Noise Map Guidance: Inversion with Spatial Context for Real Image
  Editing
Hansam Cho Jonghyun Lee Seoung Bum Kim Tae-Hyun Oh Yonghyun Jeong
Text-guided diffusion models have become a popular tool in image synthesis,
known for producing high-quality and diverse images. However, their application
to editing real images often encounters hurdles primarily due to the text
condition deteriorating the reconstruction quality and subsequently affecting
editing fidelity. Null-text Inversion (NTI) has made strides in this area, but
it fails to capture spatial context and requires computationally intensive
per-timestep optimization. Addressing these challenges, we present Noise Map
Guidance (NMG), an inversion method rich in a spatial context, tailored for
real-image editing. Significantly, NMG achieves this without necessitating
optimization, yet preserves the editing quality. Our empirical investigations
highlight NMG's adaptability across various editing techniques and its
robustness to variants of DDIM inversions.