Work

Differential Diffusion: Giving Each Pixel Its Strength

Year: 2025

Type: article

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 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 published and integrated into several platforms.

Source: Computer Graphics Forum

DOI: 10.1111/cgf.70040

Authors: Eran Levin, Ohad Fried

Institutions: Tel Aviv University, Reichman University

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,710

Funders: Israel Science Foundation, Tel Aviv University