StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style
  Adapter
Gongye Liu Menghan Xia Yong Zhang Haoxin Chen Jinbo Xing Xintao Wang Yujiu Yang Ying Shan
Text-to-video (T2V) models have shown remarkable capabilities in generating
diverse videos. However, they struggle to produce user-desired stylized videos
due to (i) text's inherent clumsiness in expressing specific styles and (ii)
the generally degraded style fidelity. To address these challenges, we
introduce StyleCrafter, a generic method that enhances pre-trained T2V models
with a style control adapter, enabling video generation in any style by
providing a reference image. Considering the scarcity of stylized video
datasets, we propose to first train a style control adapter using style-rich
image datasets, then transfer the learned stylization ability to video
generation through a tailor-made finetuning paradigm. To promote content-style
disentanglement, we remove style descriptions from the text prompt and extract
style information solely from the reference image using a decoupling learning
strategy. Additionally, we design a scale-adaptive fusion module to balance the
influences of text-based content features and image-based style features, which
helps generalization across various text and style combinations. StyleCrafter
efficiently generates high-quality stylized videos that align with the content
of the texts and resemble the style of the reference images. Experiments
demonstrate that our approach is more flexible and efficient than existing
competitors.