MirrorDiffusion: Stabilizing Diffusion Process in Zero-shot Image
  Translation by Prompts Redescription and Beyond
Yupei Lin Xiaoyu Xian Yukai Shi Liang Lin
Recently, text-to-image diffusion models become a new paradigm in image
processing fields, including content generation, image restoration and
image-to-image translation. Given a target prompt, Denoising Diffusion
Probabilistic Models (DDPM) are able to generate realistic yet eligible images.
With this appealing property, the image translation task has the potential to
be free from target image samples for supervision. By using a target text
prompt for domain adaption, the diffusion model is able to implement zero-shot
image-to-image translation advantageously. However, the sampling and inversion
processes of DDPM are stochastic, and thus the inversion process often fail to
reconstruct the input content. Specifically, the displacement effect will
gradually accumulated during the diffusion and inversion processes, which led
to the reconstructed results deviating from the source domain. To make
reconstruction explicit, we propose a prompt redescription strategy to realize
a mirror effect between the source and reconstructed image in the diffusion
model (MirrorDiffusion). More specifically, a prompt redescription mechanism is
investigated to align the text prompts with latent code at each time step of
the Denoising Diffusion Implicit Models (DDIM) inversion to pursue a
structure-preserving reconstruction. With the revised DDIM inversion,
MirrorDiffusion is able to realize accurate zero-shot image translation by
editing optimized text prompts and latent code. Extensive experiments
demonstrate that MirrorDiffusion achieves superior performance over the
state-of-the-art methods on zero-shot image translation benchmarks by clear
margins and practical model stability.