  Text-conditioned image editing has recently attracted considerable interest.
However, most methods are currently either limited to specific editing types
(e.g., object overlay, style transfer), or apply to synthetically generated
images, or require multiple input images of a common object. In this paper we
demonstrate, for the very first time, the ability to apply complex (e.g.,
non-rigid) text-guided semantic edits to a single real image. For example, we
can change the posture and composition of one or multiple objects inside an
image, while preserving its original characteristics. Our method can make a
standing dog sit down or jump, cause a bird to spread its wings, etc. -- each
within its single high-resolution natural image provided by the user. Contrary
to previous work, our proposed method requires only a single input image and a
target text (the desired edit). It operates on real images, and does not
require any additional inputs (such as image masks or additional views of the
object). Our method, which we call "Imagic", leverages a pre-trained
text-to-image diffusion model for this task. It produces a text embedding that
aligns with both the input image and the target text, while fine-tuning the
diffusion model to capture the image-specific appearance. We demonstrate the
quality and versatility of our method on numerous inputs from various domains,
showcasing a plethora of high quality complex semantic image edits, all within
a single unified framework.
