  Diffusion-based text-to-image generation models like GLIDE and DALLE-2 have
gained wide success recently for their superior performance in turning complex
text inputs into images of high quality and wide diversity. In particular, they
are proven to be very powerful in creating graphic arts of various formats and
styles. Although current models supported specifying style formats like oil
painting or pencil drawing, fine-grained style features like color
distributions and brush strokes are hard to specify as they are randomly picked
from a conditional distribution based on the given text input. Here we propose
a novel style guidance method to support generating images using arbitrary
style guided by a reference image. The generation method does not require a
separate style transfer model to generate desired styles while maintaining
image quality in generated content as controlled by the text input.
Additionally, the guidance method can be applied without a style reference,
denoted as self style guidance, to generate images of more diverse styles.
Comprehensive experiments prove that the proposed method remains robust and
effective in a wide range of conditions, including diverse graphic art forms,
image content types and diffusion models.
