Image style transfer aims to imbue digital imagery with the distinctive
attributes of style targets, such as colors, brushstrokes, shapes, whilst
concurrently preserving the semantic integrity of the content. Despite the
advancements in arbitrary style transfer methods, a prevalent challenge remains
the delicate equilibrium between content semantics and style attributes. Recent
developments in large-scale text-to-image diffusion models have heralded
unprecedented synthesis capabilities, albeit at the expense of relying on
extensive and often imprecise textual descriptions to delineate artistic
styles. Addressing these limitations, this paper introduces DiffStyler, a novel
approach that facilitates efficient and precise arbitrary image style transfer.
DiffStyler lies the utilization of a text-to-image Stable Diffusion model-based
LoRA to encapsulate the essence of style targets. This approach, coupled with
strategic cross-LoRA feature and attention injection, guides the style transfer
process. The foundation of our methodology is rooted in the observation that
LoRA maintains the spatial feature consistency of UNet, a discovery that
further inspired the development of a mask-wise style transfer technique. This
technique employs masks extracted through a pre-trained FastSAM model,
utilizing mask prompts to facilitate feature fusion during the denoising
process, thereby enabling localized style transfer that preserves the original
image's unaffected regions. Moreover, our approach accommodates multiple style
targets through the use of corresponding masks. Through extensive
experimentation, we demonstrate that DiffStyler surpasses previous methods in
achieving a more harmonious balance between content preservation and style
integration.

DiffStyler 是一种新方法，通过利用基于 LoRA 的文本到图像扩散模型来实现高效且精确的任意图像风格转移，该方法结合了跨 LoRA 特征和注意注入的策略，旨在在内容保持和风格整合之间取得更和谐的平衡。

DiffStyler：基于扩散的局部图像风格转移

DiffStyler: Diffusion-based Localized Image Style Transfer

Large language models generate high-quality responses with potential
misinformation, underscoring the need for regulation by distinguishing
AI-generated and human-written texts. Watermarking is pivotal in this context,
which involves embedding hidden markers in texts during the LLM inference
phase, which is imperceptible to humans. Current watermarking algorithms,
however, face the challenge of achieving both the detectability of inserted
watermarks and the semantic integrity of generated texts, where enhancing one
aspect often undermines the other. To overcome this, we introduce a novel
multi-objective optimization (MOO) approach for watermarking that utilizes
lightweight networks to generate token-specific watermarking logits and
splitting ratios. By leveraging MOO to optimize for both detection and semantic
objective functions, our method simultaneously achieves detectability and
semantic integrity. Experimental results show that our method outperforms
current watermarking techniques in enhancing the detectability of texts
generated by LLMs while maintaining their semantic coherence. Our code is
available at this https URL .

利用多目标优化方法实现识别性与语义完整性，我们提出了一种在大型语言模型生成的文本中嵌入水印的新方法。实验证明，我们的方法在提高大型语言模型生成文本的可识别性的同时保持其语义连贯性方面优于当前的水印技术。