  Denoising diffusion probabilistic models (DDPMs) employ a sequence of white
Gaussian noise samples to generate an image. In analogy with GANs, those noise
maps could be considered as the latent code associated with the generated
image. However, this native noise space does not possess a convenient
structure, and is thus challenging to work with in editing tasks. Here, we
propose an alternative latent noise space for DDPM that enables a wide range of
editing operations via simple means, and present an inversion method for
extracting these edit-friendly noise maps for any given image (real or
synthetically generated). As opposed to the native DDPM noise space, the
edit-friendly noise maps do not have a standard normal distribution and are not
statistically independent across timesteps. However, they allow perfect
reconstruction of any desired image, and simple transformations on them
translate into meaningful manipulations of the output image (e.g. shifting,
color edits). Moreover, in text-conditional models, fixing those noise maps
while changing the text prompt, modifies semantics while retaining structure.
We illustrate how this property enables text-based editing of real images via
the diverse DDPM sampling scheme (in contrast to the popular non-diverse DDIM
inversion). We also show how it can be used within existing diffusion-based
editing methods to improve their quality and diversity. Webpage:
https://inbarhub.github.io/DDPM_inversion
