  Recent large-scale text-driven synthesis models have attracted much attention
thanks to their remarkable capabilities of generating highly diverse images
that follow given text prompts. Such text-based synthesis methods are
particularly appealing to humans who are used to verbally describe their
intent. Therefore, it is only natural to extend the text-driven image synthesis
to text-driven image editing. Editing is challenging for these generative
models, since an innate property of an editing technique is to preserve most of
the original image, while in the text-based models, even a small modification
of the text prompt often leads to a completely different outcome.
State-of-the-art methods mitigate this by requiring the users to provide a
spatial mask to localize the edit, hence, ignoring the original structure and
content within the masked region. In this paper, we pursue an intuitive
prompt-to-prompt editing framework, where the edits are controlled by text
only. To this end, we analyze a text-conditioned model in depth and observe
that the cross-attention layers are the key to controlling the relation between
the spatial layout of the image to each word in the prompt. With this
observation, we present several applications which monitor the image synthesis
by editing the textual prompt only. This includes localized editing by
replacing a word, global editing by adding a specification, and even delicately
controlling the extent to which a word is reflected in the image. We present
our results over diverse images and prompts, demonstrating high-quality
synthesis and fidelity to the edited prompts.
