Balancing fidelity and editability is essential in text-based image editing
(TIE), where failures commonly lead to over- or under-editing issues. Existing
methods typically rely on attention injections for structure preservation and
leverage the inherent text alignment capabilities of pre-trained text-to-image
(T2I) models for editability, but they lack explicit and unified mechanisms to
properly balance these two objectives. In this work, we introduce UnifyEdit, a
tuning-free method that performs diffusion latent optimization to enable a
balanced integration of fidelity and editability within a unified framework.
Unlike direct attention injections, we develop two attention-based constraints:
a self-attention (SA) preservation constraint for structural fidelity, and a
cross-attention (CA) alignment constraint to enhance text alignment for
improved editability. However, simultaneously applying both constraints can
lead to gradient conflicts, where the dominance of one constraint results in
over- or under-editing. To address this challenge, we introduce an adaptive
time-step scheduler that dynamically adjusts the influence of these
constraints, guiding the diffusion latent toward an optimal balance. Extensive
quantitative and qualitative experiments validate the effectiveness of our
approach, demonstrating its superiority in achieving a robust balance between
structure preservation and text alignment across various editing tasks,
outperforming other state-of-the-art methods. The source code will be available
at https://github.com/CUC-MIPG/UnifyEdit.
