CLIP-Guided StyleGAN Inversion for Text-Driven Real Image Editing

TL;DR

This paper introduces CLIPInverter, a novel method for real image editing using StyleGAN and CLIP embeddings, enabling efficient, multi-attribute, text-driven edits with improved accuracy and realism across diverse image domains.

Contribution

The paper proposes lightweight, text-conditioned adapter layers within pretrained GAN-inversion networks, enhancing multi-attribute editing guided by CLIP embeddings.

Findings

Outperforms existing methods in manipulation accuracy

Achieves more photo-realistic image edits

Effective across multiple domains like faces, cats, and birds

Abstract

Researchers have recently begun exploring the use of StyleGAN-based models for real image editing. One particularly interesting application is using natural language descriptions to guide the editing process. Existing approaches for editing images using language either resort to instance-level latent code optimization or map predefined text prompts to some editing directions in the latent space. However, these approaches have inherent limitations. The former is not very efficient, while the latter often struggles to effectively handle multi-attribute changes. To address these weaknesses, we present CLIPInverter, a new text-driven image editing approach that is able to efficiently and reliably perform multi-attribute changes. The core of our method is the use of novel, lightweight text-conditioned adapter layers integrated into pretrained GAN-inversion networks. We demonstrate that by conditioning the initial inversion step on the CLIP embedding of the target description, we are able to obtain more successful edit directions. Additionally, we use a CLIP-guided refinement step to make corrections in the resulting residual latent codes, which further improves the alignment with the text prompt. Our method outperforms competing approaches in terms of manipulation accuracy and photo-realism on various domains including human faces, cats, and birds, as shown by our qualitative and quantitative results.