LASPA: Latent Spatial Alignment for Fast Training-free Single Image
  Editing
Yazeed Alharbi Peter Wonka
We present a novel, training-free approach for textual editing of real images
using diffusion models. Unlike prior methods that rely on computationally
expensive finetuning, our approach leverages LAtent SPatial Alignment (LASPA)
to efficiently preserve image details. We demonstrate how the diffusion process
is amenable to spatial guidance using a reference image, leading to
semantically coherent edits. This eliminates the need for complex optimization
and costly model finetuning, resulting in significantly faster editing compared
to previous methods. Additionally, our method avoids the storage requirements
associated with large finetuned models. These advantages make our approach
particularly well-suited for editing on mobile devices and applications
demanding rapid response times. While simple and fast, our method achieves
62-71\% preference in a user-study and significantly better model-based editing
strength and image preservation scores.