# Training-and-Prompt-Free General Painterly Harmonization via Zero-Shot Disentenglement on Style and Content References

arXiv: https://arxiv.org/abs/2404.12900 · HTML: https://mlsanity.com/paper/2404.12900/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2024.

## Built on by (most cited first, 1 of 1)

- [TF-TI2I: Training-Free Text-and-Image-to-Image Generation via Multi-Modal Implicit-Context Learning in Text-to-Image Models](https://mlsanity.com/paper/2503.15283/) (2025)

## Abstract (arXiv)

Painterly image harmonization aims at seamlessly blending disparate visual elements within a single image. However, previous approaches often struggle due to limitations in training data or reliance on additional prompts, leading to inharmonious and content-disrupted output. To surmount these hurdles, we design a Training-and-prompt-Free General Painterly Harmonization method (TF-GPH). TF-GPH incorporates a novel ``Similarity Disentangle Mask'', which disentangles the foreground content and background image by redirecting their attention to corresponding reference images, enhancing the attention mechanism for multi-image inputs. Additionally, we propose a ``Similarity Reweighting'' mechanism to balance harmonization between stylization and content preservation. This mechanism minimizes content disruption by prioritizing the content-similar features within the given background style reference. Finally, we address the deficiencies in existing benchmarks by proposing novel range-based evaluation metrics and a new benchmark to better reflect real-world applications. Extensive experiments demonstrate the efficacy of our method in all benchmarks. More detailed in https://github.com/BlueDyee/TF-GPH.