  Large-scale text-to-image diffusion models have been a ground-breaking
development in generating convincing images following an input text prompt. The
goal of image editing research is to give users control over the generated
images by modifying the text prompt. Current image editing techniques
predominantly hinge on DDIM inversion as a prevalent practice rooted in Latent
Diffusion Models (LDM). However, the large pretrained T2I models working on the
latent space suffer from losing details due to the first compression stage with
an autoencoder mechanism. Instead, other mainstream T2I pipeline working on the
pixel level, such as Imagen and DeepFloyd-IF, circumvents the above problem.
They are commonly composed of multiple stages, typically starting with a
text-to-image stage and followed by several super-resolution stages. In this
pipeline, the DDIM inversion fails to find the initial noise and generate the
original image given that the super-resolution diffusion models are not
compatible with the DDIM technique. According to our experimental findings,
iteratively concatenating the noisy image as the condition is the root of this
problem. Based on this observation, we develop an iterative inversion (IterInv)
technique for this category of T2I models and verify IterInv with the
open-source DeepFloyd-IF model.Specifically, IterInv employ NTI as the
inversion and reconstruction of low-resolution image generation. In stages 2
and 3, we update the latent variance at each timestep to find the deterministic
inversion trace and promote the reconstruction process. By combining our method
with a popular image editing method, we prove the application prospects of
IterInv. The code will be released upon acceptance. The code is available at
\url{https://github.com/Tchuanm/IterInv.git}.
