FreeDiff: Progressive Frequency Truncation for Image Editing with
  Diffusion Models
Wei Wu Qingnan Fan Shuai Qin Hong Gu Ruoyu Zhao Antoni B. Chan
Precise image editing with text-to-image models has attracted increasing
interest due to their remarkable generative capabilities and user-friendly
nature. However, such attempts face the pivotal challenge of misalignment
between the intended precise editing target regions and the broader area
impacted by the guidance in practice. Despite excellent methods leveraging
attention mechanisms that have been developed to refine the editing guidance,
these approaches necessitate modifications through complex network architecture
and are limited to specific editing tasks. In this work, we re-examine the
diffusion process and misalignment problem from a frequency perspective,
revealing that, due to the power law of natural images and the decaying noise
schedule, the denoising network primarily recovers low-frequency image
components during the earlier timesteps and thus brings excessive low-frequency
signals for editing. Leveraging this insight, we introduce a novel fine-tuning
free approach that employs progressive $\textbf{Fre}$qu$\textbf{e}$ncy
truncation to refine the guidance of $\textbf{Diff}$usion models for universal
editing tasks ($\textbf{FreeDiff}$). Our method achieves comparable results
with state-of-the-art methods across a variety of editing tasks and on a
diverse set of images, highlighting its potential as a versatile tool in image
editing applications.