  Hair editing has made tremendous progress in recent years. Early hair editing
methods use well-drawn sketches or masks to specify the editing conditions.
Even though they can enable very fine-grained local control, such interaction
modes are inefficient for the editing conditions that can be easily specified
by language descriptions or reference images. Thanks to the recent breakthrough
of cross-modal models (e.g., CLIP), HairCLIP is the first work that enables
hair editing based on text descriptions or reference images. However, such
text-driven and reference-driven interaction modes make HairCLIP unable to
support fine-grained controls specified by sketch or mask. In this paper, we
propose HairCLIPv2, aiming to support all the aforementioned interactions with
one unified framework. Simultaneously, it improves upon HairCLIP with better
irrelevant attributes (e.g., identity, background) preservation and unseen text
descriptions support. The key idea is to convert all the hair editing tasks
into hair transfer tasks, with editing conditions converted into different
proxies accordingly. The editing effects are added upon the input image by
blending the corresponding proxy features within the hairstyle or hair color
feature spaces. Besides the unprecedented user interaction mode support,
quantitative and qualitative experiments demonstrate the superiority of
HairCLIPv2 in terms of editing effects, irrelevant attribute preservation and
visual naturalness. Our code is available at
\url{https://github.com/wty-ustc/HairCLIPv2}.
