  Large-scale text-to-image generative models have been a revolutionary
breakthrough in the evolution of generative AI, allowing us to synthesize
diverse images that convey highly complex visual concepts. However, a pivotal
challenge in leveraging such models for real-world content creation tasks is
providing users with control over the generated content. In this paper, we
present a new framework that takes text-to-image synthesis to the realm of
image-to-image translation -- given a guidance image and a target text prompt,
our method harnesses the power of a pre-trained text-to-image diffusion model
to generate a new image that complies with the target text, while preserving
the semantic layout of the source image. Specifically, we observe and
empirically demonstrate that fine-grained control over the generated structure
can be achieved by manipulating spatial features and their self-attention
inside the model. This results in a simple and effective approach, where
features extracted from the guidance image are directly injected into the
generation process of the target image, requiring no training or fine-tuning
and applicable for both real or generated guidance images. We demonstrate
high-quality results on versatile text-guided image translation tasks,
including translating sketches, rough drawings and animations into realistic
images, changing of the class and appearance of objects in a given image, and
modifications of global qualities such as lighting and color.
