ReGeneration Learning of Diffusion Models with Rich Prompts for
  Zero-Shot Image Translation
Yupei Lin Sen Zhang Xiaojun Yang Xiao Wang Yukai Shi
Large-scale text-to-image models have demonstrated amazing ability to
synthesize diverse and high-fidelity images. However, these models are often
violated by several limitations. Firstly, they require the user to provide
precise and contextually relevant descriptions for the desired image
modifications. Secondly, current models can impose significant changes to the
original image content during the editing process. In this paper, we explore
ReGeneration learning in an image-to-image Diffusion model (ReDiffuser), that
preserves the content of the original image without human prompting and the
requisite editing direction is automatically discovered within the text
embedding space. To ensure consistent preservation of the shape during image
editing, we propose cross-attention guidance based on regeneration learning.
This novel approach allows for enhanced expression of the target domain
features while preserving the original shape of the image. In addition, we
introduce a cooperative update strategy, which allows for efficient
preservation of the original shape of an image, thereby improving the quality
and consistency of shape preservation throughout the editing process. Our
proposed method leverages an existing pre-trained text-image diffusion model
without any additional training. Extensive experiments show that the proposed
method outperforms existing work in both real and synthetic image editing.