Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing

Understand

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.