochastic equivalents rectified flow models Flux nonlinearities drift diffusion additional parameters latent variables Rectified Flows RFs dynamic optimal control linear quadratic regulator vector field rectified stochastic differential equation stochastic sampler zero-shot inversion stroke-to-image synthesis semantic image editing large-scale human evaluations user preference