FlashFace: Human Image Personalization with High-fidelity Identity
  Preservation
Shilong Zhang Lianghua Huang Xi Chen Yifei Zhang Zhi-Fan Wu Yutong Feng Wei Wang Yujun Shen Yu Liu Ping Luo
This work presents FlashFace, a practical tool with which users can easily
personalize their own photos on the fly by providing one or a few reference
face images and a text prompt. Our approach is distinguishable from existing
human photo customization methods by higher-fidelity identity preservation and
better instruction following, benefiting from two subtle designs. First, we
encode the face identity into a series of feature maps instead of one image
token as in prior arts, allowing the model to retain more details of the
reference faces (e.g., scars, tattoos, and face shape ). Second, we introduce a
disentangled integration strategy to balance the text and image guidance during
the text-to-image generation process, alleviating the conflict between the
reference faces and the text prompts (e.g., personalizing an adult into a
"child" or an "elder"). Extensive experimental results demonstrate the
effectiveness of our method on various applications, including human image
personalization, face swapping under language prompts, making virtual
characters into real people, etc. Project Page:
https://jshilong.github.io/flashface-page.