  Recently, inversion methods have focused on additional high-rate information
in the generator (e.g., weights or intermediate features) to refine inversion
and editing results from embedded latent codes. Although these techniques gain
reasonable improvement in reconstruction, they decrease editing capability,
especially on complex images (e.g., containing occlusions, detailed
backgrounds, and artifacts). A vital crux is refining inversion results,
avoiding editing capability degradation. To tackle this problem, we introduce
Domain-Specific Hybrid Refinement (DHR), which draws on the advantages and
disadvantages of two mainstream refinement techniques to maintain editing
ability with fidelity improvement. Specifically, we first propose
Domain-Specific Segmentation to segment images into two parts: in-domain and
out-of-domain parts. The refinement process aims to maintain the editability
for in-domain areas and improve two domains' fidelity. We refine these two
parts by weight modulation and feature modulation, which we call Hybrid
Modulation Refinement. Our proposed method is compatible with all latent code
embedding methods. Extension experiments demonstrate that our approach achieves
state-of-the-art in real image inversion and editing. Code is available at
https://github.com/caopulan/Domain-Specific_Hybrid_Refinement_Inversion.
