  Text-guided non-rigid editing involves complex edits for input images, such
as changing motion or compositions within their surroundings. Since it requires
manipulating the input structure, existing methods often struggle with
preserving object identity and background, particularly when combined with
Stable Diffusion. In this work, we propose a training-free approach for
non-rigid editing with Stable Diffusion, aimed at improving the identity
preservation quality without compromising editability. Our approach comprises
three stages: text optimization, latent inversion, and timestep-aware text
injection sampling. Inspired by the recent success of Imagic, we employ their
text optimization for smooth editing. Then, we introduce latent inversion to
preserve the input image's identity without additional model fine-tuning. To
fully utilize the input reconstruction ability of latent inversion, we suggest
timestep-aware text inject sampling. This effectively retains the structure of
the input image by injecting the source text prompt in early sampling steps and
then transitioning to the target prompt in subsequent sampling steps. This
strategic approach seamlessly harmonizes with text optimization, facilitating
complex non-rigid edits to the input without losing the original identity. We
demonstrate the effectiveness of our method in terms of identity preservation,
editability, and aesthetic quality through extensive experiments.
