  Editing real facial images is a crucial task in computer vision with
significant demand in various real-world applications. While GAN-based methods
have showed potential in manipulating images especially when combined with
CLIP, these methods are limited in their ability to reconstruct real images due
to challenging GAN inversion capability. Despite the successful image
reconstruction achieved by diffusion-based methods, there are still challenges
in effectively manipulating fine-gained facial attributes with textual
instructions.To address these issues and facilitate convenient manipulation of
real facial images, we propose a novel approach that conduct text-driven image
editing in the semantic latent space of diffusion model. By aligning the
temporal feature of the diffusion model with the semantic condition at
generative process, we introduce a stable manipulation strategy, which perform
precise zero-shot manipulation effectively. Furthermore, we develop an
interactive system named ChatFace, which combines the zero-shot reasoning
ability of large language models to perform efficient manipulations in
diffusion semantic latent space. This system enables users to perform complex
multi-attribute manipulations through dialogue, opening up new possibilities
for interactive image editing. Extensive experiments confirmed that our
approach outperforms previous methods and enables precise editing of real
facial images, making it a promising candidate for real-world applications.
Project page: https://dongxuyue.github.io/chatface/
