Inversion-Free Image Editing with Natural Language
Sihan Xu Yidong Huang Jiayi Pan Ziqiao Ma Joyce Chai
Despite recent advances in inversion-based editing, text-guided image
manipulation remains challenging for diffusion models. The primary bottlenecks
include 1) the time-consuming nature of the inversion process; 2) the struggle
to balance consistency with accuracy; 3) the lack of compatibility with
efficient consistency sampling methods used in consistency models. To address
the above issues, we start by asking ourselves if the inversion process can be
eliminated for editing. We show that when the initial sample is known, a
special variance schedule reduces the denoising step to the same form as the
multi-step consistency sampling. We name this Denoising Diffusion Consistent
Model (DDCM), and note that it implies a virtual inversion strategy without
explicit inversion in sampling. We further unify the attention control
mechanisms in a tuning-free framework for text-guided editing. Combining them,
we present inversion-free editing (InfEdit), which allows for consistent and
faithful editing for both rigid and non-rigid semantic changes, catering to
intricate modifications without compromising on the image's integrity and
explicit inversion. Through extensive experiments, InfEdit shows strong
performance in various editing tasks and also maintains a seamless workflow
(less than 3 seconds on one single A40), demonstrating the potential for
real-time applications. Project Page: https://sled-group.github.io/InfEdit/