Text-based image editing has advanced significantly in recent years. With the
rise of diffusion models, image editing via textual instructions has become
ubiquitous. Unfortunately, current models lack the ability to customize the
quantity of the change per pixel or per image fragment, resorting to changing
the entire image in an equal amount, or editing a specific region using a
binary mask. In this paper, we suggest a new framework which enables the user
to customize the quantity of change for each image fragment, thereby enhancing
the flexibility and verbosity of modern diffusion models. Our framework does
not require model training or fine-tuning, but instead performs everything at
inference time, making it easily applicable to an existing model. We show both
qualitatively and quantitatively that our method allows better controllability
and can produce results which are unattainable by existing models. Our code is
available at: this https URL

提出一种新的框架，使用户能够为每个图像碎片自定义更改量，增强了现代扩散模型的灵活性和表达能力，且无需模型训练或微调，可直接应用于现有模型，量化和定性结果表明，该方法实现了更好的可控性，并可产生现有模型无法达到的结果。