  Large-scale text-to-image generative models have shown their remarkable
ability to synthesize diverse and high-quality images. However, it is still
challenging to directly apply these models for editing real images for two
reasons. First, it is hard for users to come up with a perfect text prompt that
accurately describes every visual detail in the input image. Second, while
existing models can introduce desirable changes in certain regions, they often
dramatically alter the input content and introduce unexpected changes in
unwanted regions. In this work, we propose pix2pix-zero, an image-to-image
translation method that can preserve the content of the original image without
manual prompting. We first automatically discover editing directions that
reflect desired edits in the text embedding space. To preserve the general
content structure after editing, we further propose cross-attention guidance,
which aims to retain the cross-attention maps of the input image throughout the
diffusion process. In addition, our method does not need additional training
for these edits and can directly use the existing pre-trained text-to-image
diffusion model. We conduct extensive experiments and show that our method
outperforms existing and concurrent works for both real and synthetic image
editing.
