StyleDrop: Text

AI绘画模型—

We present StyleDrop that enables the generation of images that faithfully follow a specific style,
powered by Muse, a text-to-image generative
vision transformer.
StyleDrop is extremely versatile and captures nuances and details of a user-provided style, such as color
schemes, shading, design patterns, and local and global effects. StyleDrop works by efficiently learning
a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters), and improving
the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able
to deliver impressive results even when the user supplies only a single image specifying the desired
style. An extensive study shows that, for the task of style tuning text-to-image models, Styledrop on Muse convincingly outperforms other methods,
including DreamBooth and Textual
Inversion on Imagen or Stable Diffusion.

First of all, we thank owners of images for sharing their valuable assets. We provide links to image assets used in our experiments.
We thank Varun Jampani, Jason Baldridge, Forrester Cole, José Lezama, Steven Hickson, Kfir Aberman for their valuable feedback on our manuscript.