Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations
Keywords: Inverse Problems, Generative Modeling, Diffusion Models, Rectified Flows, Posterior Sampling, Optimal Control
TL;DR: We present an efficient inversion method for rectified flow models, including Flux, that requires no additional parameter training, latent variable optimization, prompt tuning, or complex attention processors.
Abstract: 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.
Primary Area: generative models
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Submission Number: 1202
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