CoralStyleCLIP: Co-optimized Region and Layer Selection for Image Editing

TL;DR

CoralStyleCLIP introduces a multi-layer attention-guided blending strategy in StyleGAN2 to achieve high-fidelity, controllable image edits, improving quality while maintaining simplicity compared to existing CLIP-based methods.

Contribution

It proposes a novel co-optimized region and layer selection strategy with multi-layer attention in StyleGAN2 for superior image editing quality.

Findings

High-quality edits achieved with CoralStyleCLIP

Method outperforms state-of-the-art CLIP-based approaches

Maintains ease of use and simplicity

Abstract

Edit fidelity is a significant issue in open-world controllable generative image editing. Recently, CLIP-based approaches have traded off simplicity to alleviate these problems by introducing spatial attention in a handpicked layer of a StyleGAN. In this paper, we propose CoralStyleCLIP, which incorporates a multi-layer attention-guided blending strategy in the feature space of StyleGAN2 for obtaining high-fidelity edits. We propose multiple forms of our co-optimized region and layer selection strategy to demonstrate the variation of time complexity with the quality of edits over different architectural intricacies while preserving simplicity. We conduct extensive experimental analysis and benchmark our method against state-of-the-art CLIP-based methods. Our findings suggest that CoralStyleCLIP results in high-quality edits while preserving the ease of use.