  Virtual Try-On (VTON) has become a transformative technology, empowering
users to experiment with fashion without ever having to physically try on
clothing. However, existing methods often struggle with generating
high-fidelity and detail-consistent results. While diffusion models, such as
Stable Diffusion series, have shown their capability in creating high-quality
and photorealistic images, they encounter formidable challenges in conditional
generation scenarios like VTON. Specifically, these models struggle to maintain
a balance between control and consistency when generating images for virtual
clothing trials. OutfitAnyone addresses these limitations by leveraging a
two-stream conditional diffusion model, enabling it to adeptly handle garment
deformation for more lifelike results. It distinguishes itself with
scalability-modulating factors such as pose, body shape and broad
applicability, extending from anime to in-the-wild images. OutfitAnyone's
performance in diverse scenarios underscores its utility and readiness for
real-world deployment. For more details and animated results, please see
\url{https://humanaigc.github.io/outfit-anyone/}.
