StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image
  Generation
This paper presents a LoRA-free method for stylized image generation that
takes a text prompt and style reference images as inputs and produces an output
image in a single pass. Unlike existing methods that rely on training a
separate LoRA for each style, our method can adapt to various styles with a
unified model. However, this poses two challenges: 1) the prompt loses
controllability over the generated content, and 2) the output image inherits
both the semantic and style features of the style reference image, compromising
its content fidelity. To address these challenges, we introduce StyleAdapter, a
model that comprises two components: a two-path cross-attention module (TPCA)
and three decoupling strategies. These components enable our model to process
the prompt and style reference features separately and reduce the strong
coupling between the semantic and style information in the style references.
StyleAdapter can generate high-quality images that match the content of the
prompts and adopt the style of the references (even for unseen styles) in a
single pass, which is more flexible and efficient than previous methods.
Experiments have been conducted to demonstrate the superiority of our method
over previous works.
64f7f5e4bcf14b0c9166c6fd
Zhouxia Wang
admin_assigned
2023-09-06T15:19:48.675Z
64db92a5858f8a41c11669b7
/avatars/e834d8f1d4781e3bb0b5d6d25b3b3505.svg
Zhouxia Wang
user
wzhouxiff
64f7f5e4bcf14b0c9166c6fe
Xintao Wang
admin_assigned
2023-09-06T15:20:36.238Z
60e272ca6c78a8c122b12127
https://cdn-avatars.huggingface.co/v1/production/uploads/60e272ca6c78a8c122b12127/xldEGBzGrU-bX6IwAw0Ie.jpeg
Xintao Wang
user
Xintao
64f7f5e4bcf14b0c9166c6ff
Liangbin Xie
admin_assigned
2023-09-06T15:20:46.947Z
6315dcb493fdd4a5633f7b30
/avatars/c58d228dbdf4f1edb5aa4e635dacb51d.svg
Liangbin Xie
user
Liangbin
64f7f5e4bcf14b0c9166c700
Zhongang Qi
claimed_verified
2024-11-19T08:40:32.053Z
660cc64f1b14f691990c0ea0
/avatars/f172d3120b22d745a41cc3f2eb499ce6.svg
Zhongang Qi
user
phoenixqza
64f7f5e4bcf14b0c9166c701
Ying Shan
64f7f5e4bcf14b0c9166c702
Wenping Wang
64f7f5e4bcf14b0c9166c703
Ping Luo
StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image
  Generation
This paper presents a LoRA-free method for stylized image generation that
takes a text prompt and style reference images as inputs and produces an output
image in a single pass. Unlike existing methods that rely on training a
separate LoRA for each style, our method can adapt to various styles with a
unified model. However, this poses two challenges: 1) the prompt loses
controllability over the generated content, and 2) the output image inherits
both the semantic and style features of the style reference image, compromising
its content fidelity. To address these challenges, we introduce StyleAdapter, a
model that comprises two components: a two-path cross-attention module (TPCA)
and three decoupling strategies. These components enable our model to process
the prompt and style reference features separately and reduce the strong
coupling between the semantic and style information in the style references.
StyleAdapter can generate high-quality images that match the content of the
prompts and adopt the style of the references (even for unseen styles) in a
single pass, which is more flexible and efficient than previous methods.
Experiments have been conducted to demonstrate the superiority of our method
over previous works.
StyleAdapter introduces a method for generating high-quality, stylized images in a single pass by processing prompt and style reference features separately within a unified model.
LoRA-free single pass text prompt style reference images adaptability controllability content fidelity StyleAdapter two-path cross-attention module decoupling strategies