In the ever-evolving landscape of image editing technology, a groundbreaking framework has emerged, promising unprecedented control and precision in the editing process. Dubbed "Differential Diffusion," this innovative approach revolutionizes the way users interact with images, enabling them to tailor the extent of change for each pixel or region without the need for extensive model training.

Traditionally, image editing tools have offered limited control over the degree of change, often restricting users to global modifications across entire regions. However, with Differential Diffusion, users can now exercise granular control, adjusting the quantity of change at a pixel-level granularity. This level of flexibility opens up a myriad of possibilities, allowing for the precise modification of individual objects or the gradual alteration of spatial elements within an image.

At the heart of the Differential Diffusion framework lies a set of sophisticated techniques, including change map down-sampling, fragment injection, gradual injection, and future hinting. These components work in harmony to ensure seamless integration and precise control over the editing process, empowering users to achieve their desired outcomes with unparalleled accuracy.

The versatility of the Differential Diffusion framework is demonstrated through a range of applications, including localized style transfer, heterogeneous editing, conveying progression, and augmented reality. Through rigorous qualitative and quantitative evaluations, the framework showcases its ability to produce visually stunning results, surpassing the capabilities of existing models in terms of controllability and naturalness.

Furthermore, Differential Diffusion introduces novel tools for exploring the effects of different change quantities, offering users a comprehensive understanding of the editing process. Remarkably, this framework operates solely during inference, eliminating the need for extensive model training or fine-tuning and ensuring seamless integration into existing image generation pipelines.