Text-to-Image Synthesis for Any Artistic Styles: Advancements in
  Personalized Artistic Image Generation via Subdivision and Dual Binding
Junseo Park Beomseok Ko Hyeryung Jang
Recent advancements in text-to-image models, such as Stable Diffusion, have
demonstrated their ability to synthesize visual images through natural language
prompts. One approach of personalizing text-to-image models, exemplified by
DreamBooth, fine-tunes the pre-trained model by binding unique text identifiers
with a few images of a specific subject. Although existing fine-tuning methods
have demonstrated competence in rendering images according to the styles of
famous painters, it is still challenging to learn to produce images
encapsulating distinct art styles due to abstract and broad visual perceptions
of stylistic attributes such as lines, shapes, textures, and colors. In this
paper, we introduce a new method, Single-StyleForge, for personalization. It
fine-tunes pre-trained text-to-image diffusion models to generate diverse
images in specified styles from text prompts. By using around 15-20 images of
the target style, the approach establishes a foundational binding of a unique
token identifier with a broad range of the target style. It also utilizes
auxiliary images to strengthen this binding, resulting in offering specific
guidance on representing elements such as persons in a target style-consistent
manner. In addition, we present ways to improve the quality of style and
text-image alignment through a method called Multi-StyleForge, which inherits
the strategy used in StyleForge and learns tokens in multiple. Experimental
evaluation conducted on six distinct artistic styles demonstrates substantial
improvements in both the quality of generated images and the perceptual
fidelity metrics, such as FID, KID, and CLIP scores.