Wavelet-Guided Acceleration of Text Inversion in Diffusion-Based Image
  Editing
Gwanhyeong Koo Sunjae Yoon Chang D. Yoo
In the field of image editing, Null-text Inversion (NTI) enables fine-grained
editing while preserving the structure of the original image by optimizing null
embeddings during the DDIM sampling process. However, the NTI process is
time-consuming, taking more than two minutes per image. To address this, we
introduce an innovative method that maintains the principles of the NTI while
accelerating the image editing process. We propose the WaveOpt-Estimator, which
determines the text optimization endpoint based on frequency characteristics.
Utilizing wavelet transform analysis to identify the image's frequency
characteristics, we can limit text optimization to specific timesteps during
the DDIM sampling process. By adopting the Negative-Prompt Inversion (NPI)
concept, a target prompt representing the original image serves as the initial
text value for optimization. This approach maintains performance comparable to
NTI while reducing the average editing time by over 80% compared to the NTI
method. Our method presents a promising approach for efficient, high-quality
image editing based on diffusion models.