Customized image generation, which seeks to synthesize images with consistent
characters, holds significant relevance for applications such as storytelling,
portrait generation, and character design. However, previous approaches have
encountered challenges in preserving characters with high-fidelity consistency
due to inadequate feature extraction and concept confusion of reference
characters. Therefore, we propose Character-Adapter, a plug-and-play framework
designed to generate images that preserve the details of reference characters,
ensuring high-fidelity consistency. Character-Adapter employs prompt-guided
segmentation to ensure fine-grained regional features of reference characters
and dynamic region-level adapters to mitigate concept confusion. Extensive
experiments are conducted to validate the effectiveness of Character-Adapter.
Both quantitative and qualitative results demonstrate that Character-Adapter
achieves the state-of-the-art performance of consistent character generation,
with an improvement of 24.8% compared with other methods

通过引入 Character-Adapter 框架，我们成功地解决了定制图像生成中保持高保真度一致性的挑战，实现了参考角色细节的保留，进而获得了一致的角色生成结果，并且在实验证明 Character-Adapter 相比其他方法有 24.8% 的性能提升。

角色适配器：基于提示引导的区域控制实现高保真度的角色定制

Character-Adapter: Prompt-Guided Region Control for High-Fidelity  Character Customization

Benefiting from large-scale pre-trained text-to-image (T2I) generative
models, impressive progress has been achieved in customized image generation,
which aims to generate user-specified concepts. Existing approaches have
extensively focused on single-concept customization and still encounter
challenges when it comes to complex scenarios that involve combining multiple
concepts. These approaches often require retraining/fine-tuning using a few
images, leading to time-consuming training processes and impeding their swift
implementation. Furthermore, the reliance on multiple images to represent a
singular concept increases the difficulty of customization. To this end, we
propose FreeCustom, a novel tuning-free method to generate customized images of
multi-concept composition based on reference concepts, using only one image per
concept as input. Specifically, we introduce a new multi-reference
self-attention (MRSA) mechanism and a weighted mask strategy that enables the
generated image to access and focus more on the reference concepts. In
addition, MRSA leverages our key finding that input concepts are better
preserved when providing images with context interactions. Experiments show
that our method's produced images are consistent with the given concepts and
better aligned with the input text. Our method outperforms or performs on par
with other training-based methods in terms of multi-concept composition and
single-concept customization, but is simpler. Codes can be found at
this https URL

提出一种基于参考概念的无需微调的方法生成多概念组合的定制图像，仅需每个概念一幅图像作为输入。通过引入多参考自注意力机制和加权蒙版策略，产生与给定概念一致且更好地与输入文本对齐的图像。与其他基于训练的方法相比，在多概念组合和单概念定制方面，方法表现优秀且更简单。