  Diffusion models have revolutionized image generation and editing, producing
state-of-the-art results in conditioned and unconditioned image synthesis.
While current techniques enable user control over the degree of change in an
image edit, the controllability is limited to global changes over an entire
edited region. This paper introduces a novel framework that enables
customization of the amount of change per pixel or per image region. Our
framework can be integrated into any existing diffusion model, enhancing it
with this capability. Such granular control on the quantity of change opens up
a diverse array of new editing capabilities, such as control of the extent to
which individual objects are modified, or the ability to introduce gradual
spatial changes. Furthermore, we showcase the framework's effectiveness in
soft-inpainting -- the completion of portions of an image while subtly
adjusting the surrounding areas to ensure seamless integration. Additionally,
we introduce a new tool for exploring the effects of different change
quantities. Our framework operates solely during inference, requiring no model
training or fine-tuning. We demonstrate our method with the current open
state-of-the-art models, and validate it via both quantitative and qualitative
comparisons, and a user study. Our code is available at:
https://github.com/exx8/differential-diffusion
