Painterly Image Harmonization aims at seamlessly blending disparate visual
elements within a single coherent image. However, previous approaches often
encounter significant limitations due to training data constraints, the need
for time-consuming fine-tuning, or reliance on additional prompts. To surmount
these hurdles, we design a Training-and-prompt-Free General Painterly
Harmonization method using image-wise attention sharing (TF-GPH), which
integrates a novel "share-attention module". This module redefines the
traditional self-attention mechanism by allowing for comprehensive image-wise
attention, facilitating the use of a state-of-the-art pretrained latent
diffusion model without the typical training data limitations. Additionally, we
further introduce "similarity reweighting" mechanism enhances performance by
effectively harnessing cross-image information, surpassing the capabilities of
fine-tuning or prompt-based approaches. At last, we recognize the deficiencies
in existing benchmarks and propose the "General Painterly Harmonization
Benchmark", which employs range-based evaluation metrics to more accurately
reflect real-world application. Extensive experiments demonstrate the superior
efficacy of our method across various benchmarks. The code and web demo are
available at this https URL

无需训练和提示的综合画家感知融合方法通过利用图像间注意力共享，重新定义自注意机制并跨图像信息跟踪与提升性能，同时提出新的综合画家感知融合基准测评方法，实验证明了其在多个基准测试中的优越性能。