StoryMaker: Towards Holistic Consistent Characters in Text-to-image
  Generation
Authors: name: Zhengguang Zhou, name: Jing Li, name: Huaxia Li, name: Nemo Chen, name: Xu Tang

Tuning-free personalized image generation methods have achieved significant
success in maintaining facial consistency, i.e., identities, even with multiple
characters. However, the lack of holistic consistency in scenes with multiple
characters hampers these methods' ability to create a cohesive narrative. In
this paper, we introduce StoryMaker, a personalization solution that preserves
not only facial consistency but also clothing, hairstyles, and body
consistency, thus facilitating the creation of a story through a series of
images. StoryMaker incorporates conditions based on face identities and cropped
character images, which include clothing, hairstyles, and bodies. Specifically,
we integrate the facial identity information with the cropped character images
using the Positional-aware Perceiver Resampler (PPR) to obtain distinct
character features. To prevent intermingling of multiple characters and the
background, we separately constrain the cross-attention impact regions of
different characters and the background using MSE loss with segmentation masks.
Additionally, we train the generation network conditioned on poses to promote
decoupling from poses. A LoRA is also employed to enhance fidelity and quality.
Experiments underscore the effectiveness of our approach. StoryMaker supports
numerous applications and is compatible with other societal plug-ins. Our
source codes and model weights are available at
https://github.com/RedAIGC/StoryMaker.