Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing

TL;DR

This paper introduces a novel inversion and invariance control framework for flow transformers, enabling accurate and flexible image editing across various tasks by refining inversion and manipulating text features within adaptive normalization.

Contribution

It proposes a two-stage inversion method and a new invariance control mechanism that together improve the versatility and accuracy of image editing with flow transformers.

Findings

Two-stage inversion refines velocity estimation for better editing.

Invariance control preserves non-target content across diverse edits.

Framework achieves flexible, accurate editing in multiple scenarios.

Abstract

Leveraging the large generative prior of the flow transformer for tuning-free image editing requires authentic inversion to project the image into the model's domain and a flexible invariance control mechanism to preserve non-target contents. However, the prevailing diffusion inversion performs deficiently in flow-based models, and the invariance control cannot reconcile diverse rigid and non-rigid editing tasks. To address these, we systematically analyze the \textbf{inversion and invariance} control based on the flow transformer. Specifically, we unveil that the Euler inversion shares a similar structure to DDIM yet is more susceptible to the approximation error. Thus, we propose a two-stage inversion to first refine the velocity estimation and then compensate for the leftover error, which pivots closely to the model prior and benefits editing. Meanwhile, we propose the invariance control that manipulates the text features within the adaptive layer normalization, connecting the changes in the text prompt to image semantics. This mechanism can simultaneously preserve the non-target contents while allowing rigid and non-rigid manipulation, enabling a wide range of editing types such as visual text, quantity, facial expression, etc. Experiments on versatile scenarios validate that our framework achieves flexible and accurate editing, unlocking the potential of the flow transformer for versatile image editing.