Differential Diffusion: Giving Each Pixel Its Strength

TL;DR

This paper presents a new diffusion model framework that allows pixel-level control over the amount of change during image editing, enabling more precise and gradual modifications without retraining the model.

Contribution

It introduces a versatile, inference-only method to customize change intensity per pixel or region in diffusion-based image editing, enhancing control and flexibility.

Findings

Enables granular control of image edits at pixel level

Improves seamless inpainting with subtle adjustments

Validated through quantitative, qualitative, and user studies

Abstract

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